Simulation of soil moisture on a hillslope using multiple hydrologic models in comparison to field measurements

- Soil moisture in a hillslope was simulated using three multi-dimensional hydrologic models.
- Model parameters and correlations among them were estimated via a Monte Carlo simulation compared with field measurements.
- Ensemble-based simulations have benefits to address uncertainties of parameters and model structures in a hillslope.

SummarySoil moisture in a hillslope is simulated using three multi-dimensional hydrologic models: a 3D surface-subsurface integrated model and two 2D distributed hydrologic models, MIKE-SHE and WEP, which adopt the Richards equation at different levels of approximation. High-resolution topographic data (1 m in horizontal accuracy), soil depth, hydraulic conductivity, porosity, and soil characteristics obtained from the literature and in-situ measurements were used as prior information for modeling. Numerical simulations were compared with multiple TDR sensor measurements from different locations and depths. Using available input data, the models had limited ability to reproduce the soil moisture dynamics shown in field measurements. The 3D model estimated the spatial diversity of the infiltration process of soil water movement more accurately than the distributed hydrologic models, MIKE-SHE and WEP. Suitable model parameters and correlations among them were estimated through Monte Carlo simulation using the 3D model. Parameters selected through the Monte Carlo method were used to simulate soil moisture variations at measurement sites. Relatively high correlations were found among the van Genuchten model parameters and the bottom boundary condition (bed rock). An increasing pattern of correlation between porosity to the downstream direction was found, which shows connectivity between parameter correlation and identifiability. Simulation results imply that multi-dimensional modeling of soil moisture in a hillslope may benefit from ensemble-based simulations that consider inherent uncertainty from model parameters and structures.