我们已将水文学模型开源，以支持各国国家气象与水文服务机构将基于人工智能的先进洪水预报能力集成至其自有工作流程中。如需开始部署，请参阅该 Python 交互式教程笔记本，以及配套的 YouTube 视频教程——后者讲解如何浏览模型代码。

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. 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.

我们已将水文模型开源，以支持各国气象与水文服务机构将其集成至自身业务流程中，从而实现基于先进人工智能的洪水预报。如需开始部署，请参阅该 Python 交互式教程笔记本，以及配套的 YouTube 视频教程——后者详细演示了如何浏览模型代码。即用型开源工具对于弥合技术创新与洪水灾害系统实际效能之间的鸿沟至关重要，尤其有助于加快早期预警系统能力建设。除捷克水文气象研究所（CHMI）等大型机构外，此次开源模型发布还提供了一种可扩展、易获取的工具，推动先进预报能力的普及化，使资源受限地区及本地团队无需依赖昂贵的传统预报基础设施，即可获得高质量的预报洞见。模型架构、完整文档及培训材料现已在 GitHub 上线，采用 Apache 2.0 许可协议，确保研究人员与业务预报从业人员均可全面使用该框架。

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. 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. 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. 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 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.