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In hydrological modeling, model structures are developed in an  iterative cycle as more and different types of measurements become  available and our understanding of the hillslope or watershed  improves. However, with increasing complexity of the model, it  becomes more and more difficult to detect which parts of the model  are deficient, or which processes should also be incorporated into  the model during the next development step. In this study, we first compare  two methods (the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) and  the Simultaneous parameter Optimization and Data Assimilation algorithm (SODA)) to calibrate a purposely deficient  3-D hillslope-scale model to error-free, artificially  generated measurements. We use a multi-objective approach based on  distributed pressure head at the soil–bedrock interface and  hillslope-scale discharge and water balance. For these idealized circumstances,  SODA's usefulness as  a diagnostic methodology is demonstrated by its ability to identify  the timing and location of processes that are missing in the  model. We show that SODA's state updates provide information  that could readily be incorporated into an improved model structure,  and that this type of information cannot be gained from parameter  estimation methods such as SCEM-UA. We then expand on the SODA result  by performing yet another calibration, in which we investigate whether  SODA's state updating patterns are still capable of providing insight  into model structure deficiencies when there are fewer measurements,  which are moreover subject to measurement noise. We conclude that SODA  can help guide the discussion between experimentalists and modelers by  providing accurate and detailed information on how to improve  spatially distributed hydrologic models

Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates