Estimating root zone soil moisture using the SMAR model and regression method at a headwater catchment with complex terrain

• RZSM is estimated from SSM in a catchment with complex topography by two methods. • Both the physically based and data-driven methods obtain accurate estimates of RZSM. • Both methods perform better in drier areas at the catchment or in the wet-cold season. • Regression method is suited to complex environments with long-term data while SMAR model is suited for flat areas with less spatial variability. Obtaining accurate information regarding root zone soil moisture (RZSM) is a critical element of effective hydrological and agricultural management practices. Previous studies have relied on surface soil moisture (SSM) values, which are more easily measured, to estimate RZSM using the Soil Moisture Analytical Relationship (SMAR) model or regression method. However, the performance of these two types of methods in areas with complex topography still needs more exploration. Here, we assess the accuracy of these two types of methods in a forested mountainous catchment, using daily SSM measurements from 32 monitoring sites. The results show that both methods are capable of accurately estimating RZSM with a high NSE (>0.950) during the validation period. Additionally, they exhibit excellent model transferability at ungauged sites. Spatially, both methods perform better in drier areas than in wetter areas. Temporally, both methods are better in the wet–cold season than in the dry–warm season. Overall, both methods demonstrate comparable performance in the catchment, with NSE values of 0.986 and 0.951 during the validation period, respectively. The regression method is more suited to complex hydropedological environments characterized by long-term soil moisture monitoring and nonlinear hydropedological behaviors. Conversely, the SMAR model is better suited for flat areas and less spatial variability in microtopography. Moreover, the estimation of RZSM by both methods is influenced not only by soil moisture conditions but also by local factors including terrain topography, soil depth, and the degree of subsurface hydrological connectivity. This study adds to our understanding of RZSM estimation from SSM in complex terrain and will act as a reference for selecting appropriate methods of RZSM estimation. The results of this study underscore a discernible relationship between surface and deep soil moisture across varying spatial and temporal scales.