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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2018-18
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
31 Jan 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Multi-source data assimilation for physically-based hydrological modeling of an experimental hillslope
Anna Botto, Enrica Belluco, and Matteo Camporese Department of Civil, Environmental and Architectural Engineering, University of Padova, Italy
Abstract. Data assimilation has been recently the focus of much attention for integrated surface-subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) have been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant trade-offs: the assimilation of additional variables can lead to degradation of model predictions for other variables that were otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.
Citation: Botto, A., Belluco, E., and Camporese, M.: Multi-source data assimilation for physically-based hydrological modeling of an experimental hillslope, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-18, in review, 2018.
Anna Botto et al.
Anna Botto et al.
Anna Botto et al.

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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 non-linearities. 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.
We present a multivariate application of the ensemble Kalman filter (EnKF) in hydrological...
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