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Abstract Forest thinning and prescribed fire are expected to improve the climate resilience and water security of forests in the western U.S., but few studies have directly modeled the hydrological effects of multi‐decadal landscape‐scale forest disturbance. By updating a distributed process‐based hydrological model (DHSVM) with vegetation maps from a distributed forest ecosystem model (LANDIS‐II), we simulate the water resource impacts of forest management scenarios targeting partial or full restoration of the pre‐colonial disturbance return interval in the central Sierra Nevada mountains. In a fully restored disturbance regime that includes fire, thinning, and insect mortality, reservoir inflow increases by 4%–9% total and 8%–14% in dry years. At sub‐watershed scales (10–100 km²), thinning dense forests can increase streamflow by >20% in dry years. In a thinner forest, increased understory transpiration compensates for decreased overstory transpiration. Consequentially, 73% of streamflow gains are attributable to decreased overstory rain and snow interception loss. Thinner forests can increase headwater peak flows, but reservoir‐scale peak flows are almost exclusively influenced by climate. Uncertainty in future precipitation causes high uncertainty in future water yield, but the additional water yield attributable to forest disturbance is about five times less sensitive to annual precipitation uncertainty. This partial decoupling of the streamflow disturbance response from annual precipitation makes disturbance especially valuable for water supply during dry years. Our study can increase confidence in the water resource benefits of restoring historic forest disturbance frequencies in the central Sierra Nevada mountains, and our modeling framework is widely applicable to other forested mountain landscapes. 

Abstract Water temperatures in mountain streams are likely to rise under future climate change, with negative impacts on ecosystems and water quality. However, it is difficult to predict which streams are most vulnerable due to sparse historical records of mountain stream temperatures as well as complex interactions between snowpack, groundwater, streamflow and water temperature. Minimum flow volumes are a potentially useful proxy for stream temperature, since daily streamflow records are much more common. We confirmed that there is a strong inverse relationship between annual low flows and peak water temperature using observed data from unimpaired streams throughout the montane regions of the United States' west coast. We then used linear models to explore the relationships between snowpack, potential evapotranspiration and other climate‐related variables with annual low flow volumes and peak water temperatures. We also incorporated previous years' flow volumes into these models to account for groundwater carryover from year to year. We found that annual peak snowpack water storage is a strong predictor of summer low flows in the more arid watersheds studied. This relationship is mediated by atmospheric water demand and carryover subsurface water storage from previous years, such that multi‐year droughts with high evapotranspiration lead to especially low flow volumes. We conclude that watershed management to help retain snow and increase baseflows may help counteract some of the streamflow temperature rises expected from a warming climate, especially in arid watersheds. 

Abstract Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada. Rain-on-snow (ROS) events are a common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981 and 2019. Observations across a 500-m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a one-dimensional, physically based snow model, initiated with the Richards equation and calibrated with collocated snow pillow observations. We compare observed “historical” and “scenario” ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g cm⁻³) and colder (<−0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding. Significance StatementThe purpose of this study is to understand how the snowpack modulates quantities of water that reach the land surface during rain-on-snow (ROS) events. While the amount of near-term storm rainfall is reasonably predicted by meteorologists, major floods associated with ROS are more difficult to predict and are expected to increase in frequency. Our key findings are that liquid water inputs to the land surface vary with snowpack characteristics, and although many hydrologic models incorporate snowpack cold content and density to some degree, the complexity of ROS events justifies the need for additional observations to improve operational forecasting model results. Our findings suggest additional comparisons between existing forecasting models and those that physically represent the snowpack, as well as field-based observations of cold content and density and liquid water content, would be useful follow-up investigations. 

Abstract Bark beetles have impacted over 58 million acres of coniferous forest in the Western US since 2000. Most beetle impacted forests are in snow dominated, water limited headwater basins, which generate a disproportionate fraction of water supplies. Previous studies show mixed impacts of bark beetle outbreaks on streamflow with the potential to cause increased or decreased flows, but these studies either predate long‐term snowpack data, are model‐based, or examine only mountain pine beetle outbreaks. Ours is the first study to use an empirical, climate‐normalized paired catchment approach to quantify streamflow response to spruce beetle kill. Using 27 years of climate and streamflow observations from southwest Colorado, we show that in three of the six beetle impacted study basins, annual climate‐normalized streamflow increased by 22%–37% for at least three to 6 years after the beetle outbreak. Impacted basins exhibited no decreased flows and flows in unimpacted control basins remained unchanged. Among impacted basins, no single basin characteristic clearly explained variation of streamflow response. Higher runoff ratios during snowmelt contribute anywhere from 9% to 64% of streamflow increases, implying the importance of both snow and growing season processes in driving streamflow increases. These findings show variable, sometimes substantial streamflow increases in critical water supply basins following beetle kill in subalpine spruce forests, and contrast with evidence of unchanged or decreased streamflow following beetle kill in lower elevation pine forests in colder northern Colorado basins, highlighting the importance of climate and forest composition in refining hydrologic predictions following mountain forest disturbances. 

