Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments

This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication. Please cite the paper as follows: Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118本数据集存档包含EXP-HYDRO、标准深度学习（Deep Learning, DL）、hybrid-J及hybrid-Z模型的源代码，同时涵盖本论文中五种模型在黄河源区三个子流域的逐日模拟径流数据（单位：毫米/日）。更多细节请参阅该已发表文献。请按以下方式引用该论文：Zhong, L., Lei, H., & Gao, B. (2023). 开发物理引导深度学习（physics-informed deep learning）模型以模拟高寒流域径流对气候变化的响应. 《水资源研究》(Water Resources Research), 59, e2022WR034118. https://doi.org/10.1029/2022WR034118