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For simulations in basins where soil information is limited to soil type maps, a methodology is presented to quantify the uncertainty of soil hydraulic parameters arising from within-soil-class variability and to assess the impact of this uncertainty on soil moisture modeling. Continuous pedotransfer functions were applied to samples with different texture within each soil class to construct discrete probability distributions of the soil hydraulic parameters. When propagating the parameter distributions through a hydrologic model, a wide range of simulated soil moisture was generated within a single soil class. The pedotransfer function was found to play a crucial role in assessing the uncertainty in the modeled soil moisture, and the geographic origin of the pedotransfer function (region specific versus nonregion specific) highly affected the range and shape of the probability distribution of the soil hydraulic parameters. Furthermore, the modeled soil moisture distribution was found to be non-Gaussian. An accurate uncertainty assessment therefore requires the characterization of its higher-order moments. As an extension of this research, we have shown that applying continuous region-specific pedotransfer functions to the central point of a soil class is a better alternative to standard (often nonregion-specific) class pedotransfer functions for determining an average set of soil hydraulic parameters

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Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output w.r.t. a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. We applied this method to a hydrologic model and assessed the sensitivity of the simulated soil moisture content to local changes in soil texture. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moisture retention curve

Abstract.Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moisture retention curve.

Abstract:Spatially distributed hydrological models require information on the land cover pattern, as it directly influences the generation of run-off. However, it is not clear which detail of land cover information is suitable for simulating the catchment hydrology. A better understanding of the relationship between the land cover detail and the hydrological processes is therefore required as it would enhance a successful application of the hydrological model. This study investigates the relevance of accurate information about the crop types in the catchment for the simulation of run-off, baseflow and evapotranspiration. Results reveal that adding knowledge about the crop type in the model simulation is redundant when area-averaged water flux predictions are intended. On the contrary, when a spatial distribution of water flux predictions is desired, it is meaningful to increase the land cover detail because an increased heterogeneity in the land cover map induces an increased heterogeneity in the hydrological response and locally reduces the uncertainty in the model prediction up to 50%. To assess the land cover uncertainty and to locate the regions for which the reduction in prediction uncertainty is the highest, the thematic uncertainty map (constructed through fuzzy logic) is shown to be a useful tool.