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As the climate warms, snow‐dominated regions are increasingly susceptible to precipitation phase changes and earlier snowmelt. In alpine catchments, snowmelt is the majority of runoff, governed by heterogeneous interactions among snowfall, wind, topography and vegetation. The role of the alpine snowpack in delaying the downstream release of accumulated snowfall remains understudied, despite its fundamental role in sustaining lower‐elevation ecosystems and populations. We evaluated fine‐scale changes in the magnitude and duration of alpine snow water storage and release by comparing effective precipitation seasonality to that of surface water inputs using a Snow Storage Index (SSI). We forced the Distributed Hydrology Soil Vegetation Model with historical climate conditions and an end‐of‐century warming signal down‐scaled from a 4‐km regional climate model, to simulate future changes in snow water storage and partitioning of precipitation to runoff and evapotranspiration. The hydrological modelling was conducted on a 2‐m spatial grid to resolve fine scale processes over a 0.6‐km2 headwater catchment within the Niwot Ridge Long‐Term Ecological Research site located in the Rocky Mountains, Colorado, USA. Proportionally more rainfall, reduced snowpack size and extent, and earlier snowmelt occurred in the future scenario with decreased catchment average SSI and thus snow water storing capabilities, with inordinately large decreases occurring in areas of the catchment with reconstructed snowdrifts (−57% average and −100% maximum). Using the Budyko framework to evaluate annual hydrologic partitioning, precipitation inputs to streamflow increased by a maximum 10% in large SSI regions with snowdrifts under warming conditions, signifying a potential buffer for total catchment and annual streamflow loss in a future climate. The fine‐scale analysis of hydrologic sensitivities to warming presented herein are important because climate conditions are expected to further reduce alpine snow water storage, alter hydrologic partitioning, and runoff generation for ecosystems and people living downstream. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance. Plain Language Summary: As global temperatures rise, there's growing concern about an increase in the frequency of extreme precipitation events. However, projections of future changes to large‐magnitude precipitation events often rely on coarse‐resolution models which simulate meteorology on a spatial scale larger than most storms. To address this, we explored the potential of higher resolution models from the Coupled Model Intercomparison Project HighResMIP ensemble to improve the fidelity of simulated precipitation variability across the western United States. We compared four HighResMIP models with their coarse‐resolution counterparts from 1950 to 2014 and found that coarse‐resolution models consistently underestimated the dominance of large storms compared to observed data. While some HighResMIP models showed improvement, others remained unchanged or even showed increased bias. This suggests that besides spatial resolution alone, factors like model structure and numerical representation of fine scale precipitation processes play a role in accurately simulating intense storms. These findings highlight the importance of considering both resolution and climate model structure in predicting future extreme precipitation events, especially in regions where precipitation is generated by processes that operate on a wide range of spatial scales. Key Points: Increasing spatial resolution of general circulation models (GCMs) does not consistently improve simulation of large‐storm dominance across the western United StatesCoarse resolution GCMs simulate large‐storm dominance well in regions where the largest daily precipitation events are due to synoptic scale precipitationModel structure and parameterization of precipitation generation appear to be equally as important as resolution in the simulation of large‐storm dominance [ABSTRACT FROM AUTHOR]

