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.