{"built":"2026-10-10 13:21:54","what":"HISAAB (AI Geo Navigators), Pakistan hazard, impact and loss screening, client build: hazard, exposure and the connected CLIMADA run.","credit":{"agn":"Analytics, interface and simulation: AI Geo Navigators.","inputs":"Hazard and exposure inputs, all built by AGN from open data: river flood (JRC/Copernicus GloFAS maps checked against the 2010-2025 flood outlines), flash and urban flood (Copernicus DEM drainage heights and ERA5-Land rainfall), landslide (GIRI, NGI for UNDRR) with AGN's terrain model, glacial lake outburst (AGN lake register: national inventory, Hi-MAG, RGI 7.0, HMAGLOFDB), avalanche (Copernicus DEM), cyclone wind and storm surge (IBTrACS with CLIMADA), earthquake (Building Code of Pakistan 2007 and 2021), tsunami (Makran scenarios), heat, drought and rainfall (AGN's ERA5-Land analyses), air quality and smog (CAMS EAC4), wildfire (NASA MODIS burned area), observed floods (government outlines and Sentinel-1); CMIP6 (Copernicus Climate Data Store); PBS Census 2017 and 2023; GHSL 2020; Google Open Buildings v3; ESA WorldCover 2021; OpenStreetMap; AGN school and health registers.","boundaries":"Boundaries: AGN administrative set; AJK and Gilgit-Baltistan shown as Pakistan; IIOJK shown as disputed."},"withheld":"Loss is shown only from CLIMADA run AGN-2026-10-10 (cyc, drought, eq, ff, rf, uf), a structurally valid run; its scientific status is set per component by its tests (see plugin.results.evidence). Other perils need their own runs. Source losses and deaths are not used. Components with a passing out-of-sample test: none; the rest are exploratory (partly passed, in-sample only, a failed test, or untested).","plugin":{"name":"CLIMADA","marker":"Needs plugin with CLIMADA","contract":1,"expects":"data/plugins/climada/results.json","run_id":"AGN-2026-10-10","run_date":"2026-10-10","version":"6.1.0","perils_in_run":["cyc","drought","eq","ff","rf","uf"],"calibration":{"rule":"Housing damage each run gives for recorded disasters, as destroyed + 0.4 x partly damaged houses, against the published assessments. Floods: the fit year (2022) within 1.5x, other years within 2x; 2010, 2014, 2025 are in-sample, because the rain-on-homes fit uses every season 2010-2025. Cyclones: the one recorded cyclone (May 1999) is the fit event, within 1.5x; no other cyclone is tested. Earthquakes: the damage model is fitted on eight events and each is tested with the model refit without it; the set's typical error must be within 2.5x and every event within 5x, because moderate-earthquake records are early counts. Every decision is recomputed from recorded and modelled counts. Tests outside the fits (the 2026 season, earthquakes never used in the fit, flood damage beyond buildings) are in the evidence block, which gives each loss component its scientific status.","tests":[{"event":"2022 floods","peril":"flood","role":"fit","recorded":1289751,"modelled":1020638,"ratio":0.79,"band":[0.67,1.5],"unit":"destroyed-house equivalents","source":"Government of Pakistan et al., Pakistan Floods 2022 Post-Disaster Needs Assessment, Table 3 (houses) and Table 4 (PKR million, structures CD+PD, 2022 prices)","by_province":{"BALOCHISTAN":2.28,"KP":0.26,"PUNJAB":1.06,"SINDH":0.65,"SPECIAL REGIONS":0.17},"pass":true},{"event":"2010 floods","peril":"flood","role":"in-sample (rain fit uses 2010-2025)","recorded":1191258,"modelled":977525,"ratio":0.82,"band":[0.5,2.0],"unit":"destroyed-house equivalents","source":"ADB/World Bank, Pakistan Floods 2010 Preliminary Damage and Needs Assessment, Table-Annex-6.2 (houses) and 6.3 (US$ million, depreciated direct damage)","by_province":{"AJK":0.16,"BALOCHISTAN":0.49,"KP":1.12,"GILGIT BALTISTAN":1.39,"PUNJAB":1.01,"SINDH":0.73},"pass":true},{"event":"2014 floods","peril":"flood","role":"in-sample (rain fit uses 2010-2025)","recorded":99300,"modelled":138635,"ratio":1.4,"band":[0.5,2.0],"unit":"destroyed-house equivalents","source":"Pakistan Economic Survey 2014-15, Annex III (Flood 2014): Punjab 101,515 houses in 16 districts; AJK 28,365 in 10 districts. Punjab split from PDMA Punjab via Pakistan Academy of Sciences (2018), 'Damages caused by the 2014 flood'.","by_province":{"PUNJAB":1.39,"AJK":null},"pass":true},{"event":"2025 floods","peril":"flood","role":"in-sample (rain fit uses 2010-2025)","recorded":127432,"modelled":306184,"ratio":2.4,"band":[0.5,2.0],"unit":"destroyed-house equivalents","source":"NDMA, Monsoon 2025 Final Consolidated Situation Report No. 87 (30 Sep 2025), cumulative house damage","by_province":{"PUNJAB":2.53,"KP":1.08,"SINDH":2.31,"BALOCHISTAN":0.22,"GILGIT BALTISTAN":0.86,"AJK":0.03},"pass":false},{"event":"2005 Kashmir M7.6 earthquake","id":"usp000e12e","peril":"earthquake","role":"held out (refit without it)","recorded":501892,"modelled":263124.88384481164,"ratio":0.5242754239768155,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"ERRA: 436,486 destroyed rural homes; about 600,000 destroyed or damaged (ADB 2007)","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2013 Awaran M7.7 earthquake","id":"usb000jyiv","peril":"earthquake","role":"held out (refit without it)","recorded":21000,"modelled":37130.57245609038,"ratio":1.7677568993855486,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"WHO situation report 3: 21,000 houses partly or fully collapsed","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2015 Hindu Kush M7.5 earthquake","id":"us10003re5","peril":"earthquake","role":"held out (refit without it)","recorded":109123,"modelled":117117.04121409467,"ratio":1.0732504486644248,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"houses damaged (moderate damage or worse)","source":"Pakistan: 109,123 buildings damaged","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2019 Mirpur M5.4 earthquake","id":"us60005mqp","peril":"earthquake","role":"held out (refit without it)","recorded":6200,"modelled":1832.2748271752475,"ratio":0.2966626130716985,"offset":10,"band":[0.33,3.0],"pass":false,"unit":"destroyed-house equivalents","source":"2,000 houses destroyed; 12,500 fully or partly damaged and not livable","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"May 2026 Jalalpur Sharif M4.6 earthquake","id":"us7000snly","peril":"earthquake","role":"held out (refit without it)","recorded":10,"modelled":45.57960447421862,"ratio":2.778980223710931,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"Pajhwok/PMD 27 May 2026: about 10 houses severely damaged","record_confidence":"medium","shaking":"model ground motion","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"Aug 2026 Jhelum M4.5 earthquake","id":"us6000tj67","peril":"earthquake","role":"held out (refit without it)","recorded":0,"modelled":0.0,"ratio":1.0,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"Pakistan Connect 8 Aug 2026: felt, no damage reported","record_confidence":"medium","shaking":"model ground motion","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2021 Harnai M5.9 earthquake","id":"us6000fsg9","peril":"earthquake","role":"held out (refit without it)","recorded":2579.8,"modelled":1155.5226842329148,"ratio":0.450043510785742,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"PDMA via Islamic Relief rapid needs assessment, 10 Oct 2021: 315 fully and 5,662 partially damaged households","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2008 Ziarat M6.4 doublet earthquake","id":"usp000gm8g","peril":"earthquake","role":"held out (refit without it)","recorded":5137.0,"modelled":16749.02412394116,"ratio":3.2560761849506816,"offset":10,"band":[0.33,3.0],"pass":false,"unit":"destroyed-house equivalents","source":"OCHA situation report 5 (McRAM, 36 villages, initial): 3,487 destroyed, 4,125 partially destroyed houses (both shocks)","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"May 1999 Sindh cyclone","peril":"cyclone","role":"fit","recorded":98600,"modelled":98600,"ratio":1.0,"band":[0.67,1.5],"pass":true,"unit":"destroyed-house equivalents","source":"ReliefWeb/OCHA: 75,000 houses destroyed and 59,000 damaged in Thatta and Badin; v_half fitted to this event, no held-out wind-damage record exists (Phet 2010 and Yemyin 2007 damage was rain-driven)"},{"event":"June 2023 Cyclone Biparjoy (Sindh coast)","peril":"cyclone","role":"held-out (not used in the fit)","recorded":206.4,"modelled":0,"offset":10,"ratio":0.05,"band":[0.5,2.0],"pass":false,"unit":"destroyed-house equivalents","source":"PDMA Sindh situation report, 16 Jun 2023: 516 houses partly damaged (515 in Thatta), none destroyed","note":"The modelled wind on Pakistan's homes peaked at 19.6 m/s, below the 25.7 m/s damage threshold, so the model gives no damage; the record does not say whether wind, surge or rain did it. 10 houses added to both sides."},{"event":"1984 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":96891367733,"modelled":12504945503,"offset":100000000.0,"ratio":0.13,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"1985 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":34174712806,"modelled":26930168487,"offset":100000000.0,"ratio":0.79,"band":[0.5,2.0],"pass":true,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"1988 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":38958924301,"modelled":17909247289,"offset":100000000.0,"ratio":0.46,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2001 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":49353921575,"offset":100000000.0,"ratio":494.54,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2002 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":65619942450,"modelled":27042551754,"offset":100000000.0,"ratio":0.41,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2004 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":36149890602,"modelled":33307345211,"offset":100000000.0,"ratio":0.92,"band":[0.5,2.0],"pass":true,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2008 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":88586851383,"modelled":29371793723,"offset":100000000.0,"ratio":0.33,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2010 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":62813694067,"modelled":37568565145,"offset":100000000.0,"ratio":0.6,"band":[0.5,2.0],"pass":true,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"1987 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":4140150635,"offset":100000000.0,"ratio":42.4,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2000 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":30008830154,"modelled":3384980906,"offset":100000000.0,"ratio":0.12,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2002 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":41601937109,"modelled":3012571898,"offset":100000000.0,"ratio":0.07,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2004 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":15507848285,"offset":100000000.0,"ratio":156.08,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2009 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":13476713504,"offset":100000000.0,"ratio":135.77,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"}],"passed":false},"scientific_status":{"flood_buildings":"failed test","earthquake":"partly passed","cyclone":"failed test","urban_flood":"untested","drought":"partly passed","flood_sectors.crops":"partly passed","flood_sectors.irrigation":"failed test","flood_sectors.livestock":"partly passed","flood_sectors.power":"failed test","flood_sectors.rail":"failed test","flood_sectors.roads":"partly passed","flood_sectors.telecom":"failed test"},"stale":[],"status":"connected","results":{"engine":{"name":"CLIMADA","version":"6.1.0","agn_build":"AGN multi-peril loss: flood (CLIMADA event set), earthquake (OpenQuake), cyclone (CLIMADA), drought crops (FAOSTAT yield response)","run_id":"AGN-2026-10-10","run_date":"2026-10-10","currency":"PKR","price_year":2025},"perils":{"rf":{"aal":{"AWARAN":2373262.0,"BAKRAN":626682.0,"CHAGAI":23917976.0,"DERA BUGTI":1278728.0,"GWADAR":36408524.0,"HARNAI":72938.0,"JAFFARABAD":1158885458.0,"JHAL MAGSI":131490705.0,"KACHHI(BOLAN)":96879225.0,"KACHI":37536773.0,"KALAT":4672724.0,"KECH":10859981.0,"KHARAN":12467930.0,"KHUZDAR":18909154.0,"KILLA ABDULLAH":2399913.0,"KILLA SAIFULLAH":6935782.0,"KOHLU":178657.0,"LASBELA":160533096.0,"LORALAI":7452843.0,"MASTUNG":5897595.0,"MUSA 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Rabi, 0.99 Kharif over 1991-2025)."},"sectors":{"roads":{"physical":39,"value_pkr":1523896657},"rail":{"physical":1,"value_pkr":242828598},"livestock":{"physical":3049,"value_pkr":110902973},"crops":{"physical":11012713771,"value_pkr":11012713771},"power":{"physical":125781717,"value_pkr":125781717},"telecom":{"physical":89197613,"value_pkr":89197613},"irrigation":{"physical":535068956,"value_pkr":535068956}}}},"flood_sectors":{"method":", matching the two ways the 2010 and 2022 floods did damage (PDNA 2022; DNA 2010):\n  river   assets inside the river flood footprint of the year's return period. 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Assets in the footprint at each return period are the\n          platform's riverine-flood exposure layers (data/tables/agn_hazards.json, perils.rf).\n  rain    the district's assets times the share of its katcha homes that the year's rain destroys (the rain-on-homes model,\n          fitted to 2022): heavy rain on flat land and in the hills damages roads, crops and canals the rivers never reach.\ndamage_s = a_s x (assets in river footprint) + b_s x (all assets in the district) x (rain damage share), summed over districts.\na_s, b_s >= 0 are fitted to every complete record at once (squared log error, each year alike): the national assessments 2010 (DNA)\nand 2022 (PDNA), 2011 (Sindh and Balochistan damage and needs assessment, all sectors), 2014 (satellite crop estimate) and 2025\n(NDMA roads; crops an analyst's estimate). Each complete record is tested by refitting without it (leave one record out); counts\nknown to be incomplete (NDMA livestock and road tallies 2012, 2016, 2023, 2024) are never fitted and pass if the model is 0.5-5 times\nthem. The rain part is the engine's own rain-on-homes share of the same run (flood_state.npz), the same rain as the building losses.\nOther fit (shown beside it): 2010 and 2022 only, two parameters on two records, the main fit until 9 Oct 2026; with the engine's IMERG\nrain it gave crops 87,558 capped district-years and failed all three crop tests. Physical damage x unit value, 2025 prices\n(data/tables/flood_sectors.json).\nCap (physics): a district cannot lose more in a year than it holds. damage_s(d, y) = min(a_s x FP + b_s x assets x rain share,\ncap_s(d)), with cap_s(d) = the district's Kharif production value (crops), its km of road or railway (roads, rail: km damaged\ncannot exceed km held), its animals (livestock), or its asset stock x a published replacement value per unit (power, telecom,\nirrigation; REPLACEMENT_FLOOR). The cap is applied inside every fit, every test, the historical replay, the events and the\nsimulated years, so the fitted a_s and b_s are the ones that reproduce the records with the cap in force.\nA record's geography: the 2011 assessment covers Sindh and Balochistan, the 2014 satellite crop estimate Punjab and AJK; the model\nis summed over the same provinces for those tests (RECORD_SCOPE).\nEvidence per sector (EVIDENCE_RULE): out-of-sample tests against complete records decide it, two-sided, pass band 0.5-2x on the\ntypical error (geometric mean factor); a count known to be incomplete (floor) is a lower bound, so it can only reject a model\nbelow half of it.","limits":"Records differ in definition between years (2010 'damaged', 2022 destroyed and partly damaged, 2011 Sindh and Balochistan only). The model over-predicts moderate flood years for the networks (2011 rain floods in Sindh and Balochistan: rail 347.6x, power 4.32x, telecom 10.49x, irrigation 5.2x), because a district whose river exceeds its defences is taken to flood its whole footprint at that return period, and dense urban networks (Karachi in 2011) then count; no single a and b matches every year. Livestock follows the homes the flood destroys (animals x share of housing units destroyed), fitted to NDMA and assessment counts by province; it misses quiet years where the engine itself floods homes that were not flooded (2016, 2024), and NDMA counts in moderate years are incomplete. Roads and crops hold up best. Livestock and crops are valued at meat value and production lost (floors). A second form was tested (sector damage in proportion to the district's building damage that year): power improves, roads and crops get worse, railways, telecom and irrigation still fail; neither form passes all tests, so the main fit stays for those sectors. District-level damage records are what would settle it. Each district-year is capped at what the district holds (crop value of the season, km of road or rail, animals, network stock at a published replacement value); the fits are made with the cap in force. Sectors whose evidence is not tested (evidence field) are exploratory: computed and shown, kept apart from the tested total (total.aal_pkr_tested_pass).","test_summary":{"roads":{"tests":6,"passed":1,"aal_pkr":111236069718,"aal_pkr_other_fit":113859090957,"aal_pkr_high_case":111236069718,"confidence":"partly","evidence":"partly passed","typical_error":2.68,"deciding_tests":{"2010":0.2,"2011":2.48,"2022":3.41,"2025":1.22},"exploratory":true,"district_years_capped":0},"rail":{"tests":3,"passed":0,"aal_pkr":25231896066,"aal_pkr_other_fit":200708117376,"aal_pkr_high_case":25231896066,"confidence":"failed tests","evidence":"failed test","typical_error":105.03,"deciding_tests":{"2010":0.01,"2011":347.6,"2022":0.03},"exploratory":true,"district_years_capped":257},"livestock":{"tests":7,"passed":1,"aal_pkr":5569159225,"aal_pkr_other_fit":5569159225,"aal_pkr_high_case":17306545732,"confidence":"partly","evidence":"partly passed","typical_error":5.24,"deciding_tests":{"2010 NATIONAL":0.408,"2022 BALOCHISTAN":0.427,"2022 SINDH":0.75,"2022 PUNJAB":0.08,"2022 KP":0.228,"2022 GILGIT BALTISTAN":0.216,"2022 AJK":0.018},"exploratory":true,"district_years_capped":0},"crops":{"tests":5,"passed":2,"aal_pkr":259521032175,"aal_pkr_other_fit":723372170839,"aal_pkr_high_case":259521032175,"confidence":"partly","evidence":"partly passed","typical_error":2.03,"deciding_tests":{"2010":0.37,"2011":0.96,"2014":4.52,"2022":0.49,"2025":0.76},"exploratory":true,"district_years_capped":11001},"power":{"tests":3,"passed":0,"aal_pkr":7467224411,"aal_pkr_other_fit":11045969514,"aal_pkr_high_case":7467224411,"confidence":"failed tests","evidence":"failed test","typical_error":3.68,"deciding_tests":{"2010":0.2,"2011":4.32,"2022":2.3},"exploratory":true,"district_years_capped":0},"telecom":{"tests":3,"passed":0,"aal_pkr":3395340875,"aal_pkr_other_fit":6909094505,"aal_pkr_high_case":3395340875,"confidence":"failed tests","evidence":"failed test","typical_error":4.49,"deciding_tests":{"2010":0.29,"2011":10.49,"2022":0.4},"exploratory":true,"district_years_capped":0},"irrigation":{"tests":3,"passed":0,"aal_pkr":27468585977,"aal_pkr_other_fit":47390258776,"aal_pkr_high_case":27468585977,"confidence":"failed tests","evidence":"failed test","typical_error":4.87,"deciding_tests":{"2010":0.18,"2011":5.2,"2022":0.25},"exploratory":true,"district_years_capped":0}},"total":{"aal_pkr":439889308447,"ep_pkr":{"10":1116190391825,"20":1626343001332,"50":2231043352695,"100":2686453613583,"200":3177810001443,"250":3338778289357,"500":3721268816008,"1000":4074234328800},"aal_pkr_tested_pass":0,"aal_pkr_exploratory":439889308447,"exploratory_sectors":["roads","rail","livestock","crops","power","telecom","irrigation"],"tested_pass_ep_pkr":{"10":0,"20":0,"50":0,"100":0,"200":0,"250":0,"500":0,"1000":0},"aal_pkr_other_fit":1108853861192},"with_buildings":{"aal_pkr":940884856916,"ep_pkr":{"10":2685840844325,"20":3968017626941,"50":5474268812190,"100":6784485646704,"200":7813140524301,"250":8089194830373,"500":8730604798394,"1000":9617256051628},"var995":7813140524301},"evidence_rule":"Same rule as the platform's evidence classes (tools/plugin_contract.py): each out-of-sample test passes when the model/record factor is within 0.5-2x on a complete record, or within 0.5-5x on a floor (an incomplete count, a lower bound on the true damage). tested: every test passes; partly passed: some pass, some fail; failed test: none passes; in-sample only: no test outside the fit. Typical error (geometric mean factor) is reported beside the class.","historical_years":{"mean_pkr":440844949314,"sim_mean_pkr":439889308447,"top5":[2022,1988,2012,1995,1992],"note":"The same model run on each observed year 1979-2025 (river flows and rain of that year)."},"items":{"roads":{"label":"Roads and road bridges","unit":"km damaged","a_river":0.07705093088866126,"b_rain":0.30486819895420725,"fit":{"2010":{"recorded":25000,"modelled":7875,"ratio":0.32,"river_part":5029,"rain_part":2846,"capped_districts":0,"in_sample":true},"2011":{"recorded":2380,"modelled":4471,"ratio":1.88,"river_part":3359,"rain_part":1112,"capped_districts":0,"in_sample":true},"2022":{"recorded":8330,"modelled":13637,"ratio":1.64,"river_part":6492,"rain_part":7145,"capped_districts":0,"in_sample":true},"2025":{"recorded":2811,"modelled":2902,"ratio":1.03,"river_part":2689,"rain_part":213,"capped_districts":0,"in_sample":true}},"tests":{"2010":{"recorded":25000,"modelled":5085,"ratio":0.2,"floor":false,"role":"left out of the fit, refitted on the other records","pass":false,"provinces":"all"},"2011":{"recorded":2380,"modelled":5893,"ratio":2.48,"floor":false,"role":"left out of the fit, refitted on the other records","pass":false,"provinces":"SINDH+BALOCHISTAN","ratio_if_summed_nationally":2.67},"2022":{"recorded":8330,"modelled":28441,"ratio":3.41,"floor":false,"role":"left out of the fit, refitted on the other records","pass":false,"provinces":"all"},"2023":{"recorded":24,"modelled":382,"ratio":15.97,"floor":true,"role":"not in the fit (incomplete count, a floor)","pass":false,"provinces":"all"},"2024":{"recorded":501,"modelled":3802,"ratio":7.58,"floor":true,"role":"not in the fit (incomplete count, a floor)","pass":false,"provinces":"all"},"2025":{"recorded":2811,"modelled":3418,"ratio":1.22,"floor":false,"role":"left out of the fit, refitted on the other records","pass":true,"provinces":"all"}},"aal_pkr":111236069718,"aal_physical":2842,"ep_pkr":{"10":283824792474,"20":382577364622,"50":534918064242,"100":638935276254,"200":788367829325,"250":825955317032,"500":954299372374,"1000":1072977868580},"share_of_aal_from_rivers":0.68,"district_aal":{"AWARAN":23048675,"BAKRAN":1777030,"CHAGAI":2100288580,"DERA BUGTI":98689875,"GWADAR":977427750,"HARNAI":6344958,"JAFFARABAD":783851150,"JHAL MAGSI":319324474,"KACHHI(BOLAN)":187099576,"KACHI":123208665,"KALAT":75166266,"KECH":648484828,"KHARAN":150708787,"KHUZDAR":330158420,"KILLA ABDULLAH":1004916931,"KILLA SAIFULLAH":19001610,"KOHLU":10433527,"LASBELA":213437961,"LORALAI":17413157,"MASTUNG":135329239,"MUSA KHEL":12973056,"NASIRABAD":5191356929,"NUSHKI":588349764,"PANJGUR":77676362,"PISHIN":149127144,"QUETTA":353944357,"SAKANDARABAD":33600577,"SHEERANI":1711758,"SIBI":145968974,"SOHBATPUR":205861416,"WASHUK":65381356,"ZHOB":12457465,"ZIARAT":7073911,"ASTORE":127697656,"DIAMIR":144671915,"GHANCHE":211079750,"GHIZER":303467452,"GILGIT":121396701,"HUNZA":49636976,"KHARMANG":12680734,"NAGAR":45443885,"SHIGAR":101875003,"SKARDU":187130511,"ABBOTTABAD":36769465,"BAJAUR AGENCY":253681297,"MANDI BAHAUDDIN":350786218,"MIANWALI":369742933,"MULTAN":4034006642,"MUZAFFARGARH":2874921247,"NANKANA SAHIB":1171296538,"NAROWAL":447017013,"RAHIM YAR KHAN":2561072135,"RAJANPUR":1535460915,"RAWALPINDI":385271486,"SARGODHA":788660349,"SHEIKHUPURA":2716908784,"SIALKOT":2237127636,"TOBA TEK SINGH":695610737,"VEHARI":306020570,"BADIN":2188262468,"DADU":2494304904,"GHOTKI":5053072004,"OKARA":1471259412,"PAKPATTAN":116889410,"SAHIWAL":275554749,"BANNU":59177628,"BATTAGRAM":63723646,"BUNER":33491422,"MUZAFFARABAD":37199925,"MIRPUR":5092544,"KOTLI":17768059,"CHARSADDA":117712975,"D.I.KHAN":2853092981,"HANGU":138223279,"HARIPUR":107138791,"KARAK":128573520,"KHYBER AGENCY":503535369,"KOHAT":141012814,"HAVELI":24401799,"SUDHNUTI":4435899,"KOHISTAN LOWER":18956640,"KOLAI PALLAS KOHISTAN":12237129,"KURRAM AGENCY":85138632,"LAKKI MARWAT":256112663,"LOWER CHITRAL":392447797,"LOWER DIR":183117317,"MALAKAND":59086149,"MANSEHRA":246550013,"MARDAN":63831725,"MOHMAND AGENCY":77899153,"NORTH WAZIRISTAN AGENCY":42466511,"NOWSHERA":388692464,"ORAKZAI AGENCY":43171441,"PESHAWAR":756105139,"SHANGLA":93950491,"SOUTH WAZIRISTAN":126116632,"SWABI":21684919,"SWAT":233089522,"TANK":196620500,"TOR GHAR":14219807,"UPPER CHITRAL":267540412,"UPPER DIR":162755462,"UPPER KOHISTAN":39072175,"ATTOCK":133183218,"BAHAWALNAGAR":990114783,"BAHAWALPUR":1889930305,"BHAKKAR":596326605,"CHAKWAL":172506552,"CHINIOT":121209801,"DERA GHAZI KHAN":660935395,"FAISALABAD":546525377,"GUJRANWALA":614112633,"GUJRAT":473721820,"HAFIZABAD":344766042,"JHANG":1929471289,"JHELUM":208324267,"KASUR":1030187324,"KHANEWAL":1209334142,"KHUSHAB":317488105,"LAHORE":467371626,"LAYYAH":1829139568,"LODHRAN":193902052,"HYDERABAD":1243508152,"JACOBABAD":2852360380,"JAMSHORO":949432702,"KARACHI CENTRAL":169664056,"KARACHI EAST":124507212,"KARACHI SOUTH":422902980,"KARACHI WEST":822265879,"KASHMORE":1790140192,"KHAIRPUR":8766397570,"KORANGI":80431899,"LARKANA":1980415145,"MALIR":318245085,"MATIARI":608816710,"MIRPUR KHAS":417484414,"NAUSHAHRO FEROZE":1244241463,"SANGHAR":1467697170,"SHAHDADKOT":1765250923,"SHAHEED BENAZIRABAD":2246614633,"SHIKARPUR":3146901613,"SUJAWAL":1879316852,"SUKKUR":5583176335,"TANDO ALLAHYAR":232512011,"TANDO MOHAMMAD KHAN":868747432,"THARPARKAR":164168102,"THATTA":2221712204,"UMERKOT":279999671,"ISLAMABAD":23100523,"POONCH":22215697,"NEELUM":17225083,"HATTIAN":17934595,"BHIMBER":17793785,"BAGH":6522982},"fit_years":["2010","2011","2022","2025"],"fit_kind":"all complete records, each left out for its test","other_fit":{"what":"two-assessment fit (2010 DNA and 2022 PDNA only; the main fit until 9 Oct 2026)","a_river":0.11660603743459289,"b_rain":0.0,"years":["2010","2022"],"ratios":{"2010":0.3,"2011":2.14,"2022":1.18,"2023":0.86,"2024":10.0,"2025":1.45},"aal_pkr":113859090957},"records":{"2022":"PDNA 2022, Vol. II, Table 21: 4,885.1 km destroyed + 3,444.5 km partially damaged (all provinces and cross-provincial); 347 bridges","2010":"Pakistan Floods 2010 DNA (ADB/World Bank), para 56: about 25,000 km (10% of the network) damaged; preliminary","2025":"NDMA IA&PD, Post-Monsoon 2025 Infrastructure Damage Report, Table 1: 2,811 km roads, 790 bridges","2011":"Pakistan Economic Survey 2011-12, special section on the 2011 floods, citing the ADB/World Bank 2011 Pakistan Floods Preliminary Damage and Needs Assessment (Sindh and Balochistan): road damage PKR 26,027 million (Sindh 24,824, Balochistan 1,203)","2023":"NDMA Monsoon 2023 Situation Report No. 94 (27 Sep 2023): roads damaged 23.92 km (Balochistan 22, GB 1.92), 5 bridges","2024":"NDMA Monsoon 2024 Situation Report No. 75 (13 Sep 2024): roads damaged 501.5 km (Sindh 293, Balochistan 206), 42 bridges; preliminary"},"unit_value":"PDNA 2022 Vol. II Table 24 road damage PKR 213,752.3 million / 8,329.6 km damaged (bridges and structures included)","cap":{"basis":"km damaged cannot exceed the district's km of roads; valued at the same PDNA cost per km damaged","pkr_per_unit":null,"district_years_capped_simulated":0,"district_years_simulated":1540000,"district_years_capped_historical":0,"historical_years_with_a_capped_district":[],"aal_pkr_without_cap_same_fit":111236069718,"fit_without_cap":{"a_river":0.11660603739808208,"b_rain":0.0},"highest_simulated_damage_per_unit_pkr":null},"evidence":"partly passed","typical_error":2.68,"n_complete":4,"deciding_tests":{"2010":0.2,"2011":2.48,"2022":3.41,"2025":1.22},"floor_tests_below_half":[],"floor_tests_above":{"2023":15.97,"2024":7.58},"rule":"Same rule as the platform's evidence classes (tools/plugin_contract.py): each out-of-sample test passes when the model/record factor is within 0.5-2x on a complete record, or within 0.5-5x on a floor (an incomplete count, a lower bound on the true damage). tested: every test passes; partly passed: some pass, some fail; failed test: none passes; in-sample only: no test outside the fit. Typical error (geometric mean factor) is reported beside the class."