我们已将水文模型开源，使各国气象水文部门能够将基于先进 AI 的洪水预报集成到自身工作流程中。
快速链接
水文框架
洪水中心
-
- 复制链接 ×
洪水是全球最具破坏性的自然灾害之一，往往预警时间极短，却造成长期损害。多年来，Google Research 构建了先进的 AI 模型以实现更精准的洪水预报，确保这项技术能够触达一线响应人员，为他们争取行动时间。为进一步保护脆弱社区，我们现已在 GitHub 上开源水文建模框架，供他人使用和在此基础上继续开发。
这一开源建模框架使研究人员和预报员能够训练 AI 洪水预报模型，其架构和训练数据与 Google 洪水中心用于河流洪水预报的模型及数据类似。该框架旨在让水文科学家能够基于我们在 Google Research 所做的工作，通过添加和测试新模型、新数据及新方法进行拓展。同时，它也使业务预报员——即负责为特定区域提供可操作的洪水预警的人员——能够将本地数据和知识融入先进的 AI 洪水预报中。此项举措加入了 Google Earth AI 地理空间模型与数据集系列，进一步践行了我们在危机韧性领域保持科学领导力的承诺。
我们认为，只有当一项科学突破能够赋能他人复现并拓展其成果，确保创新成为全球进步的催化剂时，它才能发挥最大潜力。正因如此，我们在内部开发了这一框架，并与捷克水文气象研究所（CHMI）等合作伙伴进行了测试。开放我们的模型架构和训练流程，标志着全球洪水防备工作迎来根本性转变，使各国气象水文部门（NMHS）、其他气象机构及主管部门能够完全掌控自身数据，同时赋能本地专家利用专业数据集优化模型。
工作原理
我们的水文模型是一个 Python 包，利用开源 PyTorch 机器学习建模包来实现驱动 Google Flood Hub 的河流预测模型。这些模型以与气候、土壤、地形和土地覆盖相关的地理特征，以及与降雨、温度和其他天气条件相关的气象预报作为输入数据，来预测全球河流的每日流量。
该水文建模包包含基于长短期记忆（LSTM）网络的模型架构，以及一个训练流水线，可利用开源 Caravan 数据集中的历史河流数据来训练这些模型。研究人员和洪水预报机构可以将自己的数据添加到这个开源数据存储库中，从而针对当地流域训练或微调模型。
要开始实施，请查看这个 Python 交互式教程笔记本，以及 YouTube 上关于浏览模型代码的相关视频教程。
模型改进
此代码仓库包含我们水文模型的两个不同版本：一个是在我们 2024 年发布的基准测试研究中测试过的原始版本，另一个是当前为 Flood Hub 提供实时全球洪水预报的升级版模型。新模型在初始研究的基础成功之上，通过转向新的模型架构进行了改进。该框架使我们能够将多样化的多源气象输入处理成一个统一的洪水预测系统，如下图所示。我们最近的基准测试研究表明，与之前的版本相比，新模型在有水文测站的流域将可靠预测期延长了六天，在无水文测站的流域延长了一天。
v2 模型采用 ME-LSTM 架构，将不同的天气数据处理为统一的洪水预测。每种天气产品由不同的网络进行嵌入；这些输出被输入到一个 LSTM 网络中，该网络生成河流流量的概率分布。时间步长通过预报和提前时间进行标注。该系统整合了全球天气产品：Graphcast（GC）；欧洲中期天气预报中心（IFS）；NASA 的卫星降雨估算数据（IMERG）；CPC = NOAA 的 CPC 基于雨量计的日降水量。
从理论到业务化现实
在世界气象组织发布的《2025 年多灾种早期预警系统全球现状》报告中，该组织认识到，本地数据以及本土与地方知识（ILK）是有效灾害预警的关键组成部分，并指出“将 ILK 系统性地整合到风险知识生产中，目前仍是例外而非普遍做法。”我们的开源洪水预报工作流程正是针对该报告的发现，允许区域预报员直接、亲手控制基于 AI 的预报模型。这些框架训练起来相对简单且成本低廉，在提供准确性的同时，避免了传统水文预报模型的复杂性，并允许用户整合自己的专业数据进行训练和预测。
易于采用的开源工具对于弥合技术创新与洪水灾害系统实际有效性之间的差距至关重要，尤其是在加速围绕早期预警系统的能力建设方面。
此次发布的实际应用潜力，在我们与捷克水文气象研究所（CHMI）的合作中得到了最佳体现。他们的协作对于验证我们基于AI的模型能够提供与传统本地校准概念模型质量相当的预报起到了关键作用。CHMI还开发了一个适配器，将这套水文开源框架集成到Delft-FEWS平台中。该平台是一款广受欢迎的实用洪水预报工具，被各国及地方洪水预报机构、非政府组织和私营公司用于驱动预测模型。Delft-FEWS由三角洲研究院（Deltares）运营和维护。这使得CHMI及全球其他水文服务机构能够在其标准工作流程中使用该模型。此次集成为全球机构如何将机器学习纳入其水资源管理工作流程提供了范本。
除了像CHMI这样的大型机构，该开源模型的发布还提供了一种可扩展、易获取的工具，使先进预报技术更加普及，为资源受限的地区和地方团队打开了大门，让他们无需依赖成本高昂的传统预报基础设施，就能利用高质量的预测见解。
国际气象界已认识到这种开放科学方法的价值。世界气象组织水文建模与预报处处长Hwirin Kim博士指出：“我欢迎开源水文建模工具的扩展，这些工具对于支持社会管理水资源和应对环境挑战至关重要。我们世界气象组织热切支持开源、可互操作、由会员驱动的模型和工具，它们有助于拯救生命，并推动全球使命——确保各地社区能提前收到灾害预警，以保护他们的生命和生计。”
气候行动的统一框架
该模型架构、全面文档及训练材料现已根据Apache 2.0许可证在GitHub上发布，使研究人员和业务预报专业人员都能完全访问该框架。
通过将我们的水文模型交到全球水文学界手中，我们可以构建一个更具抗洪韧性的世界。关于谷歌更广泛洪水预测计划与资源的更多详情，可在谷歌研究网站上查阅。我们邀请全球水文学界在这些开放工具的基础上继续发展。
致谢
许多人为这项工作的开发做出了贡献。我们特别感谢捷克水文气象研究所的 Jakub Krejci 和 Jan Daňhelka 提供的合作与反馈，以及谷歌研究团队和社会影响合作团队中的以下成员：Amit Markel、Avinatan Hassidim、Deborah Cohen、Emily Reinstein、Gila Loike、Grey Nearing、Nina Bekele、Omri Shefi、Reuven Sayag、Rony Amira、Shmulik Fronman、Stephanie Rees 和 Yossi Matias。
快速链接
水文框架
洪水中心
-
- 复制链接 ×
其他相关文章
- 2026年7月9日 SensorFM：迈向可穿戴健康数据的通用智能与接口 * 生成式 AI · * 机器智能
- 2026年7月7日 协作的力量：我们如何减少交通拥堵 * 算法与理论 · * 气候与可持续发展 · * 数据挖掘与建模
- 2026年6月30日 将我们的高温韧性数据扩展至全球50多个城市 * 气候与可持续发展 · * 地球 AI · * 开源模型与数据集
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.
