Tuolumne River Research Articles

Rescuing and monitoring White Sturgeon during drought on the Tuolumne River

Rescuing and monitoring White Sturgeon during drought on the Tuolumne River

Water Supply Planning in the Face of Drought and Ecosystem Flows: Examining the Impact of the Bay-Delta Plan on Bay Area Water Supply.

In California, recent Bay-Delta Plan legislation attempts to balance water supply and ecosystem protection by requiring 40% of the flow to remain in-stream in the Tuolumne River from February through June. Serious questions remain about what this means for the Bay Area water supply, especially during drought. Our work develops a new approach to analyze how in-stream flow policy coupled with climate change could impact regional water supply over the coming decades. Results show that the new in-stream flow demand would exceed urban water deliveries in a typical year. In wet years, water supply performance is minimally impacted, but in drought, the policy can lead to less water in storage, delayed reservoir recovery, and increased time at critically low storage. Storage impact exceeding 50 000 acre-feet (60 million m3) is anticipated with at least 18% frequency, demonstrating that, climate uncertainty notwithstanding, this impact must be planned for and managed to ensure a reliable future water supply.

Mountain snow depth retrievals from customized processing of ICESat-2 satellite laser altimetry

Snow depth is highly variable across basins, yet most snow depth data in the western U.S. come from sparse in situ point measurements. The water resources community needs accurate snow depth data for improved basin-wide snow depth estimates. The NASA ICESat-2 mission has provided over four years of global satellite laser altimetry measurements since October 2018. Previous studies have shown that standard ICESat-2 data products, when combined with snow-off digital terrain models (DTMs) from airborne laser scanning, have the potential to provide snow depth measurements with varying accuracy depending on factors such as surface slope and canopy cover. In this study we show that ICESat-2 snow depth measurements can be improved with customized data products generated using the SlideRule Earth service. Here we investigate the accuracy of our ICESat-2 SlideRule snow depth method using four years (2019–2022) of reference in situ snow depth measurements and airborne lidar snow depth observations for two watersheds with varying terrain characteristics: the Tuolumne River basin above Hetch Hetchy, CA and the Methow Valley, WA. We observe median differences of −0.14 m (RMSE of 0.18 m) and −0.20 m (RMSE of 0.33 m) between our ICESat-2 snow depth measurements and reference snow depth measurements for the Tuolumne Basin and Methow Valley sites, respectively. While individual ICESat-2 elevation measurements can contain noise, basin-scale aggregation offers robust statistics for snow depth. Differences in accuracy between sites are attributed to terrain characteristics and their spatial distributions. The customized ICESat-2 SlideRule data products used in this study resulted in more accurate median snow depth values, including under canopy, than those found by previous studies using standard ICESat-2 data products in mid-latitude mountainous regions. When combined with snow-off DTMs, the aggregated snow-on ICESat-2 SlideRule observations could provide a new snow depth dataset across the western U.S. and potentially global land surfaces.

Estimating spatiotemporally continuous snow water equivalent from intermittent satellite observations: an evaluation using synthetic data

Abstract. Accurate estimates of snow water equivalent (SWE) based on remote sensing have been elusive, particularly in mountain areas. However, there now appears to be some potential for direct satellite-based SWE observations along ground tracks that only cover a portion of a spatial domain (e.g., watershed). Fortunately, spatiotemporally continuous meteorological and surface variables could be leveraged to infer SWE in the gaps between satellite ground tracks. Here, we evaluate statistical and machine learning (ML) approaches to performing track-to-area (TTA) transformations of SWE observations in California's upper Tuolumne River watershed using synthetic data. The synthetic SWE measurements are designed to mimic a potential future P-band Signals of Opportunity (P-SoOP) satellite mission with a (along-track) spatial resolution of about 500 m. We construct relationships between multiple meteorological and surface variables and synthetic SWE observations along observation tracks, and we then extend these relationships to unobserved areas between ground tracks to estimate SWE over the entire watershed. Domain-wide, SWE inferred on 1 April using two synthetic satellite tracks (∼4.5 % basin coverage) led to percent errors of basin-averaged SWE (PEBAS) of 24.5 %, 4.5 % and 6.3 % in an extremely dry water year (WY2015), a normal water year (WY2008) and an extraordinarily wet water year (WY2017), respectively. Assuming a 10 d overpass interval, percent errors of basin-averaged SWE during both snow accumulation and snowmelt seasons were mostly less than 10 %. We employ a feature sensitivity analysis to overcome the black-box nature of ML methods and increase the explainability of the ML results. Our feature sensitivity analysis shows that precipitation is the dominant variable controlling the TTA SWE estimation, followed by net long-wave radiation (NetLong). We find that a modest increase in the accuracy of SWE estimation occurs when more than two ground tracks are leveraged. The accuracy of 1 April SWE estimation is only modestly improved for track repeats more often than about 15 d.

Process and perturbation in a high elevation alluvial system, Yosemite National Park, USA

High-elevation alluvial river environments are little studied by geomorphologists despite their sensitivity to disturbance and their critical role as the landform supporting montane meadow ecosystems. Here, we establish the fundamental fluvial functioning and legacy factors inherent to the upper Tuolumne River in Tuolumne Meadows (elevation 2620 m asl), Yosemite National Park, USA. Evidence was drawn from structured field reconnaissance, morphological surveys, and sediment analysis; integrated with LiDAR monitoring and assessments of flood frequency, flood inundation, and sediment transport potential. Results indicate a sinuous single-thread channel in dynamic equilibrium with fluvial processes but with reduced rates of meander activity over the past century. Geomorphic processes are dominated by the influence of snowmelt discharge, partly because atmospheric river events rarely influence this high elevation. Process intensity is thus directly related to snowpack depth with monitored bank erosion rates substantially higher following the deep snowpack of 2016–17. Diurnal cycling of flow means that bedload-transport potential scales very well with total annual discharge volume. The legacy of human activity is unique here, with perturbations caused primarily by early-to-mid twentieth century infrastructure development rather than typical patterns of watershed development. Pre-eminent was 1930s instream aggregate extraction for road improvements. In conjunction with naturally very low rates of sediment supply, the resulting pit is still only one-quarter full and could disconnect downstream sediment supplies for several centuries more. The gravel pit appears to explain incision of the upper channel reach and reduced rates of lateral activity farther downstream. Future impacts will be dominated by climate change, including the potential increased influence of atmospheric river events, reduced snowpack depth, and tradeoffs in sediment production and connectivity as headwater glaciers recede. The upper Tuolumne River and its meadow typify the unusual, but not singular, geomorphology of high-elevation alluvial rivers, whose high ecological and social values justify greater attention.

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System

AbstractThe NASA LIS/WRF‐Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi‐scale surface hydrological modeling capabilities of the WRF‐Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub‐surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF‐Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open‐loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF‐Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Capacity of Two Sierra Nevada Rivers for Reintroduction of Anadromous Salmonids: Insights from a High‐Resolution View