},"rail":{"label":"Railways","unit":"km damaged","a_river":0.11346625762269073,"b_rain":1.0302165248577753,"fit":{"2010":{"recorded":1224,"modelled":165,"ratio":0.13,"river_part":138,"rain_part":26,"capped_districts":0,"in_sample":true},"2011":{"recorded":5,"modelled":134,"ratio":29.11,"river_part":77,"rain_part":57,"capped_districts":0,"in_sample":true},"2022":{"recorded":3335,"modelled":852,"ratio":0.26,"river_part":157,"rain_part":695,"capped_districts":0,"in_sample":true}},"tests":{"2010":{"recorded":1224,"modelled":16,"ratio":0.01,"floor":false,"role":"left out of the fit, refitted on the other records","pass":false,"provinces":"all"},"2011":{"recorded":5,"modelled":1603,"ratio":347.6,"floor":false,"role":"left out of the fit, refitted on the other records","pass":false,"provinces":"SINDH+BALOCHISTAN","ratio_if_summed_nationally":389.35},"2022":{"recorded":3335,"modelled":114,"ratio":0.03,"floor":false,"role":"left out of the fit, refitted on the other records","pass":false,"provinces":"all"}},"aal_pkr":25231896066,"aal_physical":117,"ep_pkr":{"10":68204825893,"20":101710794566,"50":139529177189,"100":173973146532,"200":202479975605,"250":223045391183,"500":253192588010,"1000":274203494976},"share_of_aal_from_rivers":0.4,"district_aal":{"CHAGAI":6412016381,"HARNAI":2723,"JAFFARABAD":228265698,"KACHHI(BOLAN)":541746611,"KILLA ABDULLAH":348540805,"MASTUNG":282706493,"NASIRABAD":254675561,"NUSHKI":1823102721,"PISHIN":16454690,"QUETTA":65970791,"SIBI":109353865,"ABBOTTABAD":809131,"MANDI BAHAUDDIN":10964361,"MIANWALI":34875250,"MULTAN":340780263,"MUZAFFARGARH":809031462,"NANKANA SAHIB":80724215,"NAROWAL":103710729,"RAHIM YAR KHAN":219745002,"RAJANPUR":913856949,"RAWALPINDI":24510561,"SARGODHA":112227861,"SHEIKHUPURA":589412727,"SIALKOT":111953505,"TOBA TEK SINGH":193029473,"VEHARI":25407695,"DADU":588153104,"GHOTKI":529552596,"OKARA":113887804,"PAKPATTAN":21867731,"SAHIWAL":20373666,"HARIPUR":29242226,"KHYBER AGENCY":13679292,"KOHAT":44921583,"MALAKAND":71947,"MARDAN":15108,"NOWSHERA":105370144,"PESHAWAR":78886047,"SWABI":285985,"ATTOCK":61844019,"BAHAWALNAGAR":202731321,"BAHAWALPUR":398100351,"BHAKKAR":5160746,"CHINIOT":15753163,"DERA GHAZI KHAN":202391751,"FAISALABAD":48578806,"GUJRANWALA":66537838,"GUJRAT":63432616,"HAFIZABAD":39181146,"JHANG":264759269,"JHELUM":141500728,"KASUR":85787313,"KHANEWAL":178521568,"KHUSHAB":224376132,"LAHORE":15415770,"LAYYAH":343359075,"LODHRAN":85828439,"HYDERABAD":316869851,"JACOBABAD":1137229465,"JAMSHORO":930253096,"KARACHI CENTRAL":9304129,"KARACHI EAST":37456095,"KARACHI SOUTH":54929685,"KARACHI WEST":27271428,"KASHMORE":557648613,"KHAIRPUR":976899477,"LARKANA":431788721,"MALIR":58918361,"MATIARI":48437525,"MIRPUR KHAS":9475851,"NAUSHAHRO FEROZE":396271154,"SANGHAR":154728648,"SHAHDADKOT":337157984,"SHAHEED BENAZIRABAD":493891933,"SHIKARPUR":473424054,"SUKKUR":801192927,"TANDO ALLAHYAR":31510072,"THATTA":136951550,"UMERKOT":163180670,"ISLAMABAD":3661966},"fit_years":["2010","2011","2022"],"fit_kind":"all complete records, each left out for its test","other_fit":{"what":"two-assessment fit (2010 DNA and 2022 PDNA only; the main fit until 9 Oct 2026)","a_river":0.0480810774476448,"b_rain":95.58953038512217,"years":["2010","2022"],"ratios":{"2010":1.01,"2011":347.56,"2022":0.95},"aal_pkr":200708117376},"records":{"2022":"PDNA 2022, Vol. II, Table 22: 3,127 km completely and 208 km partially damaged track and signalling","2010":"Pakistan Floods 2010 DNA, Annex 11 para 4: 382 km completely and 842 km partially damaged","2011":"Pakistan Economic Survey 2011-12, special section on the 2011 floods, citing the ADB/World Bank 2011 Pakistan Floods Preliminary Damage and Needs Assessment (Sindh and Balochistan): railways direct damage PKR 277 million (Sindh)"},"unit_value":"PDNA 2022 Vol. II Table 24 railway damage PKR 470,062 million / 3,335 km (tracks, bridges, stations, buildings)","cap":{"basis":"km damaged cannot exceed the district's km of rail; valued at the same PDNA cost per km damaged","pkr_per_unit":null,"district_years_capped_simulated":257,"district_years_simulated":1540000,"district_years_capped_historical":1,"historical_years_with_a_capped_district":[2007],"aal_pkr_without_cap_same_fit":25263056045,"fit_without_cap":{"a_river":0.9408235237192168,"b_rain":3.0112159387806874},"highest_simulated_damage_per_unit_pkr":null},"evidence":"failed test","typical_error":105.03,"n_complete":3,"deciding_tests":{"2010":0.01,"2011":347.6,"2022":0.03},"floor_tests_below_half":[],"floor_tests_above":{},"rule":"Same rule as the platform's evidence classes (tools/plugin_contract.py): each out-of-sample test passes when the model/record factor is within 0.5-2x on a complete record, or within 0.5-5x on a floor (an incomplete count, a lower bound on the true damage). tested: every test passes; partly passed: some pass, some fail; failed test: none passes; in-sample only: no test outside the fit. Typical error (geometric mean factor) is reported beside the class."},"livestock":{"form":"animals lost = m x animals in the district x share of its housing units the flood destroys","m_houses":0.07159328535003853,"m_houses_high_case":0.22248106349151664,"a_river":0.0,"b_rain":0.0,"fit":{"2010":{"recorded":1500000,"modelled":612390,"ratio":0.41,"held_out":true},"2022":{"recorded":1098694,"modelled":534419,"ratio":0.49,"held_out":true}},"tests":{"2011":{"recorded":115500,"modelled":194906,"ratio":1.69,"floor":true,"pass":true,"provinces":"SINDH"},"2012":{"recorded":12121,"modelled":535683,"ratio":44.19,"floor":true,"pass":false,"provinces":"BALOCHISTAN+PUNJAB+SINDH"},"2014":{"recorded":1925,"modelled":73252,"ratio":38.05,"floor":true,"pass":false,"provinces":"PUNJAB+AJK"},"2016":{"recorded":194,"modelled":8753,"ratio":45.12,"floor":true,"pass":false,"provinces":"AJK+BALOCHISTAN+GILGIT BALTISTAN"},"2023":{"recorded":1260,"modelled":26418,"ratio":20.97,"floor":true,"pass":false,"provinces":"AJK+BALOCHISTAN+GILGIT BALTISTAN+KP+PUNJAB+SINDH"},"2024":{"recorded":2282,"modelled":100635,"ratio":44.1,"floor":true,"pass":false,"provinces":"AJK+BALOCHISTAN+GILGIT BALTISTAN+KP+PUNJAB+SINDH"},"2025":{"recorded":16332,"modelled":104533,"ratio":6.4,"floor":true,"pass":false,"provinces":"BALOCHISTAN+GILGIT 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partly passed: some pass, some fail; failed test: none passes; in-sample only: no test outside the fit. Typical error (geometric mean factor) is reported beside the class."}}},"all_hazards":{"what":"All-hazard national loss, 10,000 simulated years: flood (buildings and other sectors), earthquake, cyclone wind, urban flood, drought. PKR, 2025 prices.","method":"keeps its own engine and its own simulated years:\n  flood       river + flash + rain on homes (buildings) and the other sectors (crops, livestock, roads, railways, power,\n              telecommunications, irrigation): work/flood_years.npz, written by tools/climada_flood_events.py and agn_flood_sectors.py\n  earthquake  OpenQuake event-based ground motions (3.23.5 rerun of 9 Oct 2026), 10,000 years (2 GMPE branches x 5,000), losses recomputed with the\n              damage model refitted on eight Pakistan earthquakes (research/agn_v3/scripts/eq_recalc.py): work/eq_years.npz\n  cyclone     CLIMADA 6, 3,200 synthetic tracks, each with rate 1/2,300 a year: work/cyc_events.npz; a year holds a Poisson\n              number of storms drawn from the set\n  urban flood AGN 2D model, loss at 1-in-5 ... 1-in-100 per city and district: work/uf_v1.json; the loss read from the curve\n              (linear in log return period, nothing below 1-in-2, held at 1-in-100 above)\n  drought     crop loss by year 1980-2025: work/drought_years.json; a year drawn from the record\n\nDependence. Earthquakes and cyclones (May-June and October-November storms) are independent of the monsoon.\nUrban flood is driven by the same day of rain as rain on homes: the 2D model is run on each city's 1-day rain return level, and the\nflood engine's rain on homes uses each district's wettest day (rain_window 1). In a simulated year each city takes the return period\nof its own districts' simulated wettest day (the rank of the engine's rain-on-homes draw in those districts, which is monotone in the\nrain), so urban flood inherits the engine's dependence on the river years (a Gaussian copula fitted to the 47 observed years). Test:\nthe rank correlation (Spearman) of urban flood with the flood year in the simulation must sit inside the 90% bootstrap interval of the\nsame correlation in 1979-2025, with urban flood in each past year read from the city curves at the return period of that year's\nwettest day in the city (rank within the 47 years; no recorded urban flood loss series exists); otherwise a Gaussian copula at the\nestimate replaces it.\nDrought takes the flood year's opposite rank by a Gaussian copula on ranks (Iman & Conover 1982) whose parameter is set from the\nSpearman correlation of the 1980-2025 drought crop loss with the historical flood loss (buildings and sectors) of the same year,\nr = 2 sin(pi rho_s / 6); the estimate is used when its 90% bootstrap interval (10,000 resamples of years) excludes zero, otherwise\nthe stated assumption DR_ASSUMED is kept and the interval ends run as scenarios.\nOverlap. Rain on homes counts katcha homes destroyed by rain in every district, cities included; urban flood counts damage to the\ncity's buildings (district housing mix). In a city district, in the same year, the katcha part of the urban flood loss (urban loss x\nthe district's katcha share of residential value) and the rain-on-homes loss are the same homes hit by the same rain, and the\nrain-on-homes fit already counts them (it is fitted to all-cause house damage counts). Rule: keep the larger of the two per\ndistrict-year, i.e. urban flood net = urban flood - min(rain on homes, katcha part of urban flood). Flash and river losses in the\ncity districts are reported as an upper bound on their overlap with urban flood (their location inside the district is not known\nrelative to the city box), not removed.\nDependence scenarios: the total is recomputed with the urban-flood and drought rank correlations at their estimates and at the ends\nof their 90% intervals, with all hazards independent, and with all hazards comonotonic (each hazard's years sorted together). They are\ncomparison scenarios, not limits on a quantile: VaR is not subadditive, so the comonotonic 1-in-200 is not a worst case for the\n1-in-200 of the sum, and the independent case is not a floor.","years":10000,"seed":20261007,"dependence":{"urban_flood_vs_flood_rank":0.603,"drought_vs_flood_rank":-0.307,"note":"Spearman rank correlation with the flood year (buildings + sectors) in the simulated years"},"dependence_estimate":{"urban_flood":{"spearman":0.501,"interval_90":[0.281,0.68],"years":47,"first_year":1979,"last_year":2025,"flood_series":"historical flood loss of the year, buildings (river + flash + rain on homes) and sectors, from the same engines replayed on 1979-2025","other_series":"urban flood read from the city curves at the return period of the year's wettest day in the city (flood_state rain_record: IMERG 2001-2025, ERA5-Land before, quantile-mapped); no recorded urban flood loss series exists","supported":true,"spearman_buildings_only":0.534,"simulated_rain_linked_spearman":0.603,"adopted":"rain-linked (each city at the return period of its districts' simulated wettest day)"},"drought":{"spearman":-0.316,"interval_90":[-0.519,-0.081],"years":46,"first_year":1980,"last_year":2025,"flood_series":"historical flood loss of the year, buildings (river + flash + rain on homes) and sectors, from the same engines replayed on 1979-2025","other_series":"drought crop loss by year (work/drought_years.json)","supported":true,"spearman_buildings_only":-0.203,"adopted":-0.316,"adopted_from":"data"}},"aal":1093367876210,"ep":{"2":513953475102,"5":1685370214190,"10":2884690367058,"20":4221287070193,"25":4575430821648,"50":5850176572814,"100":7182660846150,"200":8153200048616,"250":8470286296493,"500":9103308834119,"1000":10593998234183},"var995":8153200048616,"tvar995":9524475181884,"aal_by_hazard":{"flood_buildings":500995548469,"flood_sectors":439889308722,"earthquake":63100628977,"cyclone":5673981362,"urban_flood":59641930375,"drought":24066478306},"share_of_aal":{"flood_buildings":0.458,"flood_sectors":0.402,"earthquake":0.058,"cyclone":0.005,"urban_flood":0.055,"drought":0.022},"share_of_1in200_tail":{"flood_buildings":0.547,"flood_sectors":0.341,"earthquake":0.085,"cyclone":0.0,"urban_flood":0.026,"drought":0.001},"ep_by_hazard":{"flood_buildings":{"2":110257876992,"5":712521351168,"10":1585281105920,"20":2405841489101,"25":2728437166899,"50":3624625184440,"100":4479250496225,"200":5067139058237,"250":5316112916939,"500":5908831917834,"1000":6207720499708},"flood_sectors":{"2":235634114560,"5":708253804134,"10":1116190421811,"20":1626342988186,"25":1774833452974,"50":2231043395420,"100":2686453722644,"200":3177810071388,"250":3338778207846,"500":3721268837220,"1000":4074234318160},"earthquake":{"2":10837673619,"5":59218350963,"10":131146583055,"20":239084994207,"25":291468728312,"50":501050230385,"100":789456669910,"200":1168397802014,"250":1395806254649,"500":2098226376480,"1000":3972905010103},"cyclone":{"2":0,"5":0,"10":0,"20":802767001,"25":2537019828,"50":43205074096,"100":168393699249,"200":338431245599,"250":549544541616,"500":633218156721,"1000":815799523780},"urban_flood":{"2":33436993267,"5":100323214290,"10":157006072742,"20":210926924744,"25":226939520892,"50":267503062955,"100":321524886624,"200":357421069632,"250":371030922422,"500":400451691160,"1000":433740732076},"drought":{"2":14393607201,"5":43858851394,"10":68899194820,"20":71923421357,"25":92505535969,"50":92505535969,"100":93877206853,"200":93877206853,"250":93877206853,"500":93877206853,"1000":93877206853}},"flood_buildings_parts":{"river":{"aal":431440821454,"ep":{"2":73195642880,"5":613094955418,"10":1396772162765,"20":2184487233126,"25":2429838656799,"50":3266599709573,"100":3990829885030,"200":4534013353001,"250":4649993073852,"500":5095371451662,"1000":5556761718161}},"rain_driven":{"aal":69554726834,"ep":{"2":21273482000,"5":83536925594,"10":170948290970,"20":307505923814,"25":362278435820,"50":543002584934,"100":710900151173,"200":943152940741,"250":1008622707845,"500":1266411908413,"1000":1437312283904}}},"dependence_scenarios":{"adopted":{"aal":1093367876210,"ep100":7182660846150,"var995":8153200048616,"tvar995":9524475181884,"urban_flood":"rain-linked (each city at the return period of its districts' simulated wettest day)","drought_rho_s":-0.316,"achieved_spearman":{"urban_flood":0.603,"drought":-0.307}},"copula_uf_low_dr_low":{"aal":1093367876210,"ep100":7050261024227,"var995":8002596363010,"tvar995":9409177751818,"urban_flood_rho_s":0.281,"drought_rho_s":-0.519},"copula_uf_low_dr_estimate":{"aal":1093367876210,"ep100":7052043120616,"var995":8002624195040,"tvar995":9416336341495,"urban_flood_rho_s":0.281,"drought_rho_s":-0.316},"copula_uf_low_dr_high":{"aal":1093367876210,"ep100":7064808161003,"var995":8002696163076,"tvar995":9428591752368,"urban_flood_rho_s":0.281,"drought_rho_s":-0.081},"copula_uf_estimate_dr_low":{"aal":1093367876210,"ep100":7092651271744,"var995":8053982868828,"tvar995":9480245199278,"urban_flood_rho_s":0.501,"drought_rho_s":-0.519},"copula_uf_estimate_dr_estimate":{"aal":1093367876210,"ep100":7095403336839,"var995":8053986676128,"tvar995":9487403788955,"urban_flood_rho_s":0.501,"drought_rho_s":-0.316},"copula_uf_estimate_dr_high":{"aal":1093367876210,"ep100":7103434141604,"var995":8064177620980,"tvar995":9499659199827,"urban_flood_rho_s":0.501,"drought_rho_s":-0.081},"copula_uf_high_dr_low":{"aal":1093367876210,"ep100":7145470026734,"var995":8114253022634,"tvar995":9544913201205,"urban_flood_rho_s":0.68,"drought_rho_s":-0.519},"copula_uf_high_dr_estimate":{"aal":1093367876210,"ep100":7147153691504,"var995":8114393345484,"tvar995":9552071790882,"urban_flood_rho_s":0.68,"drought_rho_s":-0.316},"copula_uf_high_dr_high":{"aal":1093367876210,"ep100":7159936011029,"var995":8114474479198,"tvar995":9564327201754,"urban_flood_rho_s":0.68,"drought_rho_s":-0.081},"independent":{"aal":1093367876210,"ep100":7058174392706,"var995":8077653938861,"tvar995":9354474571946,"ep":{"2":529036336317,"5":1665872394373,"10":2852359922205,"20":4143716588931,"25":4502564416421,"50":5722850388157,"100":7058174392706,"200":8077653938861,"250":8344888180646,"500":8986968499349,"1000":10564138467909},"note":"every hazard independent of the others"},"comonotonic":{"aal":1093367876210,"ep100":8157738236440,"var995":9771267788370,"tvar995":13049686769311,"ep":{"2":425683464615,"5":1682721620155,"10":3042892695007,"20":4490755634911,"25":4922418236866,"50":6378532603094,"100":8157738236440,"200":9771267788370,"250":10499453800863,"500":11956378360239,"1000":14933578607026},"note":"each hazard's years sorted together (worst with worst); not an upper limit on the 1-in-200 of the sum"}},"dependence_scenarios_note":"Comparison scenarios, not limits on a quantile: VaR is not subadditive, so the comonotonic 1-in-200 is not a worst case for the 1-in-200 of the sum and the independent case is not a floor. copula_uf_*_dr_*: urban flood and drought placed on the flood rank by a Gaussian copula at the low end, estimate and high end of the 90% bootstrap interval of their historical Spearman correlation with flood loss.","bounds":{"independent":{"ep":{"2":529036336317,"5":1665872394373,"10":2852359922205,"20":4143716588931,"25":4502564416421,"50":5722850388157,"100":7058174392706,"200":8077653938861,"250":8344888180646,"500":8986968499349,"1000":10564138467909},"var995":8077653938861},"comonotonic":{"ep":{"2":425683464615,"5":1682721620155,"10":3042892695007,"20":4490755634911,"25":4922418236866,"50":6378532603094,"100":8157738236440,"200":9771267788370,"250":10499453800863,"500":11956378360239,"1000":14933578607026},"var995":9771267788370},"note":"alias of dependence_scenarios.independent / .comonotonic kept for existing readers; comparison scenarios, not bounds"},"overlap":{"rule":"In each urban-flood district and year: urban flood net = urban flood - min(rain-on-homes loss, urban flood loss x the district's katcha share of residential value). The katcha homes the urban model floods and the katcha homes rain on homes destroys are the same homes in the same rain; rain on homes is fitted to all-cause house counts, so it keeps them.","urban_flood_gross_aal":60773895076,"removed_aal":1131964701,"urban_flood_net_aal":59641930375,"share_removed":0.0186,"district_years_with_overlap":111999,"all_hazard_aal_without_removal":1094499840911,"all_hazard_var995_without_removal":8158015536116,"upper_bound_not_removed":{"flash_in_urban_flood_districts_aal":822769211,"river_in_urban_flood_districts_aal":11726542450,"note":"min(flash or river building loss, urban flood loss) per district-year: the most the two could share if every flooded building were the same; not removed (different flood sources; corridor and river cells are not located relative to the city box)"},"historical_years_largest_overlap":{"1995":{"rain_on_homes_pkr":194303115831,"urban_flood_katcha_part_pkr":6000708533,"overlap_removed_pkr":5737090133},"1991":{"rain_on_homes_pkr":60730853141,"urban_flood_katcha_part_pkr":3697099474,"overlap_removed_pkr":3470529313},"1985":{"rain_on_homes_pkr":22202091150,"urban_flood_katcha_part_pkr":3756441815,"overlap_removed_pkr":3402750797},"1999":{"rain_on_homes_pkr":48758134495,"urban_flood_katcha_part_pkr":2854205363,"overlap_removed_pkr":2810376212},"2010":{"rain_on_homes_pkr":71939705375,"urban_flood_katcha_part_pkr":3055686757,"overlap_removed_pkr":2779058754},"2020":{"rain_on_homes_pkr":9824603124,"urban_flood_katcha_part_pkr":3242980362,"overlap_removed_pkr":2413935122},"1989":{"rain_on_homes_pkr":20970809357,"urban_flood_katcha_part_pkr":2203323217,"overlap_removed_pkr":2194755467},"1983":{"rain_on_homes_pkr":71524828915,"urban_flood_katcha_part_pkr":2962643258,"overlap_removed_pkr":2045065303}},"historical_mean_removed":1014089067},"checks":{"cyclone_aal_vs_engine":0.955,"urban_aal_vs_engine":1.0,"earthquake_aal_vs_engine":1.0},"buildings_only":{"what":"Buildings only (flood, earthquake, cyclone, urban flood): structures, without infrastructure, crops and livestock. Evidence differs by hazard: each component carries its own class in results.json evidence (none is in the tested class)","aal":629412089183,"ep":{"2":232360751326,"5":923981275386,"10":1775906837050,"20":2674144600384,"25":2962442836976,"50":3992165368084,"100":4847049645603,"200":5471975052375,"250":5726655884659,"500":6349920440991,"1000":6972955236031},"var995":5471975052375,"tvar995":6609627160531},"without_exploratory_sectors":{"what":"All hazards less the flood sectors whose evidence is not tested-pass (sectors_v1.json total.exploratory_sectors)","sectors_left_out":["roads","rail","livestock","crops","power","telecom","irrigation"],"aal":653478567650,"ep":{"2":258533638568,"5":945005484302,"10":1796709681399,"20":2686042622008,"25":2969205351646,"50":3992383547297,"100":4861673156235,"200":5471991867401,"250":5729381697998,"500":6349929305206,"1000":6972955221652},"var995":5471991867401,"tvar995":6620243030057},"aal_uncertainty":{"sampling_band":{"river_flood_buildings":{"aal":[393555284225,453266596105],"rp100":[3361860445153,4477616300527],"rp200":[3922296553110,5177058511610],"tvar995":[4469154988270,5831254470602],"what":"5th-95th percentile over 100 bootstrap resamples of the 47 observed years (2,000 simulated years each), copula refitted each time","holds_fixed":"defence standards, damage factor, damage curves and hazard maps; flash floods and rain on homes are not resampled"},"flood_sectors":"not computed","earthquake":"not computed","cyclone":"not computed","urban_flood":"not computed","drought":"not computed"},"model_error":{"flood_buildings":{"factor":1.7,"n":8,"tests":{"2010":0.82,"2011":0.41,"2012":1.07,"2013":0.62,"2014":1.19,"2020":0.97,"2024":4.56,"2025":2.4},"test":"national house-damage equivalents by season, each season left out of the rain-on-homes fit (seasons of 10,000+ recorded houses; 2022 left out as the river calibration's fit season); river damage factor and defence standard held at their fitted values","aal":500995548469,"low":294703263805,"high":851692432397},"flood_sectors":{"by_sector":{"roads":{"aal":111236069718,"factor":2.68,"n":4,"tests":{"2010":0.2,"2011":2.48,"2022":3.41,"2025":1.22},"evidence":"partly passed","low":41505996163,"high":298112666844,"status":"out-of-sample tests"},"rail":{"aal":25231896066,"factor":105.03,"n":3,"tests":{"2010":0.01,"2011":347.6,"2022":0.03},"evidence":"failed test","low":240235133,"high":2650106043812,"status":"out-of-sample tests"},"livestock":{"aal":5569159225,"factor":5.24,"n":7,"tests":{"2010 NATIONAL":0.408,"2022 BALOCHISTAN":0.427,"2022 SINDH":0.75,"2022 PUNJAB":0.08,"2022 KP":0.228,"2022 GILGIT BALTISTAN":0.216,"2022 AJK":0.018},"evidence":"partly passed","low":1062816646,"high":29182394339,"status":"out-of-sample tests"},"crops":{"aal":259521032175,"factor":2.03,"n":5,"tests":{"2010":0.37,"2011":0.96,"2014":4.52,"2022":0.49,"2025":0.76},"evidence":"partly passed","low":127842872993,"high":526827695315,"status":"out-of-sample tests"},"power":{"aal":7467224411,"factor":3.68,"n":3,"tests":{"2010":0.2,"2011":4.32,"2022":2.3},"evidence":"failed test","low":2029137068,"high":27479385832,"status":"out-of-sample tests"},"telecom":{"aal":3395340875,"factor":4.49,"n":3,"tests":{"2010":0.29,"2011":10.49,"2022":0.4},"evidence":"failed test","low":756200640,"high":15245080529,"status":"out-of-sample tests"},"irrigation":{"aal":27468585977,"factor":4.87,"n":3,"tests":{"2010":0.18,"2011":5.2,"2022":0.25},"evidence":"failed test","low":5640366730,"high":133772013708,"status":"out-of-sample tests"}},"test":"each sector's model/record factor on complete records, each left out of the fit in turn (2010, 2011, 2014, 2022, 2025 where recorded; livestock 2010 and 2022 by province, each left out)","aal":439889308722,"low":179077625485,"high":3680725282680},"earthquake":{"factor":3.91,"n":19,"test":"earthquakes each left out of the damage refit, and earthquakes never used in it (eq_validation.json)","aal":63100628977,"low":16138268281,"high":246723459300},"cyclone":{"factor":null,"status":"not quantified","test":"1999 used in the fit; the held-out 2023 record includes damage from all causes and does not isolate wind damage. Separate wind-model error is not quantified.","aal":5673981362,"low":5673981362,"high":5673981362},"urban_flood":{"factor":null,"status":"not quantified","test":"no recorded urban flood loss; hotspot hit rates (Lahore, Karachi) test the flooded places, not the loss","aal":59641930375,"low":59641930375,"high":59641930375},"drought":{"factor":10.32,"n":13,"test":"drought years, each left out of the yield-response fit, against the national production shortfall (FAOSTAT, all causes); leave-one-year-out skill about zero","aal":24066478306,"low":2332023092,"high":248366056118}},"sensitivity_range":{"low":557567092399,"high":5092823142232,"aal":1093367876210,"varied":["flood_buildings","flood_sectors.roads","flood_sectors.rail","flood_sectors.livestock","flood_sectors.crops","flood_sectors.power","flood_sectors.telecom","flood_sectors.irrigation","earthquake","drought"],"held_at_central":["cyclone","urban_flood"],"what":"Sensitivity range, not an interval with a stated probability: every quantified component at its AAL divided, then multiplied, by its own out-of-sample error factor, all at once; components without an out-of-sample test held at their central AAL (they add no width, which understates the spread)."},"sensitivity_range_without_exploratory_sectors":{"low":378489466915,"high":1412097859552,"sectors_left_out":["roads","rail","livestock","crops","power","telecom","irrigation"],"what":"The same, with the flood sectors whose evidence is not tested-pass left out entirely (their AAL is not in either end)."},"note":"Three separate things: the sampling band (how much 47 years of record move the river result), each component's model error from its own tests, and a sensitivity range built from them. They are not added into one interval."}},"aq_health":{"what":"Deaths attributable to outdoor PM2.5 each year, adults 25 and over: a model estimate, not recorded deaths.","method":"district annual population-weighted PM2.5 from the AGN air-quality chain (CAMS EAC4 calibrated to ground monitors and\n          sensor-fused): data/tables/aq_district_annual.json. Central: 2021-2025 mean.\nCurve     GEMM NCD+LRI, adults 25+ (Burnett et al. 2018, PNAS 115:9592; built from 41 cohorts in 16 countries, including the Chinese\n          cohort with exposures up to 84 ug/m3): HR(z) = exp(theta log(z/alpha + 1) / (1 + exp(-(z - mu)/nu))), z = max(0, PM2.5 - 2.4),\n          theta 0.1430 (SE 0.01807), alpha 1.6, mu 15.5, nu 36.8. Attributable fraction 1 - 1/HR.\nBaseline  deaths from noncommunicable diseases and lower respiratory infections at age 25+. Pakistan deaths = crude death rate x population\n          (World Bank WDI: SP.DYN.CDRT.IN 2024 6.445 per 1,000; SP.POP.TOTL 2025). Range: NCD deaths at 25+ only (WDI SH.DTH.NCOM.ZS 2019,\n          57.2% of deaths, the last year before COVID-19; 94% of NCD deaths assumed at 25+) to all non-injury deaths at 25+ (70.7% of deaths\n          at 25+, UN WPP via WDI; injuries 8.4%, SH.DTH.INJR.ZS 2019). Spread over districts by Census 2023 population.\nUncertainty  4,000 draws: theta from its standard error, baseline uniform over the range, exposure x a national factor (log-sd 0.10) and a\n          district factor (log-sd 0.12, the leave-one-site-out error of the fused map). Reported: median and 95% interval.\nCheck     State of Global Air 2024 (HEI/IHME, GBD 2021): 256,000 deaths from all air pollution in Pakistan in 2021, 58% from outdoor PM2.5\n          (about 148,000). GBD's curves flatten more at high exposure than GEMM, so GEMM gives about twice as many at Pakistan's levels;\n          the same gap exists worldwide (GEMM 8.9 million vs GBD 4.2 million deaths in 2015). 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Each district takes the nearest station with 15+ usable years (a year is usable\n         with 150+ days reported); districts further than 200 km from one are counted with it but flagged.\nRisk     relative risk of dying on a hot day, from the Ahmedabad heat-mortality curve before its heat action plan (Hess et al. 2018,\n         J Environ Public Health, 2007-2010: RR 2.34, 95% CI 1.98-2.76, at 47 C against 40 C, same day), taken as log-linear:\n         7 degrees above the reference more than doubles deaths. Pakistan had no heat action plans in the years modelled. The\n         reference is moved to each station's own climate (people are adapted to their usual heat): its 90th percentile of\n         April-September days, 1991-2020, the usual heat-wave percentile. Not tuned to Karachi. Why not lower: Ahmedabad's 40 C is\n         its 69th percentile, but the curve is flat just above 40 C, and a straight line from there puts 5.9% of all deaths in\n         ordinary summers, six times the global heat share (0.91%, 95% CI 0.56-1.36%, Zhao et al. 2021, Lancet Planet Health);\n         the 90th percentile gives 1.2%.\nDeaths   extra deaths on a day = expected deaths x (RR - 1); expected deaths = crude death rate (World Bank WDI 2024, 6.445 per 1,000)\n         x district population (PBS Census 2023; GHSL 2020 where the census name does not match) / 365.25.