Controls on hydrologic partitioning: Using a mechanistic model for comparative hydrology across ungauged sub-catchments in a mountain headwater basin

Headwater streams are the most abundant portion of the river network but the least monitored. As such, we have a limited understanding of headwater stream behaviours and how they are influenced by catchment properties such as topography, geology, and vegetation. Given the lack of runoff monitoring within headwater streams, improving an understanding of how catchment properties influence hydrologic behaviour is necessary for transferring information from instrumented areas to ungauged sites. We utilize this concept to understand physical controls on similarities and differences in hydrologic behaviour for five adjacent sub-catchments located in the Tenderfoot Creek Experimental Forest in central Montana with variable topographies and vegetative cover. We use an uncalibrated, distributed, physically-based hydrologic model, the Distributed Hydrology-Soil-Vegetation Model (DHSVM) combined with global sensitivity analysis to investigate physical controls on a range of model-predicted hydrologic behaviour (i.e. states) across multiple time scales. We implement comparative hydrology to improve our understanding of headwater catchment runoff behaviour within this framework by directly relating physical properties of a given catchment to process-based predictions of hydrologic behaviour. We find that across different hydrologic fluxes, including streamflow, evapotranspiration, and snow water equivalent change; only a few vegetation, soil, and snow parameters control the variability in hydrologic behaviour for all sub-catchments. One of the five catchments exhibited different controls on hydrologic behaviour, likely resulting from past vegetation treatments and differing surficial geology within this sub-catchment. This framework has strong potential to inform how similarities and differences in headwater catchment characteristics can influence variability in spatially and temporally varying hydrologic behaviour. We ultimately demonstrate that the influences of soil and vegetation across headwaters vary, using a modelling framework to understand physical controls on hydrologic behaviour at a high resolution. We suggest that this approach can especially enhance estimation of controls on headwater behaviour at unmonitored sites.