groundsource 0.1.1

Python package for Google's Groundsource flash flood dataset — 2.6M events, 150+ countries, 2000–2026

groundsource

Python package for Google's Groundsource flash flood dataset.

Google used Gemini to extract 2.6 million flash flood events from news articles across 150+ countries (2000-2026). The raw data is a 667MB Parquet file with undocumented WKB geometries and no location labels. This package decodes the geometries, tags every event with country and continent, and provides a clean search and analysis API.

from groundsource import FloodDB

db = FloodDB()  # auto-downloads + enriches on first run
floods = db.search(country="India", year_range=(2020, 2025))

Installation

pip install groundsource

Requirements: Python 3.9+, pandas, pyarrow, geopandas, shapely, matplotlib

On first run, the package downloads the dataset from Zenodo (~667MB), decodes 2.6M WKB polygons, and performs a spatial join against Natural Earth boundaries. This takes 2-3 minutes and is cached locally for instant subsequent loads.

Usage

Search

from groundsource import FloodDB
db = FloodDB()

# By country (supports common aliases: "USA", "UK", "UAE", etc.)
db.search(country="India")
db.search(country="USA", year_range=(2020, 2025))

# By city (98 major cities built-in, default 100km radius)
db.search(city="Houston", radius_km=50)

# By continent or bounding box
db.search(continent="Asia")
db.search(bbox=[0, 95, 25, 120])  # [min_lat, min_lon, max_lat, max_lon]

Trend Analysis

db.trend(country="India")                        # yearly event counts
db.growth(country="India")                       # growth rate between two periods
db.compare(["USA", "UK", "India", "Indonesia"])  # side-by-side comparison
db.top_countries(20)                             # ranked by total events
db.country_growth_ranking(20)                    # ranked by growth acceleration
db.bias_check()                                  # global yearly counts for bias analysis

Built-in Charts

db.plot_hockey_stick(save_path="hockey_stick.png")
db.plot_bias(save_path="bias.png")
db.plot_top_countries(save_path="top_countries.png")
db.plot_country_growth(save_path="growth.png")

Raw DataFrame Access

df = db.to_dataframe()
# Columns: uuid, area_km2, start_date, end_date, centroid_lon, centroid_lat,
#           country, iso_a3, continent, year

What This Package Does

The raw Parquet from Zenodo has 5 columns with no documentation:

Raw Column	Type	Issue
uuid	string	ID only
area_km2	float	Usable as-is
geometry	WKB binary	Requires shapely to decode
start_date	string	Not parsed as datetime
end_date	string	Not parsed as datetime

This package enriches each event with:

Added Column	Source
centroid_lon, centroid_lat	Decoded from WKB polygons
country, iso_a3	Spatial join against Natural Earth
continent	Natural Earth
year	Extracted from start_date

Reporting Bias

The dataset shows 498 events in 2000 and 402,012 in 2024. This does not mean floods increased 807x. The data is extracted from news articles, and digital news coverage grew dramatically over this period. Any trend analysis should account for this reporting bias. Use db.bias_check() and db.plot_bias() to visualize this.

Top Countries by Events Detected

Dataset

• Source: Google Groundsource
• Download: Zenodo (CC BY 4.0)
• Records: 2,646,302 events across 175 countries, 2000-2026
• Method: Gemini parsed ~5M news articles
• Accuracy: 60% location+timing, 82% practically useful (per Google)

License

MIT. The underlying dataset is licensed CC BY 4.0 by Google.

Citation

Google Research. Groundsource: Turning News Reports into Data with Gemini. Zenodo, 2026. DOI: 10.5281/zenodo.18647054