AbstractHistorically, anadromous steelhead Oncorhynchus mykiss and spring‐run Chinook Salmon O. tshawytscha used high‐elevation rivers in the Sierra Nevada of California but were extirpated in the 20th century by construction of impassable dams. Plans to reintroduce the fish by opening migratory passage across the dams and reservoirs can only succeed if upstream habitats have the capacity to support viable populations of each species. To estimate capacity in the Tuolumne and Merced rivers of the central Sierra Nevada, we used a high‐resolution approach based on remote sensing and dynamic habitat modeling. Our results suggested that for both species in both systems, sediment grain sizes would support widespread spawning and the water temperatures, depths, and velocities would generate ample capacity for fry and juveniles. However, the unregulated Merced River was consistently too warm for adult Chinook Salmon to hold in the dry season prior to spawning, while the regulated Tuolumne River maintained a cooler, more stable thermal regime with a capacity for thousands of holding adults. In our high‐resolution approach, we also discovered several specific physical controls on life history expression, including thermal constraints on the timing of spawning, hydraulic prompts for downstream migration of fry, divergence of the hydraulic niches of steelhead and Chinook Salmon, and a key but uncertain role for thermal tolerance in adult Chinook Salmon. Our results suggested that steelhead reintroduction could succeed in either system and Chinook Salmon could succeed in the Tuolumne River if passage strategies account for large numbers of migrant fry and juveniles driven downstream by winter storms and snowmelt. The Merced River appeared too warm for adult Chinook Salmon, which raises questions about the current limited understanding of thermal tolerance in holding adults. Our study shows how a high‐resolution approach can provide valuable insights on specific limiting factors that must be addressed for reintroduction to succeed.

Downscaling Snow Deposition Using Historic Snow Depth Patterns: Diagnosing Limitations From Snowfall Biases, Winter Snow Losses, and Interannual Snow Pattern Repeatability

AbstractRepeatable snow depth patterns have been identified in many regions between years with similar meteorological characteristics. This suggests that snow patterns from previous years could adjust snow deposition in space as a substitution for unmodeled snow processes. Here, we tested a pattern‐based snow deposition downscaling routine which assumes (a) a spatially consistent relationship between snow deposition and snow depth, (b) interannually repeatable snow patterns, and (c) unbiased mean snowfall. We investigated these assumptions, and future avenues for improvement, in water‐year 2014 over the California Tuolumne River Watershed. 6 km snowfall from an atmospheric model was downscaled to 25 m resolution using snow depth patterns from seven different years, and was compared to a more common terrain‐based downscaling method. Snow depth patterns were influenced not only by snow accumulation, but also snowmelt, snow sublimation, and snow density, resulting in pattern‐based snow deposition downscaling that was too spatially heterogeneous. However, snow depth simulated using terrain‐based downscaling was too spatially homogeneous, and less spatially correlated with observations (r = 0.27), than simulations with pattern‐based downscaling using snow depth patterns from the simulation season (r = 0.76), or from a different year (r = 0.52). Overall, modeled snow depth errors at peak‐snowpack timing were driven more by atmospheric model snowfall biases than different downscaling methods. In order of most‐ to least‐importance, future research should focus on bias‐correcting coarse‐scale snowfall estimates, correcting snow deposition patterns for winter snow losses and snow density spatial variability, and identifying the historic periods of most‐similar snow accumulation.

Evaluating Wind Fields for Use in Basin‐Scale Distributed Snow Models

AbstractMountain winds are the driving force behind snow accumulation patterns in mountainous catchments, making accurate wind fields a prerequisite to accurate simulations of snow depth for ecological or water resource applications. In this study, we examine the effect that wind fields derived from different coarse data sets and downscaling schemes has on simulations of modeled snow depth at resolutions suitable for basin‐scale modeling (>50 m). Simulations are run over the Tuolumne River Basin, CA for the accumulation season of Water Year 2017 using the distributed snow model, SnowModel. We derived wind fields using observations from either sensor networks, the North American Land Data Assimilation System (12.5 km resolution), or High‐Resolution Rapid Refresh (HRRR, 3 km resolution) data, and downscaled using terrain‐based multipliers (MicroMet), a mass‐conserving flow model (WindNinja), or bilinear interpolation. Two wind fields derived from 3 km HRRR data and downscaled with respect to terrain produced snow depth maps that best matched observations of snow depth from airborne LiDAR. We find that modeling these wind fields at 100 and 50 m resolutions do not produce improvements in simulated snow depth when compared to wind fields modeled at a 150 m resolution due to their inability to represent wind dynamics at these scales. For input to distributed snow models at the basin‐scale, we recommend deriving wind fields from high resolution numerical weather prediction model output and downscaling with respect to terrain. Future studies should compare suspension schemes used in blowing snow models and investigate wind downscaling schemes of complexity between statistical and fluid dynamic models.

