Using data from a water‐balance instrument cluster with spatially distributed sensors we determined the magnitude and within‐catchment variability of components of the catchment‐scale water balance, focusing on the relationship of seasonal evapotranspiration to changes in snowpack and soil moisture storage. Co‐located, continuous snow depth and soil moisture measurements were deployed in a rain–snow transition catchment in the mixed‐conifer forest in the Southern Sierra Nevada. At each elevation sensors were placed in the open, under the canopy, and at the drip edge on both north‐ and south‐facing slopes. Snow sensors were placed at 27 locations, with soil moisture and temperature sensors placed at depths of 10, 30, 60, and 90 cm beneath the snow sensor. Soils are weakly developed (Inceptisols and Entisols) and formed from decomposed granite with properties that change with elevation. The soil–bedrock interface is hard in upper reaches of the basin (>2000 m) where glaciers have scoured the parent material approximately 18,000 yr ago. Below an elevation of 2000 m soils have a paralithic contact (weathered saprolite) that can extend beyond a depth of 1.5 m, facilitating pathways for deep percolation. Soils are wet and not frozen in winter, and dry out in the weeks following spring snowmelt and rain. Based on data from two snowmelt seasons, it was found that soils dry out following snowmelt at relatively uniform rates; however, the timing of drying at a given site may be offset by up to 4 wk because of heterogeneity in snowmelt at different elevations and aspects. Spring and summer rainfall mainly affected sites in the open, with drying after a rain event being faster than following snowmelt. Water loss rates from soil of 0.5 to 1.0 cm d−1 during the winter and snowmelt season reflect a combination of evapotranspiration and deep drainage, as stream baseflow remains relatively low. About one‐third of annual evapotranspiration comes from water storage below the 1‐m depth, that is, below mapped soil. We speculate that much of the deep drainage is stored locally in the deeper regolith during periods of high precipitation, being available for tree transpiration during summer and fall months when shallow soil water storage is limiting. Total annual evapotranspiration for water year 2009 was estimated to be approximately 76 cm.

Airborne-based Light Detection and Ranging (LiDAR) offers the potential to measure snow depth and vegetation structure at high spatial resolution over large extents and thereby increase our ability to quantify snow water resources. Here we present airborne LiDAR data products at four Critical Zone Observatories (CZO) in the Western United States: Jemez River Basin, NM, Boulder Creek Watershed, CO, Kings River Experimental Watershed, CA, and Wolverton Basin, CA. We make publicly available snow depth data products (1 m 2 resolution) derived from LiDAR with an estimated accuracy of <30 cm compared to limited in situ snow depth observations.

Western US forest ecosystems and downstream water supplies are reliant on seasonal snowmelt. Complex feedbacks govern forest–snow interactions in which forests influence the distribution of snow and the timing of snowmelt but are also sensitive to snow water availability. Notwithstanding, few studies have investigated the influence of forest structure on snow distribution, snowmelt and soil moisture response. Using a multi‐year record from co‐located observations of snow depth and soil moisture, we evaluated the influence of forest‐canopy position on snow accumulation and snow depth depletion, and associated controls on the timing of soil moisture response at Boulder Creek, Colorado, Jemez River Basin, New Mexico, and the Wolverton Basin, California. Forest‐canopy controls on snow accumulation led to 12–42 cm greater peak snow depths in open versus under‐canopy positions. Differences in accumulation and melt across sites resulted in earlier snow disappearance in open positions at Jemez and earlier snow disappearance in under‐canopy positions at Boulder and Wolverton sites. Irrespective of net snow accumulation, we found that peak annual soil moisture was nearly synchronous with the date of snow disappearance at all sites with an average deviation of 12, 3 and 22 days at Jemez, Boulder and Wolverton sites, respectively. Interestingly, sites in the Sierra Nevada showed peak soil moisture prior to snow disappearance at both our intensive study site and the nearby snow telemetry stations. Our results imply that the duration of soil water stress may increase as regional warming or forest disturbance lead to earlier snow disappearance and soil moisture recession in subalpine forests. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract. We present results from snow-on and snow-off airborne-scanning LiDAR measurements over a 53 km 2 area in the southern Sierra Nevada. We found that snow depth as a function of elevation increased approximately 15 cm per 100 m, until reaching an elevation of 3300 m, where depth sharply decreased at a rate of 48 cm per 100 m. Departures from the 15 cm per 100 m trend, based on 1 m elevation-band means of regression residuals, showed slightly less steep increases below 2050 m; steeper increases between 2050 and 3300 m; and less steep increases above 3300 m. Although the study area is partly forested, only measurements in open areas were used. Below approximately 2050 m elevation, ablation and rainfall are the primary causes of departure from the orographic trend. From 2050 to 3300 m, greater snow depths than predicted were found on the steeper terrain of the northwest and the less steep northeast-facing slopes, suggesting that ablation, aspect, slope and wind redistribution all play a role in local snow-depth variability. At elevations above 3300 m, orographic processes mask the effect of wind deposition when averaging over large areas. Also, terrain in this basin becomes less steep above 3300 m. This suggests a reduction in precipitation from upslope lifting and/or the exhaustion of precipitable water from ascending air masses. Our results suggest a cumulative precipitation lapse rate for the 2100-3300 m range of about 6 cm per 100 m elevation for the accumulation period of 3 December 2009 to 23 March 2010. This is a higher gradient than the widely used PRISM (Parameter-elevation Relationships on Independent Slopes Model) precipitation products, but similar to that from reconstruction of snowmelt amounts from satellite snow-cover data. Our findings provide a unique characterization of the consistent, steep average increase in precipitation with elevation in snow-dominated terrain, using high-resolution, highly accurate data and highlighs the importance of solar radiation, wind redistribution and mid-winter melt with regard to snow distribution.

Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover's spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack. Understanding the little studied effects of changing snowscapes on its wildlife communities is critical. The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Via consideration of existing knowledge of wildlife-snow interactions, currently available snow products for focus region, and results of three case studies, we conclude that improving snow science in the ABR will be best achieved by focusing efforts on developing data-model fusion approaches to produce fitfor-purpose snow products that include, but are not limited to, wildlife ecology. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of NASA's Arctic Boreal Vulnerability Experiment and SnowEx campaigns, for example, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties.