On using AI‐based large‐sample emulators for land/hydrology model calibration and regionalization

Such emulators require large, spatially diverse data sets for training, however, which—in the land/hydrology context—contrasts with parameter estimation approaches that have traditionally emphasized optimizing model performance for individual basins, followed by similarity‐based transfer schemes for parameter regionalization.

Compared to calibrating basins individually, direct land/hydrology process model calibration approaches typically perform worse when trained jointly on large collections of basins. Building on insights from large‐sample deep learning hydrologic modeling, this study introduces a Large‐Sample Emulator (LSE) approach that unifies and streamlines process model parameter calibration and regionalization. Tested across 627 basins in the continental United States using the Community Terrestrial Systems Model (CTSM), the LSE approach consistently improves runoff predictions in all basins, outperforming the Single‐Site Emulator (SSE) in both single‐objective and multi‐objective calibration tasks.