26 Sep 2022
26 Sep 2022
Status: this preprint is currently under review for the journal HESS.

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

Natascha Brandhorst and Insa Neuweiler Natascha Brandhorst and Insa Neuweiler
  • Institute of Fluid Mechanics and Environmental Physics in Civil Engineering, University of Hannover, Hannover, Germany

Abstract. Models of variably saturated subsurface flow 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, 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 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.

Natascha Brandhorst and Insa Neuweiler

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-311', Anonymous Referee #1, 26 Oct 2022
  • RC2: 'Comment on hess-2022-311', Anonymous Referee #2, 01 Dec 2022

Natascha Brandhorst and Insa Neuweiler

Natascha Brandhorst and Insa Neuweiler


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Short summary
Data assimilation aims at quantifying and minimizing model uncertainty. In hydrological models, this uncertainty is mainly caused by the uncertain soil hydraulic parameters and their spatial variability. In this study, the impact of updating these parameters along with the model states on the estimated soil moisture is investigated. It is shown that parameter updates are beneficial and that it is advisable to resolve heterogeneous structures instead of applying a simplified soil structure.