The next chapter in flood resilience: Open sourcing Google’s hydrology framework (opens in new tab)
Google Research has open-sourced the hydrology framework behind its Flood Hub river forecasts. The Python/PyTorch package lets researchers and national forecasting agencies train AI models with global and local data while retaining control over their information. Google argues that open access, local expertise, and interoperable tools can make advanced flood warnings more accurate, affordable, and widely deployable. ## The Open-Source Hydrology Framework - The framework is available on GitHub under an Apache 2.0 license. - It provides model architectures, training pipelines, documentation, and tutorials. - Users can train models with climate, soil, topography, land-cover, and weather data. - Historical river observations come from the open Caravan dataset, which agencies can extend with local measurements. - The package is built with PyTorch and is intended for both researchers and operational forecasters. ## Model Versions and Improvements - The release includes: - The original model used in Google’s 2024 benchmarking study. - An upgraded v2 model currently used for real-time global forecasts in Flood Hub. - The v2 model uses a multi-input ME-LSTM architecture. - Separate networks embed different meteorological products before combining them in an LSTM. - Inputs include GraphCast, ECMWF forecasts, NASA IMERG satellite rainfall estimates, and NOAA CPC precipitation data. - Benchmarking showed the newer model extends the reliable forecast horizon by: - Six days in gauged river basins. - One day in ungauged basins. ## Local Data and Operational Forecasting - Agencies can fine-tune models for specific watersheds using local observations and expert knowledge. - The approach supports the integration of Indigenous and Local Knowledge, which the World Meteorological Organization says is still rarely incorporated systematically. - Models are designed to be relatively inexpensive and easier to train than traditional conceptual hydrological systems. - Local organizations can preserve control over their data while adapting the models to regional conditions. ## Partnership with the Czech Hydrometeorological Institute - Google worked with CHMI to validate the model against locally calibrated traditional forecasting models. - CHMI created an adapter connecting the framework to Delft-FEWS, a widely used operational forecasting platform. - This integration demonstrates how machine-learning forecasts can fit into existing workflows used by government agencies, NGOs, and private organizations. - The partnership provides a practical model for other national hydrological services. ## Broader Flood-Resilience Goals - Open-source distribution could help resource-constrained regions access advanced forecasting without expensive infrastructure. - The framework is intended to support capacity building for early-warning systems worldwide. - Google presents the release as a way to let the global hydrology community reproduce, improve, and localize its research. National hydrological agencies and researchers should evaluate the open-source framework using their own watershed data, integrate it with existing forecasting systems, and validate its predictions against established local models before operational deployment.