Data for LSE-CTSM parameter calibration and regionalization paper

This dataset supports the study "On AI-based large-sample emulators for land/hydrology model calibration and regionalization", 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 Community Terrestrial Systems Model (CTSM) across 627 basins from the CAMELS dataset in the continental United States. Due to the large size of the complete CTSM forcing and output files, only essential components are included here. Full CTSM meteorological forcings and model outputs used in the experiments are available via the NCAR Research Data Archive. For questions about this dataset or related methods, please contact: Guoqiang Tang ([e-mail]) and/or Andy Wood ([e-mail]). When using this dataset, please cite: Guoqiang Tang, Andy Wood, Sean Swenson (2025). On AI-based large-sample emulators for land/hydrology model calibration and regionalization. Water Resources Research.本数据集支撑研究论文《面向陆面/水文模型参数率定与区域化的人工智能大样本模拟器》（*On AI-based large-sample emulators for land/hydrology model calibration and regionalization*），该研究提出了大样本模拟器（Large-Sample Emulator, LSE）方法，用于陆面/水文模型的参数率定与区域化。本数据集包含复现与拓展基于该模拟器的参数率定实验所需的关键文件与资源，相关实验采用陆面系统模式（Community Terrestrial Systems Model, CTSM），覆盖美国本土CAMELS数据集的627个流域。 鉴于完整CTSM强迫场与模型输出文件体积庞大，本数据集仅收录必要组件。实验所用的全部CTSM气象强迫场与模型输出数据，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存在疑问，请联系：唐国强（[e-mail]）及/或安迪·伍德（[e-mail]）。 使用本数据集时，请引用下述文献：Guoqiang Tang, Andy Wood, Sean Swenson (2025). 面向陆面/水文模型参数率定与区域化的人工智能大样本模拟器. 《水资源研究》（Water Resources Research）。