\nYears    the heat deaths of a year are counted as EXCESS OVER A NORMAL SUMMER (the station's 1991-2020 mean), which is what a heatwave\n         death toll measures. Normal summers also carry heat deaths under this curve (shown separately, less certain: the curve was\n         measured in one city).\nTest     Karachi, 15-30 June 2015, against the about 1,200 deaths recorded in the city (1,233 Sindh government, 1,260 ARY News;\n         Edhi and press estimates up to 2,000). Karachi population from the 2017 census (16.05 million).","sources":["Zhao Q. et al. 2021, Global, regional, and national burden of mortality associated with non-optimal ambient temperatures, Lancet Planet Health 5:e415-e425","Hess J.J. et al. 2018, Building resilience to climate change: pilot evaluation of the impact of India's first heat action plan on all-cause mortality, J Environ Public Health 2018:7973519","NOAA NCEI Global Surface Summary of the Day (GSOD)","World Bank WDI crude death rate","PBS Census 2017 and 2023; GHSL 2020","Reported Karachi 2015 death counts (Sindh government; ARY News; Edhi Foundation via press)"],"stations_used":22,"districts_far_from_station":13},"whatif":{"what":"Past disasters replayed on today's Pakistan, and one hypothetical earthquake","built":"2026-10-10","method":"What-if scenarios: past disasters replayed on today's Pakistan (today's population, housing and assets), by district.\n\n    python tools/agn_scenarios.py          # needs internet once for the earthquake ShakeMaps (USGS); floods use the engine's own replays\n\nEarthquakes: the historical event's USGS ShakeMap (atlas or event ShakeMap: recordings, felt reports, the fault where known) through\nAGN's earthquake damage model (the alert chain, tools/agn_quake_alert.py: Lagomarsino-Giovinazzi vulnerability on Census 2023\nhousing, duration term fitted on eight Pakistan earthquakes). One hypothetical event, clearly marked, uses AGN's own ground motion, as does 1935 Quetta (its atlas ShakeMap has only a\npoint source at the ISC-GEM epicentre near Kalat, too far south to reproduce the recorded destruction of Quetta).\nRange: intensity -0.5 / +0.5. 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Corrected PBS district-workbook exposure; MMI rounded to 0.01.","adopted":true}]},"flood_seasons_test":{"what":"Every monsoon season with a published house-damage count by province, against the flood engine (rivers, flash floods, rain on homes) for the same provinces. Destroyed-house equivalents (destroyed + 0.4 x partly damaged); counts include all causes.","seasons":{"2010":{"recorded_equiv":1191258,"model_equiv":977526,"ratio":0.82,"source":"ADB/World Bank, Pakistan Floods 2010 Preliminary Damage and Needs Assessment, Table-Annex-6.2","final":true,"provinces":6},"2011":{"recorded_equiv":707926,"model_equiv":290873,"ratio":0.41,"source":"Pakistan Economic Survey 2011-12, Special Section 'Pakistan: Flood Impact Assessment' (WB/ADB 2011 Damage and Needs Assessment)","final":true,"provinces":2},"2012":{"recorded_equiv":432453,"model_equiv":458956,"ratio":1.06,"source":"NDMA Annual Report 2012, Annex B 'Damages / Losses - Floods 2012'","final":true,"provinces":6},"2013":{"recorded_equiv":52235,"model_equiv":32473,"ratio":0.62,"source":"NDMA Annual Report 2013, Section II.5(b) 'Floods - 2013'","final":true,"provinces":5},"2014":{"recorded_equiv":115682,"model_equiv":138635,"ratio":1.2,"source":"Pakistan Economic Survey 2014-15, Annex III (Flood 2014); Punjab split from PDMA Punjab via Pakistan Academy of Sciences (2018)","final":true,"provinces":2},"2015":{"recorded_equiv":6189,"model_equiv":48080,"ratio":7.76,"source":"NDMA Annual Report 2014-15, Annex H 'Post Disaster Report - Floods 2015'","final":true,"provinces":5},"2016":{"recorded_equiv":839,"model_equiv":4514,"ratio":5.33,"source":"NDMA Annual Report 2016, Annex D Post Disaster Report Monsoon 2016","final":true,"provinces":6},"2017":{"recorded_equiv":471,"model_equiv":557,"ratio":1.18,"source":"NDMA Annual Report 2017, Annex C (Post Monsoon Report 2017)","final":true,"provinces":7},"2018":{"recorded_equiv":932,"model_equiv":52553,"ratio":55.82,"source":"NDMA Annual Report 2018, 'Losses / Damages - Monsoon 2018 (June - September 2018)'","final":true,"provinces":1},"2019":{"recorded_equiv":390,"model_equiv":44030,"ratio":110.15,"source":"Pakistan Bureau of Statistics, Compendium on Environment Statistics 2020, Table B-23 'Monsoon 2019'","final":true,"provinces":5},"2020":{"recorded_equiv":202864,"model_equiv":197569,"ratio":0.97,"source":"Sindh: PDMA Sindh Provincial Monsoon Contingency Plan 2024, Table 3.2.3.1; national: NDMA figures in IFRC DREF Final Report MDRPK019","final":true,"provinces":2},"2021":{"recorded_equiv":515,"model_equiv":5425,"ratio":10.35,"source":"NDMA Annual Report 2021, Monsoon 2021 losses (respective PDMAs and DC Office Islamabad)","final":true,"provinces":7},"2022":{"recorded_equiv":1289752,"model_equiv":1020638,"ratio":0.79,"source":"Pakistan Floods 2022 Post-Disaster Needs Assessment, Table 3","final":true,"provinces":5},"2023":{"recorded_equiv":3532,"model_equiv":39640,"ratio":11.19,"source":"NDMA Monsoon 2023 Daily Situation Report No. 94 (27 Sep 2023, cumulative from 25 Jun)","final":false,"provinces":7},"2024":{"recorded_equiv":32258,"model_equiv":144994,"ratio":4.49,"source":"NDMA Monsoon 2024 Daily Situation Report No. 75 (13 Sep 2024, cumulative from 1 Jul)","final":false,"provinces":7},"2025":{"recorded_equiv":127460,"model_equiv":306184,"ratio":2.4,"source":"NDMA, Monsoon 2025 Final Consolidated Situation Report No. 87 (30 Sep 2025)","final":true,"provinces":7},"2026":{"recorded_equiv":1109,"model_equiv":3104,"ratio":2.78,"source":"NDMA, Monsoon 2026 Daily Situation Report No. 97 (30 Sep 2026, cumulative from 26 Jun)","final":false,"provinces":7}},"all_seasons_total_ratio":0.9,"big_seasons":["2010","2011","2012","2013","2014","2020","2022","2024","2025"],"big_seasons_typical_error":1.64,"rain_fit":{"observations":80,"years":[2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025],"total_only_equiv_share":0.578,"form":"joint: 2022 by district (quasi-binomial) + other seasons by province (lognormal)","dispersion_2022":18322.3,"window_days":1,"sigma":1.863,"tests_by_year_left_out":{"2010":{"recorded_equiv":1191258,"model_equiv":975639,"ratio":0.82,"provinces":6},"2011":{"recorded_equiv":707926,"model_equiv":293326,"ratio":0.41,"provinces":2},"2012":{"recorded_equiv":432453,"model_equiv":463934,"ratio":1.07,"provinces":6},"2013":{"recorded_equiv":52235,"model_equiv":32320,"ratio":0.62,"provinces":5},"2014":{"recorded_equiv":115682,"model_equiv":138194,"ratio":1.19,"provinces":2},"2015":{"recorded_equiv":6189,"model_equiv":47049,"ratio":7.59,"provinces":5},"2016":{"recorded_equiv":839,"model_equiv":4686,"ratio":5.53,"provinces":6},"2017":{"recorded_equiv":471,"model_equiv":539,"ratio":1.14,"provinces":7},"2018":{"recorded_equiv":932,"model_equiv":52551,"ratio":55.82,"provinces":1},"2019":{"recorded_equiv":390,"model_equiv":43746,"ratio":109.44,"provinces":5},"2020":{"recorded_equiv":202864,"model_equiv":195775,"ratio":0.97,"provinces":2},"2021":{"recorded_equiv":515,"model_equiv":6320,"ratio":12.06,"provinces":7},"2022":{"recorded_equiv":1289752,"model_equiv":753111,"ratio":0.58,"provinces":5},"2023":{"recorded_equiv":3532,"model_equiv":44024,"ratio":12.43,"provinces":7},"2024":{"recorded_equiv":32258,"model_equiv":147063,"ratio":4.56,"provinces":7},"2025":{"recorded_equiv":127460,"model_equiv":305632,"ratio":2.4,"provinces":7}},"typical_error_left_out":3.86,"within_3x_left_out":0.51,"typical_error_in_sample":3.69,"unit":"destroyed-house equivalents (destroyed + 0.4 x partly damaged), all causes in the season, by province"},"changes_tested":{"what":"Changes to the flood engine tested on 8-9 Oct 2026 against every monsoon season with a published house-damage count (16 seasons 2010-2025, 80 province figures; data/tables/observed_monsoon_houses.json). Adopted only if the season record improves and the engine's own house tests (2010, 2014, 2025 within 2x; 2022 within 1.5x) still pass. Scores are model over recorded; typical error is the geometric mean factor.","baseline":{"label":"engine as installed on 8 Oct (gauge anchoring, Punjab 1-in-24, rain on homes on ERA5-Land fitted to 2022)","province_typical_error":4.41,"within_3x":0.44,"big_seasons_typical_error":2.41,"all_seasons_total_ratio":0.78,"house_tests":{"2010":0.66,"2014":0.77,"2022":0.99,"2025":0.8},"quiet_seasons":{"2015":6.89,"2016":6.26,"2017":10.9,"2019":179.58,"2021":4.31,"2023":1.03,"2024":5.17},"rain_seasons":{"2011":0.59,"2012":0.5,"2013":0.23,"2020":0.07}},"tested":[{"change":"Gauge-anchored river years: GloFAS annual maxima reordered per reach to follow observed barrage and headworks peaks (22 control points, FFC / PMD FFD / World Bank / ADB; data/tables/river_annual_peaks.json); 908 of 3,468 river cells follow a control point","province_typical_error":4.53,"big_seasons_typical_error":1.9,"all_seasons_total_ratio":0.85,"house_tests":{"2010":0.67,"2014":1.3,"2022":0.98,"2025":2.28},"result":"adopted together with the Punjab defence standard below. Alone it gave better timing in quiet years (2018 Sutlej false flood 137x -> 54x; 2014 0.70 -> 1.11) but put 2025, the record Chenab-Ravi-Sutlej year, at 2.3x the recorded houses."},{"change":"Rain on homes fitted to all 16 seasons (lognormal likelihood on province figures, each season left out in turn) instead of 2022 by district","province_typical_error_left_out":5.69,"result":"not adopted: the fit switches rain damage almost off, because river false floods in quiet years dominate the record; Sindh's rain years 2011, 2012, 2020 and 2022 then fall to 0.1-0.5x. An absolute-rainfall measure did no better. Available as --rain-fit multi."},{"change":"Buildings linked only to rivers with a mean annual flood of at least 100 or 300 m3/s (search 22-33 km), so small plains channels carry no river flood","province_typical_error":6.05,"big_seasons_typical_error":3.75,"result":"not adopted: removes the 2015 Rahim Yar Khan false flood but loses most of 2011, 2012 and 2020 in Sindh. Available as --min-qbar."},{"change":"Wider river link (radius 4 or 6 cells, 22-33 km)","province_typical_error":5.2,"result":"not adopted"},{"change":"IMERG rain fitted to 2022 by district only (IMERG V07, satellite rain merged with GPCC gauges; data/tables/imerg_monsoon_rain.json, tools/imerg_district_rain.py; ERA5-Land quantile-mapped onto IMERG before 2001)","province_typical_error":8.09,"within_3x":0.38,"big_seasons_typical_error":1.66,"all_seasons_total_ratio":1.02,"house_tests":{"2010":0.79,"2014":0.95,"2022":0.97,"2025":1.23},"quiet_seasons":{"2015":17.68,"2016":31.0,"2017":69.13,"2019":174.82,"2021":63.63,"2023":53.03,"2024":7.84},"rain_seasons":{"2011":0.56,"2012":1.11,"2013":1.68,"2020":0.46},"result":"not adopted: big seasons improve (x1.66) but the 2022-only fit gives a wide fragility that damages homes every quiet year (2017 69x, 2021 64x, 2023 53x)"},{"change":"Rain on homes on IMERG, fitted to every season by province only","province_typical_error":3.59,"within_3x":0.47,"big_seasons_typical_error":2.33,"all_seasons_total_ratio":0.6,"house_tests":{"2010":0.64,"2014":0.79,"2022":0.58,"2025":0.77},"quiet_seasons":{"2015":6.21,"2016":4.77,"2017":1.75,"2019":101.47,"2021":6.43,"2023":5.78,"2024":3.78},"rain_seasons":{"2011":0.33,"2012":0.69,"2013":0.35,"2020":0.1},"result":"not adopted: quiet years improve but 2022 falls to 0.58x (fails its 1.5x test)"},{"change":"Rain on homes on ERA5-Land with the joint fit (2022 by district + every other season by province)","province_typical_error":4.35,"within_3x":0.44,"big_seasons_typical_error":2.16,"all_seasons_total_ratio":0.87,"house_tests":{"2010":0.76,"2014":0.77,"2022":0.93,"2025":0.79},"quiet_seasons":{"2015":9.52,"2016":5.49,"2017":5.26,"2019":160.14,"2021":3.12,"2023":0.89,"2024":7.55},"rain_seasons":{"2011":0.86,"2012":0.64,"2013":0.17,"2020":0.18},"result":"not adopted: little change (x4.35); the gain comes from the IMERG rain, not the fit"},{"change":"Small GloFAS channels (mean annual flood under 100 m3/s) carry no river flood, with IMERG rain and the joint fit","province_typical_error":3.71,"within_3x":0.48,"big_seasons_typical_error":1.9,"all_seasons_total_ratio":0.72,"house_tests":{"2010":0.72,"2014":0.3,"2022":0.75,"2025":0.82},"quiet_seasons":{"2015":2.97,"2016":5.55,"2017":1.27,"2019":16.92,"2021":17.73,"2023":18.91,"2024":4.4},"rain_seasons":{"2011":0.33,"2012":0.95,"2013":1.0,"2020":0.32},"result":"not adopted: removes much of the 2015 and 2019 false floods (7x to 3x, 106x to 17x) but Punjab 2014 falls to 0.30x (fails), because the 2014 damage in Okara, Pakpattan and Kasur sits on those channels"},{"change":"The same only inside the canal-irrigated plains (Irrigation Departments' canal network, 5,446 canals; data/protection/irrigation_canals.geojson), hill torrents and mountain streams kept","province_typical_error":3.8,"within_3x":0.49,"big_seasons_typical_error":1.89,"all_seasons_total_ratio":0.72,"house_tests":{"2010":0.71,"2014":0.32,"2022":0.75,"2025":0.82},"quiet_seasons":{"2015":2.95,"2016":5.69,"2017":1.3,"2019":37.89,"2021":17.53,"2023":18.53,"2024":4.45},"rain_seasons":{"2011":0.34,"2012":0.95,"2013":1.03,"2020":0.33},"result":"not adopted: same trade-off, 2014 at 0.32x; 2019 38x"},{"change":"IMERG rain and the joint fit without the Punjab 1-in-24 standard","province_typical_error":3.52,"within_3x":0.52,"big_seasons_typical_error":2.01,"all_seasons_total_ratio":0.8,"house_tests":{"2010":0.75,"2014":1.15,"2022":0.76,"2025":2.31},"quiet_seasons":{"2015":7.61,"2016":4.79,"2017":1.29,"2019":104.02,"2021":9.49,"2023":10.06,"2024":4.21},"rain_seasons":{"2011":0.39,"2012":0.98,"2013":0.56,"2020":0.17},"result":"not adopted: 2025 runs 2.31x (fails its 2x test). With the new rain Punjab's own leave-one-season-out test no longer favours its standard (2.8x against 2.2x national); the standard is kept for the 2025 test and flagged for review"},{"change":"Building linked to the river through the connected JRC water patch it sits in, instead of the strongest GloFAS cell within about 11 km","result":"not run through the engine: on the flat plains the 1-in-10 water forms one connected sheet, so every plains building would link to the lower Indus"},{"change":"Rain-anchored river years for small catchments: GloFAS years of river cells with catchments under 20,000 km2 (not governed by a barrage record) reordered to follow their 3-day catchment rain (HydroBASINS level 7 from NIGRAAN; IMERG 2001-2025, ERA5-Land before; data/tables/catchment_rain.json)","province_typical_error":4.09,"within_3x":0.49,"big_seasons_typical_error":2.07,"all_seasons_total_ratio":0.69,"house_tests":{"2010":0.52,"2014":0.73,"2022":0.9,"2025":0.78},"punjab_2022":0.54,"kp_2010":0.16,"kp_2022":0.2,"district_2022":{"RAJANPUR":0.12,"DERA GHAZI KHAN":0.04,"SWAT":0.14,"NOWSHERA":0.37,"D.I.KHAN":0.07,"TANK":0.28},"result":"not adopted: typical error 4.09x against 3.60x; Rajanpur 2022 0.02x to 0.12x only; KP 2010 falls to 0.16x and Balochistan 2010 to 0.09x"},{"change":"The same with the 10-day catchment rain","province_typical_error":4.17,"within_3x":0.47,"big_seasons_typical_error":1.97,"all_seasons_total_ratio":0.74,"house_tests":{"2010":0.53,"2014":0.83,"2022":0.92,"2025":0.91},"punjab_2022":0.75,"kp_2010":0.15,"kp_2022":0.18,"district_2022":{"RAJANPUR":0.36,"DERA GHAZI KHAN":0.04,"SWAT":0.14,"NOWSHERA":0.36,"D.I.KHAN":0.06,"TANK":0.13},"result":"not adopted: Punjab 2022 0.27x to 0.75x and Rajanpur to 0.36x, but typical error 4.17x, KP 2010 0.15x, Punjab 2023 421x"},{"change":"Flash floods driven by the IMERG 3-day district rain instead of ERA5-Land","province_typical_error":3.69,"within_3x":0.49,"big_seasons_typical_error":1.85,"all_seasons_total_ratio":0.73,"house_tests":{"2010":0.71,"2014":0.83,"2022":0.75,"2025":0.82},"punjab_2022":0.29,"kp_2010":0.86,"kp_2022":0.24,"district_2022":{"RAJANPUR":0.03,"DERA GHAZI KHAN":0.11,"SWAT":0.18,"NOWSHERA":0.37,"D.I.KHAN":0.09,"TANK":0.25},"result":"not adopted: the joint fit sets the flash part to almost nothing (factor 0.001); typical error 3.69x"},{"change":"Rain anchoring (10-day) with IMERG flash floods","province_typical_error":4.22,"within_3x":0.48,"big_seasons_typical_error":1.95,"all_seasons_total_ratio":0.75,"house_tests":{"2010":0.53,"2014":0.83,"2022":0.92,"2025":0.93},"punjab_2022":0.78,"kp_2010":0.15,"kp_2022":0.19,"district_2022":{"RAJANPUR":0.37,"DERA GHAZI KHAN":0.04,"SWAT":0.21,"NOWSHERA":0.36,"D.I.KHAN":0.06,"TANK":0.14},"result":"not adopted: typical error 4.22x"},{"change":"Rain on homes scored by the rain over each district's contributing catchments (piedmont districts take the hills' rain; data/tables/contrib_rain.json)","province_typical_error":3.71,"within_3x":0.47,"big_seasons_typical_error":1.79,"all_seasons_total_ratio":0.81,"house_tests":{"2010":0.76,"2014":0.83,"2022":0.79,"2025":0.84},"punjab_2022":0.28,"kp_2010":1.37,"kp_2022":0.22,"district_2022":{"RAJANPUR":0.05,"DERA GHAZI KHAN":0.05,"SWAT":0.16,"NOWSHERA":0.37,"D.I.KHAN":0.06,"TANK":0.24},"result":"not adopted: big seasons improve (1.79x) and all seasons 0.81x, but typical error 3.71x and Dera Ghazi Khan and Rajanpur 2022 stay at 0.05x: IMERG puts the 2022 rain over the Koh-e-Suleman catchments at only 2-3 times normal, a top-five year, not an extreme one"},{"change":"River re-link (tools/main_rivers.py, engine option --river-relink): buildings within 15 km of the Indus, Jhelum, Chenab, Ravi, Sutlej or Kabul whose GloFAS link carries under a quarter of that river's mean annual flood are tied to the river's governing barrage or headworks instead (297,091 building cells; 138,068 on the Chenab below Qadirabad, where GloFAS gives the Chiniot Bridge station 6 m3/s)","date":"2026-10-09","province_typical_error":3.58,"within_3x":0.49,"big_seasons_typical_error":1.88,"all_seasons_total_ratio":0.72,"house_tests":{"2010":0.71,"2014":0.71,"2022":0.75,"2025":0.82},"extent_check_joint":{"2010":[0.336,0.343],"2014":[0.274,0.21],"2022":[0.211,0.202],"2025":[0.394,0.428],"note":"share of buildings inside each year's mapped flood that the replay floods, installed vs re-linked"},"result":"not adopted in the engine: no gain overall (typical error 3.60x to 3.58x, big seasons 1.87x to 1.88x); 2010 and 2025 extents improve, 2014 falls (the Chenab's 2014 peak ranks only about 1-in-13 at Qadirabad in the gauge-anchored record, below the Punjab defence standard). Used in the location tool, where it changes only which river year a cell replays and which reach is named (research/site_2026-10-09/relink_test.json)."},{"change":"Record extension (tools/main_rivers.py extend_peaks; engine default since 9 Oct 2026, off with --no-anchor-extend): each barrage or headworks record's missing years are filled by quantile mapping from the best-correlated record (rho 0.8 or more, 10 or more shared years) in the same reach group (upper Indus, lower Indus, Jhelum, upper and lower Chenab, Ravi, Sutlej, Kabul), so more years of the 1979-2025 replay follow a gauge instead of GloFAS's ranking","date":"2026-10-09","province_typical_error":3.6,"within_3x":0.49,"big_seasons_typical_error":1.85,"all_seasons_total_ratio":0.74,"house_tests":{"2010":0.71,"2014":0.88,"2022":0.76,"2025":0.84},"extent_check_joint":{"2010":[0.336,0.337],"2014":[0.274,0.288],"2022":[0.211,0.211],"2025":[0.394,0.399],"note":"share of buildings inside each year's mapped flood that the replay floods, before vs extended"},"aal_bn":[352,357],"result":"adopted: equal or better on every check (big seasons 1.87x to 1.85x; 2014 0.83 to 0.88, 2025 0.81 to 0.84; extents 2014 and 2025 up, 2010 and 2022 equal). A first version that took donors from any river let the Guddu record fill upper-Indus years and was dropped. Production rerun reproduces the test run exactly (AAL PKR 357bn; chain tests all pass)."},{"change":"Record extension plus river re-link in the engine (--river-relink)","date":"2026-10-09","province_typical_error":3.57,"within_3x":0.49,"big_seasons_typical_error":1.85,"all_seasons_total_ratio":0.73,"house_tests":{"2010":0.72,"2014":0.76,"2022":0.75,"2025":0.88},"extent_check_joint":{"2010":0.343,"2014":0.262,"2022":0.202,"2025":0.44},"result":"not adopted: 2010 and 2025 improve but 2014 (0.88 to 0.76 houses; extent 0.288 to 0.262) and 2022 extent (0.211 to 0.202) fall; the re-link stays in the location tool only"},{"change":"Housing units and katcha share from the PBS Census 2023 district tables (tools/housing_census_2023.py: households, kacha + half of semi-pakka; AJK and GB by proxy), replacing the 2017-based table","date":"2026-10-09","province_typical_error":3.61,"within_3x":0.51,"big_seasons_typical_error":1.84,"all_seasons_total_ratio":0.75,"house_tests":{"2010":0.77,"2014":1.02,"2022":0.73,"2025":0.84},"extent_check_joint":{"2010":0.335,"2014":0.293,"2022":0.213,"2025":0.396},"national_by_season":{"2010":0.77,"2011":0.4,"2012":0.95,"2013":0.61,"2014":0.88,"2015":7.13,"2016":8.03,"2017":1.37,"2019":108.29,"2020":0.19,"2021":11.07,"2022":0.73,"2023":11.84,"2024":4.46,"2025":0.84},"result":"adopted: equal on the province figures (3.61x against 3.60x; within 3x 51% against 49%; big seasons 1.84x against 1.85x) and the housing input is now the published census, not the 2017-based table. 2016 gets worse (5.1x to 8.0x); AAL unchanged at PKR 357bn because the joint fit re-sets the damage factor (0.356 to 0.352)"},{"change":"Rain on homes driven by CHIRPS v2.0 daily rain (tools/chirps_district_rain.py, 1981-2025) instead of IMERG","date":"2026-10-09","rain_on_homes_left_out_typical_error":6.1,"within_3x":0.38,"house_tests":{"2010":0.64,"2014":0.95,"2022":0.51,"2025":0.86},"replay_2026_houses":253015,"recorded_2026_houses":1109,"result":"not adopted: left-out typical error 6.1x against 3.91x with IMERG, 2022 falls to 0.51, and the partial 2026 season is replayed at 253,015 houses against about 1,109 recorded. CHIRPS stays a candidate cross-check only"}],"adopted":[{"change":"Gauge-anchored river years (22 barrage and headworks peak records)","why":"the barrages' own records of which years were big replace GloFAS's ranking where a river cell follows a control point"},{"change":"Punjab defence standard 1-in-24 (between the 1-in-18 and 1-in-30 rank levels), fitted to Punjab's recorded house damage in 2010, 2012, 2013, 2014, 2022 and 2025 (tools/fit_regional_defences.py; data/tables/regional_defences.json); the national pair (1-in-16, damage x0.35) stays elsewhere. Sindh and Balochistan tested and kept national (their own standard did not beat it with each season left out).","why":"Punjab left-out typical error 1.86x against 2.32x with the national standard"},{"change":"Rain on homes from GPM IMERG V07 (2001-2025; ERA5-Land quantile-mapped before), fitted jointly to 2022 by district (quasi-binomial, Pearson dispersion; McCullagh and Nelder 1989) and every other recorded season by province (lognormal)","why":"ERA5-Land scored the August 2020 Karachi-Hyderabad rains at 1.1-1.4x normal and the dry 2019 at 1.5-3x; IMERG gets both right. All 80 province figures: typical error 4.41x to 3.60x, within 3x 44% to 49%; big seasons 2.41x to 1.87x; house tests 2010 0.71, 2014 0.83, 2022 0.75, 2025 0.81"},{"change":"Record extension of the barrage peak records within each reach group (default since 9 Oct 2026)","why":"equal or better on every check; more replay years follow a gauge"},{"change":"Census 2023 housing (PBS district tables) in place of the 2017-based housing table","why":"same skill, published source, removes the last third-party input to the building exposure"}],"result_adopted":{"province_typical_error":3.61,"within_3x":0.51,"big_seasons_typical_error":1.84,"all_seasons_total_ratio":0.75,"house_tests":{"2010":0.77,"2014":1.02,"2022":0.73,"2025":0.84},"quiet_seasons":{"2015":7.13,"2016":8.03,"2017":1.37,"2019":108.29,"2021":11.07,"2023":11.84,"2024":4.46},"rain_seasons":{"2011":0.4,"2012":0.95,"2013":0.61,"2020":0.19},"rain_fit_left_out_typical_error":3.91},"where_the_engine_is_weakest":"Quiet seasons still: small GloFAS channels in the irrigated plains give river floods in years with few recorded losses (2019 106x, 2015 7x, 2024 4x), and the same channels carry real damage in other years (Punjab 2014), so no rule tested so far removes one without the other. By province, Sindh improves with the new rain (typical 4.85x to 4.17x; big seasons 3.76x to 2.31x) and Punjab gets worse (3.21x to 4.22x; Punjab 2022 0.27x: the Koh-e-Suleman hill torrents of Dera Ghazi Khan and Rajanpur). Rain on homes in quiet years: 2021 and 2023 nationally 10-11x. 2020 (Karachi) 0.20x. Hill torrents of the Koh-e-Suleman piedmont (Dera Ghazi Khan, Rajanpur, Dera Ismail Khan, Tank): 2022 modelled at 0.02-0.1x. Neither satellite rain over their catchments nor GloFAS flows show 2022 as an extreme year there, and the JRC river maps and the HAND flash corridors both miss sheet flooding on alluvial fans; a fan (piedmont) flood layer built from a DEM is what is missing.","previous":{"date":"2026-10-08","file":"research/flood_rebuild/tested_2026-10-08.json"}}},"evidence":{"aal_tested_components":0.0,"aal_exploratory_components":1095141356194.6113,"aal_by_class":{"tested":0.0,"partly passed":463870708720.6113,"in-sample only":0.0,"failed test":570496739803.0,"untested":60773907671.0},"tested_components":[],"exploratory_components":["cyclone","drought","earthquake","flood_buildings","flood_sectors.crops","flood_sectors.irrigation","flood_sectors.livestock","flood_sectors.power","flood_sectors.rail","flood_sectors.roads","flood_sectors.telecom","urban_flood"],"rule":"Scientific status from each component's tests against recorded damage, recomputed from recorded and modelled counts: tested = at least one held-out event and every test inside its band; in-sample only = only events used in the fit (or in the season record the engine was tuned on), all inside their band; partly passed = some held-out events inside their band and some test outside it; failed test = no held-out event inside its band and at least one test outside it; untested = no damage test. Flood buildings: 2022 is the fit, 2010, 2014 and 2025 are in-sample (the rain-on-homes fit uses every season 2010-2025); only seasons after 2025 are out of sample.","classes":["tested","partly passed","in-sample only","failed test","untested"],"perils":{"rf":"failed test","ff":"failed test","eq":"partly passed","cyc":"failed test","uf":"untested","drought":"partly passed"},"components":[{"name":"flood_buildings","class":"failed test","perils":["rf","ff"],"aal":500995548622.0,"note":"21 tests (1 fit, 3 in-sample, 1 held-out, 16 partly independent); 11 outside their band: 2025 floods 2.403x vs [0.5, 2.0]; 2026 floods 2.801x vs [0.5, 2.0] (record not final); 2011 floods, rain fit refit without 2011 0.414x vs [0.5, 2.0]; 2015 floods, rain fit refit without 2015 7.591x vs [0.5, 2.0] ...","tests":[{"event":"2022 floods","role":"fit","recorded":1289751,"modelled":1020638,"ratio":0.791,"band":[0.67,1.5],"pass":true,"from":"calibration"},{"event":"2010 floods","role":"in-sample (rain fit uses 2010-2025)","recorded":1191258,"modelled":977525,"ratio":0.821,"band":[0.5,2.0],"pass":true,"from":"calibration"},{"event":"2014 floods","role":"in-sample (rain fit uses 2010-2025)","recorded":99300,"modelled":138635,"ratio":1.396,"band":[0.5,2.0],"pass":true,"from":"calibration"},{"event":"2025 floods","role":"in-sample (rain fit uses 2010-2025)","recorded":127432,"modelled":306184,"ratio":2.403,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2026 floods","role":"held-out (season after every fit)","recorded":1109,"modelled":3106,"ratio":2.801,"band":[0.5,2.0],"pass":false,"from":"events.2026.recorded","final":false},{"event":"2010 floods, rain fit refit without 2010","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":1191258,"modelled":975639,"ratio":0.819,"band":[0.5,2.0],"pass":true,"from":"flood_seasons_test.rain_fit"},{"event":"2011 floods, rain fit refit without 2011","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":707926,"modelled":293326,"ratio":0.414,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"},{"event":"2012 floods, rain fit refit without 2012","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":432453,"modelled":463934,"ratio":1.073,"band":[0.5,2.0],"pass":true,"from":"flood_seasons_test.rain_fit"},{"event":"2013 floods, rain fit refit without 2013","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":52235,"modelled":32320,"ratio":0.619,"band":[0.5,2.0],"pass":true,"from":"flood_seasons_test.rain_fit"},{"event":"2014 floods, rain fit refit without 2014","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":115682,"modelled":138194,"ratio":1.195,"band":[0.5,2.0],"pass":true,"from":"flood_seasons_test.rain_fit"},{"event":"2015 floods, rain fit refit without 2015","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":6189,"modelled":47049,"ratio":7.591,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"},{"event":"2016 floods, rain fit refit without 