Population Exposure Analysis

Hazard alone does not determine impact β€” a high-risk lake above an uninhabited valley poses far less threat than a moderate-risk lake above a densely settled floodplain. This page estimates the population and buildings within each lake's downstream flood corridor, combining WorldPop Nepal 2020 (100 m resolution) with OpenStreetMap building footprints.

The population and building counts here are real, but they are summed over flood corridors derived from the simulated lake inventory β€” 8 corridors digitised from valley topography and 17 straight Β±2 km centroid paths. The totals are real people counted inside illustrative corridors, not a flood model.

Population at risk
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Buildings at risk
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Corridor area (kmΒ²)
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All lakes β€” population exposure ranking

RankLakePopulation at riskBuildings at riskCorridor area (kmΒ²)Data source
1Gosainkunda8,1053,92892.5βšͺ synthetic
2Lower Barun7,8832,999200.3🟒 real
3Dolpo Lake 13,7031,83692.3βšͺ synthetic
4Dolpo Lake 23,3081,39892.3βšͺ synthetic
5Annapurna Lake 12,2611,20692.4βšͺ synthetic
6Annapurna Lake 21,36127792.4βšͺ synthetic
7Phoksundo Lake1,0907792.3βšͺ synthetic
8Thulagi9050113.0🟒 real
9Sabai Tsho5071,71997.3🟒 real
10Mera Lake4711,45092.6βšͺ synthetic
11Lumding Tsho4353692.6βšͺ synthetic
12Imja Tsho3343149.8🟒 real
13Langtang Lake 126819292.5βšͺ synthetic
14Langtang Lake 2267892.5βšͺ synthetic
15Tsho Rolpa26253130.6🟒 real
16Ngozumpa Tsho226447187.2🟒 real
17Chamlang South214092.6βšͺ synthetic
18Kanchenjunga Lake 21865492.8βšͺ synthetic
19Tilicho Lake1802150.8🟒 real
20Chamlang North150092.6βšͺ synthetic
21Kanchenjunga Lake 11415882.3🟒 real
22Dig Tsho915192.6βšͺ synthetic
23Ama Dablam Lake781392.6βšͺ synthetic
24Spillway Lake61492.6βšͺ synthetic
25Hongu 257392.6βšͺ synthetic

Population: WorldPop Nepal 2020 (100 m resolution, Β© WorldPop). Buildings: OpenStreetMap contributors. Corridors marked synthetic use buffered centroid paths β€” treat as indicative only.