A framework for parameterization of a geomorphic, process-based model for soil thickness prediction

Spatial distribution of soil thickness plays a critical role in upland hydrologic response as it decides the water storage and flow path. The physically, process-based approaches sought by Hydrologist and geomorphologists for soil thickness predictions, however, rely on model parameter determination, which is limited at specific areas by using expensive and time consuming laboratory works (e.g., cosmogenic radionuclide measurement). So it is essential to develop a framework for parameterization of the geomorphic, process-based model on the basis of measured soil information. In this study, based on two local steady assumptions (i.e., local steady-state for ridge area and local steady-state for sideslope area), a framework for model parameterization has been derived for two models, i.e., the linear, slope-dependent model and the nonlinear depth- and slope-dependent model. In both of the assumptions, the same set of parameters (i.e, characteristic decay depth and the ratio of diffusion coefficient and soil production rate for bare bedrock) can be determined. Under a specified value of the ratio, the optimal results of the two models can converge to the same point without regards of the individual value of diffusion coefficient and soil production rate. Furthermore, for the linear model, a power function between the optimal simulation time and the diffusion coefficient was found and then an analytical expression was derived for determine the optimal simulation time, which matches the optimal time through model calibrations well. As to the nonlinear model, we found that the simulation results can be significantly impacted by the lower limit of ridge width under the local steady-state for sideslope area. The theoretical framework is used to predict the spatial distribution of soil thickness in a headwater hillslope, the 0.31 ha H1 in Hemuqiao catchment in eastern China. Field observations and model simulations indicate that two models can achieve comparative results under the framework through appropriate derivations of parameters. The framework suggested in this study provides an applicable approach for parameterizing the geomorphic, process-based model for soil thickness predictions.