Articles | Volume 22, issue 8
Hydrol. Earth Syst. Sci., 22, 4251–4266, 2018
Hydrol. Earth Syst. Sci., 22, 4251–4266, 2018

Research article 13 Aug 2018

Research article | 13 Aug 2018

Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope

Anna Botto et al.

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Cited articles

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Brandhorst, N., Erdal, D., and Neuweiler, I.: Soil moisture prediction with the ensemble Kalman filter: Handling uncertainty of soil hydraulic parameters, Adv. Water Resour., 110, 360–370,, 2017. a, b
Short summary
We present a multivariate application of the ensemble Kalman filter (EnKF) in hydrological modeling of a real-world hillslope test case with dominant unsaturated dynamics and strong nonlinearities. Overall, the EnKF is able to correctly update system state and soil parameters. However, multivariate data assimilation may lead to significant tradeoffs between model predictions of different variables, if the observation data are not high quality or representative.