Incorporating Catchment Attributes Grouping into Model Parameter Regionalization To Enhance Root Zone Soil Moisture Estimation

Accurate prediction of root zone soil moisture (RZSM) is critical for advancing hydrological modeling and water cycle characterization. To improve RZSM estimation in ungauged regions and elucidate the role of catchment attributes in RZSM dynamics in time and space, this study proposed a novel regionalization framework that integrates catchment attribute classification with surface soil moisture (SSM) similarity metrics. We investigate the viability of extrapolating RZSM data from gauged to ungauged catchments, with emphasis on the adaptability of the Soil Moisture Analytical Relationship (SMAR) model and the influence of catchment attributes on prediction performance. The results show that the calibrated SMAR model effectively simulates RZSM patterns, achieving a mean root mean square error (RMSE) of 0.040 cm³/cm³ for the validation periods. Additionally, the results reveal significant disparities between SSM and RZSM dynamics across the catchment, underscoring the pronounced influence of catchment attributes on SSM-RZSM coupling. Notably, parameter regionalization strategies combining catchment attribute-based site grouping, including topographic wetness index (TWI), soil depth, and leaf area index (LAI), produced more accurate RZSM predictions (mean RMSE = 0.081 cm³/cm³) than results from relying solely on SSM similarity (mean RMSE = 0.145 cm³/cm³). The superior performance of TWI-based groupings highlights topography’s essential role in modulating nonlinear SSM-RZSM relationships. These insights underscore the interdependence between soil moisture dynamics and catchment attributes in headwater catchments, illustrating the value of catchment physiographic features in constraining predictive uncertainty for RZSM in ungauged regions.