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Updated: Mar 14, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Model-based analysis of the influence of catchment properties on hydrologic partitioning across five mountain
Christa Kelleher1, Thorsten Wagener2, Brian McGlynn1
1Department of Earth and Ocean Sciences, Nicholas School of the Environment Duke University Durham North Carolina USA.
Abstract:
Ungauged headwater basins are an abundant part of the river network, but dominant influences on headwater hydrologic response remain difficult to predict. To address this gap, we investigated the ability of a physically based watershed model (the Distributed Hydrology-Soil-Vegetation Model) to represent controls on metrics of hydrologic partitioning across five adjacent headwater subcatchments. The five study subcatchments, located in Tenderfoot Creek Experimental Forest in central Montana, have similar climate but variable topography and vegetation distribution. This facilitated a comparative hydrology approach to interpret how parameters that influence partitioning, detected via global sensitivity analysis, differ across catchments. Model parameters were constrained a priori using existing regional information and expert knowledge. Influential parameters were compared to perceptions of catchment functioning and its variability across subcatchments. Despite between-catchment differences in topography and vegetation, hydrologic partitioning across all metrics and all subcatchments was sensitive to a similar subset of snow, vegetation, and soil parameters. Results also highlighted one subcatchment with low certainty in parameter sensitivity, indicating that the model poorly represented some complexities in this subcatchment likely because an important process is missing or poorly characterized in the mechanistic model. For use in other basins, this method can assess parameter sensitivities as a function of the specific ungauged system to which it is applied. Overall, this approach can be employed to identify dominant modeled controls on catchment response and their agreement with system understanding.
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