Presentation is loading. Please wait.

Presentation is loading. Please wait.

Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of.

Similar presentations


Presentation on theme: "Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of."— Presentation transcript:

1 Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of California, Merced 2 UCLA, Department of Civil and Environmental Engineering Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of California, Merced 2 UCLA, Department of Civil and Environmental Engineering Introduction Scaling point observations of snow water equivalent (SWE) to model grid-element scales is particularly challenging given the considerable sub-grid variability in snow accumulation over complex terrain. In an effort to capture this sub-grid variability and provide spatially explicit ground-truth snow data an embedded snow sensor network was designed and installed in Yosemite and in the Valles Caldera National Preserve. Extensive snow surveys were used to guide the installation of the network and to relate the observations to more detailed spatial SWE fields. Four years of continuous spatial and temporal data from Yosemite National Park and the and three-years in the Valles Caldera indicate that accumulation and ablation rates can vary as much as 50% based on variability in topography and vegetation. These snow distribution patterns are especially apparent in the open forests of Yosemite and the Valles Caldera where vegetation structure largely controls variability in snow distribution. Results and discussion-Gin Flat Distributed snow measurements: The distributed snow measurement network is located at Gin Flat in Yosemite National Park at an elevation of 2100-m and deployed The distributed network consists of 10 ultra sonic snow depth sensors continuously logging snow depth every 1-hr. since December Gin Flat is located near the existing snow course (1930-present) and snow sensor (1980-present) sites. This site was chosen because of its close proximity to existing long term data sets, ease of access, and variable terrain. Distributed snow measurements: The distributed snow measurement network is located at Gin Flat in Yosemite National Park at 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 consists of 10 ultra sonic snow depth sensors continuously logging snow depth every 1-hr. since December Gin Flat is located near the existing snow course (1930-present) and snow sensor (1980-present) sites. This site was chosen because of its close proximity to existing long term data sets, ease of access, and variable terrain. An additional network was deployed within the 36,000-hectare Valles Caldera National Preserve (VCNP) in the Jemez Mountains, New Mexico (35° 53’N, 106° 32’W). The primary forest type of the study site is a mixed conifer forest, consisting of Douglas fir, white fir, blue spruce, and southwestern white pine. The region’s principal sources of moisture are the Pacific Ocean and the Gulf of California, approximately 800 km to the west and southwest, respectively; approximately 50% of the 720 mm of average annual precipitation ( ) falls between November and April in the form of snow. Snow depth was recorded hourly using a network of nine ultrasonic snow depth sensors placed under the canopy, at the edge of the canopy, and in open areas. Conclusions The specific objective of measurement network is capture the accumulation and ablation across topographic variables with the aim of providing guidance for future larger scale observation network designs.These spatial and temporal measurement arrays will improve remotely sensed and modeled SWE estimates across complex terrain by providing robust, spatially explicit ground-truth values of snowpack states. The specific objective of measurement network is capture the accumulation and ablation across topographic variables with the aim of providing guidance for future larger scale observation network designs. These spatial and temporal measurement arrays will improve remotely sensed and modeled SWE estimates across complex terrain by providing robust, spatially explicit ground-truth values of snowpack states. A distributed network is currently being expanded in Yosemite National Park along an elevational transect using Tioga Pass Road (HWY 120). This will extend the current measurement array at Gin Flat from 1500-m to 2700-m. In addition, this will complement the basin transects that are being installed in Sequoia National Park and the Kings River Experimental Watershed. Acknowledgements Support is provided by NASA Grant NNG04GC52AREASoN CAN “Multi- resolution snow products for the hydrologic sciences”. Distributed snow data was made available through the Sierra Nevada and San Joaquin Virtual Observatory (https://eng.ucmerced.edu/dev00/snsjno) which is supported by the National Science Foundation.In addition, UC Merced, with the cooperation of Yosemite National Park is acknowledged. https://eng.ucmerced.edu/dev00/snsjno Snow Survey Gin Flat-2006 : Comparisons of snow depth estimates with historical snow course data shows that a single point measurement is a poor estimator of snow depth over a homogenous area, but 4 or more measurement points can reduce the uncertainty by 50%. Range of snow depth estimates from choosing 1-10 points with identical physiographic features (flat, open) for 3 different years, as % on mean snow depth: historical peak (1983), low (1988), & average (1982). This analysis from the historical snowcouse data indicated that an optimal snow depth network should consists of 7 to 10 snow depth sensors. Range of snow depth estimates from choosing 1-10 snow depth sensors within the distributed measurement network with terrain characterized by varying physiographic features (mixed conifer, slope, aspect). Again, as with the snow course 10 only slightly replicate better than 3. However, given the variability in the terrain the uncertainty is far greater than when compared to homogenous terrain. Using 4 or more snow depth sensors can reduce the uncertainty by 40%. Having sensors per cluster provides for replication. Continuous (hourly) measurements of the ultra sonic depth sensors from The snow, as measured by the distributed network can vary as much as 50%, where tree canopy density of >60% influence distribution patterns. The plot demonstrates that the Gin Flat snow pillow and snow course overestimate snow depth by 25% and indicate that these point measurements are not good indicators of the spatial average. In addition, the distributed snow measurement network is depleted of snow as much as 4 weeks earlier than the snow pillow. Upper Merced River Basin Gin Flat: Elevation:2100-mForested Complex terrain Ease of access Accumulation & ablation rates over a 0.4 ha m 2 of as much as 50%. Extensive snow surveys in February and April 2006 verified that the existing location of the distributed snow depth network provides details on the spatial variability of snow depth for both accumulation and ablation. The box plots represent the modeled snow depth values over the 1-, 4-, and 16- km 2 study areas for the 1st and 3rd quartiles with the spatial average. The plot demonstrates that at Gin Flat the now pillow and snow course overestimate SWE by 25% and indicate that these point measurements are not good indicators of the spatial average, but provides a point within the distribution of modeled snow depth values. Results and discussion-Valles Caldera Observations from ultra-sonic snow depth sensors showed a 56% increase in snow depth in open versus under-canopy locations at maximum accumulation. Snow settling rates between 15 March and 1 April were greater in open areas versus those at the canopy edge; likely a result of more rapid snow metamorphism in areas exposed to greater solar irradiance. Total ablation rates after maximum accumulation on 15 March, were 54 and 46% lower in under-canopy locations versus canopy edge and open areas, respectively kilometers Instrument sites Strategy: rather than spreading instruments across a whole basin, this transect statistically samples the variability in the Tuolumne & Merced basins, taking advantage of the Tioga Pass Road as infrastructure Instrument sites leverage operational & research investments Elevation (m) Snow pillow The box plots represent the embedded sensors and the modeled snow depth over the 1-, 4-, and 16- km 2 study areas for the 1st and 3rd quartiles with the spatial average. The plot demonstrates, that in February, the embedded sensor network was able to capture the variability as well as the spatial average. However, April which was more indicative of accumulation, showed the embedded sensor network over estimating the spatial average by as much as 10%, but the embedded sensors were able to capture the shallowest modeled snow depth.


Download ppt "Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of."

Similar presentations


Ads by Google