Abstract Snow disappearance date (SDD) affects the ecohydrological dynamics of montane forests, by altering water availability, forest fire regime, and the land surface energy budget. The forest canopy modulates SDD through competing processes; dense canopy intercepts snowfall and enhances longwave radiation while shading snowpack from shortwave radiation and sheltering it from the wind. Limited ground‐based observations of snow presence and absence have restricted our ability to unravel the dominant processes affecting SDD in montane forests. We apply a lidar‐derived method to estimate fractional snow cover area (fSCA) at two relatively warm sites in the Sierra Nevada and two colder sites in the Rocky Mountains, which we link to SDD. With the exception of late season snowpack and low fSCA, snow retention is longer under low vegetation density than under high vegetation density in both warm and cold sites. Warm forests consistently have longer snow retention in open areas compared to dense under canopy areas, particularly on south‐facing slopes. Cold forests tend to have longer snow retention under lower density canopy compared to open areas, particularly on north‐facing slopes. We use this empirical analysis to make process inferences and develop an initial framework to predict SDD that incorporates the role of topography and vegetation structure. Building on our framework will be necessary to provide better forest management recommendations for snowpack retention across complex terrain and heterogenous canopy structure. 

Montane snowpack in the Sierra Nevada provides critical water resources for ecological functions and downstream communities. Forest removal allows us to manage the snowpack in montane forests and mitigate the effect of climate on water resources. Little is known about the mid- to long-term effects that changing snowpack following forest disturbance has on tree re-growth, and how tree re-growth might in turn affect snowpack accumulation and melt. We use a 1-m resolution process-based snow model (SnowPALM) coupled with a stand-scale ecohydrological model (RHESSys) that resolves water, energy and carbon cycling to represent tree growth, and to quantify how trees and snowpack co-evolve following two disturbance scenarios (thinning and clearcutting) over a period of 40 years in a small 100 m x 234 m mid-elevation forested area in the Sierra Nevada, California. We first calculate the impact of forest disturbance on the snowpack assuming no tree regrowth and then we compare it with scenarios that include the feedback of trees regrowth on the snowpack. Without tree regrowth, snow accumulation and melt volume increase on average by roughly 5 % and 13 % following thinning and clearcutting, respectively. With tree regrowth, a regrowth rate of 0.75 and 1.15 m/decade are found for thinning and clearcutting, respectively, along with a decrease of melt volumes of 2.5 to 0.9 mm/decade, respectively. About 50 % of the snowmelt volume gains from forest thinning are lost after 40 years of regrowth, whereas only about 7 % is lost from clearcutting after the same period, which are largely explained by changes to canopy interception and sublimation. This proof-of-concept study is expected to shed light into the coevolution of montane forests and snowpack response to forest disturbance. 

Abstract. Climate warming will cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Quantifying how sensitive streamflow timing is to climate change and where it is most sensitive remain key questions. Physically based hydrological models are often used for this purpose; however, they have embedded assumptions that translate into uncertain hydrological projections that need to be quantified and constrained to provide reliable inferences. The purpose of this study is to evaluate differences in projected end-of-century changes to streamflow timing between a new empirical model based on diel (daily) streamflow cycles and regional land surface simulations across the mountainous western USA. We develop an observational technique for detecting streamflow responses to snowmelt using diel cycles of incoming solar radiation and streamflow to detect when snowmelt occurs. We measure the date of the 20th percentile of snowmelt days (DOS20) across 31 western USA watersheds affected by snow, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May among our sites, with warmer basins having earlier snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2=0.85), suggesting that a 1 d earlier DOS20 corresponds with a 1 d earlier DOQ25 and 0.7 d earlier DOQ50. Empirical projections of future DOS20 based on a stepwise multiple linear regression across sites and years under the RCP8.5 scenario for the late 21st century show that DOS20 will occur on average 11±4 d earlier per 1 ∘C of warming. However, DOS20 in colder watersheds (mean November–February air temperature, TNDJF<-8 ∘C) is on average 70 % more sensitive to climate change than in warmer watersheds (TNDJF>0 ∘C). Moreover, empirical projections of DOQ25 and DOQ50 based on DOS20 are about four and two times more sensitive to climate change, respectively, than those simulated by a state-of-the-art land surface model (NoahMP-WRF) under the same scenario. Given the importance of changes in streamflow timing for water resources, and the significant discrepancies found in projected streamflow sensitivity, snowmelt detection methods such as DOS20 based on diel streamflow cycles may help to constrain model parameters, improve hydrological predictions, and inform process understanding.