Terrestrial hydrology is altered by fires, particularly in snow‐dominated catchments. However, fire impacts on catchment hydrology are often neglected from land surface model (LSM) simulations. Western U.S. wildfire activity has been increasing in recent decades and is projected to continue increasing over at least the next three decades, and thus it is important to evaluate if neglecting fire impacts in operational land surface models (LSMs) is a significant error source that has a noticeable signal among other sources of uncertainty. We evaluate a widely used state‐of‐the‐art LSM (Noah‐MP) in runoff and snowpack simulations at two representative fire‐affected snow‐dominated catchments in the Pacific Northwest: Andrew's Creek in Washington and Johnson Creek in Idaho. These two catchments are selected across all western U.S. fire‐affected catchments because they are snow‐dominated and experienced more than 50% burning in a single fire event with minimal burning outside of this event, which allows analyses of distinct pre‐ and post‐fire periods. There are statistically significant shifts in model skills from pre‐to post‐fire years in simulating runoff and snowpack. At both study catchments, simulations miss enhancements in early‐spring runoff and annual runoff efficiency during post‐fire years, resulting in persistent underestimates of annual runoff anomalies throughout the 12‐year post‐fire analysis periods. Enhanced post‐fire snow accumulation and melt contributes to observed but unmodeled increases of spring runoff and annual runoff efficiency at these catchments. Informing simulations with satellite observed land cover classifications, leaf area index, and green fraction do not consistently improve the model ability to simulate hydrologic responses to fire disturbances. Plain Language Summary: Western U.S. fire activity has been increasing in recent decades and is expected to continue to increase in coming decades. Fires remove vegetation and alter soils which in turn alters terrestrial hydrology. Fire effects on hydrology are particularly significant over snowy catchments that serve as natural water towers for major western U.S. rivers. Sophisticated models often neglect or underrepresent fire effects on land surface properties, and thus are susceptible to larger errors after fires. This study compares runoff and snow simulations from a widely used land surface model (LSM) with observations to quantify fire‐induced changes in model accuracy over two snow dominated catchments in the Pacific Northwest. Simulations persistently underestimate enhanced spring runoff and annual runoff anomalies in post‐fire years. These underestimates are consistent with observed enhancements in post‐fire snow accumulation and melt, which the model mostly failed to capture. The finding that post‐fire model errors are consistent with previously published fire impacts on hydrology supports that fire is an important error source in LSM simulations that should be accounted for. Key Points: Noah‐MP underestimates annual runoff anomalies relative to observations following fires in the Pacific‐NorthwestIn post‐fire years, Noah‐MP fails to simulate deeper snowpacks that ablate faster and the associated enhanced spring runoffInforming the model with satellite‐observed vegetation characteristics (including fire effect) did not resolve post‐fire model deficiencies [ABSTRACT FROM AUTHOR]

Lakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record‐low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high‐quality hypsometry data. Here, we propose a novel proxy‐based method to reconstruct multi‐decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003–2009) and recently launched ICESat‐2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km2. Reconstructed water levels have a median root‐mean‐square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%–32%. The improvement is attributable to the new method's robust construction of high‐quality hypsometry, with a median R2 value of 0.92. Most reconstructed water level time series have a bi‐monthly or higher frequency. Given that ICESat‐2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time‐varying lake levels and thus inform water resource management more broadly than existing methods. Key Points: Landsat images and laser altimeters were leveraged to reconstruct multi‐decadal lake levels of both large and small lakesReconstructed water levels were validated against observed levels on 342 global lakes with a median error of 0.66 mMost of the reconstructed lake level time series have a bi‐monthly or higher frequency [ABSTRACT FROM AUTHOR]

In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters. Plain Language Summary: Reservoir storage capacity is steadily lost due to sediment filling, which threatens freshwater supplies both now and in the future. Yet, lost reservoir storage capacities to sediment are largely unknown. Here, we develop a generic method to estimate capacity losses and reservoir sedimentation rates by leveraging remote sensing techniques. We tested on eight reservoirs across the central and western United States and found capacity losses and sedimentation rates vary across reservoirs. The proposed method offers a promising alternative to evaluate and predict capacity losses in reservoirs nationwide and globally, and thus supports effective water managements and planning for sustainable freshwater supplies in the future. Key Points: High‐resolution Sentinel‐2 images and daily in situ water levels were used to estimate reservoir sedimentation rates and capacity lossesEstimated reservoir sedimentation rates and storage capacity losses have a mean error of 0.05% yr−1 of full storage capacityPotential applications of this method to ungauged reservoirs are feasible with sub‐monthly level data from recent satellite altimeters [ABSTRACT FROM AUTHOR]

