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. Key Points The hydrological signal of snowmelt timing was strongest in shallow soil Effects of snowmelt timing on soil moisture lasted 2-4 months Advancing snowmelt timing by 2-3 weeks depleted shallow soil water by one third © 2014. American Geophysical Union. All Rights Reserved.

The release of water during snowmelt orchestrates a variety of important belowground biogeochemical processes in seasonally snow-covered ecosystems, including the production and consumption of greenhouse gases (GHGs) by soil microorganisms. Snowmelt timing is advancing rapidly in these ecosystems, but there is still a need to isolate the effects of earlier snowmelt on soil GHG fluxes. For an improved mechanistic understanding of the biogeochemical effects of snowmelt timing during the snow-free period, we manipulated a high-elevation forest that typically receives over two meters of snowfall but little summer precipitation to influence legacy effects of snowmelt timing. We altered snowmelt rates for two years using black sand to accelerate snowmelt and white fabric to postpone snowmelt, thus creating a two- to three-week disparity in snowmelt timing. Soil microclimate and fluxes of carbon dioxide (CO₂ ), methane (CH₄ ), and nitrous oxide (N₂ O) were monitored weekly to monthly during the snow-free period. Microbial abundances were estimated by potential assays near the end of each snow-free period. Although earlier snowmelt caused soil drying, we found no statistically significant effects (p < 0.05) of altered snowmelt timing on fluxes of CO₂ or N₂ O, or soil microbial abundances. Soil CH₄ fluxes, however, did respond to snowmelt timing, with 18% lower rates of CH₄ uptake in the earlier snowmelt treatment, but only after a dry winter. Cumulative CO₂ emission and CH₄ uptake were 43% and 88% greater, respectively, after the dry winter. We conclude that soil GHG fluxes can be surprisingly resistant to hydrological changes associated with earlier snowmelt, likely because of persistent moisture and microbial activities in deeper mineral soils. As a result, a drier California in the future may cause seasonally snow-covered soils in the Sierra Nevada to emit more GHGs, not less.

[1] A wireless sensor network (WSN) was deployed as part of a water balance instrument cluster across a forested 1 km2 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. © 2012. American Geophysical Union.