---
id: engines/google-hydrological-modeling-framework-unnamed-in-export
about:
  label: OpenHydroNet
  description: >-
    Google Research's open-source hydrology framework, a Python package that
    replicates the LSTM river-flow models behind Flood Hub so forecasters can
    train or fine-tune them on their own watersheds.
  tags:
    - hydrology
    - flood
    - forecast
    - lstm
    - open source
    - river
tool:
  homepage: 'https://github.com/google-research/flood-forecasting'
  form: open-source library
  licence: Apache-2.0
  cost: free
provenance:
  origin: agent
  sources:
    - title: GitHub - google-research/flood-forecasting
      url: 'https://github.com/google-research/flood-forecasting'
    - title: >-
        The next chapter in flood resilience: Open sourcing Google's hydrology
        framework
      url: >-
        https://research.google/blog/the-next-chapter-in-flood-resilience-open-sourcing-googles-hydrology-framework/
    - title: >-
        Google opens its hydrology framework — what does this mean for
        Delft-FEWS users?
      url: >-
        https://oss.deltares.nl/web/delft-fews/-/google-opens-its-hydrology-framework-what-does-this-mean-for-delft-fews-users-
    - title: >-
        Delft-FEWS now supports direct import of Google FloodHub forecasts -
        Delft-FEWS - Deltares Community Portal
      url: >-
        https://oss.deltares.nl/web/delft-fews/-/delft-fews-now-supports-direct-import-of-google-floodhub-forecasts
    - title: Flood Forecasting
      url: 'https://sites.research.google/gr/floodforecasting/'
  created: '2026-10-17'
  reviewed: '2026-10-17'
  reviewed_by: 'Claude, from the sources listed'
  status: draft
import:
  source: openai-linkedin-scrape 2026-10-17
  id: DT020
  kind: enabling_framework
  family: enabling_technology_and_adjacent_work
  priority: supporting
  verified: false
  posts:
    - P025
  posters:
    - Juliet Rothenberg
domains:
  - water
---
OpenHydroNet is the name the repository gives to the hydrology modelling framework Google Research released on GitHub on 3 June 2026 (the announcement, by Grey Nearing and Deborah Cohen, calls it Google's hydrology framework). It is a Python package on PyTorch, forked from NeuralHydrology and reworked for forecast sequences, and it replicates the river-flow models that run in Flood Hub. Two architectures are included: Mean-Embedding-Forecast-LSTM, the production model as of December 2025, which uses separate embedding networks for hindcast and forecast inputs and combines GraphCast, ECMWF IFS, NASA IMERG and NOAA CPC precipitation; and Handoff-Forecast-LSTM, the earlier production model described in a 2024 Nature paper. An optional data assimilation step adjusts the model's latent state against recent streamflow observations without retraining. Training uses the open Caravan dataset, and the package ships tools to delineate catchments from gauge coordinates and to pull static attributes from HydroATLAS and ERA5-Land for a user's own watersheds. The README says it is not an officially supported Google product.

The stated purpose is to let hydrologists test new models and data, and to let national forecasting services add local data and knowledge while keeping control of that data. The Czech Hydrometeorological Institute tested the framework and found its forecasts comparable in quality to locally calibrated conceptual models; it also built a Python adapter connecting the framework to Delft-FEWS, which it provides on request. Deltares and Google are working on a model adapter to embed the framework in Delft-FEWS workflows, still in progress in October 2026.

It is free under Apache 2.0 and installs locally; documentation is at openhydronet.readthedocs.io, with a tutorial notebook and video.

Status: released June 2026. Google states that the newer model extends the reliable forecast horizon by six days in gauged basins and one day in ungauged basins compared with its predecessor.