| 04 Jan 2026
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.
I reviewed the manuscript “Hydrologic Model Parameter Estimation in Snow-Dominated Headwater Catchments Using Multiple Observation Datasets” by North et al. This study investigates the value of integrating diverse observational datasets (streamflow, satellite-derived snow, soil moisture, and evapotranspiration) for parameter sensitivity and estimation in the pywatershed hydrological model across four snow-dominated headwater catchments. Using Morris screening and Monte Carlo filtering, we find that while alternative observations consistently identify more informative parameters than streamflow alone, their impact on final streamflow performance is highly catchment-specific. Only actual evapotranspiration (AET) data reliably improved simulations, whereas snow and soil moisture datasets yielded inconsistent results, sometimes degrading performance. The work underscores the context-dependent utility of multi-observational calibration, highlighting challenges such as equifinality, spatial representativeness, and model-observation alignment that must be addressed to effectively leverage new data sources in hydrological forecasting.
This is a timely, well-executed, and intellectually rigorous study that makes a valuable contribution to the field of hydrological model calibration. The experimental design is robust, leveraging state-of-the-art sensitivity and uncertainty analysis techniques (Morris method, LHS Monte Carlo filtering) applied to a relevant and modern modeling code (pywatershed). The manuscript is clearly written, and the analysis convincingly demonstrates both the potential and the pitfalls of integrating diverse, often spatially mismatched, observational datasets. The conclusions are supported by the data presented. I only have a few specific comments in the annotated manuscript file for further clarify the narrative and implications of the work. I would recommend publication after minor revisions.