A physically based hydrological model for the upper Baker River basin (UBRB) in Patagonia was developed using the modular Cold Regions Hydrological Model (CRHM) in order to better understand the processes that drive the hydrological response of one of the largest rivers in this region. The model includes a full suite of blowing snow, intercepted snow, and energy balance snowmelt modules that can be used to describe the hydrology of this cold region. Within this watershed, snowfall, wind speed, and radiation are not measured; there are no high-elevation weather stations; and existing weather stations are sparsely distributed. The impact of atmospheric data from ECMWF interim reanalysis (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) on improving model performance by enhancing the representation of forcing variables was evaluated. CRHM parameters were assigned for local physiographic and vegetation characteristics based on satellite land cover classification, a digital elevation model, and parameter transfer from cold region environments in western Canada. It was found that observed precipitation has almost no predictive power [Nash–Sutcliffe coefficient (NS) < 0.3] when used to force the hydrologic model, whereas model performance using any of the reanalysis products—after bias correction—was acceptable with very little calibration (NS > 0.7). The modeled water balance shows that snowfall amounts to about 28% of the total precipitation and that 26% of total river flow stems from snowmelt. Evapotranspiration losses account for 7.2% of total precipitation, whereas sublimation and canopy interception losses represent about 1%. The soil component is the dominant modulator of runoff, with infiltration contributing as much as 73.7% to total basin outflow.

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Climate change is altering the seasonal accumulation and ablation of snow across mid-latitude mountainous regions in the Northern Hemisphere with profound implications for the water resources available to downstream communities and environments. Despite decades of empirical and model-based research on snowmelt-driven streamflow, our ability to predict whether streamflow will increase or decrease in a changing climate remains limited by two factors. First, predictions are fundamentally limited by the high spatial and temporal variability in the processes that control net snow accumulation and ablation across mountainous environments. Second, we lack a consistent and testable framework to coordinate research to determine which dominant mechanisms influencing seasonal snow dynamics are most/least important for streamflow generation in different basins. Our data-driven review marks a step towards the development of such a framework. We first conduct a systematic literature review that synthesizes knowledge about seasonal snowmelt-driven streamflow and how it is altered by climate change, highlighting unsettled questions about how annual streamflow volume is shaped by changing snow dynamics. Drawing from literature, we then propose a framework comprised of three testable, inter-related mechanisms—snow season mass and energy exchanges, the intensity of snow season liquid water inputs, and the synchrony of energy and water availability. Using data for 537 catchments in the United States, we demonstrate the utility of each mechanism and suggest that streamflow prediction will be more challenging in regions with multiple interacting mechanisms. This framework is intended to inform the research community and improve management predictions as it is tested and refined.

Reductions in snow accumulation and melt in headwater basins are increasing the water stress on forest ecosystems across the western US. Forest thinning has the potential to reduce water stress by decreasing sublimation losses from canopy interception; however, it can also increase snowpack exposure to sun and wind. We used the high-resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model to investigate the effect of two virtual forest thinning scenarios on the snowpack of two adjacent watersheds (54 km 2 total) in the Lake Tahoe Basin, California, where forest thinning is being planned. SnowPALM realistically represents small-scale snow-forest interactions to simulate the impact of virtual thinning experiments in which trees <10 and <20 m are removed. In general, thinning results in an overall increase in peak snow water equivalent and snowmelt. Areas around sheltered tree clusters have the largest increases of snowmelt due to decreases of canopy sublimation, while more open and exposed areas show a small decrease due to increases in snowpack sublimation. At the 30-m forest stand scale, existing forest structure controls the efficacy of thinning, where forest stands with mean leaf area index (LAI) >3 m 2 /m 2 and 5-15-m tall show the largest increases in snow accumulation (up to 450 mm) and melt volume (up to 650 mm). Despite the role of tree-and stand-scale thinning on snowmelt, macroscale effects were limited to slightly larger increases in melt volumes at mid to low elevation slopes (<2,300 masl) and south facing areas per unit of LAI removed. A decision support tool using machine learning (random forest) was developed to synthesize SnowPALM results, and was applied to neighboring watersheds. These results will inform ongoing forest management practices in California, and improve our understanding of the effects of snow-forest interactions at scales relevant to water management.