Thresholds can be used to interpret environmental data in a way that is easily communicated and useful for decision-making purposes. However, thresholds are often developed for specific data products and time periods, changing findings when the same threshold is applied to datasets or periods with different characteristics. Here, we test the impact of different spatial discretizations of snow on annual estimates of wolverine denning opportunities in the Colorado Rocky Mountains, defined using a snow water equivalent (SWE) threshold (0.20 m) and threshold date (15 May) from previous habitat assessments. Annual potential wolverine denning area (PWDA) was thresholded from a 36-year (1985–2020) snow reanalysis model with three different spatial discretizations: (1) 480 m grid cells (D480), (2) 90 m grid cells (D90), and (3) 480 m grid cells with implicit representations of subgrid snow spatial heterogeneity (S480). Relative to the D480 and S480 discretizations, D90 resolved shallower snow deposits on slopes between 3050 and 3350 m elevation, decreasing PWDA by 10 %, on average. In years with warmer and/or drier winters, S480 discretizations with subgrid representations of snow heterogeneity increased PWDA, even within grid cells where mean 15 May SWE was less than the SWE threshold. These simulations increased PWDA by upwards of 30 % in low-snow years, as compared to the D480 and D90 simulations without subgrid snow heterogeneity. Despite PWDA sensitivity to different snow spatial discretizations, PWDA was controlled more by annual variations in winter precipitation and temperature. However, small changes to the SWE threshold (±0.07 m) and threshold date (±2 weeks) also affected PWDA by as much as 82 %. Across these threshold ranges, PWDA was approximately 18 % more sensitive to the SWE threshold than the threshold date. However, the sensitivity to the threshold date was larger in years with late spring snowfall, when PWDA depended on whether modeled SWE was thresholded before, during, or after spring snow accumulation. Our results demonstrate that snow thresholds are useful but may not always provide a complete picture of the annual variability in snow-adapted wildlife denning opportunities. Studies thresholding spatiotemporal datasets could be improved by including (1) information about the fidelity of thresholds across multiple spatial discretizations and (2) uncertainties related to ranges of realistic thresholds. [ABSTRACT FROM AUTHOR]

Climate change and human activities increasingly threaten lakes that store 87% of Earth’s liquid surface fresh water. Yet, recent trends and drivers of lake volume change remain largely unknown globally. Here, we analyze the 1972 largest global lakes using three decades of satellite observations, climate data, and hydrologic models, finding statistically significant storage declines for 53% of these water bodies over the period 1992–2020. The net volume loss in natural lakes is largely attributable to climate warming, increasing evaporative demand, and human water consumption, whereas sedimentation dominates storage losses in reservoirs. We estimate that roughly one-quarter of the world’s population resides in a basin of a drying lake, underscoring the necessity of incorporating climate change and sedimentation impacts into sustainable water resources management. [ABSTRACT FROM AUTHOR]

Wildfires change the hydrologic and geomorphic response of watersheds, which has been associated with cascades of additional hazards and management challenges. Among these post-wildfire events are shallow landslides and debris flows. This study evaluates post-wildfire mass movement trigger characteristics by comparing the precipitation preceding events at both burned and unburned locations. Landslide events are selected from the NASA Global Landslide Catalog (GLC). Since this catalog contains events from multiple regions worldwide, it allows a greater degree of inter-region comparison than many more localized catalogs. Fire and precipitation histories for each site are established using Moderate Resolution Imagine Spectroradiometer (MODIS) Burned Area and Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation data, respectively. Analysis of normalized 7 d accumulated precipitation for sites across all regions shows that, globally, mass movements at burned sites are preceded by less precipitation than mass movements without antecedent burn events. This supports the hypothesis that fire increases rainfall-driven mass movement hazards. An analysis of the seasonality of mass movements at burned and unburned locations shows that mass-movement-triggering storms in burned locations tend to exhibit different seasonality from rainfall-triggered mass movements in areas undisturbed by recent fire, with a variety of seasonal shifts ranging from approximately 6 months in the Pacific Northwest of North America to 1 week in the Himalayan region. Overall, this paper offers an exploration of regional differences in the characteristics of rainfall-triggered mass movements at burned and unburned sites over a broad spatial scale and encompassing a variety of climates and geographies. [ABSTRACT FROM AUTHOR]

In the Upper Colorado River Basin (UCRB), there is a deep reliance on seasonal snowpack for maintenance of water resources. The term "snow drought" has recently emerged to describe periods of anomalously low snowpack. Unique seasonal patterns in precipitation and temperature that drive snow drought can have distinct hydrologic signatures, and these relationships have not been carefully studied in the UCRB. Here we examine snow drought with a new classification scheme using peak snow water equivalent (SWE) and the ratio of basin‐wide modeled peak SWE to accumulated (onset to peak) precipitation (SWE/P) that clusters snow drought years into three distinct groups—"warm," "dry," and "warm & dry"—that minimize within‐group variance. Over the period 1916–2018, we identify 14 warm years (P¯ = 160 mm; SWE/P¯ = 0.24), 24 dry years (P¯ = 117 mm; SWE/P¯ = 0.35), and 21 warm & dry years (P¯ = 94 mm; SWE/P¯ = 0.23). An elevation‐based analysis reveals two distinct patterns: warm snow droughts see severe SWE reductions primarily at lower (<2600 m) elevations (65% at lower elevations, 37% overall), whereas "dry" scenarios exhibit a consistent reduction across all elevations (39% overall). Using naturalized streamflow data, we also differentiate snow droughts by their earlier streamflow timing and decreased peakedness (warm: 7 days, 2%; dry: 7 days, 2%; warm & dry: 13 days, 5%). This research provides new insights into snow drought patterns relevant for regional water management. [ABSTRACT FROM AUTHOR]

Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived Day of Snow Disappearance (DSD)—the date at which snow has completely disappeared—and the seasonal water supply, i.e., the April—July total streamflow volume, for 15 snow dominated basins across the western U.S. A Monte Carlo framework was applied, using linear regression models to evaluate the predictive skill—defined here as a model's ability to accurately predict seasonal flow volumes—of varied predictors, including DSD and in situ snow water equivalent (SWE), across a range of spring forecast dates. In all basins there is a statistically significant relationship between mean DSD and seasonal water supply (p ≤ 0.05), with mean DSD explaining roughly half of the variance. Satellite-based model skill improves later in the forecast season, surpassing the skill of in-situ-based (SWE) models in skill in 10 of the 15 basins by the latest forecast date. We found little to no correlation between model error and basin characteristics such as elevation and the ratio of snow water equivalent to total precipitation. Despite a relatively short data record, this exploratory analysis shows promise for improving seasonal water supply prediction, in particular for snow dominated basins lacking in situ observations. [ABSTRACT FROM AUTHOR]

Accurate representation of channel properties is important for forecasting in hydrologic models, as it affects the height, celerity, and attenuation of flood waves. Yet, considerable uncertainty in the parameterization of channel geometry and hydraulic roughness (Manning's n) exists within the NOAA National Water Model (NWM), due largely to data scarcity; only ∼2800 out of the 2.7×106 river reach segments in the NWM have measured channel properties. In this study, we seek to improve channel representativeness by updating channel geometry and roughness parameters using a large, previously unpublished hydraulic geometry (HyG) dataset of approximately 48 000 gauges. We begin with a Sobol' sensitivity analysis of channel geometry parameters for 12 small, semi-natural basins across the continental U.S., which reveals an outsized sensitivity of simulated flow to Manning's n relative to channel geometry parameters. We then develop and evaluate a set of regression-based regionalizations of channel parameters estimated using the HyG dataset. Finally, we compare the model output generated from updated channel parameter sets to observations and the current NWM v2.1 parameterization. We find that while the NWM land surface model holds the most influence over flow, given its control over total volume, the updated channel parameterization leads to improvements in simulated streamflow performance relative to observed flows, with a statistically significant mean R2 increase from 0.479 to 0.494 across approximately 7400 gauge locations. HyG-based channel geometry and roughness provide a substantial overall improvement in channel representation over the default parameterization, updating the previous set value for most reaches of Manning's n=0.060 to a new range between 0.006 and 0.537 (median 0.077). This research provides a more representative, observationally based channel parameter dataset for the NWM routing module and new insight into the influence of the routing module within the overall modeling framework. [ABSTRACT FROM AUTHOR]

In many parts of the world including the western United States, the allocation of water is governed by complex water laws that dictate who receives water, how much they receive, and when. Because these rules are generally based on the seniority of water rights, they are not necessarily focused on maximizing economic value across the entire economy. The maximization of value from water use economy-wide is a complex optimization problem that must explicitly consider each user's water demand, willingness to pay (WTP) function, and the feedbacks among users in a coupled natural-human system model. In this study, we distill these complexities into a simple MATLAB® model developed to represent a two-user economy with water-dependent sectors representative of agriculture and industry. We feed the model with realistic values of relative water use, relative willingness to pay, and return flows to explore the relationships among these factors in water-limited systems. We find that the total economic value generated from water-dependent users depends primarily on the total water available in the system. However, for a given volume of water available, economic value is not necessarily maximized when all the water is appropriated to the user with the highest WTP. Rather, total economic value depends on the amount of water available, the relative WTP between the two users, and on the return flows generated from each sector's water use. While our simple two-user model is a significant abstraction of the complexities inherent in natural systems, our study provides important insights into the coupled natural-human system dynamics of water allocation and use in water-limited environments. [ABSTRACT FROM AUTHOR]

Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western United States. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April–July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gauges and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, that is, years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, that is, below-median years (P15, P57.5], minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid- to late April for colder regions. We report similar findings using a modified National Resources Conservation Service (NRCS) procedure in nine large Upper Colorado River basin (UCRB) basins, highlighting the importance of the snowpack–streamflow relationship in streamflow predictability. We propose an "adaptive sampling" approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of up to 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning. Significance Statement: Seasonal water supply forecasts based on the relationship between peak snowpack and water supply exhibit unique errors in drought years due to low snow and streamflow variability, presenting a major challenge for water supply prediction. Here, we assess the reliability of snow-based streamflow predictability in drought years using a fixed forecast date or fixed model training period. We critically evaluate different training protocols that evaluate predictive performance and identify sources of error during historical drought years. We also propose and test an "adaptive sampling" application that dynamically selects training years based on antecedent SWE conditions providing to overcome persistent errors and provide new insights and strategies for snow-guided forecasts. [ABSTRACT FROM AUTHOR]

This review addresses the critical knowledge gap of techniques simulating combustion and heating characteristics present in natural wildfires and their use in assessing postfire impacts on water quality and quantity. Our assessment includes both laboratory and plot-scale techniques with burn and rainfall simulation components. Studies included focus on advancing understanding of changes in chemical and physical properties of soil, as well as subsequent runoff changes. Advantages of simulation experiments include: overcoming logistical challenges of collecting in situ wildfire data, reducing the high spatial variability observed in natural settings (i.e., the heterogeneity of burn intensity and the underlying vegetation and soil properties), and controlling the magnitude of key drivers of wildfire impacts. In sum, simulation experiments allow for more direct attribution of water quality and quantity responses to specific drivers than experiments conducted in situ. Drawbacks of simulation techniques include the limitation of observing only local-scale processes, the potential misrepresentation of natural settings (i.e., lack of spatial variability in vegetation, soil structure, burn intensity, etc.), uncertainty introduced through experimental error, and subsequent challenges in upscaling results to larger scales relevant for water management. This review focuses primarily on simulation techniques, with the goal of providing a foundation of knowledge for the design of future simulation experiments. [ABSTRACT FROM AUTHOR]

Thresholds can be used to interpret environmental data in a way that is easily communicated and useful for decision making purposes. However, thresholds are often developed for specific data products and time periods, changing findings when the same threshold is applied to datasets or periods with different characteristics. Here, we test the impact of different spatial discretizations of snow on annual estimates of wolverine habitat in the Colorado Rocky Mountains, defined using a snow water equivalent (SWE) threshold (0.20 m) and threshold date (15 May) from previous habitat assessments. Annual wolverine habitable area (WHA) was thresholded from a 36-year (1985 - 2020) snow reanalysis at three different spatial discretizations: 1) 480 m grid cells, 2) 90 m grid cells, and 3) 480 m grid cells with implicit representations of subgrid snow spatial heterogeneity. Relative to the 480 m grid cells, 90 m grid cells resolved shallower snow deposits on slopes between 3050 and 3350 m elevation, decreasing WHA by 10%, on average. In years with warmer and/or drier winters, grid cells with subgrid representations of snow heterogeneity increased the prevalence of 15 May snow deposits that exceeded the 0.20 m SWE threshold, even within grid cells where mean SWE was less than the threshold. These simulations increased WHA by upwards of 30% in low snow years, as compared to simulations without subgrid snow heterogeneity. Despite WHA sensitivity to different snow spatial discretizations, WHA was controlled more by annual variations in winter precipitation and temperature. However, small changes to the SWE threshold (± 0.07 m) and threshold date (± 2 weeks) also affected WHA by as much as 82%. Across these threshold ranges, WHA was approximately 18% more sensitive to the SWE threshold than the threshold date. However, the sensitivity to the threshold date was larger in years with late spring snowfall, when WHA depended greatly on whether the date SWE was thresholded was before, during, or after spring snow accumulation. Our results demonstrate that snow thresholds are useful but may not always provide a complete picture of the annual variability in snow-adapted wildlife habitat. Studies thresholding spatiotemporal datasets could be improved by including 1) information about the fidelity of thresholds across multiple spatial discretizations, and 2) uncertainties related to ranges of realistic thresholds. [ABSTRACT FROM AUTHOR]

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs (P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire–climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water suppliesand runoff-related risks. [ABSTRACT FROM AUTHOR]