Articles | Volume 22, issue 8
Hydrol. Earth Syst. Sci., 22, 4251–4266, 2018
https://doi.org/10.5194/hess-22-4251-2018
Hydrol. Earth Syst. Sci., 22, 4251–4266, 2018
https://doi.org/10.5194/hess-22-4251-2018

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

Baatz, D., Kurtz, W., Franssen, H. H., Vereecken, H., and Kollet, S.: Catchment tomography – An approach for spatial parameter estimation, Adv. Water Resour., 107, 147–159, https://doi.org/10.1016/j.advwatres.2017.06.006, 2017. a
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Bauser, H. H., Jaumann, S., Berg, D., and Roth, K.: EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology, Hydrol. Earth Syst. Sci., 20, 4999–5014, 10.5194/hess-20-4999-2016, 2016. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a
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, https://doi.org/10.1016/j.advwatres.2017.10.022, 2017. a, b
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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.