From Hetch Hetchy to the Cuyahoga: How Rivers Shaped the American Environmental Movement

Rivers are common natural features through which the environment is experienced. They provide important economic benefits to society, and the spatial organization of watersheds makes them uniquely vulnerable to environmental change. For these reasons, the history of the U.S. environmental movement can be read as the history of society’s relationship with rivers. Here, an overview of this history is presented using five rivers that represent critical turning points in U.S. environmentalism. The Tuolumne River in the Hetch Hetchy Valley was an early battleground between differing visions of river aesthetics and recreation. The Colorado River in the Grand Canyon was a symbol of the last remaining wilderness. The infamous burning of the Cuyahoga River in Cleveland is widely credited with increasing awareness of pollution that led to the first Earth Day and landmark federal environmental legislation. The controversy over the Tellico Dam on the Little Tennessee River was an early sign of strong backlash against new environmental laws. Finally, the ongoing revitalization of the Los Angeles River, the paradigmatic urban stream, represents the promise and pitfalls of the environmental movement of the future.

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

Abstract. Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80 m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40 m for snow depth) when averaged to a 36 m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.

Estimation of Daily Spatial Snow Water Equivalent from Historical Snow Maps and Limited In-Situ Measurements

We present a scheme aimed at estimating daily spatial snow water equivalent (SWE) maps in real time and at high spatial resolution from scarce in-situ SWE measurements from Internet of Things (IoT) devices at actual sensor locations and historical SWE maps. The method consists of finding a background SWE field, followed by an update step using ensemble optimal interpolation to estimate the residuals. This novel approach allowed for areas with parsimonious sensors to have accurate estimates of spatial SWE without explicitly discovering and specifying the spatial-interpolation features. The scheme is evaluated across the Tuolumne River basin on a 50 m grid using an existing LiDAR-based product as the historical dataset. Results show a minimum RMSE of 30% at 50 m resolutions. Compared with the operational SNODAS product, reduction in error is up to 80% with historical LiDAR-measured snow depth as input data.

River food chains lead to riparian bats and birds in two mid‐order rivers

AbstractRiver regulation can modify natural flow regimes with deleterious effects on aquatic communities. While the effects of flow manipulation on the physical environment and populations and assemblages of aquatic organisms have been described thoroughly, how and to what extent river regulation influences ecosystem processes like food web architecture is less studied. Emergent aquatic insect prey can provide an important food resource to riparian consumers like birds and bats with concomitant consequences for nutrient cycling through aquatic–terrestrial food webs, thus potentially increasing the spatial influence of river regulation into the riparian zone and beyond. We used naturally abundant stable isotopes of carbon and nitrogen to compare food web architecture (trophic position and reliance on an aquatic nutritional pathway) leading to birds and bats between a regulated river, the Tuolumne River downstream of Hetch Hetchy Reservoir, and an adjacent unregulated river, the Merced River, located in Yosemite National Park on the west slope of the Sierra Nevada, California, USA. We found that both birds and bats derived >50% of their nutrition from food webs originating in photosynthesis by algae. In addition, birds and bats occupied a similar trophic position to predatory fish in other systems. Both birds and bats seemed to rely more strongly on an aquatic nutritional pathway during the dryer year of our study period, underscoring the potential importance of emergent aquatic prey as a water subsidy in dry systems and in dry years. In the Tuolumne River, reservoir managers strive to simulate characteristics of the natural flow regime, including seasonal scouring flows and prolonged floodplain inundation. Although we found no conclusive evidence of an effect of river regulation on food web responses, our study suggests that nutrient cycling through aquatic–terrestrial food webs expands the potential influence of river regulation to organisms and ecosystems typically characterized as terrestrial.

