EuroFlood

Query Europe's satellite flood-depth maps as GeoDataFrames — in a few lines.

Discover where and when floods happened across Europe from a published index (zero config), then download only the depth rasters you want — no 35 GB bulk archive, no setup.

Get started Tutorials API reference

Install

pipuv

pip install "euroflood[viz]"

uv add "euroflood[viz]"

Three lines

import euroflood as ef

cat = ef.floods("Zutphen, Netherlands")  # discover — a GeoDataFrame, one row per flood
cat.plot()                                # visualize — recurrence heatmap, no download
cat.download().stats()                    # measure — fetch depth rasters + per-event stats

Zero configuration: the published index is read remotely and cached on first use.

Why EuroFlood

• One API, all of Europe

  ---

  Continental coverage from ~3,280 Sentinel-1 flood-depth maps (2015–2024), indexed so a query streams a few MB — never the 35 GB archive.

  How it works

• GeoDataFrames, not tiles

  ---

  Every query returns a GeoPandas GeoDataFrame — filter, clip, join, and export with the tools you already use.

  Discover & filter

• Visualize without downloading

  ---

  Recurrence heatmaps, per-event footprints, and interactive Leaflet maps straight from the index — no rasters fetched.

  Visualize

• Global hazard maps

  ---

  The same API queries modelled CEMS-GLOFAS return-period depth (10–500 yr): ef.hazard("Zutphen", return_period=100).

  Hazard

• CLI & offline

  ---

  Every query is also a euroflood command; mirror the index once for fully offline / HPC use.

  CLI & configuration

• Cited & reproducible

  ---

  A versioned, checksummed index bundle with a manifest — pin a version, cite the source, reproduce a result.

  Get started

Learn by doing

The tutorials take you from a first query to hazard maps and quantitative analysis — runnable notebooks that follow one place (Zutphen, on the IJssel) throughout.

---

EuroFlood code is MIT-licensed. The index and flood-depth maps derive from the JRC / Copernicus CEMS-EFAS dataset (CC-BY-4.0) — please cite Betterle & Salamon (2025). Developed at Princeton University (Complex Infrastructure Systems Group).