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Prediction of soil thicknesses in a headwater hillslope with constrained sampling data

Liu, Jintao, Han, Xiaole, Chen, Xi, He, Ruimin, Wu, Pengfei
Catena 2019 v.177 pp. 101-113
biogeochemistry, data collection, diffusivity, highlands, linear models, prediction, soil depth, topographic slope, water, watersheds, China
The spatial distribution of soil thickness plays a critical role in upland hydrological and biogeochemical processes. Process-based models are thought to have great potentials for predicting soil thickness. However, applications of process-based approaches have been greatly limited by parameter estimation, which typically requires specialized laboratories. In this study, on the basis of field works, an approach for parameter estimation under steady-state assumption is developed using linear and nonlinear soil transport models. Under each model, parameter, namely the ratios between the maximum soil production rate and the diffusion coefficient can be determined. Moreover, the optimal simulation time is derived by a power function of the diffusion coefficient derived under the linear model. The method is then used in a 0.31 ha headwater hillslope in the Hemuqiao catchment, China. According to the suggested method, parameters are determined using soil thickness data collected at 45 different locations on the hillslope, and then soil thickness is predicted. We find the distribution of predicted soil thickness and the root mean square error between the measured and predicted soil thickness for all the soil pits are all the same with the estimated ratios in both models. Furthermore, the derived power function between the optimal simulation time and the diffusion coefficient is verified and the optimal simulation time is determined. Comparisons of simulations and field observations indicate that both models can achieve comparable results, although the procedure developed using the linear model is more suitable for the study area. Furthermore, the linear model is more efficient in parameter estimation, and thus is simpler to employ in practical applications. With our framework, field studies are constrained either on ridges or sideslopes, thus reducing the aimless hunting for sampling sites. This framework provides a useful approach for applying process-based geomorphic models in real catchments.