2016","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":839,"modelled":4686,"ratio":5.531,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"},{"event":"2017 floods, rain fit refit without 2017","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":471,"modelled":539,"ratio":1.141,"band":[0.5,2.0],"pass":true,"from":"flood_seasons_test.rain_fit"},{"event":"2018 floods, rain fit refit without 2018","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":932,"modelled":52551,"ratio":55.82,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"},{"event":"2019 floods, rain fit refit without 2019","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":390,"modelled":43746,"ratio":109.44,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"},{"event":"2020 floods, rain fit refit without 2020","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":202864,"modelled":195775,"ratio":0.965,"band":[0.5,2.0],"pass":true,"from":"flood_seasons_test.rain_fit"},{"event":"2021 floods, rain fit refit without 2021","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":515,"modelled":6320,"ratio":12.057,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"},{"event":"2022 floods, rain fit refit without 2022","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":1289752,"modelled":753111,"ratio":0.584,"band":[0.5,2.0],"pass":true,"from":"flood_seasons_test.rain_fit"},{"event":"2023 floods, rain fit refit without 2023","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":3532,"modelled":44024,"ratio":12.432,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"},{"event":"2024 floods, rain fit refit without 2024","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":32258,"modelled":147063,"ratio":4.558,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"},{"event":"2025 floods, rain fit refit without 2025","role":"partly independent (rain fit left this year out; engine changes were chosen on the 2010-2025 seasons)","recorded":127460,"modelled":305632,"ratio":2.398,"band":[0.5,2.0],"pass":false,"from":"flood_seasons_test.rain_fit"}],"n_tests":21,"n_failed":11},{"name":"earthquake","class":"partly passed","perils":["eq"],"aal":63100628977.35721,"note":"19 tests (19 held-out); 8 outside their band: 2019 Mirpur M5.4 earthquake 0.297x vs [0.33, 3.0]; 2008 Ziarat M6.4 doublet earthquake 3.256x vs [0.33, 3.0]; 2011 Dalbandin M7.2 (68 km deep) earthquake 81.238x vs [0.3333, 3.0]; 2013 Saravan (Iran) M7.7, Mashkel earthquake 73.171x vs [0.3333, 3.0] ...","tests":[{"event":"2005 Kashmir M7.6 earthquake","role":"held out (refit without it)","recorded":501892,"modelled":263124.88384481164,"ratio":0.524,"band":[0.33,3.0],"pass":true,"from":"calibration"},{"event":"2013 Awaran M7.7 earthquake","role":"held out (refit without it)","recorded":21000,"modelled":37130.57245609038,"ratio":1.768,"band":[0.33,3.0],"pass":true,"from":"calibration"},{"event":"2015 Hindu Kush M7.5 earthquake","role":"held out (refit without it)","recorded":109123,"modelled":117117.04121409467,"ratio":1.073,"band":[0.33,3.0],"pass":true,"from":"calibration"},{"event":"2019 Mirpur M5.4 earthquake","role":"held out (refit without it)","recorded":6200,"modelled":1832.2748271752475,"ratio":0.297,"band":[0.33,3.0],"pass":false,"from":"calibration"},{"event":"May 2026 Jalalpur Sharif M4.6 earthquake","role":"held out (refit without it)","recorded":10,"modelled":45.57960447421862,"ratio":2.779,"band":[0.33,3.0],"pass":true,"from":"calibration"},{"event":"Aug 2026 Jhelum M4.5 earthquake","role":"held out (refit without it)","recorded":0,"modelled":0.0,"ratio":1.0,"band":[0.33,3.0],"pass":true,"from":"calibration"},{"event":"2021 Harnai M5.9 earthquake","role":"held out (refit without it)","recorded":2579.8,"modelled":1155.5226842329148,"ratio":0.45,"band":[0.33,3.0],"pass":true,"from":"calibration"},{"event":"2008 Ziarat M6.4 doublet earthquake","role":"held out (refit without it)","recorded":5137.0,"modelled":16749.02412394116,"ratio":3.256,"band":[0.33,3.0],"pass":false,"from":"calibration"},{"event":"2011 Dalbandin M7.2 (68 km deep) earthquake","role":"held-out (never used in the fit)","recorded":200,"modelled":17050.02838344994,"ratio":81.238,"band":[0.3333,3.0],"pass":false,"from":"eq_validation"},{"event":"2013 Saravan (Iran) M7.7, Mashkel earthquake","role":"held-out (never used in the fit)","recorded":700,"modelled":51941.125674096395,"ratio":73.171,"band":[0.3333,3.0],"pass":false,"from":"eq_validation"},{"event":"2016 Hindu Kush M6.6 (212 km deep) earthquake","role":"held-out (never used in the fit)","recorded":20,"modelled":0.0,"ratio":0.333,"band":[0.3333,3.0],"pass":true,"from":"eq_validation"},{"event":"Feb 2015 Battagram M5.4 earthquake","role":"held-out (never used in the fit)","recorded":2,"modelled":0.0,"ratio":0.833,"band":[0.3333,3.0],"pass":true,"from":"eq_validation"},{"event":"Jul 2015 Murree M5.1 earthquake","role":"held-out (never used in the fit)","recorded":3,"modelled":0.0,"ratio":0.769,"band":[0.3333,3.0],"pass":true,"from":"eq_validation"},{"event":"May 2022 Khuzdar M5.1 earthquake","role":"held-out (never used in the fit)","recorded":184.0,"modelled":18.38046721688718,"ratio":0.146,"band":[0.3333,3.0],"pass":false,"from":"eq_validation"},{"event":"2023 Hindu Kush M6.5 (192 km deep) earthquake","role":"held-out (never used in the fit)","recorded":82.0,"modelled":0.0,"ratio":0.109,"band":[0.3333,3.0],"pass":false,"from":"eq_validation"},{"event":"Jan 2024 Hindu Kush M6.4 (204 km deep) earthquake","role":"held-out (never used in the fit)","recorded":0,"modelled":0.0,"ratio":1.0,"band":[0.3333,3.0],"pass":true,"from":"eq_validation"},{"event":"Jan 2026 Upper Hunza M5.6 earthquake","role":"held-out (never used in the fit)","recorded":450,"modelled":0.05073488520841174,"ratio":0.022,"band":[0.3333,3.0],"pass":false,"from":"eq_validation"},{"event":"Jun 2026 Musakhail doublet M5.2 and M5.3 earthquake","role":"held-out (never used in the fit)","recorded":117,"modelled":1149.6671917989145,"ratio":9.131,"band":[0.3333,3.0],"pass":false,"from":"eq_validation"},{"event":"Jun 2026 Hindu Kush M6.1 (199 km deep) earthquake","role":"held-out (never used in the fit)","recorded":0,"modelled":0.0,"ratio":1.0,"band":[0.3333,3.0],"pass":true,"from":"eq_validation"}],"n_tests":19,"n_failed":8},{"name":"cyclone","class":"failed test","perils":["cyc"],"aal":5938143852.0,"note":"2 tests (1 fit, 1 held-out); 1 outside their band: June 2023 Cyclone Biparjoy (Sindh coast) 0.046x vs [0.5, 2.0]","tests":[{"event":"May 1999 Sindh cyclone","role":"fit","recorded":98600,"modelled":98600,"ratio":1.0,"band":[0.67,1.5],"pass":true,"from":"calibration"},{"event":"June 2023 Cyclone Biparjoy (Sindh coast)","role":"held-out (not used in the fit)","recorded":206.4,"modelled":0,"ratio":0.046,"band":[0.5,2.0],"pass":false,"from":"calibration"}],"n_tests":2,"n_failed":1},{"name":"urban_flood","class":"untested","perils":["uf"],"aal":60773907671.0,"note":"no recorded urban-flood damage to test against","tests":[],"n_tests":0,"n_failed":0},{"name":"drought","class":"partly passed","perils":["drought"],"aal":24443818625.254112,"note":"13 tests (13 held-out); 10 outside their band: 1984 drought, wheat (rabi) 0.13x vs [0.5, 2.0]; 1988 drought, wheat (rabi) 0.461x vs [0.5, 2.0]; 2001 drought, wheat (rabi) 494.539x vs [0.5, 2.0]; 2002 drought, wheat (rabi) 0.413x vs [0.5, 2.0] ...","tests":[{"event":"1984 drought, wheat (rabi)","role":"held-out (left out of the yield-response fit)","recorded":96891367733,"modelled":12504945503,"ratio":0.13,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"1985 drought, wheat (rabi)","role":"held-out (left out of the yield-response fit)","recorded":34174712806,"modelled":26930168487,"ratio":0.789,"band":[0.5,2.0],"pass":true,"from":"calibration"},{"event":"1988 drought, wheat (rabi)","role":"held-out (left out of the yield-response fit)","recorded":38958924301,"modelled":17909247289,"ratio":0.461,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2001 drought, wheat (rabi)","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":49353921575,"ratio":494.539,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2002 drought, wheat (rabi)","role":"held-out (left out of the yield-response fit)","recorded":65619942450,"modelled":27042551754,"ratio":0.413,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2004 drought, wheat (rabi)","role":"held-out (left out of the yield-response fit)","recorded":36149890602,"modelled":33307345211,"ratio":0.922,"band":[0.5,2.0],"pass":true,"from":"calibration"},{"event":"2008 drought, wheat (rabi)","role":"held-out (left out of the yield-response fit)","recorded":88586851383,"modelled":29371793723,"ratio":0.332,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2010 drought, wheat (rabi)","role":"held-out (left out of the yield-response fit)","recorded":62813694067,"modelled":37568565145,"ratio":0.599,"band":[0.5,2.0],"pass":true,"from":"calibration"},{"event":"1987 drought, maize (kharif)","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":4140150635,"ratio":42.402,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2000 drought, maize (kharif)","role":"held-out (left out of the yield-response fit)","recorded":30008830154,"modelled":3384980906,"ratio":0.116,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2002 drought, maize (kharif)","role":"held-out (left out of the yield-response fit)","recorded":41601937109,"modelled":3012571898,"ratio":0.075,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2004 drought, maize (kharif)","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":15507848285,"ratio":156.078,"band":[0.5,2.0],"pass":false,"from":"calibration"},{"event":"2009 drought, maize (kharif)","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":13476713504,"ratio":135.767,"band":[0.5,2.0],"pass":false,"from":"calibration"}],"n_tests":13,"n_failed":10},{"name":"flood_sectors.crops","class":"partly passed","perils":[],"aal":259521032175,"note":"5 tests (5 held-out); 3 outside their band: 2010 floods, crops 0.37x vs [0.5, 2.0]; 2014 floods, crops 4.516x vs [0.5, 2.0]; 2022 floods, crops 0.487x vs [0.5, 2.0]. Fitted to 2010, 2011, 2014, 2022, 2025, each complete record left out for its test","tests":[{"event":"2010 floods, crops","role":"held-out (left out of the fit, refitted on the other records)","recorded":1014615400000,"modelled":375866624306,"ratio":0.37,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.crops.tests"},{"event":"2011 floods, crops","role":"held-out (left out of the fit, refitted on the other records)","recorded":520122937904,"modelled":500637876015,"ratio":0.963,"band":[0.5,2.0],"pass":true,"from":"flood_sectors.items.crops.tests"},{"event":"2014 floods, crops","role":"held-out (left out of the fit, refitted on the other records)","recorded":99029317000,"modelled":447240842899,"ratio":4.516,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.crops.tests"},{"event":"2022 floods, crops","role":"held-out (left out of the fit, refitted on the other records)","recorded":2343734589928,"modelled":1141832710988,"ratio":0.487,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.crops.tests"},{"event":"2025 floods, crops","role":"held-out (left out of the fit, refitted on the other records)","recorded":302000000000,"modelled":228503520871,"ratio":0.757,"band":[0.5,2.0],"pass":true,"from":"flood_sectors.items.crops.tests"}],"n_tests":5,"n_failed":3},{"name":"flood_sectors.irrigation","class":"failed test","perils":[],"aal":27468585977,"note":"3 tests (3 held-out); 3 outside their band: 2010 floods, irrigation 0.176x vs [0.5, 2.0]; 2011 floods, irrigation 5.199x vs [0.5, 2.0]; 2022 floods, irrigation 0.248x vs [0.5, 2.0]. Fitted to 2010, 2011, 2022, each complete record left out for its test","tests":[{"event":"2010 floods, irrigation","role":"held-out (left out of the fit, refitted on the other records)","recorded":94522248000,"modelled":16627149009,"ratio":0.176,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.irrigation.tests"},{"event":"2011 floods, irrigation","role":"held-out (left out of the fit, refitted on the other records)","recorded":17045527306,"modelled":88618742573,"ratio":5.199,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.irrigation.tests"},{"event":"2022 floods, irrigation","role":"held-out (left out of the fit, refitted on the other records)","recorded":232796188125,"modelled":57725767730,"ratio":0.248,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.irrigation.tests"}],"n_tests":3,"n_failed":3},{"name":"flood_sectors.livestock","class":"partly passed","perils":[],"aal":5569159225,"note":"7 tests (7 held-out); 6 outside their band: 2012 floods, livestock 44.195x vs [0.5, 5.0]; 2014 floods, livestock 38.053x vs [0.5, 5.0]; 2016 floods, livestock 45.119x vs [0.5, 5.0]; 2023 floods, livestock 20.967x vs [0.5, 5.0] .... Fitted to 2010, 2022; a record that is an incomplete count may sit up to 5x below the model","tests":[{"event":"2011 floods, livestock","role":"held-out (year outside the 2010 and 2022 fit)","recorded":115500,"modelled":194906,"ratio":1.687,"band":[0.5,5.0],"pass":true,"from":"flood_sectors.items.livestock.tests"},{"event":"2012 floods, livestock","role":"held-out (year outside the 2010 and 2022 fit)","recorded":12121,"modelled":535683,"ratio":44.195,"band":[0.5,5.0],"pass":false,"from":"flood_sectors.items.livestock.tests"},{"event":"2014 floods, livestock","role":"held-out (year outside the 2010 and 2022 fit)","recorded":1925,"modelled":73252,"ratio":38.053,"band":[0.5,5.0],"pass":false,"from":"flood_sectors.items.livestock.tests"},{"event":"2016 floods, livestock","role":"held-out (year outside the 2010 and 2022 fit)","recorded":194,"modelled":8753,"ratio":45.119,"band":[0.5,5.0],"pass":false,"from":"flood_sectors.items.livestock.tests"},{"event":"2023 floods, livestock","role":"held-out (year outside the 2010 and 2022 fit)","recorded":1260,"modelled":26418,"ratio":20.967,"band":[0.5,5.0],"pass":false,"from":"flood_sectors.items.livestock.tests"},{"event":"2024 floods, livestock","role":"held-out (year outside the 2010 and 2022 fit)","recorded":2282,"modelled":100635,"ratio":44.099,"band":[0.5,5.0],"pass":false,"from":"flood_sectors.items.livestock.tests"},{"event":"2025 floods, livestock","role":"held-out (year outside the 2010 and 2022 fit)","recorded":16332,"modelled":104533,"ratio":6.401,"band":[0.5,5.0],"pass":false,"from":"flood_sectors.items.livestock.tests"}],"n_tests":7,"n_failed":6},{"name":"flood_sectors.power","class":"failed test","perils":[],"aal":7467224411,"note":"3 tests (3 held-out); 3 outside their band: 2010 floods, power 0.203x vs [0.5, 2.0]; 2011 floods, power 4.319x vs [0.5, 2.0]; 2022 floods, power 2.295x vs [0.5, 2.0]. 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Fitted to 2010, 2011, 2022, each complete record left out for its test","tests":[{"event":"2010 floods, rail","role":"held-out (left out of the fit, refitted on the other records)","recorded":1224,"modelled":16,"ratio":0.013,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.rail.tests"},{"event":"2011 floods, rail","role":"held-out (left out of the fit, refitted on the other records)","recorded":5,"modelled":1603,"ratio":347.6,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.rail.tests"},{"event":"2022 floods, rail","role":"held-out (left out of the fit, refitted on the other records)","recorded":3335,"modelled":114,"ratio":0.034,"band":[0.5,2.0],"pass":false,"from":"flood_sectors.items.rail.tests"}],"n_tests":3,"n_failed":3},{"name":"flood_sectors.roads","class":"partly passed","perils":[],"aal":111236069718,"note":"6 tests (6 held-out); 5 outside their band: 2010 floods, roads 0.203x vs [0.5, 2.0]; 2011 floods, roads 2.476x vs [0.5, 2.0]; 2022 floods, roads 3.414x vs [0.5, 2.0]; 2023 floods, roads 15.97x vs [0.5, 5.0] .... 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Service; Inness et al. 2019), 0.75 degrees, calibrated to ground monitors, interpolated to 12 arc-sec and weighted by GHSL 2020 population. Calibration: ln(ground) = a + b ln(CAMS) + seasonal terms, fitted to 280 monitor-months (2019-05 to 2025-12) at the four US diplomatic-post monitors (Islamabad, Karachi, Lahore, Peshawar). Uncalibrated EAC4 reads x1.49 high there (most in summer dust months); calibrated, the bias is x0.99 with each city left out in turn (log error 0.535 -> 0.37). Checked against 46 low-cost sensor clusters away from those cities (AirGradient, Hawanama, Clarity, corrected to the reference where co-located): bias x1.2 -> x0.83, correlation 0.722 -> 0.853. City differences left by the calibration (Islamabad x1.37, Peshawar x0.63) are taken out by sensor fusion: a smooth bias field kriged from 23 monitor sites (100 km correlation length, chosen by cross-validation) that returns to the calibrated map away from monitors; leave-one-site-out log error 0.171 -> 0.121. Monthly means are above Pakistan's 24-hour PM2.5 standard (35 \u00b5g/m\u00b3, Punjab Environmental Quality Standards 2016) almost everywhere in every month, so exposure counts cells at the US EPA 'Very unhealthy' level (125.5 \u00b5g/m\u00b3, 2024 AQI breakpoints).","agn":true,"checks":{"US diplomatic-post monitors (monthly PM2.5)":{"Uncalibrated CAMS":{"bias_factor":1.49,"rmse_log":0.535,"r_log":0.839},"Calibrated, each city left out":{"bias_factor":0.99,"rmse_log":0.37,"r_log":0.822}},"By city: ground and CAMS means (\u00b5g/m\u00b3), bias after calibration (each city left out)":{"Islamabad":{"months":78,"ground_mean":52.8,"cams_mean":89.0,"bias_factor_calibrated":1.37},"Karachi":{"months":64,"ground_mean":48.4,"cams_mean":68.4,"bias_factor_calibrated":1.14},"Lahore":{"months":68,"ground_mean":124.0,"cams_mean":187.1,"bias_factor_calibrated":0.96},"Peshawar":{"months":70,"ground_mean":84.2,"cams_mean":79.4,"bias_factor_calibrated":0.63}},"Low-cost sensor clusters away from the reference cities":{"Uncalibrated CAMS":{"bias_factor":1.2,"rmse_log":0.476,"r_log":0.722},"Calibrated":{"bias_factor":0.83,"rmse_log":0.375,"r_log":0.853}},"Sensor fusion, each monitor site left out":{"calibrated CAMS":{"sites":23,"bias_factor":0.98,"rmse_log":0.171},"fused, each site left out":{"sites":23,"bias_factor":0.99,"rmse_log":0.121},"Short-record clusters not used in the fit (fused)":{"sites":25,"bias_factor":0.91,"rmse_log":0.543}},"note":"Bias factor = model over ground (1 = no bias); r on logs. Sensor corrections rest on Islamabad (AirGradient, Hawanama) and Lahore/Karachi (Clarity) co-locations only, so this check is indicative. Full table: data/tables/aq_ground_check.json."},"limits":"Calibrated to four monitors (Islamabad, Karachi, Lahore, Peshawar); elsewhere less certain."},"smog":{"label":"Smog","axis":{"kind":"month","keys":["1","2","3","4","5","6","7","8","9","10","11","12"],"labels":["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]},"metrics":{"smog_days":{"label":"Smog days in the month (daily PM2.5 'Very unhealthy', 125.5 \u00b5g/m\u00b3 or more)","unit":"days","kind":"hazard","source_unit":"days","table":"AGN","v":{"1":{"AWARAN":0.0,"CHAGAI":0.1,"DERA BUGTI":0.0,"GWADAR":0.0,"JAFFARABAD":0.3,"JHAL MAGSI":0.0,"KALAT":0.0,"KECH":0.0,"KHUZDAR":0.0,"KILLA ABDULLAH":0.0,"LASBELA":0.1,"MASTUNG":0.0,"MUSA KHEL":1.3,"NASIRABAD":0.1,"NUSHKI":0.2,"PANJGUR":0.2,"PISHIN":0.0,"QUETTA":0.0,"SHEERANI":0.3,"SOHBATPUR":0.2,"WASHUK":0.1,"ABBOTTABAD":0.1,"BAJAUR AGENCY":0.0,"MANDI BAHAUDDIN":25.0,"MIANWALI":0.5,"MULTAN":21.7,"MUZAFFARGARH":13.1,"NANKANA SAHIB":29.3,"NAROWAL":27.0,"RAHIM YAR 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degree grid and weighted by GHSL 2020 population. Smog day: daily PM2.5 at or above the US EPA 'Very unhealthy' level (125.5 \u00b5g/m\u00b3, 2024 AQI breakpoints); hazardous: 225.5 \u00b5g/m\u00b3. Average of 2003-2025. Exposure: people where 10 or more days in the month are 'Very unhealthy'. Checked against Punjab EPD district AQI for October-December 2025.","agn":true,"checks":{"Daily PM2.5 at the US diplomatic-post monitors, each city left out of the fit":{"least_squares":{"bias_factor":1.0,"rmse_log":0.51,"r_log":0.744,"smog_days_per_year_ground":46.4,"smog_days_per_year_model":27.9,"city_years":25},"matched":{"bias_factor":1.01,"rmse_log":0.54,"r_log":0.758,"smog_days_per_year_ground":46.4,"smog_days_per_year_model":46.5,"city_years":25}},"Smog days a year by city (ground / model)":{"Islamabad":"17.0 / 36.3","Karachi":"13.3 / 15.2","Lahore":"105.3 / 136.0","Peshawar":"54.7 / 0.3"},"Smog days a year, final layer with fusion (ground / model, 2019-2025; monitors are in the fusion fit)":{"Peshawar":"54.7 / 46.1","Karachi":"13.3 / 16.6","Islamabad":"17.0 / 7.7","Lahore":"105.3 / 155.6"},"Punjab EPD district AQI, Oct-Dec 2025 (not used in any fit)":{"districts":11,"period":"2025-10-01 to 2025-12-31","smog_days_ground_mean":53.4,"smog_days_model_mean":60.0,"r":-0.13,"note":"Punjab EPD district daily AQI converted at the US EPA breakpoints (AQI 201 = PM2.5 125.5). 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A river cell floods only above the calibrated defence standard (1-in-15); a 1-in-T year is the (1-1/T) quantile of people flooded nationally, mapped as the mean of the simulated years of that size. Other metrics: the floodplain layer (JRC/GloFAS maps, people, buildings, schools, roads, crops, livestock, infrastructure) scaled per district by the share of its floodplain people flooded. Unlike the floodplain maps, which show every reach at its own 1-in-T flow with no embankments, this shows what a whole year of that rarity looks like.","agn":true,"checks":{"years":{"2010":{"people_modelled":18757648,"area_km2_modelled":41986,"people_in_observed_outline":25717524,"area_km2_observed_outline":70023,"national_rank_in_simulation":null,"people_affected_recorded":20000000,"ratio_to_recorded":0.94,"source":"DNA 2010 (ADB/World Bank): over 20 million people affected, all causes","url":"https://www.adb.org/sites/default/files/linked-documents/44372-01-pak-oth-02.pdf"},"2014":{"people_modelled":6797891,"area_km2_modelled":13161,"people_in_observed_outline":7736963,"area_km2_observed_outline":17500,"national_rank_in_simulation":null,"people_affected_recorded":2530000,"ratio_to_recorded":2.69,"source":"NDMA 2014, as compiled in '2014 India-Pakistan floods' (about 2.5 million affected)","url":"https://en.wikipedia.org/wiki/2014_India%E2%80%93Pakistan_floods"},"2022":{"people_modelled":13095996,"area_km2_modelled":45163,"people_in_observed_outline":6131850,"area_km2_observed_outline":34771,"national_rank_in_simulation":null,"note":"2022 was a rain flood (Sindh, Balochistan): about 33 million affected in all, most of them outside the river footprint; the river part is what this layer models."},"2025":{"people_modelled":11418917,"area_km2_modelled":16227,"people_in_observed_outline":11186271,"area_km2_observed_outline":25762,"national_rank_in_simulation":null,"people_affected_recorded":4700000,"ratio_to_recorded":2.43,"source":"PDMA Punjab, reported by Aaj News: 4.7 million affected in Punjab","url":"https://english.aaj.tv/news/amp/330436028"}},"national_people_by_year_rp":{"2":813441,"5":5806904,"10":12398979,"25":20951597,"50":27638846,"100":33598598,"200":38514729,"500":42539212},"method":"1. The engine's 10,000 simulated years (Gaussian copula over 47 years of GloFAS v4 annual maxima, 3,468 river cells; the same\n     draws as the loss figures, seed in climada_flood_events.py) give each river cell a return period every year.\n  2. A (river cell, district) unit floods only when its return period is above the calibrated defence standard (1-in-15, the\n     joint calibration); its people inside the water at that return period come from the engine's own depth maps.\n  3. Years are ranked by people flooded nationally. The 1-in-T year is the (1-1/T) quantile; its map is the mean of the\n     simulated years ranked within a factor 1.25 of N/T, so the districts show a typical year of that size, not a sum of\n     local extremes.\n  4. Every metric of the floodplain layer (people, area, buildings, schools, roads, crops, livestock, infrastructure) at 1-in-500,\n     taken over the whole floodplain (the part kept near observed floods plus the protected zone, since protection is already in\n     step 2), is scaled district by district by the share of the district's floodplain people flooded in those years."},"limits":"Simulated years from GloFAS river flows with one national defence standard (about 1-in-16); regional defence standards were tried and withdrawn. Over-estimated the 2025 Punjab season about 2.4 times."},"ffy":{"label":"Flash flood years","axis":{"kind":"rp","keys":["2","5","10","25","50","100","200","500"],"labels":["1-in-2 year","1-in-5 year","1-in-10 year","1-in-25 year","1-in-50 year","1-in-100 year","1-in-200 year","1-in-500 year"],"ticks":["2","5","10","25","50","100","200","500"]},"metrics":{"hazard_km2":{"label":"Footprint area","unit":"km\u00b2","kind":"hazard","source_unit":"km\u00b2","table":"AGN","v":{"2":{"AWARAN":688.8,"BAKRAN":145.6,"CHAGAI":596.8,"DERA BUGTI":172.4,"GWADAR":259.6,"HARNAI":42.5,"JHAL MAGSI":19.2,"KACHHI(BOLAN)":68.7,"KALAT":337.4,"KECH":461.7,"KHARAN":116.8,"KHUZDAR":787.6,"KILLA ABDULLAH":87.3,"KILLA SAIFULLAH":306.9,"KOHLU":244.6,"LASBELA":394.9,"LORALAI":313.1,"MASTUNG":163.3,"MUSA 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A district's flash corridors flood to the extent mapped for that return period (AGN flash flood layer, HAND corridors); a 1-in-T year is the (1-1/T) quantile of people in flash corridors nationally, mapped as the mean of the simulated years of that size. 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Smog has risen since 2003 (about 270 million person-days a season more each year), so only the 12 seasons from 2013-14 to 2024-25 are ranked, to reflect today's level. The 1-in-T season map is the mean of the seasons ranked near N/T, so places differ as they did in those seasons. With 12 seasons, 1-in-10 is about the worst on record; rarer seasons are not shown because they would be extrapolation. Exposure: people with 30 or more smog days in the season.","agn":true,"checks":{"seasons_ranked":{"2013-14":{"rank":10,"person_days_millions":11138},"2014-15":{"rank":11,"person_days_millions":10886},"2015-16":{"rank":7,"person_days_millions":12041},"2016-17":{"rank":6,"person_days_millions":12553},"2017-18":{"rank":3,"person_days_millions":13358},"2018-19":{"rank":8,"person_days_millions":11489},"2019-20":{"rank":12,"person_days_millions":9831},"2020-21":{"rank":4,"person_days_millions":12688},"2021-22":{"rank":5,"person_days_millions":12623},"2022-23":{"rank":9,"person_days_millions":11474},"2023-24":{"rank":1,"person_days_millions":14084},"2024-25":{"rank":2,"person_days_millions":13501}},"all_seasons_person_days_millions":{"2003-04":7709,"2004-05":7433,"2005-06":8807,"2006-07":7918,"2007-08":9007,"2008-09":8355,"2009-10":10495,"2010-11":10428,"2011-12":9361,"2012-13":10199,"2013-14":11138,"2014-15":10886,"2015-16":12041,"2016-17":12553,"2017-18":13358,"2018-19":11489,"2019-20":9831,"2020-21":12688,"2021-22":12623,"2022-23":11474,"2023-24":14084,"2024-25":13501},"trend_person_days_millions_per_season":270,"national":{"2":{"seasons":["2015-16","2016-17","2018-19"],"person_days_millions":12028,"people_30plus_smog_days_millions":115.7},"5":{"seasons":["2017-18"],"person_days_millions":13358,"people_30plus_smog_days_millions":123.3},"10":{"seasons":["2024-25"],"person_days_millions":13501,"people_30plus_smog_days_millions":125.0}},"Daily PM2.5 at the US diplomatic-post monitors, each city left out of the fit":{"least_squares":{"bias_factor":1.0,"rmse_log":0.51,"r_log":0.744,"smog_days_per_year_ground":46.4,"smog_days_per_year_model":27.9,"city_years":25},"matched":{"bias_factor":1.01,"rmse_log":0.54,"r_log":0.758,"smog_days_per_year_ground":46.4,"smog_days_per_year_model":46.5,"city_years":25}},"Smog days a year, final layer with fusion (ground / model, 2019-2025; monitors are in the fusion fit)":{"Peshawar":"54.7 / 46.1","Karachi":"13.3 / 16.6","Islamabad":"17.0 / 7.7","Lahore":"105.3 / 155.6"},"Punjab EPD district AQI, Oct-Dec 2025 (not used in any fit)":{"districts":11,"period":"2025-10-01 to 2025-12-31","smog_days_ground_mean":53.4,"smog_days_model_mean":60.0,"r":-0.13,"note":"Punjab EPD district daily AQI converted at the US EPA breakpoints (AQI 201 = PM2.5 125.5). Not used in any fit."},"Sensor fusion (monthly bias field)":{"sites":23,"length_km":100,"cv_rmse_log_calibrated":0.171,"cv_rmse_log_fused":0.121},"note":"Daily calibration used: matched (chosen because it gets the count of smog days right). Full tables: data/tables/aq_ground_check.json and aq_fusion.json."},"limits":"Only 12 seasons ranked, because smog has risen since 2003."