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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 The Sierra Nevada has experienced unprecedented wildfires and reduced snowmelt runoff in recent decades, due partially to anthropogenic climate change and over a century of fire suppression. To address these challenges, public land agencies are planning forest restoration treatments, which have the potential to both increase water availability and reduce the likelihood of uncontrollable wildfires. However, the impact of forest restoration on snowpack is site specific and not well understood across gradients of climate and topography. To improve our understanding of how forest restoration might impact snowpack across diverse conditions in the central Sierra Nevada, we run the high‐resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model across five 23–75 km²subdomains in the region where forest thinning is planned or recently completed. We conduct two virtual thinning experiments by removing all trees shorter than 10 or 20 m tall and rerunning SnowPALM to calculate the change in meltwater input. Our results indicate heterogeneous responses to thinning due to differences in climate and wind across our five central Sierra Nevada subdomains. We also predict the largest increases in snow retention when thinning forests with tall (7–20 m) and dense (40–70% canopy cover) trees, highlighting the importance of pre‐thinning vegetation structure. We develop a decision support tool using a random forests model to determine which regions would most benefit from thinning. In many locations, we expect major forest restoration to increase snow accumulation, while other areas with short and sparse canopies, as well as sunny and windy climates, are more likely to see decreased snowpack following thinning. Our decision support tool provides stand‐scale (30 m) information to land managers across the central Sierra Nevada region to best take advantage of climate and existing forest structure to obtain the greatest snowpack benefits from forest restoration. 

Abstract Understanding the severity and extent of near surface critical zone (CZ) disturbances and their ecosystem response is a pressing concern in the face of increasing human and natural disturbances. Predicting disturbance severity and recovery in a changing climate requires comprehensive understanding of ecosystem feedbacks among vegetation and the surrounding environment, including climate, hydrology, geomorphology, and biogeochemistry. Field surveys and satellite remote sensing have limited ability to effectively capture the spatial and temporal variability of disturbance and CZ properties. Technological advances in remote sensing using new sensors and new platforms have improved observations of changes in vegetation canopy structure and productivity; however, integrating measures of forest disturbance from various sensing platforms is complex. By connecting the potential for remote sensing technologies to observe different CZ disturbance vectors, we show that lower severity disturbance and slower vegetation recovery are more difficult to quantify. Case studies in montane forests from the western United States highlight new opportunities, including evaluating post‐disturbance forest recovery at multiple scales, shedding light on understory vegetation regrowth, detecting specific physiological responses, and refining ecohydrological modeling. Learning from regional CZ disturbance case studies, we propose future directions to synthesize fragmented findings with (a) new data analysis using new or existing sensors, (b) data fusion across multiple sensors and platforms, (c) increasing the value of ground‐based observations, (d) disturbance modeling, and (e) synthesis to improve understanding of disturbance. 

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. Large sample datasets are transforming the catchment sciences, but there are few off-the-shelf stream water chemistry datasets with complementary atmospheric deposition, streamflow, meteorology, and catchment physiographic attributes. The existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset includes data on topography, climate, streamflow, land cover, soil, and geology across the continental US. With CAMELS-Chem, we pair these existing attribute data for 516 catchments with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry and instantaneous discharge data from the US Geological Survey over the period from 1980 through 2018 in a relational database and corresponding dataset. The data include 18 common stream water chemistry constituents: Al, Ca, Cl, dissolved organic carbon, total organic carbon, HCO3, K, Mg, Na, total dissolved N, total organic N, NO3, dissolved oxygen, pH (field and lab), Si, SO4, and water temperature. Annual deposition loads and concentrations include hydrogen, NH4, NO3, total inorganic N, Cl, SO4, Ca, K, Mg, and Na. We demonstrate that CAMELS-Chem water chemistry data are sampled effectively across climates, seasons, and discharges for trend analysis and highlight the coincident sampling of stream constituents for process-based understanding. To motivate their use by the larger scientific community across a variety of disciplines, we show examples of how these publicly available datasets can be applied to trend detection and attribution, biogeochemical process understanding, and new hypothesis generation via data-driven techniques.