Data Downloads
Every dataset behind this site is available below.
Lake areas, growth rates, dam types, slopes and settlement distances in these files are simulated (data/generate_data.py), as are every hazard score, risk class and change figure derived from them. Population and building counts are real. See the Methodology page for a full provenance table before reusing any of this data.
Lake risk GeoJSON
One Point feature per lake (WGS84 / EPSG:4326) with hazard score, risk class, dam type, elevation and basin.
Lake time-series CSV
Annual area values for all 25 lakes from 2000 to 2024 (625 rows).
Flood corridors GeoJSON
8 downstream LineString corridors for the highest-risk lakes, digitised from valley topography.
Buffered corridors GeoJSON
All 25 lakes with ±2 km Polygon corridors (8 real LineStrings buffered, 17 synthetic from lake centroids).
Population exposure JSON
Pre-computed population and building counts within each lake's flood corridor, derived from WorldPop Nepal 2020 and OpenStreetMap. Real data.
Lake area cache (2016–2024)
One JSON file per lake, bundled as a zip. Each file records its own source: the cache shipped here is generated from the simulated area series, not from Sentinel-2.
GLOF event catalogue
Confirmed GLOF events used to train the classifier, curated from the ICIMOD GLOF database. Coverage is the wider Hindu Kush Himalaya — Nepal, Bhutan, Tibet and Sikkim — not Nepal alone. The events are real; the per-event attributes are unverified estimates.
Trained ML model
Random Forest classifier (scikit-learn, joblib). Cross-validation scores are omitted deliberately: the classes come from separately authored files, so any score reflects that split rather than predictive skill.
PDF summary report
Auto-generated report with key statistics and a risk table for the top 10 lakes.
WorldPop Nepal 2020 raster (~100 MB)
The WorldPop 2020 population raster is used to compute population counts within flood corridors. It is too large to bundle here — fetch it directly, or let the offline script do it:
https://data.worldpop.org/GIS/Population/Global_2000_2020/2020/NPL/npl_ppp_2020.tif pip install -r requirements-offline.txt python data/compute_exposure.py
Source: WorldPop (www.worldpop.org) — School of Geography and Environmental Science, University of Southampton. Licence: CC BY 4.0.