Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model

Abstract

Abstract. Variably saturated subsurface flow models require knowledge of the soil hydraulic parameters. However, the determination of these parameters in heterogeneous soils is not easily feasible and subject to large uncertainties. As the modeled soil moisture is very sensitive to these parameters, especially the saturated hydraulic conductivity, porosity, and the parameters describing the retention and relative permeability functions, it is likewise highly uncertain. Data assimilation can be used to handle and reduce both the state and parameter uncertainty. In this work, we apply the ensemble Kalman filter (EnKF) to a three-dimensional heterogeneous hillslope model and investigate the influence of updating the different soil hydraulic parameters on the accuracy of the estimated soil moisture. We further examine the usage of a simplified layered soil structure instead of the fully resolved heterogeneous soil structure in the ensemble. It is shown that the best estimates are obtained when performing a joint update of porosity and the van Genuchten parameters and (optionally) the saturated hydraulic conductivity. The usage of a simplified soil structure gave decent estimates of spatially averaged soil moisture in combination with parameter updates but led to a failure of the EnKF and very poor soil moisture estimates at non-observed locations.

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“…In this work, the EnKF is also used to update the most sensitive parameter (saturated hydraulic conductivities) in ParFlow. The other parameters were not updated because Brandhorst & Neuweiler, 2023 found that updating multiple parameters for the unsaturated zone is prone to causing numerical instabilities, even in synthetic studies.…”

Evaluating the effects of topography and land use change on hydrological signatures: a comparative study of two adjacent watersheds

“…The model underwent multiple spin-up sessions using 2020 meteorological data to establish an initial condition that closely mirrors the monitoring datasets. Given the heterogeneity of the basin, effective auto-calibration was challenging, leading to a preference for manual calibration as a common practice for ISSHMs (Shi et al, 2014;Thornton et al, 2022;Brandhorst and Neuweiler, 2023). We leveraged previous uncertainty analysis and parameter sensitivity studies (Baroni et al, 2010;Song et al, 2015;Liu et al, 2020) to select the most crucial parameters for this hands-on calibration process.…”