Quick links
-
- Copy link ×
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. This initiative joins the Google Earth AI family of geospatial models and datasets, reinforcing our commitment to scientific leadership in crisis resilience.
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.
How it works
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.
Model improvements
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 streamflow. Timesteps are noted by forecast & lead time. The system integrates global weather products:Graphcast(GC);European Centre for Medium-Range Weather Forecasts(IFS);NASA’s satellite rainfall estimates(IMERG);CPC = NOAA’s CPC gauge-based daily precipitation.
From theory to operational reality
In the Global Status of Multi-Hazard Early Warning Systems 2025 report, the World Meteorological Organization recognizes that both local data and Indigenous and Local Knowledge (ILK) are critical components of effective disaster warnings, and notes that “[t]he systematic integration of ILK into risk knowledge production is still the exception rather than the norm.” Our open source flood forecasting workflow addresses the report’s finding by allowing regional forecasters to take direct, hands-on control over AI-powered forecasting models. These frameworks are relatively easy and inexpensive to train, providing accuracy without the complexity of traditional hydrological forecasting models and allowing users to incorporate their own specialized data for training and prediction.
Readily adoptable open-source tools are critical for bridging the gap between technological innovation and the real-world effectiveness of flood hazard systems, particularly for accelerating capacity development around early warning systems.
The operational potential of this release is best illustrated by our partnership with CHMI. Their collaboration was key to validating that our AI-based model provides forecasts comparable in quality to traditional, locally calibrated conceptual models. CHMI also developed an adapter that integrates the hydrology open source framework into the Delft-FEWS platform, a popular operational flood forecasting tool used by national and local flood forecasting agencies, NGOs, and private companies to drive predictive models. Delft-FEWS is operated and maintained by the Deltares research institute. This allows CHMI and other hydrological services worldwide to use the model in their standard workflows. This integration serves as a blueprint for how global agencies can include machine learning in their water management workflows.
Beyond larger institutions like CHMI, the open source model release offers a scalable, accessible tool, democratizing access to advanced forecasting and opening the door for resource-constrained regions and local teams to leverage high caliber insights without the need for costly traditional forecasting infrastructure.
The international meteorological community has recognized the value of this open-science approach. Dr. Hwirin Kim, Chief of Hydrological Modelling and Forecasting Section at the World Meteorological Organization, notes: “I welcome the expansion of open-source hydrological modeling tools that are critical to supporting how societies manage water resources and respond to environmental challenges. We at WMO are keen to support open-source, interoperable, Member-driven models and tools that can help save lives and advance the global mission to ensure communities everywhere are forewarned about hazards to protect their lives and livelihoods.”
A unified framework for climate action
The model architecture, comprehensive documentation, and training materials are now live on GitHub under an Apache 2.0 license, making the framework fully accessible to both researchers and operational forecasting professionals.
By putting our hydrology model into the hands of the global hydrology community, we can build a more flood-resilient world. More details about Google’s broader flood forecasting initiatives and resources are available on the Google Research site. We invite the worldwide hydrology community to build upon these open tools.
Acknowledgements
Many people were involved in the development of this effort. We would especially like to thank Jakub Krejci and Jan Daňhelka from CHMI for their partnership and feedback as well as the following individuals across Google Research and the Social Impact Partnerships Team: Amit Markel, Avinatan Hassidim, Deborah Cohen, Emily Reinstein, Gila Loike, Grey Nearing, Nina Bekele, Omri Shefi, Reuven Sayag, Rony Amira, Shmulik Fronman, Stephanie Rees, and Yossi Matias.
Quick links
-
- Copy link ×
Other posts of interest
July 9, 2026 SensorFM: Towards a general intelligence and interface for wearable health data * Generative AI · * Machine Intelligence
July 7, 2026 The power of collaboration: How we can reduce traffic congestion * Algorithms & Theory · * Climate & Sustainability · * Data Mining & Modeling
June 30, 2026 Expanding our Heat Resilience data to 50+ global cities * Climate & Sustainability · * Earth AI · * Open Source Models & Datasets