From Drought to Flood: A Water Balance Analysis of the Tuolumne River Basin during Extreme Conditions (2015 – 2017)

The degree to which the hydrologic water balance in a snow‐dominated headwater catchment is affected by annual climate variations is difficult to quantify, primarily due to uncertainties in measuring precipitation inputs and evapotranspiration (ET) losses. Over a recent three‐year period, the snowpack in California's Sierra Nevada fluctuated from the lightest in recorded history (2015) to historically heaviest (2017), with a relatively average year in between (2016). This large dynamic range in climatic conditions presents a unique opportunity to investigate correlations between annual water availability and runoff in a snow‐dominated catchment. Here, we estimate ET using a water balance approach where the water inputs to the system are spatially constrained using a combination of remote sensing, physically based modelling, and in‐situ observations. For all 3 years of this study, the NASA Airborne Snow Observatory (ASO) combined periodic high‐resolution snow depths from airborne Lidar with snow density estimates from an energy and mass balance model to produce spatial estimates of snow water equivalent over the Tuolumne headwater catchment at 50‐m resolution. Using observed reservoir inflow at the basin outlet and the well‐quantified snowmelt model results that benefit from periodic ASO snow depth updates, we estimate annual ET, runoff efficiency (RE), and the associated uncertainty across these three dissimilar water years. Throughout the study period, estimated annual ET magnitudes remained steady (222 mm in 2015, 151 mm in 2016, and 299 mm in 2017) relative to the large differences in basin input precipitation (547 mm in 2015, 1,060 mm in 2016, and 2,211 mm in 2017). These values compare well with independent satellite‐derived ET estimates and previously published studies in this basin. Results reveal that ET in the Tuolumne does not scale linearly with the amount of available water to the basin, and that RE primarily depends on total annual snowfall proportion of precipitation.

A Joint Landsat- and MODIS-Based Reanalysis Approach for Midlatitude Montane Seasonal Snow Characterization

A new snow reanalysis method is presented that is designed to jointly assimilate Landsat- and MODIS-derived (MODSCAG) fractional snow covered area (fSCA) to characterize seasonal snow in remote regions like High Mountain Asia (HMA) where in situ data is severely lacking. The method leverages existing readily available global datasets for forcing a snow model and uses the fSCA retrievals along with the ensemble prior model estimates to derive posterior estimates using a Bayesian framework. The method addresses MODIS viewing-geometry effects on the fSCA retrievals by accounting for viewing angle dependent measurement errors and using a CDF-matching technique to improve the joint fSCA measurement consistency before assimilation. The method was verified through comparison with the Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates over the Tuolumne River watershed in California. The posterior SWE estimates were shown to be much more consistent with the independent ASO estimates across the three WYs examined. Tests over Tuolumne showed that in cases where sufficient Landsat observations are available (i.e. with multiple sensors and in areas of overlapping Landsat tiles), assimilation of only Landsat data may be optimal, which is attributable primarily to the higher spatial resolution of the raw Landsat data, but that in cases with fewer Landsat measurements (i.e. with single Landsat tiles and/or significant reduction due to clouds), the additional screened and CDF-matched MODIS-based measurements can have a positive (albeit marginal) impact. Illustrative results are presented for nine HMA test tiles to illustrate how the method can provide posterior estimates of the space-time climatology of SWE storage in areas where in situ data does not generally exist. Ongoing work is being conducted to use the method outlined herein to generate an HMA-wide reanalysis dataset that will provide an opportunity for a more thorough characterization of HMA seasonal snow storage and dynamics over the joint Landsat-MODIS era. The method is generalizable to any midlatitude montane region where seasonal snow is important.

