Embedded sensor network design for spatial snowcoverEmbedded sensor network design for spatial snowcover Robert RiceRobert Rice11,, Noah MolotchNoah Molotch22, Roger C. Bales, Roger C. Bales11

11Sierra Nevada Research Institute, University of California, Merced (Sierra Nevada Research Institute, University of California, Merced (rrice@ucmerced.edu))22UCLA, Department of Civil and Environmental EngineeringUCLA, Department of Civil and Environmental Engineering

IntroductionIntroductionScaling point observations of snow water Scaling point observations of snow water equivalent (SWE) to model grid-element scales is equivalent (SWE) to model grid-element scales is particularly challenging given the considerable particularly challenging given the considerable sub-grid variability in snow accumulation over sub-grid variability in snow accumulation over complex terrain. In an effort to capture this sub-complex terrain. In an effort to capture this sub-grid variability and provide spatially explicit grid variability and provide spatially explicit ground-truth snow data an embedded snow ground-truth snow data an embedded snow sensor network was designed and installed in sensor network was designed and installed in Yosemite and in the Valles Caldera National Yosemite and in the Valles Caldera National Preserve. Extensive snow surveys were used to Preserve. Extensive snow surveys were used to guide the installation of the network and to relate guide the installation of the network and to relate the observations to more detailed spatial SWE the observations to more detailed spatial SWE fields. Four years of continuous spatial and fields. Four years of continuous spatial and temporal data from Yosemite National Park and temporal data from Yosemite National Park and the and three-years in the Valles Caldera indicate the and three-years in the Valles Caldera indicate that accumulation and ablation rates can vary as that accumulation and ablation rates can vary as much as 50% based on variability in topography much as 50% based on variability in topography and vegetation. These snow distribution patterns and vegetation. These snow distribution patterns are especially apparent in the open forests of are especially apparent in the open forests of Yosemite and the Valles Caldera where vegetation Yosemite and the Valles Caldera where vegetation structure largely controls variability in snow structure largely controls variability in snow distribution. distribution.

Results and discussion-Gin FlatResults and discussion-Gin Flat

Distributed snow measurements: Distributed snow measurements: The distributed snow measurement network is The distributed snow measurement network is located at Gin Flat in Yosemite National Park at located at Gin Flat in Yosemite National Park at an elevation of 2100-m and deployed an elevation of 2100-m and deployed across a mixed conifer 0.4 ha site (Gin Flat) in the Upper Merced River basin. The distributed network The distributed network consists of 10 ultra sonic snow depth sensors consists of 10 ultra sonic snow depth sensors continuously logging snow depth every 1-hr. continuously logging snow depth every 1-hr. since December 2003. Gin Flat is located near since December 2003. Gin Flat is located near the existing snow course (1930-present) and the existing snow course (1930-present) and snow sensor (1980-present) sites. This site was snow sensor (1980-present) sites. This site was chosen because of its close proximity to chosen because of its close proximity to existing long term data sets, ease of access, existing long term data sets, ease of access, and variable terrain.and variable terrain.

ConclusionsConclusionsThe specific objective of measurement network is The specific objective of measurement network is capture the accumulation and ablation across capture the accumulation and ablation across topographic variables with the aim of providing topographic variables with the aim of providing guidance for future larger scale observation network guidance for future larger scale observation network designs.designs. These spatial and temporal measurement These spatial and temporal measurement arrays will improve remotely sensed and modeled arrays will improve remotely sensed and modeled SWE estimates across complex terrain by providing SWE estimates across complex terrain by providing robust, spatially explicit ground-truth values of robust, spatially explicit ground-truth values of snowpack states. snowpack states.

A distributed network is currently being installed in A distributed network is currently being installed in Yosemite National Park along an elevational transect Yosemite National Park along an elevational transect using Tioga Pass Road (HWY 120). This will extend using Tioga Pass Road (HWY 120). This will extend the current measurement array at Gin Flat from the current measurement array at Gin Flat from 1500-m to 2700-m. In addition, this will complement 1500-m to 2700-m. In addition, this will complement the basin transects that are installed in Sequoia the basin transects that are installed in Sequoia National Park and the Kings River Experimental National Park and the Kings River Experimental Watershed.Watershed.

