趋势解读：The next chapter in flood resilience，讨论数据集与基础模型

Grey Nearing and Deborah Cohen, Research Scientists, Google Research We have open-sourced our hydrology model to enable National Meteorological and Hydrological Services to integrate advanced AI-based flood forecasting into their own workflows. Floods are one of the most devastating natural hazards worldwide, often arriving with little warning and leaving long-term damage. Over several years, Google Research has built state-of-the-art AI models for more accurate flood forecasting , ensuring this technology reaches frontline responders to give them time to act. To help further protect vulnerable communities, we are now open-sourcing our hydrology modeling framework on GitHub for others to use and build upon. This open source modeling framework allows researchers and forecasters to train AI flood forecasting models with the same architecture and similar training data to what is used to power riverine flood forecasts on Google’s Flood Hub. It is developed to allow hydrological scientists to build on what we have done at Google Research by adding and testing new models, data, and approaches. It also allows operational forecasters — people whose job entails providing actionable flood warnings for specific areas — to incorporate local data and knowledge into state-of-the-art AI-based flood forecasting. We believe that a scientific breakthrough reaches its full potential when it empowers others to replicate and expand upon findings, ensuring that innovation is a catalyst for worldwide progress. That's why we developed this framework internally and tested it with partners like the Czech Hydrometeorological Institute (CHMI). Releasing our model architecture and training pipeline represents a fundamental shift in global flood preparedness, allowing National Meteorological and Hydrological Services (NMHSs), other meteorological agencies, and authorities to retain full control of their data while empowering local experts to refine models using specialized datasets. Our hydrology model is a Python package that uses the open source PyTorch machine learning modeling package to implement the river forecast model that drives the Google Flood Hub. These models take input data in the form of geographical features related to climate, soils, topography, and land cover, along with meteorological forecasts related to rainfall, temperature, and other weather conditions to predict the daily flow rate of rivers around the world. The hydrology modeling package includes model architectures based on Long Short Term Memory (LSTM) Networks, and a training pipeline that allows these models to be trained using historical river data from the open source Caravan dataset . Researchers and flood forecasting agencies can add their own data to this open source data repository to train or fine tune models to their local watersheds. To get started with implementation, check out this interactive tutorial notebook in Python and the associated video tutorial on Youtube on navigating the model code . This code repository includes two distinct versions of our hydrological model: the original version tested in our benchmarking study published in 2024 , and an upgraded model that currently powers real-time global flood forecasting on Flood Hub . The new model builds upon the foundational success of our initial research by transitioning to a new model architecture. This framework allows us to process diverse, multi-source meteorological inputs into a unified flood prediction system, illustrated in the figure below. Our recent benchmarking study shows that this new model extends the reliable predictive horizon by six days in gauged basins and by one day in ungauged basins relative to the previous version. The v2 model uses an ME-LSTM architecture to process disparate weather data into a unified flood prediction. Each weather product is embedded by a different network; these outputs are fed into an LSTM network, which generates a probability distribution for streamflo