A Weather‐Regime‐Based Stochastic Weather Generator for Climate Vulnerability Assessments of Water Systems in the Western United States

AbstractVulnerability‐based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for climate vulnerability assessments that simulate weather based on patterns of regional atmospheric flow (i.e., weather regimes) conditioned on global‐scale climate features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain for weather regime simulation; (2) block bootstrapping and a Gaussian copula for multivariate, multisite weather simulation; and (3) modules to impose thermodynamic and dynamical climate change, including Clausius‐Clapeyron precipitation scaling, elevation‐dependent warming, and shifting dynamics of the El Niño–Southern Oscillation (ENSO). In this way, the model can be used to evaluate climate impacts on water systems based on hypotheses of dynamic and thermodynamic climate change. The model is developed and tested for cold‐season climate in the Tuolumne River Basin in California but is broadly applicable across the western United States. Results show that eight weather regimes exert strong influences over local climate in the Tuolumne Basin. Model simulations adequately preserve many of the historical statistics for precipitation and temperature across sites, including the mean, variance, skew, and extreme values. Annual precipitation and temperature are somewhat underdispersed, and precipitation spell statistics are negatively biased by 1‐2 days. For simulations of future climate, the model can generate a range of Clausius‐Clapeyron scaling relationships and modes of elevation‐dependent warming. Model simulations also suggest a muted response of Tuolumne climate to changes in ENSO variability.

Snow Depth Variability at the Forest Edge in Multiple Climates in the Western United States

AbstractSnow depth variability between different sides of the forest edge was analyzed using airborne lidar in the Olympic Mountains, WA; Tuolumne River Watershed, CA; Jemez Caldera, NM; and the Boulder Creek Watershed, CO. At Boulder Creek there were statistically significant snow depth differences between leeward and windward forest edges (mean: 38%), which were likely the result of cold winter temperatures (−5 to −10°C), substantial wind speeds (>10–12 m/s), and the forest architecture. In the Olympic Mountains, wind speeds were similar to Boulder Creek, but snow depth differences between forest edges were not significantly different, likely due to winter temperatures near 0°C and a more continuous forest distribution. At Jemez, forest shading likely caused significant snow depth differences between north and south facing forest edges (mean: 32%). Tuolumne had similar incoming solar radiation to Jemez, but only two of the four sites contained significant snow depth differences between north and south facing forest edges (14% and 31%). The Tuolumne sites without significant edge differences received less direct solar radiation due to shading by surrounding topography. At Jemez, Boulder Creek, and Tuolumne, snow depth differences between forest edges were greater than or equal to differences between exposed and under canopy areas. Furthermore, at Jemez and Tuolumne, forest‐edge snow depth differences were similar to snow depth differences between different aspects in exposed areas. Therefore, snow depth differences between forest‐edge classifications are of similar magnitude to classifications currently used to represent subelement heterogeneity within hydrologic models. Therefore, representation of forest‐edge variability within models should be explored.

Direct Insertion of NASA Airborne Snow Observatory‐Derived Snow Depth Time Series Into the iSnobal Energy Balance Snow Model

AbstractAccurately simulating the spatiotemporal distribution of mountain snow water equivalent improves estimates of available meltwater and benefits the water resource management community. In this paper we present the first integration of lidar‐derived distributed snow depth data into a physics‐based snow model using direct insertion. Over four winter seasons (2013–2016) the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) Airborne Snow Observatory (ASO) performed near‐weekly lidar surveys throughout the snowmelt season to measure snow depth at high resolution over the Tuolumne River Basin above Hetch Hetchy Reservoir in the Sierra Nevada Mountains of California. The modeling component of the ASO program implements the iSnobal model to estimate snow density for converting measured depths to snow water equivalent and to provide temporally complete snow cover mass and thermal states between flights. Over the four years considered in this study, snow depths from 36 individual lidar flights were directly inserted into the model to provide updates of snow depth and distribution. Considering all updates to the model, the correlation between ASO depths and modeled depths with and without previous updates was on average r2 = 0.899 (root‐mean‐square error = 12.5 cm) and r2 = 0.162 (root‐mean‐square error = 41.5 cm), respectively. The precise definition of the snow depth distribution integrated with the iSnobal model demonstrates how the ASO program represents a new paradigm for the measurement and modeling of mountain snowpacks and reveals the potential benefits for managing water in the region.