},"cmip6":{"label":"Climate change, CMIP6","axis":{"kind":"scenario","keys":["SSP245|2011-2040","SSP245|2041-2070","SSP245|2071-2100","SSP585|2011-2040","SSP585|2041-2070","SSP585|2071-2100"],"labels":["SSP2-4.5 \u00b7 2011-2040","SSP2-4.5 \u00b7 2041-2070","SSP2-4.5 \u00b7 2071-2100","SSP5-8.5 \u00b7 2011-2040","SSP5-8.5 \u00b7 2041-2070","SSP5-8.5 \u00b7 2071-2100"],"ticks":["4.5 \u201925","4.5 \u201955","4.5 \u201985","8.5 \u201925","8.5 \u201955","8.5 \u201985"]},"metrics":{"tasmax":{"label":"Change in daily maximum temperature","unit":"\u00b0C","kind":"hazard","source_unit":"\u00b0C","table":"Copernicus CDS projections-cmip6","v":{"SSP245|2011-2040":{"AWARAN":0.68,"BAKRAN":0.724,"CHAGAI":0.942,"DERA BUGTI":0.65,"GWADAR":0.699,"HARNAI":0.904,"JAFFARABAD":0.669,"JHAL 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AGENCY":1.97,"PAKPATTAN":7.06,"PANJGUR":5.31,"PESHAWAR":7.68,"PISHIN":2.62,"POONCH":5.9,"QUETTA":4.72,"RAHIM YAR KHAN":5.87,"RAJANPUR":7.84,"RAWALPINDI":9.44,"SAHIWAL":7.44,"SAKANDARABAD":5.29,"SANGHAR":8.01,"SARGODHA":8.88,"SHAHDADKOT":7.97,"SHAHEED BENAZIRABAD":7.77,"SHANGLA":4.81,"SHEERANI":0.31,"SHEIKHUPURA":6.59,"SHIGAR":3.4,"SHIKARPUR":7.53,"SIALKOT":4.82,"SIBI":5.39,"SKARDU":3.17,"SOHBATPUR":6.29,"SOUTH WAZIRISTAN":1.93,"SUDHNUTI":5.51,"SUJAWAL":6.92,"SUKKUR":7.46,"SWABI":6.56,"SWAT":5.49,"TANDO ALLAHYAR":7.19,"TANDO MOHAMMAD KHAN":7.29,"TANK":1.88,"THARPARKAR":8.83,"THATTA":6.68,"TOBA TEK SINGH":7.77,"TOR GHAR":2.94,"UMERKOT":7.98,"UPPER CHITRAL":1.81,"UPPER DIR":4.36,"UPPER KOHISTAN":2.34,"VEHARI":7.29,"WASHUK":5.54,"ZHOB":2.15,"ZIARAT":0.42}},"cls":null,"mean":true,"signed":false}},"failed":{},"default":"risk","agn":true,"source":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed = population x share of the district in each footprint; heat on the Giles discomfort scale; drought from SPEI-12; vulnerability from Census 2023 housing, lighting, water and sanitation; coping from health facilities, roads and mobile towers per head.","method":"INFORM Index for Risk Management (Marin-Ferrer, Vernaccini and\nPoljansek 2017, JRC Technical Report EUR 28655 EN), applied to districts.\n\n  Risk = Hazard & Exposure ^ 1/3  x  Vulnerability ^ 1/3  x  Lack of coping capacity ^ 1/3\n\nEvery component is on 0-10.\n\nHazard & Exposure. For each hazard, people exposed = population x share of the\ndistrict inside the footprint (a uniform-density approximation: the source\ncrosses population for only some hazards). The hazard score is INFORM's blend\nof the absolute and the relative figure:\n    0.5 x minmax(log10(1 + people exposed)) + 0.5 x minmax(people exposed / population)\nFootprints: riverine, flash and urban flood and storm surge at 1-in-100;\nearthquake at very strong shaking or worse, 1-in-475; cyclone at Category 1+,\n1-in-100; landslide at high susceptibility; glacial lake outburst; avalanche\n(share of buildings inside at 1-in-100). Heat: the share of people who feel\ndiscomfort, from the district's peak discomfort index read on the Giles et al.\n(1990) scale (24 degC: half the population; 32 degC: medical emergency;\nlinear between). Drought: the share of months in drought on SPEI-12 (AGN,\n1980-2025). Hazards combine as INFORM does, so one severe hazard is not\naveraged away:  H = 10 - ( product of (10 - score) ) ^ (1/n).\n\nVulnerability. Mean of: kacha share of households, households without electric\nlighting, without improved water, without a toilet (PBS Census 2023; Census 2017\nkatcha share where 2023 is missing). Shares map straight to 0-10. No outside\nsocial vulnerability index is used (the code keeps a slot for one; it is empty\nin the AGN build).\n\nSensitivity (sensitivity() below, shown in the layer's About box): the ranking\nis recomputed with the three pillars weighted by 1,000 random draws (Dirichlet,\neach weight near 1/3, from about 0.17 to 0.52), with an arithmetic instead of a\ngeometric mean, and with each hazard and each indicator left out in turn.\n\nLack of coping capacity. Mean of the inverted, log-scaled min-max of: health\nfacilities per 10,000 people, road km per km2, mobile towers per 1,000 people.\n\nHeat-health risk uses the same form with heat alone:\n    heat score ^ 1/3 x heat vulnerability ^ 1/3 x lack of health access ^ 1/3\nwhere heat vulnerability is the mean of kacha share, no electric lighting and\nno improved water (no fans, hot roofs, no water to cool).\n\nClimate change pressure (not inside Risk; shown beside it): the mean min-max of\nCMIP6 SSP5-8.5 2041-2070 change in daily maximum temperature, how many times\nmore often the 1-in-50 wettest day and the 1-in-50 hottest day occur, and the\nobserved 1980-2025 warming trend.\n\nMin-max bounds are the 1st and 99th percentile across districts, so one\noutlier does not flatten the rest (the maximum, for a hazard present in fewer\nthan 1% of districts). Population: Census 2023 where present,\nelse the platform's district population.","limits":"A composite index: the broad order is stable under other weights and indicators (rank correlation 0.94-1.00), but 3 to 6 of the top 20 districts change places; use it to group districts, not to rank neighbours (sensitivity table in About)."}},"zonal":[{"id":"agn_rain_annual_mean_rainfall","t":"Annual rainfall","s":"1980-2025 mean","g":"AGN rainfall","u":"mm","v":{"AWARAN":139.819,"BAKRAN":468.261,"CHAGAI":77.979,"DERA BUGTI":239.793,"GWADAR":102.391,"HARNAI":464.644,"JAFFARABAD":199.373,"JHAL MAGSI":214.075,"KACHHI(BOLAN)":314.629,"KACHI":232.391,"KALAT":301.032,"KECH":116.656,"KHARAN":137.839,"KHUZDAR":228.471,"KILLA ABDULLAH":297.042,"KILLA SAIFULLAH":255.478,"KOHLU":332.689,"LASBELA":162.989,"LORALAI":284.709,"MASTUNG":271.794,"MUSA 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JRC/Copernicus GloFAS river flood maps v2.1.2 (Baugh et al. 2024; CC BY 4.0) with permanent water removed, kept where within 1.8 km"},{"id":"rf.cropland_km2","l":"Cropland inside","f":"agn_exposure","p":"Riverine flood (floodplain, no defences)","rp":"1-in-10, 1-in-20, 1-in-50, 1-in-100, 1-in-200, 1-in-500","s":"712 district values","a":"AGN riverine flood layer v1: JRC/Copernicus GloFAS river flood maps v2.1.2 (Baugh et al. 2024; CC BY 4.0) with permanent water removed, kept where within 1.8 km"},{"id":"rf.resid_km2","l":"Floodable but not flooded 2010-2025 (mostly behind embankments)","f":"agn_hazard","p":"Riverine flood (floodplain, no defences)","rp":"1-in-10, 1-in-20, 1-in-50, 1-in-100, 1-in-200, 1-in-500","s":"814 district values","a":"AGN riverine flood layer v1: JRC/Copernicus GloFAS river flood maps v2.1.2 (Baugh et al. 2024; CC BY 4.0) with permanent water removed, kept where within 1.8 km"},{"id":"rf.resid_population","l":"People in that protected 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v2.1.2 (Baugh et al. 2024; CC BY 4.0) with permanent water removed, kept where within 1.8 km"},{"id":"rf.livestock_tlu","l":"Livestock inside (tropical livestock units)","f":"agn_exposure","p":"Riverine flood (floodplain, no defences)","rp":"1-in-10, 1-in-20, 1-in-50, 1-in-100, 1-in-200, 1-in-500","s":"713 district values","a":"AGN riverine flood layer v1: JRC/Copernicus GloFAS river flood maps v2.1.2 (Baugh et al. 2024; CC BY 4.0) with permanent water removed, kept where within 1.8 km"},{"id":"rf.cattle_buffalo","l":"Cattle and buffalo inside","f":"agn_exposure","p":"Riverine flood (floodplain, no defences)","rp":"1-in-10, 1-in-20, 1-in-50, 1-in-100, 1-in-200, 1-in-500","s":"713 district values","a":"AGN riverine flood layer v1: JRC/Copernicus GloFAS river flood maps v2.1.2 (Baugh et al. 2024; CC BY 4.0) with permanent water removed, kept where within 1.8 km"},{"id":"rf.goats_sheep","l":"Goats and sheep inside","f":"agn_exposure","p":"Riverine flood (floodplain, no 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kept where within 1.8 km"},{"id":"rf.power_km","l":"Power lines inside","f":"agn_exposure","p":"Riverine flood (floodplain, no defences)","rp":"1-in-10, 1-in-20, 1-in-50, 1-in-100, 1-in-200, 1-in-500","s":"536 district values","a":"AGN riverine flood layer v1: JRC/Copernicus GloFAS river flood maps v2.1.2 (Baugh et al. 2024; CC BY 4.0) with permanent water removed, kept where within 1.8 km"},{"id":"rf.rail_km","l":"Railway track inside","f":"agn_exposure","p":"Riverine flood (floodplain, no defences)","rp":"1-in-10, 1-in-20, 1-in-50, 1-in-100, 1-in-200, 1-in-500","s":"313 district values","a":"AGN riverine flood layer v1: JRC/Copernicus GloFAS river flood maps v2.1.2 (Baugh et al. 2024; CC BY 4.0) with permanent water removed, kept where within 1.8 km"},{"id":"rf.rail_stations","l":"Railway stations inside","f":"agn_exposure","p":"Riverine flood (floodplain, no defences)","rp":"1-in-10, 1-in-20, 1-in-50, 1-in-100, 1-in-200, 1-in-500","s":"222 district values","a":"AGN riverine flood 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the nearest drainage (Height Above Nearest Drainage, ASF GLO-30 HAND from the Copernicus DEM), h_T"},{"id":"ff.schools","l":"Schools inside","f":"agn_exposure","p":"Flash flood (each valley at its 1-in-T rain)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"366 district values","a":"AGN flash-flood layer v1: valley floors within a stage h_T of the nearest drainage (Height Above Nearest Drainage, ASF GLO-30 HAND from the Copernicus DEM), h_T"},{"id":"ff.health","l":"Health facilities inside","f":"agn_exposure","p":"Flash flood (each valley at its 1-in-T rain)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"395 district values","a":"AGN flash-flood layer v1: valley floors within a stage h_T of the nearest drainage (Height Above Nearest Drainage, ASF GLO-30 HAND from the Copernicus DEM), h_T"},{"id":"ff.roads_km","l":"Road length inside","f":"agn_exposure","p":"Flash flood (each valley at its 1-in-T rain)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"515 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earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.hazard_severe_km2","l":"Area at high or very high susceptibility","f":"agn_hazard","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"59 district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.quake_severe_km2","l":"Earthquake-triggered: high or very high","f":"agn_hazard","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"99 district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.population","l":"People at high susceptibility","f":"agn_exposure","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"57 district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.buildings","l":"Buildings at high susceptibility (estimate)","f":"agn_exposure","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"57 district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.floor_km2","l":"Building floor area at high susceptibility","f":"agn_exposure","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"57 district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.schools","l":"Schools at high susceptibility","f":"agn_exposure","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"25 district 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district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.rabi_km2","l":"Rabi (winter) crop area at high susceptibility","f":"agn_exposure","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"6 district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.kharif_km2","l":"Kharif (summer) crop area at high susceptibility","f":"agn_exposure","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"31 district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.livestock_tlu","l":"Livestock at high susceptibility (tropical livestock 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CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"ls.rel_density","l":"AGN tehsil model: event density relative to national average","f":"agn_hazard","p":"Landslide susceptibility","rp":"Standing susceptibility","s":"126 district values","a":"GIRI landslide susceptibility, rain-triggered and earthquake-triggered (NGI for UNDRR/CDRI/UNEP-GRID-Geneva, CC BY 4.0), 12 arc-sec; exposure inside the high an"},{"id":"glof.hazard_km2","l":"Outburst flood corridor area","f":"agn_hazard","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"67 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.population","l":"People in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"66 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.buildings","l":"Buildings in outburst corridors (estimate)","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"66 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.floor_km2","l":"Building floor area in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"66 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.schools","l":"Schools in outburst corridors","f":"agn_exposure","p":"Glacial 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all CC BY 4.0; tiers"},{"id":"glof.cropland_km2","l":"Cropland in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"66 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.rabi_km2","l":"Rabi (winter) crop area in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"37 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.kharif_km2","l":"Kharif (summer) crop area in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"50 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.livestock_tlu","l":"Livestock in outburst corridors (tropical livestock units)","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"67 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.cattle_buffalo","l":"Cattle and buffalo in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"67 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.goats_sheep","l":"Goats and sheep in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"67 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.towers","l":"Mobile towers in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"57 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.power_sites","l":"Grid stations and substations in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"29 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.power_km","l":"Power lines in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"35 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.rail_km","l":"Railway track in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"0 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.rail_stations","l":"Railway stations in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"0 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.bridges","l":"Road bridges in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"64 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.airports","l":"Airports in outburst corridors","f":"agn_exposure","p":"Glacial lake outburst","rp":"Priority lakes, Priority and elevated lakes, All mapped lakes","s":"7 district values","a":"AGN glacial lake register (NIGRAAN): national inventory 2024 merged with Hi-MAG (Chen et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"glof.dams","l":"Dams and 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et al. 2021), RGI 7.0 and HMAGLOFDB (Steiner et al.), all CC BY 4.0; tiers"},{"id":"av.hazard_km2","l":"Avalanche zone area (release and runout)","f":"agn_hazard","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"111 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.population","l":"People in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"111 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.buildings","l":"Buildings in avalanche zones (estimate)","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"111 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.floor_km2","l":"Building floor area in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"111 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.schools","l":"Schools in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"74 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.health","l":"Health facilities in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"92 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.roads_km","l":"Road length in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"111 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.cropland_km2","l":"Cropland in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"110 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.rabi_km2","l":"Rabi (winter) crop area in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"34 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.kharif_km2","l":"Kharif (summer) crop area in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"75 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.livestock_tlu","l":"Livestock in avalanche zones (tropical livestock units)","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"111 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.cattle_buffalo","l":"Cattle and buffalo in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"111 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.goats_sheep","l":"Goats and sheep in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"111 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.towers","l":"Mobile towers in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"51 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.power_sites","l":"Grid stations and substations in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"21 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.power_km","l":"Power lines in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"35 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.rail_km","l":"Railway track in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"0 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.rail_stations","l":"Railway stations in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"0 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.bridges","l":"Road bridges in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"77 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.airports","l":"Airports in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"4 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.dams","l":"Dams and barrages in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"17 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"av.canal_km","l":"Irrigation canals in avalanche zones","f":"agn_exposure","p":"Avalanche","rp":"Frequent (alpha 28\u00b0), Large (alpha 24\u00b0), Extreme (alpha 20\u00b0)","s":"0 district values","a":"AGN avalanche zones: release cells on 28-55 degree slopes at or above 2,500 m (Copernicus GLO-90 DEM); runout by the alpha-angle energy-line rule (Lied & Bakkeh"},{"id":"eq.hazard_severe_km2","l":"Area at very strong shaking or worse (PGA >= 0.18 g)","f":"agn_hazard","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"275 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.pga_g","l":"Peak ground acceleration, area mean","f":"agn_hazard","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"308 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.population","l":"People at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"274 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.buildings","l":"Buildings at very strong shaking or worse (estimate)","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"274 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.floor_km2","l":"Building floor area at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"274 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.schools","l":"Schools at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"257 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.health","l":"Health facilities at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"263 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.roads_km","l":"Road length at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"275 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.cropland_km2","l":"Cropland at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"273 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.rabi_km2","l":"Rabi (winter) crop area at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"228 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.kharif_km2","l":"Kharif (summer) crop area at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"225 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.livestock_tlu","l":"Livestock at very strong shaking or worse (tropical livestock units)","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"273 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.cattle_buffalo","l":"Cattle and buffalo at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"273 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.goats_sheep","l":"Goats and sheep at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"273 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.towers","l":"Mobile towers at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"246 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.power_sites","l":"Grid stations and substations at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"235 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.power_km","l":"Power lines at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"247 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.rail_km","l":"Railway track at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"118 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.rail_stations","l":"Railway stations at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"116 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.bridges","l":"Road bridges at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"266 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.airports","l":"Airports at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"119 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.dams","l":"Dams and barrages at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"183 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"eq.canal_km","l":"Irrigation canals at very strong shaking or worse","f":"agn_exposure","p":"Earthquake (building-code maps)","rp":"1-in-475 (BCP 2007 zones), 1-in-2,475 (BCP 2021 MCE)","s":"125 district values","a":"Building Code of Pakistan: Seismic Provisions 2007 tehsil zones (Table 2.2; PGA bands of Table 2.1, 10% in 50 years) and BCP 2021 mapped MCE short-period accele"},{"id":"hw.hazard_km2","l":"Area where the hottest day reaches 40 \u00b0C","f":"agn_hazard","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"655 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.hazard_severe_km2","l":"Area in severe or extreme heat (45 \u00b0C and above)","f":"agn_hazard","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"546 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.population_severe","l":"People in severe or extreme heat (45 \u00b0C and above)","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"545 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.txx_c","l":"Hottest day of the year, area mean","f":"agn_hazard","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"770 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.population","l":"People where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"654 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.buildings","l":"Buildings where the hottest day reaches 40 \u00b0C (estimate)","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"654 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.floor_km2","l":"Building floor area where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"654 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.schools","l":"Schools where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"630 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.health","l":"Health facilities where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"648 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.roads_km","l":"Road length where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"651 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.cropland_km2","l":"Cropland where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"649 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.rabi_km2","l":"Rabi (winter) crop area where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"586 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.kharif_km2","l":"Kharif (summer) crop area where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"531 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.livestock_tlu","l":"Livestock where the hottest day reaches 40 \u00b0C (tropical livestock units)","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"645 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.cattle_buffalo","l":"Cattle and buffalo where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"645 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.goats_sheep","l":"Goats and sheep where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"645 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.towers","l":"Mobile towers where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"589 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.power_sites","l":"Grid stations and substations where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"599 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.power_km","l":"Power lines where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"633 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.rail_km","l":"Railway track where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"400 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.rail_stations","l":"Railway stations where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"404 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.bridges","l":"Road bridges where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"635 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.airports","l":"Airports where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"317 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.dams","l":"Dams and barrages where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"450 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"hw.canal_km","l":"Irrigation canals where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave (each place at its 1-in-T hottest day)","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"400 district values","a":"AGN heatwave hazard: return levels of the year's hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025; Gumbel by L-moments per cell)."