Ensemble methods for history matching and uncertainty quantification with a watershed model

History matching of large hydrologic models is challenging due to data sparsity and non-unique process combinations (and associated parameters) that can produce similar model predictions. We develop an ensemble-based history matching (and uncertainty quantification) approach using an iterative ensemble smoother (iES) method for three cutouts of the National Hydrologic Model (NHM) and qualitatively compare the results and performance to the stepwise history matching approach. In the latter approach, subsets of parameters and observations were sequentially calibrated to a diverse range of observations to mitigate non-uniqueness and local minima. In iES, localization simulates the same causal connections between parameters and observations without the need (and computational cost) of sequential history matching steps. iES uses a weighted sum-of-squared-errors objective function which allows differential weighting of multiple data sources. Formal adoption of range observation also pushes results to within ranges of observation values rather than discrete values. Overall, the ensemble approach performs similarly to the stepwise approach. Both approaches performed poorly for the cutout representing a snowmelt-dominated watershed, indicating a structural issue in the process representation of the model. The main advantage of iES is quantification of uncertainty in both the history matching and the predictions of interest.