Effective modelling of percolation at the landscape scale using data-based approaches

Process-based models have been extensively applied to assess the impact of landuse change on water quantity and quality at landscape scales. However, the routine application of those models suffers from large computational efforts, lack of transparency and the requirement of many input parameters. Data-based models such as Feed-Forward Multilayer Perceptrons (MLP) and Classification and Regression Trees (CART) may be used as effective models, i.e. simple approximations of complex process-based models. These data-based approaches can subsequently be applied for scenario analysis and as a transparent management tool provided climatic boundary conditions and the basic model assumptions of the process-based models do not change dramatically. In this study, we apply MLP, CART and Multiple Linear Regression (LR) to model the spatially distributed and spatially aggregated percolation in soils using weather, groundwater and soil data. The percolation data is obtained via numerical experiments with Hydrus1D. Thus, the complex process-based model is approximated using simpler data-based approaches. The MLP model explains most of the percolation variance in time and space without using any soil information. This reflects the effective dimensionality of the process-based model and suggests that percolation in the study area may be modelled much simpler than using Hydrus1D. The CART model shows that soil properties play a negligible role for percolation under wet climatic conditions. However, they become more important if the conditions turn drier. The LR method does not yield satisfactory predictions for the spatially distributed percolation however the spatially aggregated percolation is well approximated. This may indicate that the soils behave simpler (i.e. more linear) when percolation dynamics are upscaled.