Globally scalable alpine snow metrics

Horizontal and altitudinal redistribution of snow by wind transport and avalanches can be important controls on small- and large-scale snow accumulation patterns that control meltwater supply in alpine environments. Redistribution processes control the spatial variability of snow accumulation, which not only controls meltwater supply, but also regulates snowmelt timing, duration, and rates, as well as snow-covered area depletion and the variable contributing area for meltwater runoff generation. However, most hydrological models and land surface schemes do not consider snow redistribution processes, and those that do are difficult to verify without spatially distributed snow depth measurements. These are rarely available in both high resolution and covering large scales. As an increased number of hydrological models include snow redistribution processes there is a need for additional snowcover metrics to verify snow redistribution schemes over large areas using readily available data.This study develops novel high-resolution (20 m), snowcover indices from remotely sensed imagery (Landsat-8 and Sentinel-2) to evaluate snow redistribution models over alpine areas without in-situ or airborne snow observations. A snowcover absence (SA) index, calculated from snow-free areas during the winter, identifies areas of wind erosion or avalanche source areas. A snowcover persistence (SP) index, calculated from snow-covered areas during the summer, identifies snow deposition in drifts and avalanche deposits.The snowcover indices captured the relative differences in surface observations of snow presence and absence between exposed and sheltered sites on an intensely instrumented ridge in the Canadian Rockies Hydrological Observatory. Within the Tuolumne River Basin in central California (1100 km2), the SP index captured roughly half of the spatial variability (R2 = 0.49 to 0.56) in peak SWE as estimated from airborne LiDAR-derived snow depths. At the individual mountain ridge scale (~800 m), variability in both ablation and snow redistribution controlled the SP patterns over 7979 ridges. Differences in shortwave irradiance explained 76% of the SP variance across ridges, but could not explain smaller-scale (~100 m) SP peaks that are associated with snowdrifts and avalanche deposits. The snowcover indices can be used to evaluate snow redistribution models of the finer scale impacts of snow redistribution by wind and gravity as long as the larger scale influences of spatially variable solar irradiance effects are also simulated.

High‐Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance

AbstractHydrologic variables such as evapotranspiration (ET) and soil water storage are difficult to observe across spatial scales in complex terrain. Streamflow and lidar‐derived snow observations provide information about distributed hydrologic processes such as snowmelt, infiltration, and storage. We use a distributed streamflow data set across eight basins in the upper Tuolumne River region of Yosemite National Park in the Sierra Nevada mountain range, and the NASA Airborne Snow Observatory (ASO) lidar‐derived snow data set over 3 years (2013–2015) during a prolonged drought in California, to estimate basin‐scale water balance components. We compare snowmelt and cumulative precipitation over periods from the ASO flight to the end of the water year against cumulative streamflow observations. The basin water balance residual term (snow melt plus precipitation minus streamflow) is calculated for each basin and year. Using soil moisture observations and hydrologic model simulations, we show that the residual term represents short‐term changes in basin water storage over the snowmelt season, but that over the period from peak snow water equivalent (SWE) to the end of summer, it represents cumulative basin‐mean ET. Warm‐season ET estimated from this approach is 168 (85–252 at 95% confidence), 162 (0–326) and 191 (48–334) mm averaged across the basins in 2013, 2014, and 2015, respectively. These values are lower than previous full‐year and point ET estimates in the Sierra Nevada, potentially reflecting reduced ET during drought, the effects of spatial variability, and the part‐year time period. Using streamflow and ASO snow observations, we quantify spatially‐distributed hydrologic processes otherwise difficult to observe.