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. Data assimilation has recently been the focus of much attentionfor integrated surface–subsurface hydrological models, whereby jointassimilation of water table, soil moisture, and river discharge measurementswith the ensemble Kalman filter (EnKF) has been extensively applied. Althoughthe EnKF has been specifically developed to deal with nonlinear models,integrated hydrological models based on the Richards equation still representa challenge, due to strong nonlinearities that may significantly affect thefilter performance. Thus, more studies are needed to investigate thecapabilities of the EnKF to correct the system state and identify parametersin cases where the unsaturated zone dynamics are dominant, as well as toquantify possible tradeoffs associated with assimilation of multi-sourcedata. Here, the CATHY (CATchment HYdrology) model is applied to reproduce the hydrological dynamicsobserved in an experimental two-layered hillslope, equipped withtensiometers, water content reflectometer probes, and tipping bucket flowgages to monitor the hillslope response to a series of artificial rainfallevents. Pressure head, soil moisture, and subsurface outflow are assimilatedwith the EnKF in a number of scenarios and the challenges and issues arisingfrom the assimilation of multi-source data in this real-world test case arediscussed. Our results demonstrate that the EnKF is able to effectivelycorrect states and parameters even in a real application characterized bystrong nonlinearities. However, multi-source data assimilation may lead tosignificant tradeoffs: the assimilation of additional variables can lead todegradation of model predictions for other variables that are otherwise wellreproduced. Furthermore, we show that integrated observations such as outflowdischarge cannot compensate for the lack of well-distributed data inheterogeneous hillslopes.

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