Analysis of uncertainties associated with different methods to determine soil hydraulic properties and their propagation in the distributed hydrological MIKE SHE model

Complex hydrological models require a significant amount of data as input. The necessary measurement campaigns to determine input variables and parame ters can be extremely expensive and time consuming, particularly at catchme nt scale. A subset of the inputs of hydrological models is the set of soil hydraulic parameters. Pedo-transfer-functions (PTFs), relating easily measu rable soil properties to soil hydraulic parameters, can deliver candidate a pproximations for the required soil hydraulic properties. In the present st udy, uncertainties, resulting from four ways to obtain soil hydraulic param eters, are compared and evaluated with respect to their resulting uncertain ties on different model outputs. These four methods are: (i) moisture reten tion lab measurements, (ii) prediction via PTFs using field texture measure ments, (iii) prediction via PTFs using USDA texture classes, and (iv) predi ction through the bootstrap-neural network approach using field texture mea surements. The effect of parameter uncertainties on simulated catchment response was i nvestigated using the spatially distributed, physically based hydrological MIKE SHE model in a joint deterministic-stochastic approach, based on the L atin Hypercube Sampling. As expected, different results are found for the d ifferent model outputs: discharge, ground water level, and soil water conte nt. Including the PTF model as well as measurement fitting error, next to s oil heterogeneity, when quantifying the input distributions, has a major im pact, which cannot be neglected. Scaling issues were disregarded and parame ters presumed to be grid-effective. The assumption of equal medians of the soil hydraulic functions, providing the input for the MIKE SHE model, gener ally cannot be rejected, but the uncertainties differed. The neural network approach consistently provides the smallest uncertainty, but exhibits diff erent median values as well as uncertainty, and as such its application req uires further research. No significant conclusions can be inferred for the ground water elevations - the model behaved differently for the separate me thods, indicating even non-behavioural parameter sets. Soil water content a nd cumulative discharge uncertainty followed the iv, i, ii, iii order. K-sa t (ground water recharge, runoff-infiltration ratio) and theta (s) (soil wa ter contents) can be established as influential parameters. Methods ii and i provide for similar in- and output, however their input distributions do not necessarily correspond to grid-effective values. Depending on the objec tive of the model application, approximation methods to assess soil hydraul ic parameters can be a valid option. (C) 2001 Elsevier Science B.V. All rig hts reserved.