},{"id":"cyc.hazard_km2","l":"Area at tropical-storm force or stronger","f":"agn_hazard","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"152 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.hazard_severe_km2","l":"Area at hurricane force (Category 1+)","f":"agn_hazard","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"33 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.population","l":"People at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"151 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.buildings","l":"Buildings at storm-force wind (estimate)","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"151 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.floor_km2","l":"Building floor area at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"151 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.schools","l":"Schools at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"137 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.health","l":"Health facilities at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"136 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.roads_km","l":"Road length at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"149 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.cropland_km2","l":"Cropland at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"150 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.rabi_km2","l":"Rabi (winter) crop area at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"115 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.kharif_km2","l":"Kharif (summer) crop area at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"118 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.livestock_tlu","l":"Livestock at storm-force wind (tropical livestock units)","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"146 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.cattle_buffalo","l":"Cattle and buffalo at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"146 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.goats_sheep","l":"Goats and sheep at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"146 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.towers","l":"Mobile towers at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"132 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.power_sites","l":"Grid stations and substations at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"121 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.power_km","l":"Power lines at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"138 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.rail_km","l":"Railway track at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"89 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.rail_stations","l":"Railway stations at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"92 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.bridges","l":"Road bridges at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"138 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.airports","l":"Airports at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"71 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.dams","l":"Dams and barrages at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"61 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"cyc.canal_km","l":"Irrigation canals at storm-force wind","f":"agn_exposure","p":"Cyclone wind","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"93 district values","a":"AGN cyclone wind (CLIMADA 6): IBTrACS v04r01 North Indian tracks 1980-2025 that passed within about 800 km of the coast (64 storms), 49 synthetic tracks each (C"},{"id":"ss.hazard_km2","l":"Area flooded by surge","f":"agn_hazard","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"64 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.population","l":"People in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"54 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.buildings","l":"Buildings in the surge zone (estimate)","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"51 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.floor_km2","l":"Building floor area in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"51 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.schools","l":"Schools in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"34 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.health","l":"Health facilities in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"33 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.roads_km","l":"Road length in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"56 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.cropland_km2","l":"Cropland in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"50 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.rabi_km2","l":"Rabi (winter) crop area in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"16 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.kharif_km2","l":"Kharif (summer) crop area in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"13 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.livestock_tlu","l":"Livestock in the surge zone (tropical livestock units)","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"48 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.cattle_buffalo","l":"Cattle and buffalo in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"48 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.goats_sheep","l":"Goats and sheep in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"48 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.towers","l":"Mobile towers in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"34 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.power_sites","l":"Grid stations and substations in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"22 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.power_km","l":"Power lines in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"34 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.rail_km","l":"Railway track in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"13 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.rail_stations","l":"Railway stations in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"0 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.bridges","l":"Road bridges in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"37 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.airports","l":"Airports in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"1 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.dams","l":"Dams and barrages in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"0 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"ss.canal_km","l":"Irrigation canals in the surge zone","f":"agn_exposure","p":"Storm surge","rp":"1-in-10, 1-in-25, 1-in-50, 1-in-100, 1-in-250, 1-in-500, 1-in-1,000","s":"15 district values","a":"AGN storm surge (CLIMADA bathtub method): the 1-in-T cyclone wind turned into surge height by the Xu (2010) SLOSH-based relation, less ground height (Copernicus"},{"id":"drought.hazard_km2","l":"Drought-prone area (severe drought 1 year in 10 or more often)","f":"agn_hazard","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"77 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.p_severe","l":"Share of seasons ending in severe drought","f":"agn_hazard","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"308 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.population","l":"People in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"68 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.buildings","l":"Buildings in drought-prone area (estimate)","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"68 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.floor_km2","l":"Building floor area in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"68 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.schools","l":"Schools in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"36 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.health","l":"Health facilities in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"50 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.roads_km","l":"Road length in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"66 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.cropland_km2","l":"Cropland in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"69 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.rabi_km2","l":"Rabi (winter) crop area in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"32 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.kharif_km2","l":"Kharif (summer) crop area in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"37 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.livestock_tlu","l":"Livestock in drought-prone area (tropical livestock units)","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"76 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.cattle_buffalo","l":"Cattle and buffalo in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"76 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.goats_sheep","l":"Goats and sheep in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"76 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.towers","l":"Mobile towers in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"29 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.power_sites","l":"Grid stations and substations in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"19 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.power_km","l":"Power lines in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"24 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.rail_km","l":"Railway track in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"12 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.rail_stations","l":"Railway stations in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"9 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.bridges","l":"Road bridges in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"48 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.airports","l":"Airports in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"8 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.dams","l":"Dams and barrages in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"13 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"drought.canal_km","l":"Irrigation canals in drought-prone area","f":"agn_exposure","p":"Drought","rp":"Rabi season (Oct-Mar), Kharif season (Apr-Sep)","s":"15 district values","a":"AGN drought hazard: SPEI-6 (Vicente-Serrano et al. 2010) from AGN's ERA5-Land rainfall and temperature analysis, 1980-2025, base 1991-2020. A season is in sever"},{"id":"ts.hazard_km2","l":"Land flooded by the tsunami (0.1 m or deeper)","f":"agn_hazard","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"8 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.population","l":"People in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"8 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.buildings","l":"Buildings in the tsunami zone (estimate)","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"8 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.floor_km2","l":"Building floor area in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"8 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.schools","l":"Schools in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"5 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.health","l":"Health facilities in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"5 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.roads_km","l":"Road length in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"8 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.cropland_km2","l":"Cropland in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"7 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.rabi_km2","l":"Rabi (winter) crop area in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"0 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.kharif_km2","l":"Kharif (summer) crop area in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"0 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.livestock_tlu","l":"Livestock in the tsunami zone (tropical livestock units)","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"8 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.cattle_buffalo","l":"Cattle and buffalo in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"8 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.goats_sheep","l":"Goats and sheep in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"8 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.towers","l":"Mobile towers in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"5 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.power_sites","l":"Grid stations and substations in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"1 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.power_km","l":"Power lines in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"4 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.rail_km","l":"Railway track in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"0 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.rail_stations","l":"Railway stations in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"0 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.bridges","l":"Road bridges in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"2 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.airports","l":"Airports in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"0 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.dams","l":"Dams and barrages in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"0 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"ts.canal_km","l":"Irrigation canals in the tsunami zone","f":"agn_exposure","p":"Tsunami","rp":"1945-type, Mw 8.3, Eastern Makran, Mw 8.8, Whole Makran, Mw 9.1","s":"0 district values","a":"AGN tsunami, hydrodynamic: GeoClaw 5.9 (Clawpack; Berger, George, LeVeque & Mandli 2011) nonlinear shallow-water model with adaptive mesh refinement, three Makr"},{"id":"f2025.hazard_km2","l":"Flooded area","f":"agn_hazard","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"841 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.population","l":"People inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"810 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.buildings","l":"Buildings inside the flood (estimate)","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"806 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.floor_km2","l":"Building floor area inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"806 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.schools","l":"Schools inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"414 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.health","l":"Health facilities inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"478 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.roads_km","l":"Road length inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"784 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.cropland_km2","l":"Cropland inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"791 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.rabi_km2","l":"Rabi (winter) crop area inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"622 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.kharif_km2","l":"Kharif (summer) crop area inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"605 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.livestock_tlu","l":"Livestock inside the flood (tropical livestock units)","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"832 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.cattle_buffalo","l":"Cattle and buffalo inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"832 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.goats_sheep","l":"Goats and sheep inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"832 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.towers","l":"Mobile towers inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"506 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.power_sites","l":"Grid stations and substations inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"161 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.power_km","l":"Power lines inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"624 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.rail_km","l":"Railway track inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"330 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.rail_stations","l":"Railway stations inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"115 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.bridges","l":"Road bridges inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"574 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.airports","l":"Airports inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"44 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.dams","l":"Dams and barrages inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"245 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"f2025.canal_km","l":"Irrigation canals inside the flood","f":"agn_exposure","p":"Observed floods","rp":"2010 flood (outline), 2014 flood (outline), 2022 flood (outline), 2025 flood (outline), 2026 monsoon water (Sentinel-1), 2015 floods (UNOSAT, Jul-Aug), 2020 Sindh rain floods (UNOSAT VIIRS), 2023 floods (UNOSAT VIIRS, 26-30 Jul)","s":"490 district values","a":"Flood outlines of 2010, 2014, 2022 and 2025 (government outlines supplied to AGN); 2026: flood water mapped by AGN from Sentinel-1 radar (20 Aug - 16 Sep 2026, "},{"id":"aq.pm25","l":"PM2.5, population-weighted monthly mean","f":"agn_hazard","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"1848 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.population","l":"People where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"114 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.buildings","l":"Buildings where the monthly mean is 'Very unhealthy' (estimate)","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"114 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.floor_km2","l":"Building floor area where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"114 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.schools","l":"Schools where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"92 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.health","l":"Health facilities where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"108 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.roads_km","l":"Road length where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"113 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.cropland_km2","l":"Cropland where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"114 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.rabi_km2","l":"Rabi (winter) crop area where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"107 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.kharif_km2","l":"Kharif (summer) crop area where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"102 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.livestock_tlu","l":"Livestock where the monthly mean is 'Very unhealthy' (tropical livestock units)","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"115 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.cattle_buffalo","l":"Cattle and buffalo where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"115 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.goats_sheep","l":"Goats and sheep where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"115 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.towers","l":"Mobile towers where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"104 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.power_sites","l":"Grid stations and substations where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"92 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.power_km","l":"Power lines where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"104 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.rail_km","l":"Railway track where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"96 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.rail_stations","l":"Railway stations where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"88 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.bridges","l":"Road bridges where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"110 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.airports","l":"Airports where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"43 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.dams","l":"Dams and barrages where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"59 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"aq.canal_km","l":"Irrigation canals where the monthly mean is 'Very unhealthy'","f":"agn_exposure","p":"Air quality","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"101 district values","a":"CAMS global reanalysis EAC4 monthly mean PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), 0.75 degrees, calibrated to ground mon"},{"id":"smog.smog_days","l":"Smog days in the month (daily PM2.5 'Very unhealthy', 125.5 \u00b5g/m\u00b3 or more)","f":"agn_hazard","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"834 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.hazardous_days","l":"Hazardous days in the month (daily PM2.5 225.5 \u00b5g/m\u00b3 or more)","f":"agn_hazard","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"262 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.pm25","l":"PM2.5, population-weighted monthly mean","f":"agn_hazard","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"1848 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.population","l":"People where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"190 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.buildings","l":"Buildings where 10 or more days in the month are 'Very unhealthy' (estimate)","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"190 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.floor_km2","l":"Building floor area where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"190 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.schools","l":"Schools where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"157 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.health","l":"Health facilities where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"174 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.roads_km","l":"Road length where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"187 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.cropland_km2","l":"Cropland where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"189 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.rabi_km2","l":"Rabi (winter) crop area where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"173 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.kharif_km2","l":"Kharif (summer) crop area where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"161 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.livestock_tlu","l":"Livestock where 10 or more days in the month are 'Very unhealthy' (tropical livestock units)","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"188 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.cattle_buffalo","l":"Cattle and buffalo where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"188 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.goats_sheep","l":"Goats and sheep where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"188 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.towers","l":"Mobile towers where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"177 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.power_sites","l":"Grid stations and substations where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"157 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.power_km","l":"Power lines where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"176 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.rail_km","l":"Railway track where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"142 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.rail_stations","l":"Railway stations where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"135 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.bridges","l":"Road bridges where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"176 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.airports","l":"Airports where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"76 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.dams","l":"Dams and barrages where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"102 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"smog.canal_km","l":"Irrigation canals where 10 or more days in the month are 'Very unhealthy'","f":"agn_exposure","p":"Smog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"149 district values","a":"CAMS global reanalysis EAC4 3-hourly PM2.5, 2003-2025 (Copernicus Atmosphere Monitoring Service; Inness et al. 2019), daily means on Pakistan time, calibrated t"},{"id":"fire.hazard_km2","l":"Burned area, mean","f":"agn_hazard","p":"Wildfire","rp":"All year, Jan, Feb, Mar, Apr, May, Jun, Jul","s":"1047 district values","a":"NASA MODIS burned area MCD64A1 version 6.1 (Giglio et al. 2018, Remote Sensing of Environment 217), 500 m, monthly 2003-2025, processed by AGN: burned pixels as"},{"id":"fire.cropland_km2","l":"Cropland burned (stubble and residue), mean","f":"agn_exposure","p":"Wildfire","rp":"All year, Jan, Feb, Mar, Apr, May, Jun, Jul","s":"666 district values","a":"NASA MODIS burned area MCD64A1 version 6.1 (Giglio et al. 2018, Remote Sensing of Environment 217), 500 m, monthly 2003-2025, processed by AGN: burned pixels as"},{"id":"fire.wildland_km2","l":"Wildland burned (forest, scrub, grass), mean","f":"agn_exposure","p":"Wildfire","rp":"All year, Jan, Feb, Mar, Apr, May, Jun, Jul","s":"926 district values","a":"NASA MODIS burned area MCD64A1 version 6.1 (Giglio et al. 2018, Remote Sensing of Environment 217), 500 m, monthly 2003-2025, processed by AGN: burned pixels as"},{"id":"fire.builtup_km2","l":"Built-up land burned, mean","f":"agn_exposure","p":"Wildfire","rp":"All year, Jan, Feb, Mar, Apr, May, Jun, Jul","s":"230 district values","a":"NASA MODIS burned area MCD64A1 version 6.1 (Giglio et al. 2018, Remote Sensing of Environment 217), 500 m, monthly 2003-2025, processed by AGN: burned pixels as"},{"id":"burn.burned_km2","l":"Burned area","f":"agn_hazard","p":"Burned area (observed)","rp":"2003-2025 mean, 2003, 2004, 2005, 2006, 2007, 2008, 2009","s":"1903 district values","a":"NASA MODIS burned area MCD64A1 version 6.1 (Giglio et al. 2018, Remote Sensing of Environment 217), 500 m, monthly 2003-2025, processed by AGN: burned pixels as"},{"id":"burn.cropland_km2","l":"Cropland burned","f":"agn_exposure","p":"Burned area (observed)","rp":"2003-2025 mean, 2003, 2004, 2005, 2006, 2007, 2008, 2009","s":"1373 district values","a":"NASA MODIS burned area MCD64A1 version 6.1 (Giglio et al. 2018, Remote Sensing of Environment 217), 500 m, monthly 2003-2025, processed by AGN: burned pixels as"},{"id":"burn.wildland_km2","l":"Wildland burned","f":"agn_exposure","p":"Burned area (observed)","rp":"2003-2025 mean, 2003, 2004, 2005, 2006, 2007, 2008, 2009","s":"1619 district values","a":"NASA MODIS burned area MCD64A1 version 6.1 (Giglio et al. 2018, Remote Sensing of Environment 217), 500 m, monthly 2003-2025, processed by AGN: burned pixels as"},{"id":"hs.days_p95","l":"Days a year above the local 95th-percentile Discomfort Index","f":"agn_hazard","p":"Humid heat stress","rp":"Average 1991-2020, Average 2016-2025, Year 2024, Year 2025","s":"600 district values","a":"AGN Discomfort Index analysis (Cat_data/DI): Thom's Discomfort Index from ERA5-Land hourly 2 m temperature and humidity, 1980-2025, about 9 km; severity = hourl"},{"id":"hs.hours_p95","l":"Hours a year above the local 95th-percentile Discomfort Index","f":"agn_hazard","p":"Humid heat stress","rp":"Average 1991-2020, Average 2016-2025, Year 2024, Year 2025","s":"600 district values","a":"AGN Discomfort Index analysis (Cat_data/DI): Thom's Discomfort Index from ERA5-Land hourly 2 m temperature and humidity, 1980-2025, about 9 km; severity = hourl"},{"id":"hs.person_days","l":"Person-days of extreme humid heat a year (GHSL 2020 people x days)","f":"agn_exposure","p":"Humid heat stress","rp":"Average 1991-2020, Average 2016-2025, Year 2024, Year 2025","s":"600 district values","a":"AGN Discomfort Index analysis (Cat_data/DI): Thom's Discomfort Index from ERA5-Land hourly 2 m temperature and humidity, 1980-2025, about 9 km; severity = hourl"},{"id":"hs.trend_pct","l":"Trend in mean heat severity, 1980-2025 (x100, Theil-Sen per decade)","f":"agn_hazard","p":"Humid heat stress","rp":"Average 1991-2020, Average 2016-2025, Year 2024, Year 2025","s":"600 district values","a":"AGN Discomfort Index analysis (Cat_data/DI): Thom's Discomfort Index from ERA5-Land hourly 2 m temperature and humidity, 1980-2025, about 9 km; severity = hourl"},{"id":"uf.hazard_km2","l":"Urban area flooded 0.3 m or deeper","f":"agn_hazard","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"105 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.population","l":"People in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"105 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.buildings","l":"Buildings in water 0.3 m or deeper (estimate)","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"105 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.floor_km2","l":"Building floor area in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"105 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.schools","l":"Schools in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"84 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.health","l":"Health facilities in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"83 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.roads_km","l":"Road length in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"105 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.cropland_km2","l":"Cropland in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"98 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.rabi_km2","l":"Rabi (winter) crop area in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"40 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.kharif_km2","l":"Kharif (summer) crop area in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"40 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.livestock_tlu","l":"Livestock in water 0.3 m or deeper (tropical livestock units)","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"92 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.cattle_buffalo","l":"Cattle and buffalo in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"92 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.goats_sheep","l":"Goats and sheep