Workflow and data supporting ensemble-based history matching and uncertainty quantification for selected watersheds from the National Hydrologic 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 developed 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 compared 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.

Plain language summary: Parameter estimation, or history matching, for large hydrologic models to best align with real-world observations is difficult because there are often limited data and many ways to adjust the parameters that can produce similar results. This study tests a statistical approach, called the iterative ensemble smoother (iES), in three National Hydrologic Model cutouts. The iES method is compared with a traditional step-by-step parameterization approach. Both methods produced similar results overall, though both struggled in a watershed where snowmelt plays a major role, suggesting the model needs improvement there. The main advantage of the iES approach is that it provides an estimate of uncertainty, which helps scientists better understand how confident they can be in both the history matching and its predictions.