AcknowledgementsAcknowledgementsSupport was provided by NASA Grant NNG04GC52AREASoN Support was provided by NASA Grant NNG04GC52AREASoN CAN “CAN “Multi-resolution snow products for the hydrologic Multi-resolution snow products for the hydrologic sciences”sciences”. In addition, . In addition, UC Merced, with the cooperation of UC Merced, with the cooperation of YosemiteYosemite National Park is acknowledged.National Park is acknowledged.

Extensive snow surveys in February and April 2006 verified Extensive snow surveys in February and April 2006 verified that the existing location of the distributed snow depth that the existing location of the distributed snow depth network provides details on the spatial variability of snow network provides details on the spatial variability of snow depth. The box plots represent the modeled snow depth depth. The box plots represent the modeled snow depth over the 1-, 4-, and 16- km2 study areas for the 1st and over the 1-, 4-, and 16- km2 study areas for the 1st and 3rd quartiles with the spatial average. The plot 3rd quartiles with the spatial average. The plot demonstrates that at Gin Flat snow pillow/sensors and demonstrates that at Gin Flat snow pillow/sensors and snow courses overestimate snow depth by 25% and snow courses overestimate snow depth by 25% and indicate that these point measurements are not good indicate that these point measurements are not good indicators of the spatial average, but merely a point within indicators of the spatial average, but merely a point within variability. Results from the 2006 snow surveys variability. Results from the 2006 snow surveys determined that the current location of the distributed determined that the current location of the distributed snow depth network are in optimal locations for both snow depth network are in optimal locations for both accumulation and ablation.accumulation and ablation.

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Comparisons of snow depth estimates with Comparisons of snow depth estimates with historical snow course data shows that a historical snow course data shows that a single point measurement is a poor single point measurement is a poor estimator of snow depth over a estimator of snow depth over a homogenous area, but 4 or more homogenous area, but 4 or more measurement points can reduce the measurement points can reduce the uncertainty by 50%. Range of snow depth uncertainty by 50%. Range of snow depth estimates from choosing 1-10 points with estimates from choosing 1-10 points with identical physiographic features (flat, open) identical physiographic features (flat, open) for 3 different years, as % on mean snow for 3 different years, as % on mean snow depth: historical peak (1983), low (1988), & depth: historical peak (1983), low (1988), & average (1982). This analysis from the average (1982). This analysis from the historical snowcouse data indicated that an historical snowcouse data indicated that an optimal snow depth network should consists optimal snow depth network should consists of 7 to 10 snow depth sensors. of 7 to 10 snow depth sensors.

Range of snow depth estimates from Range of snow depth estimates from choosing 1-10 snow depth sensors within choosing 1-10 snow depth sensors within the distributed measurement network with the distributed measurement network with terrain characterized by varying terrain characterized by varying physiographic features (mixed conifer, physiographic features (mixed conifer, slope, aspect). Again, as with the snow slope, aspect). Again, as with the snow course 10 only slightly replicate better course 10 only slightly replicate better than 3. However, given the variability in than 3. However, given the variability in the terrain the uncertainty is far greater the terrain the uncertainty is far greater than when compared to homogenous than when compared to homogenous terrain. Using 4 or more snow depth terrain. Using 4 or more snow depth sensors can reduce the uncertainty by sensors can reduce the uncertainty by 40%. Having 10-15 sensors per cluster 40%. Having 10-15 sensors per cluster provides for replication. provides for replication.

Continuous (hourly) measurements of the ultra sonic depth sensors Continuous (hourly) measurements of the ultra sonic depth sensors and temperature from 2003- 2007. The Gin Flat snow depth and temperature from 2003- 2007. The Gin Flat snow depth operated by CA DWR and USGS, as well as, the monthly snow course operated by CA DWR and USGS, as well as, the monthly snow course measurements are plotted and represents the open homogenous measurements are plotted and represents the open homogenous terrain. The snow as measures by the distributed network can vary terrain. The snow as measures by the distributed network can vary as much as 50%, where tree canopy density of >60% can influence as much as 50%, where tree canopy density of >60% can influence distribution patterns. In addition, the distributed snow distribution patterns. In addition, the distributed snow measurement network is depleted of snow as much as 4 weeks measurement network is depleted of snow as much as 4 weeks earlier than the CA DWR site. earlier than the CA DWR site.

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Accumulation & ablation rates over a 0.4 Accumulation & ablation rates over a 0.4 ha mha m22 of as much as 50%. of as much as 50%.

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