Data for LSE-SUMMA parameter calibration and regionalization paper

This dataset supports the study "Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments", which presents a Large-Sample Emulator (LSE) approach for calibrating and regionalizing parameters in land/hydrology models. The dataset includes key files and resources necessary to reproduce and extend the LSE-based calibration experiments conducted with the Structure for Unifying Multiple Modeling Alternatives (SUMMA) across 627 basins from the CAMELS dataset in the continental United States.Due to the large size of the complete SUMMA forcing and output files, only essential components are included here. Full SUMMA meteorological forcings used in the experiments are available via the NCAR Research Data Archive.For questions about this dataset or related methods, please contact: Mozhgan A. Farahani ([e-mail]) and/or Andy Wood ([e-mail])When using this dataset, please cite:Farahani, MA, G Tang, N Mizukami, and AW Wood, 2025. Calibrating large-domain land/hydrology process models in the age of AI: the SUMMA CAMELS experiments. Hydrology and Earth System Sciences.本数据集支撑研究论文《人工智能时代下大尺度陆面/水文过程模型校准：SUMMA CAMELS模拟器实验》，该研究提出了一种大样本模拟器（Large-Sample Emulator, LSE）方法，用于陆面/水文模型的参数校准与区域化。本数据集包含复现与拓展基于该大样本模拟器方法开展的校准实验所需的核心文件与资源，相关实验基于统一多模型备选方案结构（Structure for Unifying Multiple Modeling Alternatives, SUMMA）开展，覆盖美国本土CAMELS数据集收录的627个流域。鉴于完整的SUMMA强迫场与输出文件体积庞大，本数据集仅收录必要组件。实验中使用的全部SUMMA气象强迫场数据可通过美国国家大气研究中心研究数据档案馆（NCAR Research Data Archive）获取。若对本数据集或相关研究方法有疑问，请联系：莫兹甘·A·法拉哈尼（[e-mail]）与/或安迪·伍德（[e-mail]）。使用本数据集时，请引用如下文献：Farahani, MA, G Tang, N Mizukami, 及 AW Wood, 2025. 《人工智能时代下大尺度陆面/水文过程模型校准：SUMMA CAMELS模拟器实验》，《Hydrology and Earth System Sciences》。