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Hydrologic model diagnostics using multiple observation datasets: a case study in the Upper Colorado River Basin
North, Lucas Hunter
North, Lucas Hunter
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2025
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2026-05-11
Abstract
Hydrologic models are traditionally calibrated to streamflow only, and the increasing availability of in situ and satellite-based observations provides numerous opportunities to constrain model outputs and improve process representation. However, as new observation data emerges, it is often unclear whether calibration with the additional data would inform or disinform streamflow prediction. In this study, we carry out multi-observational diagnostics with the pywatershed hydrologic model in four headwater catchments in the Upper Colorado River Basin. We use seven different calibration data products that pertain to discharge, snow water equivalent, snow-covered area, soil moisture, and evapotranspiration. These include both in situ and satellite-based observations. Informative model parameters are identified using the Morris screening method across all data sets, followed by a qualitative assessment of parameter estimation and streamflow performance using a Latin Hypercube Sample Monte-Carlo filtering approach. Results show that an increased number of informative parameters are determined through the screening process with the use of observation data representing terms beyond streamflow alone, and that forcing corrections and rain-snow partitioning parameters are particularly impactful to the model fit to observations. Multi-objective Monte Carlo filtering reduces the number of viable parameter sets, and the estimated parameter values depend strongly on the observation data. Evapotranspiration is informative to streamflow prediction across all catchments, but snow and soil moisture datasets are only informative in some catchments. These results provide insight into the value of alternative observation data for streamflow prediction and highlight the need for model diagnostics as new observations become available. Understanding the potential benefits of alternative observation data has implications for observational priorities, model development, and hydrologic forecasting.
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