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.

We combined observations from four eddy covariance towers with remote sensing to better understand the altitudinal patterns of climate, plant phenology, Gross Ecosystem CO2Uptake, and Evapotranspiration (ET) around the Upper Kings River basin in the southern Sierra Nevada Mountains. Precipitation (P) increased with elevation to ∼500 m, and more gradually at higher elevations, while vegetation graded from savanna at 405 m to evergreen oak and pine forest to mid‐montane forest to subalpine forest at 2700 m. CO2uptake and transpiration at 405 m peaked in spring (March to May) and declined in summer; gas exchange at 1160 and 2015 m continued year‐round; gas exchange at 2700 m peaked in summer and ceased in winter. A phenological threshold occurred between 2015 and 2700 m, associated with the development of winter dormancy. Annual ET and Gross Primary Production were greatest at 1160 and 2015 m and reduced at 405 m coincident with less P, and at 2700 m coincident with colder temperatures. The large decline in ET above 2015 m raises the possibility that an upslope redistribution of vegetation with climate change could cause a large increase in upper elevation ET. We extrapolated ET to the entire basin using remote sensing. The 2003–11 P for the entire Upper Kings River basin was 984 mm y−1 and the ET was 429 mm y−1, yielding a P‐ET of 554 mm y−1, which agrees well with the observed Kings River flow of 563 mm y−1. ET averaged across the entire basin was nearly constant from year to year.

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.

Roughly one-third of the Earth's land surface is seasonally covered by snow. In many of these ecosystems, the spring snowpack is melting earlier due to climatic warming and atmospheric dust deposition, which could greatly modify soil water resources during the growing season. Though snowmelt timing is known to influence soil water availability during summer, there is little known about the depth of the effects and how long the effects persist. We therefore manipulated the timing of seasonal snowmelt in a high-elevation mixed-conifer forest in a Mediterranean climate during consecutive wet and dry years. The snow-all-gone (SAG) date was advanced by 6 days in the wet year and 3 days in the dry year using black sand to reduce the snow surface albedo. To maximize variation in snowmelt timing, we also postponed the SAG date by 8 days in the wet year and 16 days in the dry year using white fabric to shade the snowpack from solar radiation. We found that deeper soil water (30-60 cm) did not show a statistically significant response to snowmelt timing. Shallow soil water (0-30 cm), however, responded strongly to snowmelt timing. The drying effect of accelerated snowmelt lasted 2 months in the 0-15 cm depth and at least 4 months in the 15-30 cm depth. Therefore, the legacy of snowmelt timing on soil moisture can persist through dry periods, and continued earlier snowmelt due to climatic warming and windblown dust could reduce near-surface water storage and availability to plants and soil biota.

[1] A wireless sensor network (WSN) was deployed as part of a water balance instrument cluster across a forested 1 km 2 headwater catchment in the southern Sierra Nevada of California. The network, which integrates readings from over 300 sensors, provides spatially representative measurements of snow depth, solar radiation, relative humidity, soil moisture, and matric potential. The ability of this densely instrumented watershed to capture catchment-scale snow depth and soil moisture distributions is investigated through comparison with three comprehensive gridded surveys and 1 day of detailed lidar snow data. Statistical analysis shows that the network effectively characterized catchment-wide distributions of snow depth, while offering a cost-effective, reliable, and energy-efficient means for collecting distributed data in real time. A temporal analysis of snow depth variability reveals that canopy cover is the major explanatory variable of snow depth and that under-canopy measurements persistently show higher variability compared to those in open terrain. An analysis of soil moisture shows lower variability at deeper soil depth and a correlation between mean soil moisture and variability for shallow soils. A three-phase design procedure was used to optimize the WSN deployment. First, as off-the-shelf performance of current WSN platforms for large-scale, long-term deployments cannot be guaranteed, statistics from a prototype deployment were analyzed. Two indicators of network performance, the packet delivery ratio and received signal strength indicator, showed that for our site conditions, a conservative 50 m node-to-node spacing would ensure low-power, reliable, and robust network communications. Second, results from the prototype were used to refine hardware specifications and to guide the layout of the full 57-node wireless network. Of these nodes, 23 were used actively for sensing, while the remaining 34 nodes were used as signal repeaters to ensure proper spatial radio coverage and robust network operations. Further analysis of network statistics is conducted during the third, operational, phase to validate system performance.Citation: Kerkez, B., S. D. Glaser, R. C. Bales, and M. W. Meadows (2012), Design and performance of a wireless sensor network for catchment-scale snow and soil moisture measurements, Water Resour. Res., 48, W09515,