in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"92 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.towers","l":"Mobile towers in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"92 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.power_sites","l":"Grid stations and substations in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"64 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.power_km","l":"Power lines in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"91 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.rail_km","l":"Railway track in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"67 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.rail_stations","l":"Railway stations in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"38 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.bridges","l":"Road bridges in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"80 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.airports","l":"Airports in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"6 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.dams","l":"Dams and barrages in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"15 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"uf.canal_km","l":"Irrigation canals in water 0.3 m or deeper","f":"agn_exposure","p":"Urban flood","rp":"1-in-5, 1-in-10, 1-in-25, 1-in-50, 1-in-100","s":"33 district values","a":"AGN urban flood, 2D: local inertial shallow-water model (Bates et al. 2010; de Almeida et al. 2012) on the Copernicus GLO-90 surface (about 90 m) for Pakistan's"},{"id":"rfy.hazard_km2","l":"Footprint area","f":"agn_hazard","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"976 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.hazard_severe_km2","l":"Area under 1.5 m of water or more","f":"agn_hazard","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"968 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.population","l":"People inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"936 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.buildings","l":"Buildings inside (estimate)","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"920 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.floor_km2","l":"Building floor area inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"880 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.schools_distinct","l":"Schools inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"640 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.health_distinct","l":"Health facilities inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"664 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.roads_km","l":"Road length inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"920 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.cropland_km2","l":"Cropland inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"896 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.rabi_km2","l":"Rabi (winter) crop area inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"712 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.kharif_km2","l":"Kharif (summer) crop area inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"680 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.livestock_tlu","l":"Livestock inside (tropical livestock units)","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"960 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.cattle_buffalo","l":"Cattle and buffalo inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"952 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.goats_sheep","l":"Goats and sheep inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"960 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.towers","l":"Mobile towers inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"672 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.power_sites","l":"Grid stations and substations inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"440 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.power_km","l":"Power lines inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"728 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.rail_km","l":"Railway track inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"424 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.rail_stations","l":"Railway stations inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"312 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.bridges","l":"Road bridges inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"760 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.airports","l":"Airports inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"120 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.dams","l":"Dams and barrages inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"160 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"rfy.canal_km","l":"Irrigation canals inside","f":"agn_exposure","p":"Riverine flood years (with defences)","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"560 district values","a":"AGN river flood years, from the loss engine's 10,000 simulated years (GloFAS v4 1979-2025, Gaussian copula, 3,468 river cells; the same years as the loss figure"},{"id":"ffy.hazard_km2","l":"Footprint area","f":"agn_hazard","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"824 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.population","l":"People inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"824 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.buildings","l":"Buildings inside (estimate)","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"824 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.floor_km2","l":"Building floor area inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"824 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.schools","l":"Schools inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"592 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.health","l":"Health facilities inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"656 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.roads_km","l":"Road length inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"824 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.cropland_km2","l":"Cropland inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"792 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.rabi_km2","l":"Rabi (winter) crop area inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"496 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.kharif_km2","l":"Kharif (summer) crop area inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"496 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.livestock_tlu","l":"Livestock inside (tropical livestock units)","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"816 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.cattle_buffalo","l":"Cattle and buffalo inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"816 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.goats_sheep","l":"Goats and sheep inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"816 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.towers","l":"Mobile towers inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"495 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.power_sites","l":"Grid stations and substations inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"328 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.power_km","l":"Power lines inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"680 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.rail_km","l":"Railway track inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"152 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.rail_stations","l":"Railway stations inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"111 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.bridges","l":"Road bridges inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"744 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.airports","l":"Airports inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"24 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.dams","l":"Dams and barrages inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"407 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"ffy.canal_km","l":"Irrigation canals inside","f":"agn_exposure","p":"Flash flood years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"136 district values","a":"AGN flash flood years: the loss engine's 10,000 simulated years, with each hilly district's annual maximum 3-day rain (ERA5-Land 1979-2025) turned into a return"},{"id":"hwy.population_extreme","l":"People in extreme heat (hottest day 48 \u00b0C and above)","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"950 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.hazard_extreme_km2","l":"Area in extreme heat (48 \u00b0C and above)","f":"agn_hazard","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"960 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.population_severe","l":"People in severe or extreme heat (hottest day 45 \u00b0C and above)","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1040 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.hazard_severe_km2","l":"Area in severe or extreme heat (45 \u00b0C and above)","f":"agn_hazard","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1045 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.hazard_km2","l":"Area where the hottest day reaches 40 \u00b0C","f":"agn_hazard","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1103 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.txx_c","l":"Hottest day of the year, area mean","f":"agn_hazard","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1232 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.population","l":"People where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1103 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.buildings","l":"Buildings where the hottest day reaches 40 \u00b0C (estimate)","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1103 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.floor_km2","l":"Building floor area where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1103 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.schools","l":"Schools where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1050 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.health","l":"Health facilities where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1099 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.roads_km","l":"Road length where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1101 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.cropland_km2","l":"Cropland where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1095 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.rabi_km2","l":"Rabi (winter) crop area where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"975 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.kharif_km2","l":"Kharif (summer) crop area where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"916 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.livestock_tlu","l":"Livestock where the hottest day reaches 40 \u00b0C (tropical livestock units)","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1087 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.cattle_buffalo","l":"Cattle and buffalo where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1087 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.goats_sheep","l":"Goats and sheep where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1087 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.towers","l":"Mobile towers where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1032 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.power_sites","l":"Grid stations and substations where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1026 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.power_km","l":"Power lines where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1062 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.rail_km","l":"Railway track where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"640 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.rail_stations","l":"Railway stations where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"656 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.bridges","l":"Road bridges where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1083 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.airports","l":"Airports where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"553 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.dams","l":"Dams and barrages where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"766 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"hwy.canal_km","l":"Irrigation canals where the hottest day reaches 40 \u00b0C","f":"agn_exposure","p":"Heatwave years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"647 district values","a":"AGN heatwave years: 10,000 simulated years of the hottest day (TXx) from AGN's ERA5-Land temperature analysis (0.1 deg, 1980-2025). Each place keeps its own Gum"},{"id":"eqy.population","l":"People in very strong shaking (MMI VII+)","f":"agn_exposure","p":"Earthquake years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1232 district values","a":"OpenQuake 3.23.5, same 10,000 stochastic years and saved GMFs as current earthquake losses and location curves. Years ranked by GHSL 2020 people at model sites "},{"id":"eqy.hazard_severe_km2","l":"Occupied model-grid area in very strong shaking","f":"agn_hazard","p":"Earthquake years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1232 district values","a":"OpenQuake 3.23.5, same 10,000 stochastic years and saved GMFs as current earthquake losses and location curves. Years ranked by GHSL 2020 people at model sites "},{"id":"eqy.houses","l":"Dwellings at model sites in very strong shaking","f":"agn_exposure","p":"Earthquake years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1232 district values","a":"OpenQuake 3.23.5, same 10,000 stochastic years and saved GMFs as current earthquake losses and location curves. Years ranked by GHSL 2020 people at model sites "},{"id":"eqy.floor_km2","l":"Building floor area in very strong shaking","f":"agn_exposure","p":"Earthquake years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1232 district values","a":"OpenQuake 3.23.5, same 10,000 stochastic years and saved GMFs as current earthquake losses and location curves. Years ranked by GHSL 2020 people at model sites "},{"id":"eqy.pga_g","l":"Strongest shaking of year, occupied-grid area mean","f":"agn_hazard","p":"Earthquake years","rp":"1-in-2 year, 1-in-5 year, 1-in-10 year, 1-in-25 year, 1-in-50 year, 1-in-100 year, 1-in-200 year, 1-in-500 year","s":"1232 district values","a":"OpenQuake 3.23.5, same 10,000 stochastic years and saved GMFs as current earthquake losses and location curves. Years ranked by GHSL 2020 people at model sites "},{"id":"fog.fog_days","l":"Fog days in the month (visibility under 1 km)","f":"agn_hazard","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"958 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.population","l":"People where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"199 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.buildings","l":"Buildings where fog closes in on 5 or more days in the month (estimate)","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"199 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.floor_km2","l":"Building floor area where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"199 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.schools","l":"Schools where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"176 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.health","l":"Health facilities where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"188 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.roads_km","l":"Road length where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"198 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.cropland_km2","l":"Cropland where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"200 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.rabi_km2","l":"Rabi (winter) crop area where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"179 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.kharif_km2","l":"Kharif (summer) crop area where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"178 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.livestock_tlu","l":"Livestock where fog closes in on 5 or more days in the month (tropical livestock units)","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"198 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.cattle_buffalo","l":"Cattle and buffalo where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"198 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.goats_sheep","l":"Goats and sheep where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"198 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.towers","l":"Mobile towers where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"178 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.power_sites","l":"Grid stations and substations where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"164 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.power_km","l":"Power lines where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"184 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.rail_km","l":"Railway track where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"135 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.rail_stations","l":"Railway stations where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"134 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.bridges","l":"Road bridges where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"186 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.airports","l":"Airports where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"78 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.dams","l":"Dams and barrages where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"104 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"fog.canal_km","l":"Irrigation canals where fog closes in on 5 or more days in the month","f":"agn_exposure","p":"Fog","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"154 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Fog day: f"},{"id":"dust.dust_days","l":"Dust days in the month (dust or sand reported)","f":"agn_hazard","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"998 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.population","l":"People where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"153 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.buildings","l":"Buildings where dust is reported on 2 or more days in the month (estimate)","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"153 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.floor_km2","l":"Building floor area where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"153 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.schools","l":"Schools where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"135 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.health","l":"Health facilities where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"139 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.roads_km","l":"Road length where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"154 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.cropland_km2","l":"Cropland where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"156 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.rabi_km2","l":"Rabi (winter) crop area where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"135 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.kharif_km2","l":"Kharif (summer) crop area where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"89 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.livestock_tlu","l":"Livestock where dust is reported on 2 or more days in the month (tropical livestock units)","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"157 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.cattle_buffalo","l":"Cattle and buffalo where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"157 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.goats_sheep","l":"Goats and sheep where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"157 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.towers","l":"Mobile towers where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"124 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.power_sites","l":"Grid stations and substations where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"112 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.power_km","l":"Power lines where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"135 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.rail_km","l":"Railway track where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"76 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.rail_stations","l":"Railway stations where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"73 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.bridges","l":"Road bridges where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"141 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.airports","l":"Airports where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"68 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.dams","l":"Dams and barrages where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"76 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.canal_km","l":"Irrigation canals where dust is reported on 2 or more days in the month","f":"agn_exposure","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"76 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"dust.dust_storm_days","l":"Dust-storm days in the month (visibility under 1 km)","f":"agn_hazard","p":"Dust","rp":"Jan, Feb, Mar, Apr, May, Jun, Jul, Aug","s":"541 district values","a":"NOAA Integrated Surface Database, 40 Pakistan weather stations with usable records, 2019-2026 (daily summaries of hourly and 3-hourly SYNOP reports). Dust day: "},{"id":"smogy.smog_days","l":"Smog days in the season (October-February)","f":"agn_hazard","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"351 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.hazardous_days","l":"Hazardous days in the season (October-February)","f":"agn_hazard","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"164 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.population","l":"People with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"177 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.buildings","l":"Buildings with 30 or more smog days in the season (estimate)","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"177 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.floor_km2","l":"Building floor area with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"177 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.schools","l":"Schools with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"159 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.health","l":"Health facilities with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"163 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.roads_km","l":"Road length with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"175 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.cropland_km2","l":"Cropland with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"175 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.rabi_km2","l":"Rabi (winter) crop area with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"161 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.kharif_km2","l":"Kharif (summer) crop area with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"153 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.livestock_tlu","l":"Livestock with 30 or more smog days in the season (tropical livestock units)","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"176 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.cattle_buffalo","l":"Cattle and buffalo with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"176 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.goats_sheep","l":"Goats and sheep with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"176 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.towers","l":"Mobile towers with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"167 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.power_sites","l":"Grid stations and substations with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"153 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.power_km","l":"Power lines with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"164 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.rail_km","l":"Railway track with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"130 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.rail_stations","l":"Railway stations with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"128 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.bridges","l":"Road bridges with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"169 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.airports","l":"Airports with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"74 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.dams","l":"Dams and barrages with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"105 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"smogy.canal_km","l":"Irrigation canals with 30 or more smog days in the season","f":"agn_exposure","p":"Smog season years","rp":"1-in-2 season, 1-in-5 season, 1-in-10 season","s":"140 district values","a":"Smog seasons (October-February) from the daily smog-day counts of the Smog layer, ranked by person-days of smog (people x days at 125.5 \u00b5g/m\u00b3 or more). Smog has"},{"id":"cmip6.tasmax","l":"Change in daily maximum temperature","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.tasmin","l":"Change in daily minimum temperature","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.pr","l":"Rainfall, share of 1985-2014","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.pr_p10","l":"Rainfall, dry end of models (10th pct)","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.pr_p90","l":"Rainfall, wet end of models (90th pct)","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.pr_agree","l":"Models agreeing on rain change","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.tasmax_p90","l":"Change in daily maximum, hot end (90th pct)","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.rx1day_x50","l":"1-in-50 wettest day: times more often","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.rx1day_x10","l":"1-in-10 wettest day: times more often","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.rx1day_d50","l":"1-in-50 wettest day: change in depth","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, SSP5-8.5 \u00b7 2071-2100","s":"924 district values","a":"CMIP6 monthly output from the Copernicus Climate Data Store, 8 models (NORESM2-MM, EC-EARTH3-VEG-LR, AWI-CM-1-1-MR, GFDL-ESM4, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-"},{"id":"cmip6.rx1day_agree","l":"Models: wettest day more often","f":"agn_hazard","p":"Climate change, CMIP6","rp":"SSP2-4.5 \u00b7 2011-2040, SSP2-4.5 \u00b7 2041-2070, SSP2-4.5 \u00b7 2071-2100, SSP5-8.5 \u00b7 2011-2040, SSP5-8.5 \u00b7 2041-2070, 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People exposed"},{"id":"risk.heat_health","l":"Heat-health risk","f":"agn_exposure","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"},{"id":"risk.climate_pressure","l":"Climate change pressure","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"},{"id":"risk.h_riverine_flood","l":"Exposure score \u00b7 riverine flood","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. 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People exposed"},{"id":"risk.h_earthquake","l":"Exposure score \u00b7 earthquake","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"},{"id":"risk.h_cyclone","l":"Exposure score \u00b7 cyclone","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"},{"id":"risk.h_landslide","l":"Exposure score \u00b7 landslide","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"},{"id":"risk.h_glacial_lake_outburst","l":"Exposure score \u00b7 glacial lake outburst","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"},{"id":"risk.h_avalanche","l":"Exposure score \u00b7 avalanche","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"},{"id":"risk.h_heat","l":"Exposure score \u00b7 heat","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"},{"id":"risk.h_drought","l":"Exposure score \u00b7 drought","f":"agn_hazard","p":"Risk index (AGN)","rp":"2023 exposure","s":"154 district values","a":"INFORM method (Marin-Ferrer et al. 2017, JRC EUR 28655): risk = (hazard and exposure x vulnerability x lack of coping capacity)^(1/3), each 0-10. People exposed"}],"qa":{"monotone":[{"peril":"rf","keys":["10","20","50","100","200","500"],"km2":[83368,88660,93525,95950,97826,99816],"monotone":true},{"peril":"ff","keys":["5","10","25","50","100"],"km2":[24457,29009,34910,39476,44170],"monotone":true},{"peril":"uf","keys":["5","10","25","50","100"],"km2":[4,17,48,83,135],"monotone":true},{"peril":"ss","keys":["10","25","50","100","250","500","1000"],"km2":[22,1580,2089,3603,5399,5750,5902],"monotone":true},{"peril":"cyc","keys":["10","25","50","100","250","500","1000"],"km2":[0,7706,38578,70794,120262,193409,241361],"monotone":true},{"peril":"hw","keys":["5","10","25","50","100"],"km2":[674779,684321,694312,701074,707829],"monotone":true}],"registers":[{"item":"Schools (AGN register)","bundle":65535.0,"source":65535.0},{"item":"Health facilities (AGN register)","bundle":35229.0,"source":35229.0},{"item":"Road network, km (OpenStreetMap)","bundle":606255.0,"source":606255.0},{"item":"Houses (Census 2023)","bundle":38982265.0,"source":38982265.0}],"notes":[],"schools_flood":{"records":{},"distinct":{"10":8199,"20":8787,"50":9282,"100":9611,"200":9845,"500":10140}},"area_faults":[],"unit_corrections":[],"dropped":{"null_district":0,"unknown_district":{}},"fetched":null,"gauges":[{"id":"PK000041640","name":"Lahore City","lon":74.333,"lat":31.55,"years":15,"first":2007,"last":2023,"gauge_mean_amax_mm":150.0,"era5land_mean_amax_mm":58.6,"factor":2.56,"year_to_year_r":0.04},{"id":"PK000041712","name":"Dal Bandin","lon":64.4,"lat":28.883,"years":14,"first":2009,"last":2023,"gauge_mean_amax_mm":40.0,"era5land_mean_amax_mm":21.0,"factor":1.905,"year_to_year_r":0.51},{"id":"PKM00041530","name":"Peshawar Intl","lon":71.515,"lat":33.994,"years":13,"first":2009,"last":2023,"gauge_mean_amax_mm":174.1,"era5land_mean_amax_mm":64.5,"factor":2.698,"year_to_year_r":0.6},{"id":"PKM00041598","name":"Jhelum","lon":73.717,"lat":32.933,"years":15,"first":2007,"last":2023,"gauge_mean_amax_mm":190.7,"era5land_mean_amax_mm":92.3,"factor":2.067,"year_to_year_r":0.03},{"id":"PKM00041600","name":"Sialkot","lon":74.533,"lat":32.5,"years":14,"first":2009,"last":2024,"gauge_mean_amax_mm":174.7,"era5land_mean_amax_mm":93.4,"factor":1.872,"year_to_year_r":0.13},{"id":"PKM00041675","name":"Multan Intl","lon":71.419,"lat":30.203,"years":13,"first":2009,"last":2023,"gauge_mean_amax_mm":111.7,"era5land_mean_amax_mm":38.2,"factor":2.924,"year_to_year_r":0.39},{"id":"PKM00041696","name":"Kalat","lon":66.583,"lat":29.033,"years":15,"first":2009,"last":2024,"gauge_mean_amax_mm":49.6,"era5land_mean_amax_mm":27.5,"factor":1.805,"year_to_year_r":-0.14},{"id":"PKM00041718","name":"Khanpur","lon":70.683,"lat":28.65,"years":17,"first":2007,"last":2024,"gauge_mean_amax_mm":79.9,"era5land_mean_amax_mm":36.2,"factor":2.203,"year_to_year_r":0.46},{"id":"PKM00041744","name":"Khuzdar","lon":66.633,"lat":27.833,"years":13,"first":2012,"last":2024,"gauge_mean_amax_mm":69.6,"era5land_mean_amax_mm":28.7,"factor":2.421,"year_to_year_r":0.84},{"id":"PKM00041780","name":"Jinnah Intl","lon":67.161,"lat":24.907,"years":14,"first":1997,"last":2023,"gauge_mean_amax_mm":75.1,"era5land_mean_amax_mm":48.8,"factor":1.538,"year_to_year_r":0.44}],"plugin":{"run_id":"AGN-2026-10-10","run_date":"2026-10-10","version":"6.1.0","perils_in_run":["cyc","drought","eq","ff","rf","uf"],"calibration":{"rule":"Housing damage each run gives for recorded disasters, as destroyed + 0.4 x partly damaged houses, against the published assessments. Floods: the fit year (2022) within 1.5x, other years within 2x; 2010, 2014, 2025 are in-sample, because the rain-on-homes fit uses every season 2010-2025. Cyclones: the one recorded cyclone (May 1999) is the fit event, within 1.5x; no other cyclone is tested. Earthquakes: the damage model is fitted on eight events and each is tested with the model refit without it; the set's typical error must be within 2.5x and every event within 5x, because moderate-earthquake records are early counts. Every decision is recomputed from recorded and modelled counts. Tests outside the fits (the 2026 season, earthquakes never used in the fit, flood damage beyond buildings) are in the evidence block, which gives each loss component its scientific status.","tests":[{"event":"2022 floods","peril":"flood","role":"fit","recorded":1289751,"modelled":1020638,"ratio":0.79,"band":[0.67,1.5],"unit":"destroyed-house equivalents","source":"Government of Pakistan et al., Pakistan Floods 2022 Post-Disaster Needs Assessment, Table 3 (houses) and Table 4 (PKR million, structures CD+PD, 2022 prices)","by_province":{"BALOCHISTAN":2.28,"KP":0.26,"PUNJAB":1.06,"SINDH":0.65,"SPECIAL REGIONS":0.17},"pass":true},{"event":"2010 floods","peril":"flood","role":"in-sample (rain fit uses 2010-2025)","recorded":1191258,"modelled":977525,"ratio":0.82,"band":[0.5,2.0],"unit":"destroyed-house equivalents","source":"ADB/World Bank, Pakistan Floods 2010 Preliminary Damage and Needs Assessment, Table-Annex-6.2 (houses) and 6.3 (US$ million, depreciated direct damage)","by_province":{"AJK":0.16,"BALOCHISTAN":0.49,"KP":1.12,"GILGIT BALTISTAN":1.39,"PUNJAB":1.01,"SINDH":0.73},"pass":true},{"event":"2014 floods","peril":"flood","role":"in-sample (rain fit uses 2010-2025)","recorded":99300,"modelled":138635,"ratio":1.4,"band":[0.5,2.0],"unit":"destroyed-house equivalents","source":"Pakistan Economic Survey 2014-15, Annex III (Flood 2014): Punjab 101,515 houses in 16 districts; AJK 28,365 in 10 districts. Punjab split from PDMA Punjab via Pakistan Academy of Sciences (2018), 'Damages caused by the 2014 flood'.","by_province":{"PUNJAB":1.39,"AJK":null},"pass":true},{"event":"2025 floods","peril":"flood","role":"in-sample (rain fit uses 2010-2025)","recorded":127432,"modelled":306184,"ratio":2.4,"band":[0.5,2.0],"unit":"destroyed-house equivalents","source":"NDMA, Monsoon 2025 Final Consolidated Situation Report No. 87 (30 Sep 2025), cumulative house damage","by_province":{"PUNJAB":2.53,"KP":1.08,"SINDH":2.31,"BALOCHISTAN":0.22,"GILGIT BALTISTAN":0.86,"AJK":0.03},"pass":false},{"event":"2005 Kashmir M7.6 earthquake","id":"usp000e12e","peril":"earthquake","role":"held out (refit without it)","recorded":501892,"modelled":263124.88384481164,"ratio":0.5242754239768155,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"ERRA: 436,486 destroyed rural homes; about 600,000 destroyed or damaged (ADB 2007)","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2013 Awaran M7.7 earthquake","id":"usb000jyiv","peril":"earthquake","role":"held out (refit without it)","recorded":21000,"modelled":37130.57245609038,"ratio":1.7677568993855486,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"WHO situation report 3: 21,000 houses partly or fully collapsed","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2015 Hindu Kush M7.5 earthquake","id":"us10003re5","peril":"earthquake","role":"held out (refit without it)","recorded":109123,"modelled":117117.04121409467,"ratio":1.0732504486644248,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"houses damaged (moderate damage or worse)","source":"Pakistan: 109,123 buildings damaged","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2019 Mirpur M5.4 earthquake","id":"us60005mqp","peril":"earthquake","role":"held out (refit without it)","recorded":6200,"modelled":1832.2748271752475,"ratio":0.2966626130716985,"offset":10,"band":[0.33,3.0],"pass":false,"unit":"destroyed-house equivalents","source":"2,000 houses destroyed; 12,500 fully or partly damaged and not livable","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"May 2026 Jalalpur Sharif M4.6 earthquake","id":"us7000snly","peril":"earthquake","role":"held out (refit without it)","recorded":10,"modelled":45.57960447421862,"ratio":2.778980223710931,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"Pajhwok/PMD 27 May 2026: about 10 houses severely damaged","record_confidence":"medium","shaking":"model ground motion","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"Aug 2026 Jhelum M4.5 earthquake","id":"us6000tj67","peril":"earthquake","role":"held out (refit without it)","recorded":0,"modelled":0.0,"ratio":1.0,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"Pakistan Connect 8 Aug 2026: felt, no damage reported","record_confidence":"medium","shaking":"model ground motion","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2021 Harnai M5.9 earthquake","id":"us6000fsg9","peril":"earthquake","role":"held out (refit without it)","recorded":2579.8,"modelled":1155.5226842329148,"ratio":0.450043510785742,"offset":10,"band":[0.33,3.0],"pass":true,"unit":"destroyed-house equivalents","source":"PDMA via Islamic Relief rapid needs assessment, 10 Oct 2021: 315 fully and 5,662 partially damaged households","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"2008 Ziarat M6.4 doublet earthquake","id":"usp000gm8g","peril":"earthquake","role":"held out (refit without it)","recorded":5137.0,"modelled":16749.02412394116,"ratio":3.2560761849506816,"offset":10,"band":[0.33,3.0],"pass":false,"unit":"destroyed-house equivalents","source":"OCHA situation report 5 (McRAM, 36 villages, initial): 3,487 destroyed, 4,125 partially destroyed houses (both shocks)","record_confidence":"high","shaking":"USGS ShakeMap with model ground motion beyond its extent","note":"ratio uses +10 houses on both sides; fixed eight-event fit set; corrected census housing; 0.01-MMI table"},{"event":"May 1999 Sindh cyclone","peril":"cyclone","role":"fit","recorded":98600,"modelled":98600,"ratio":1.0,"band":[0.67,1.5],"pass":true,"unit":"destroyed-house equivalents","source":"ReliefWeb/OCHA: 75,000 houses destroyed and 59,000 damaged in Thatta and Badin; v_half fitted to this event, no held-out wind-damage record exists (Phet 2010 and Yemyin 2007 damage was rain-driven)"},{"event":"June 2023 Cyclone Biparjoy (Sindh coast)","peril":"cyclone","role":"held-out (not used in the fit)","recorded":206.4,"modelled":0,"offset":10,"ratio":0.05,"band":[0.5,2.0],"pass":false,"unit":"destroyed-house equivalents","source":"PDMA Sindh situation report, 16 Jun 2023: 516 houses partly damaged (515 in Thatta), none destroyed","note":"The modelled wind on Pakistan's homes peaked at 19.6 m/s, below the 25.7 m/s damage threshold, so the model gives no damage; the record does not say whether wind, surge or rain did it. 10 houses added to both sides."},{"event":"1984 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":96891367733,"modelled":12504945503,"offset":100000000.0,"ratio":0.13,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"1985 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":34174712806,"modelled":26930168487,"offset":100000000.0,"ratio":0.79,"band":[0.5,2.0],"pass":true,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"1988 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":38958924301,"modelled":17909247289,"offset":100000000.0,"ratio":0.46,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2001 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":49353921575,"offset":100000000.0,"ratio":494.54,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2002 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":65619942450,"modelled":27042551754,"offset":100000000.0,"ratio":0.41,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2004 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":36149890602,"modelled":33307345211,"offset":100000000.0,"ratio":0.92,"band":[0.5,2.0],"pass":true,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2008 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":88586851383,"modelled":29371793723,"offset":100000000.0,"ratio":0.33,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2010 drought, wheat (rabi)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":62813694067,"modelled":37568565145,"offset":100000000.0,"ratio":0.6,"band":[0.5,2.0],"pass":true,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"1987 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":4140150635,"offset":100000000.0,"ratio":42.4,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2000 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":30008830154,"modelled":3384980906,"offset":100000000.0,"ratio":0.12,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2002 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":41601937109,"modelled":3012571898,"offset":100000000.0,"ratio":0.07,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2004 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":15507848285,"offset":100000000.0,"ratio":156.08,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"},{"event":"2009 drought, maize (kharif)","peril":"drought","role":"held-out (left out of the yield-response fit)","recorded":0,"modelled":13476713504,"offset":100000000.0,"ratio":135.77,"band":[0.5,2.0],"pass":false,"unit":"PKR, 2024 crop prices","source":"FAOSTAT production below its 2nd-order trend in a year with season SPEI-6 of -1 or lower (all causes of the shortfall); research/agn_v3/drought_test_2026-10-09","note":"PKR 0.1bn added to both sides so a year without a shortfall can be compared"}],"passed":false},"scientific_status":{"flood_buildings":"failed test","earthquake":"partly passed","cyclone":"failed test","urban_flood":"untested","drought":"partly passed","flood_sectors.crops":"partly passed","flood_sectors.irrigation":"failed test","flood_sectors.livestock":"partly passed","flood_sectors.power":"failed test","flood_sectors.rail":"failed test","flood_sectors.roads":"partly passed","flood_sectors.telecom":"failed test"},"stale":[],"status":"connected"},"fetch_log":null},"cmip6_info":{"dataset":"CMIP6 climate projections (projections-cmip6), Copernicus Climate Data Store","url":"https://cds.climate.copernicus.eu/datasets/projections-cmip6","built":"2026-10-06","baseline":"1985-2014","models":[{"model":"NorESM2-MM","family":"NCAR CAM","institution_id":"NCC","institution":"NorESM Climate Modelling Consortium (CICERO, MET Norway, NERSC, NILU, UiB, UiO, UNI)","country":"Norway","member":"r1i1p1f1","grid":"0.94\u00b0 \u00d7 1.25\u00b0","nominal_resolution":"100 km","ecs_c":2.49,"ecs_note":null,"ecs_vs_ar6":"below","score":-0.385,"rank":1,"decision":"kept: rank 1 of 8 through all gates","climwip_weight":0.0741},{"model":"EC-Earth3-Veg-LR","family":"IFS (EC-Earth)","institution_id":"EC-Earth-Consortium","institution":"EC-Earth Consortium (27 European institutes)","country":"Europe","member":"r1i1p1f1","grid":"1.12\u00b0 \u00d7 1.12\u00b0","nominal_resolution":"250 km","ecs_c":4.23,"ecs_note":null,"ecs_vs_ar6":"above","score":-0.19,"rank":2,"decision":"kept: rank 2 of 8 through all gates","climwip_weight":0.0612},{"model":"AWI-CM-1-1-MR","family":"ECHAM (MPI-M)","institution_id":"AWI","institution":"Alfred Wegener Institute","country":"Germany","member":"r1i1p1f1","grid":"0.94\u00b0 \u00d7 0.94\u00b0","nominal_resolution":"100 km","ecs_c":3.16,"ecs_note":null,"ecs_vs_ar6":"inside","score":-0.133,"rank":3,"decision":"kept: rank 3 of 8 through all gates","climwip_weight":0.0917},{"model":"GFDL-ESM4","family":"GFDL AM4","institution_id":"NOAA-GFDL","institution":"NOAA Geophysical Fluid Dynamics Laboratory","country":"USA","member":"r1i1p1f1","grid":"1.00\u00b0 \u00d7 1.25\u00b0","nominal_resolution":"100 km","ecs_c":2.65,"ecs_note":null,"ecs_vs_ar6":"inside","score":-0.016,"rank":4,"decision":"kept: rank 4 of 8 through all gates","climwip_weight":0.0477},{"model":"IPSL-CM6A-LR","family":"LMDZ (IPSL)","institution_id":"IPSL","institution":"Institut Pierre-Simon Laplace","country":"France","member":"r1i1p1f1","grid":"1.27\u00b0 \u00d7 2.50\u00b0","nominal_resolution":"250 km","ecs_c":4.7,"ecs_note":null,"ecs_vs_ar6":"above","score":0.122,"rank":5,"decision":"kept: rank 5 of 8 through all gates","climwip_weight":0.0771},{"model":"CNRM-ESM2-1","family":"ARPEGE (CNRM)","institution_id":"CNRM-CERFACS","institution":"CNRM (Meteo-France) and CERFACS","country":"France","member":"r1i1p1f2","grid":"1.40\u00b0 \u00d7 1.41\u00b0","nominal_resolution":"250 km","ecs_c":4.79,"ecs_note":null,"ecs_vs_ar6":"above","score":0.186,"rank":6,"decision":"kept: rank 6 of 8 through all gates","climwip_weight":0.0617},{"model":"MIROC-ES2L","family":"MIROC","institution_id":"MIROC","institution":"JAMSTEC, AORI University of Tokyo and NIES","country":"Japan","member":"r1i1p1f2","grid":"2.79\u00b0 \u00d7 2.81\u00b0","nominal_resolution":"500 km","ecs_c":2.66,"ecs_note":null,"ecs_vs_ar6":"inside","score":0.278,"rank":7,"decision":"kept: rank 7 of 8 through all gates","climwip_weight":0.041},{"model":"MRI-ESM2-0","family":"MRI-AGCM","institution_id":"MRI","institution":"Meteorological Research Institute","country":"Japan","member":"r1i1p1f1","grid":"1.12\u00b0 \u00d7 1.12\u00b0","nominal_resolution":"100 km","ecs_c":3.13,"ecs_note":null,"ecs_vs_ar6":"inside","score":0.363,"rank":8,"decision":"kept: rank 8 of 8 through all gates","climwip_weight":0.0352}],"models_note":"The 8 CMIP6 models behind the platform's climate-change layers, kept by tools/cmip6_select.py out of 24 complete models (58 listed) on the Copernicus store. Institution, run, grid and nominal resolution are read from the files the store returned. ECS (equilibrium climate sensitivity, warming for doubled CO2): Zelinka et al. 2020 (GRL 47, e2019GL085782), updated table. IPCC AR6 WGI: likely 2.5-4 \u00b0C, very likely 2-5 \u00b0C (best estimate 3 \u00b0C); the selection drops models outside the very likely range.","ar6_ecs_likely":[2.5,4.0],"coverage":{"monthly tasmax, tasmin, pr":{"SSP2-4.5":8,"SSP5-8.5":8},"daily pr (Rx1day)":{"SSP2-4.5":7,"SSP5-8.5":7},"daily tasmax (TXx)":{"SSP2-4.5":8,"SSP5-8.5":7}},"coverage_note":"AWI-CM-1-1-MR: no daily precipitation for 1985-2014 on the store; IPSL-CM6A-LR: no daily maximum temperature for SSP5-8.5 on the store.","scenarios":{"SSP2-4.5":{"name":"Middle of the road","forcing":"4.5 W/m\u00b2 by 2100","ar6_warming_2081_2100":"2.7 \u00b0C (very likely 2.1-3.5 \u00b0C) above 1850-1900"},"SSP5-8.5":{"name":"Fossil-fuelled development, high emissions","forcing":"8.5 W/m\u00b2 by 2100","ar6_warming_2081_2100":"4.4 \u00b0C (very likely 3.3-5.7 \u00b0C) above 1850-1900"}},"scenario_source":"IPCC AR6 WGI Summary for Policymakers, Table SPM.1","periods":["2011-2040","2041-2070","2071-2100"],"variables":[["tasmax","daily maximum near-surface air temperature","monthly means (Amon) and daily (day)"],["tasmin","daily minimum near-surface air temperature","monthly means (Amon); daily where the store has no monthly series"],["pr","precipitation","monthly means (Amon) and daily (day)"]],"method_means":[["Data","CMIP6 monthly output (Copernicus CDS, projections-cmip6): daily maximum and minimum near-surface air temperature and precipitation, historical and SSP2-4.5 / SSP5-8.5, 60-78 E, 23-38 N. Where the store has no monthly series for a piece, the daily series is read instead."],["Models","the kept set in data/tables/cmip6_selection.json, chosen by tools/cmip6_select.py from every complete model on the store by skill against Pakistan's 1985-2014 observations, independence and climate sensitivity. --models pool runs every model through the sensitivity gate instead, for the ClimWIP cross-check (Brunner et al. 2020): the skill-and-independence weighted median against the kept set's median, written back into the selection record."],["Baseline","1985-2014 from each model's own historical run."],["Change","each model's 30-year mean minus its own baseline (temperature, degC) or divided by it (precipitation, as % of baseline): the delta method, so model biases cancel. 2011-2040 joins the last historical years to the scenario run."],["District","each model's grid is sampled on a 0.05-degree district grid and averaged inside every district (coarse cells are shared by the districts they overlap)."],["Ensemble","median across the 8 models, with the 10th-90th percentile range and, for precipitation, the share of models agreeing on the sign of the change (IPCC AR6 robustness: 80% or more is \"robust\")."]],"method_extremes":[["Data","CMIP6 daily precipitation and daily maximum near-surface air temperature (Copernicus CDS, projections-cmip6), the kept models of data/tables/cmip6_selection.json (as fetch_cmip6.py) that have daily output for the piece on the store, 60-78 E, 23-38 N; historical 1985-2014 and SSP2-4.5 / SSP5-8.5 for 2011-2040, 2041-2070, 2071-2100. The scenario runs start in 2015, so the first period is 2015-2040 (26 years)."],["District","each model's daily field is averaged over the district on its own grid (share of each cell inside the district, as in fetch_cmip6.py) and the annual maximum taken: Rx1day (wettest day, mm) and TXx (hottest day, degC), 30 values per period."],["Fit","GEV by L-moments (Hosking 1990; Hosking and Wallis 1997) to each 30-year sample of annual maxima."],["Change","for the 1-in-10 and 1-in-50 year day of 1985-2014 in that model: how many times more often it happens in the future period (the ratio of return periods; capped at \"every year\") and how much bigger the future 1-in-50 day is (% for rain, degC for heat). Each model is compared only with itself, so its bias cancels."],["Ensemble","median across models, the 10th-90th percentile, and the share of models that agree the event becomes more frequent."]],"metrics":[["tasmax","Change in daily maximum temperature","\u00b0C"],["tasmin","Change in daily minimum temperature","\u00b0C"],["pr","Rainfall, share of 1985-2014","%"],["pr_p10","Rainfall, dry end of models (10th pct)","%"],["pr_p90","Rainfall, wet end of models (90th pct)","%"],["pr_agree","Models agreeing on rain change","%"],["tasmax_p90","Change in daily maximum, hot end (90th pct)","\u00b0C"],["rx1day_x50","1-in-50 wettest day: times more often","times"],["rx1day_x10","1-in-10 wettest day: times more often","times"],["rx1day_d50","1-in-50 wettest day: change in depth","%"],["rx1day_agree","Models: wettest day more often","%"],["txx_x50","1-in-50 hottest day: times more often","times"],["txx_x10","1-in-10 hottest day: times more often","times"],["txx_d50","1-in-50 hottest day: change","\u00b0C"],["txx_agree","Models: hottest day more often","%"]],"used_in":[["Map","Climate change, CMIP6: every metric, scenario and period, districts and tehsils"],["Atlas","CMIP6 maps: warming, rainfall, rare wet and hot days"],["Simulation","Scenario builder and Monte Carlo: under a CMIP6 climate, rain-driven floods and heat return m times as often, m = the change in frequency of the 1-in-10 or 1-in-50 wettest or hottest day; other perils are not rescaled"],["Risk index","Climate change pressure: SSP5-8.5 2041-2070 warming of the hottest days and how much more often the 1-in-50 wettest and hottest days come, min-max scaled"]],"limits":["8 models kept out of 24 complete on the Copernicus store, by skill against Pakistan's 1985-2014 observations, one per model family and climate sensitivity inside the IPCC AR6 very likely range. Skill in the past does not guarantee a right answer about the future; it removes models that get Pakistan's present climate badly wrong.","Observations disagree, most in the mountains (ERA5-Land has about twice GPCC's rain there). The test uses three rain references and records each model's rank under each; temperature is tested against CRU TS only and rare days against ERA5-Land only, because APHRODITE and GPM IMERG need accounts AGN does not hold yet.","One run per model (r1i1p1f1 or r1i1p1f2), so internal variability is sampled once per model.","Native grids are 100-500 km (EC-Earth3-Veg-LR, IPSL-CM6A-LR, CNRM-ESM2-1, MIROC-ES2L at 250-500 km): a district shares the model cells it overlaps, so neighbouring districts can show the same change and mountain rainfall is not resolved.","Changes are model-against-itself (delta method): no bias correction or downscaling of absolute values is needed or done.","SSP5-8.5 is a high-end pathway; SSP2-4.5 is closer to current policies.","30-year samples support the 1-in-10 and 1-in-50 year day; nothing rarer is reported."],"layer_models":{"means|SSP245":["NorESM2-MM","EC-Earth3-Veg-LR","AWI-CM-1-1-MR","GFDL-ESM4","IPSL-CM6A-LR","CNRM-ESM2-1","MIROC-ES2L","MRI-ESM2-0"],"rx1day|SSP245":["CNRM-ESM2-1","EC-Earth3-Veg-LR","GFDL-ESM4","IPSL-CM6A-LR","MIROC-ES2L","MRI-ESM2-0","NorESM2-MM"],"txx|SSP245":["AWI-CM-1-1-MR","CNRM-ESM2-1","EC-Earth3-Veg-LR","GFDL-ESM4","IPSL-CM6A-LR","MIROC-ES2L","MRI-ESM2-0","NorESM2-MM"],"means|SSP585":["NorESM2-MM","EC-Earth3-Veg-LR","AWI-CM-1-1-MR","GFDL-ESM4","IPSL-CM6A-LR","CNRM-ESM2-1","MIROC-ES2L","MRI-ESM2-0"],"rx1day|SSP585":["CNRM-ESM2-1","EC-Earth3-Veg-LR","GFDL-ESM4","IPSL-CM6A-LR","MIROC-ES2L","MRI-ESM2-0","NorESM2-MM"],"txx|SSP585":["AWI-CM-1-1-MR","CNRM-ESM2-1","EC-Earth3-Veg-LR","GFDL-ESM4","MIROC-ES2L","MRI-ESM2-0","NorESM2-MM"]},"selection":{"listed":58,"complete":24,"kept":8,"asked":"2026-10-06 15:26 PKT","scored":"2026-10-06 16:16 PKT","incomplete":["ACCESS-ESM1-5","AWI-ESM-1-1-LR","BCC-CSM2-MR","BCC-ESM1","CAMS-CSM1-0","CESM2","CESM2-FV2","CESM2-WACCM","CESM2-WACCM-FV2","CIESM","CMCC-CM2-HR4","CMCC-CM2-SR5","CanESM5","E3SM-1-0","E3SM-1-1","E3SM-1-1-ECA","EC-Earth3","EC-Earth3-AerChem","EC-Earth3-Veg","FGOALS-f3-L","GISS-E2-1-G","GISS-E2-1-H","HadGEM3-GC31-MM","IITM-ESM","IPSL-CM5A2-INCA","KIOST-ESM","MCM-UA-1-0","MIROC-ES2H","MPI-ESM-1-2-HAM","MPI-ESM1-2-HR","NorCPM1","NorESM2-LM","SAM0-UNICON","TaiESM1"],"method":[["Pool","every model on the Copernicus store (projections-cmip6) with daily maximum and minimum temperature and precipitation for historical, SSP2-4.5 and SSP5-8.5, monthly or daily (Step 0, asked of the store's own constraints service, model by model)."],["Grid","models and observations remapped to one 1 degree grid over Pakistan by conservative (area-weighted) remapping."],["Regions","Upper Indus mountains (cells above 1,500 m), monsoon plains (at or below 1,500 m where July-September brings at least half the year's rain in GPCC), arid south and west (the rest)."],["Obs","rainfall: GPCC Full Data v2022 0.25 deg, CRU TS 4.08, ERA5-Land (AGN's own daily series); temperature: CRU TS 4.08 and ERA5-Land (AGN normals and yearly extremes). APHRODITE and IMERG need accounts AGN does not hold yet; the record says so."],["Measures","1 mean bias (annual and July-September rain %, annual max and min temperature degC); 2 spatial pattern of the 30-year mean (1 - Taylor skill score, Taylor 2001); 3 seasonal cycle per region (1 - r, and RMSE of the 12 monthly means); 4 year-to-year variability (|ln(SD model / SD obs)|, July-September rain and annual max temperature); 5 rare days (wettest day, wettest 5 days, hottest day: 30-year mean and 1-in-10 level, GEV by L-moments; relative error); 6 warming trend (degC per decade, max and min temperature)."],["Score","each error as a relative error against the median model (Gleckler, Taylor & Doutriaux 2008); weights rainfall 40%, temperature 30%, rare days 20%, trend 10%; regions equal; averaged over observation sets, with the rank under each set kept. Observation against observation gives the margin within which models count as tied."],["Gates","1 complete (Step 0); 2 not in the bottom quarter for monsoon-plains rainfall under every observation set; 3 one model per family (shared atmosphere code; Knutti et al. 2013, Kuma et al. 2023), the best-scoring kept; 4 climate sensitivity inside the IPCC AR6 very likely range (ECS 2-5 degC), values from Zelinka et al. (2020, updated); a model with no published value is kept and marked."],["Keep","the best-scoring models through all gates, at most 10 and at least 6; ties (within the observation margin) ordered by daily data available. Cross-check: ClimWIP weights (Brunner et al. 2020)."]],"weights":{"rain":0.4,"temp":0.3,"rare":0.2,"trend":0.1},"tie_margin":0.24,"observation_as_model":{"ERA5-Land as a model":-0.249,"CRU TS as a model":-0.849},"references":{"rain":["GPCC","CRU TS","ERA5-Land"],"temperature":["CRU TS 4.08"],"rare days":["ERA5-Land (AGN)"]},"not_used":"APHRODITE and GPM IMERG need accounts AGN does not hold yet; rare days are therefore tested against ERA5-Land only.","regions":{"cells":[23,40,31],"rule":"mountains: tehsil mean elevation above 1500 m (AGN terrain set); monsoon plains: Jul-Sep at least half the GPCC annual rain"},"ecs_range":[2.0,5.0],"ecs_source":"Zelinka et al. 2020, GRL 47, e2019GL085782, table v2.2 of 2022-06-15 (github.com/mzelinka/cmip56_forcing_feedback_ecs; copy in data/tables/zelinka_ecs_v2.2.json)","rows":[{"model":"NorESM2-MM","family":"NCAR CAM","ecs":2.49,"ecs_note":null,"grid":"100 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