Hydrologic Model Parameter Estimation in Snow-Dominated Headwater Catchments Using Multiple Observation Datasets

Abstract. Hydrologic models are often calibrated only using streamflow, but increasing availability of in situ and satellite based observations provide numerous opportunities to constrain model outputs and improve process representation. However, as new observation data emerges, it is often unclear whether calibration with additional data would inform or misinform streamflow prediction. Here, we carry out a multi-observational sensitivity and uncertainty analysis using the U.S. Geological Survey's National Hydrologic Model (NHM) in four headwater catchments in the Upper Colorado River Basin. We use seven different observational data products that pertain to discharge, snow water equivalent, snow-covered area, soil moisture, and evapotranspiration. Informative model parameters are identified using the Morris screening method across all data sets, followed by parameter estimation and streamflow performance assessment 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, 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 behavioral parameter sets, and estimated parameter values can depend strongly on the observation data criteria. Evapotranspiration is informative for streamflow prediction across all catchments included in this study, but snow and soil moisture datasets are only informative in some. These results provide new insight into the variable value of alternative observation data for streamflow prediction and highlight challenges related to model/observation scale mismatches, compensating errors, and misinformative data.