Matthew Irwin Barker | Search USGS

USGS2026

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.

Journal of the American Water Resources Associatio↗

USGS2025

A streamflow permanence classification model for forested streams that explicitly accounts for uncertainty and extrapolation

Accurate mapping of headwater streams and their flow status has important implications for understanding and managing water resources and land uses. However, accurate information is rare, especially in rugged, forested terrain. We developed a streamflow permanence classification model for forested lands in western Oregon using the latest light detection and ranging-derived hydrography published in the National Hydrography Dataset. Models were trained using 2,518 flow/no flow field observations collected in late summer 2019–2021 across headwaters of 129 sub-watersheds. The final model, the Western Oregon WeT DRy model, used Random Forest and 13 environmental covariates for classifying every 5-m stream sub-reach across 426 sub-watersheds. The most important covariates were annual precipitation and drainage area. Model output included probabilities of late summer surface flow presence and were subsequently categorized into three streamflow permanence classes—Wet, Dry, and Ambiguous. Ambiguous denoted model probabilities and associated prediction intervals that extended over the 50% classification threshold between wet and dry. Model accuracy was 0.83 for sub-watersheds that contained training data and decreased to 0.67 for sub-watersheds that did not have observations of late summer surface flow. The model identified where predictions extrapolated beyond the domain characterized by the training data. The combination of spatially continuous estimates of late summer streamflow status along with uncertainty and extrapolation estimates provide critical information for strategic project planning and designing additional field data collection.

Oregon↗