Propagation of uncertainty of soil hydraulic parameterization in the prediction of water balance components: A stochastic analysis in kaolinitic clay soils

Abstract

Hydrological models are powerful tools to understand soil–vegetation–atmosphere transport (SVAT) processes. These processes are partially controlled by soil water retention and hydraulic conductivity, in process-based models described by analytical functions (K-θ-h) that define the dependence between pressure head (h), soil water content (θ) and hydraulic conductivity (K). Given the nonlinearity of these process-based SVAT models, stochastic analysis is an interesting tool to get insight in the pattern of model outputs given the uncertainty in the K–θ–h functions. We developed a stochastic framework to evaluate outputs of the SWAP hydrological model according to the uncertainty in the van Genuchten-Mualem K–θ–h analytical functions (VGM). A multivariate Gaussian joint distribution analysis stochastically sampled VGM parameters using two statistical methods, one considering and one disregarding parameter correlations. We evaluated the relations of VGM parameters and simulation outputs (i) for an internal drainage scenario to predict pressure head at flux-based field capacity and (ii) for a cropped scenario to predict water balance components. We also assessed the relative importance of each parameter using linear regression and random forest methods. The hydrological stochastic simulations showed that the developed algorithm allowed to perform multiple SWAP model runs in a quick and unassisted manner on a common personal computer. Although the degree of VGM uncertainty of the studied soils did not affect the SWAP simulation results considerably, model output uncertainty increased when uncorrelated stochastic realizations of VGM parameters were used. The relative propagation of parameter uncertainty showed to be dependent on the simulated scenario (boundary conditions) and on the soil itself. For the correlated VGM stochastic realizations, both criteria used to analyze the parameter importance detected the VGM parameter l as one of the most relevant in determining water balance components variation. This is an interesting outcome, as this parameter is hardly ever determined, and commonly assumed to a predefined value.