34 research outputs found
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Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products
We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions
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Comparison of ground-based and airborne snow surface albedo parameterizations in an alpine watershed: Impact on snowpack mass balance
Two commonly used snow surface albedo models were evaluated using albedo data from the Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS), and their influence on snowmelt timing and magnitude was assessed using a net radiation/temperature index snowmelt model, a series of satellite-based snow covered area scenes, and on-site snow surveys. Albedo estimates using an explicit representation of snow surface temperature, snow age, and solar illumination angle, based on the Biosphere Atmosphere Transfer Scheme (BATS), were within the 0.02 AVIRIS measurement error for 78% of the snow-covered portions of the watershed. Conversely, albedo values estimated using a simple model based solely on snow surface age underestimated AVIRIS-observed albedo. Correlations between the timing of snowmelt and observed runoff using the BATS albedo model (R2 = 0.69) were significantly better than those using the age-based approach (R2 = 0.59) and were comparable to using AVIRIS data (R2 = 0.73). Snow extent was simulated most accurately with the AVIRIS parameterization; average map accuracy was 79 and 10% greater than when using the age-based and BATS albedo parameterizations, respectively. The error in snow water equivalent for April was 14% for BATS versus 39% for the age-based albedo; however, it was less than 1% for simulations using AVIRIS albedo data. Thus the BATS albedo estimates performed better than the age-based albedo but did not outperform simulations using AVIRIS albedo data. Copyright 2006 by the American Geophysical Union
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Scaling snow observations from the point to the grid element: Implications for observation network design
[1] The spatial distribution of snow water equivalent (SWE) within 16-, 4-, and 1-km2 grid elements surrounding six snow telemetry (SNOTEL) stations in the Rio Grande headwaters was characterized using field observations of snowpack properties, satellite data, binary regression tree models, and a spatially distributed net radiation/temperature index snowpack mass balance model. In some cases, SNOTEL SWE values were 200% greater than mean grid element SWE. Analyses designed to identify the optimal location for measuring mean grid element SWE accumulation indicated that only 2.4% of each grid element satisfied the criteria of optimality. Similar analyses for the ablation season showed that point SWE and mean grid element SWE were highly correlated (r = 0.73) in areas with relatively persistent snow cover. These locations did not overlap in space with areas deemed optimal at maximum accumulation; areas with persistent snow cover have relatively high accumulation rates. Therefore future observations may need to be placed with the specific objective of representing either accumulation or ablation season processes. These results have implications for large-scale studies that require ground observations for updating purposes; we show an example of this utility using the SWE product of the National Operational Hydrologic Remote Sensing Center. Furthermore, the relatively consistent spatial patterns of snow accumulation and melt have implications for future observation network design in that results from short-term studies (e.g., 2 years) can be used to design long-term observation networks. Copyright 2005 by the American Geophysical Union
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Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt-Verde River basin, Arizona
The temporal and spatial continuity of spatially distributed estimates of snow-covered area (SCA) are limited by the availability of cloud-free satellite imagery; this also affects spatial estimates of snow water equivalent (SWE), as SCA can be used to define the extent of snow telemetry (SNOTEL) point SWE interpolation. In order to extend the continuity of these estimates in time and space to areas beneath the cloud cover, gridded temperature data were used to define the spatial domain of SWE interpolation in the Salt-Verde watershed of Arizona. Gridded positive accumulated degree-days (ADD) and binary SCA (derived from the Advanced Very High Resolution Radiometer (AVHRR)) were used to define a threshold ADD to define the area of snow cover. The optimized threshold ADD increased during snow accumulation periods, reaching a peak at maximum snow extent. The threshold then decreased dramatically during the first time period after peak snow extent owing to the low amount of energy required to melt the thin snow cover at lower elevations. The area having snow cover at this later time was then used to define the area for which SWE interpolation was done. The area simulated to have snow was compared with observed SCA from AVHRR to assess the simulated snow map accuracy. During periods without precipitation, the average commission and omission errors of the optimal technique were 7% and 11% respectively, with a map accuracy of 82%. Average map accuracy decreased to 75% during storm periods, with commission and omission errors equal to 11% and 12% respectively. The analysis shows that temperature data can be used to help estimate the snow extent beneath clouds and therefore improve the spatial and temporal continuity of SCA and SWE products. © 2004 John Wiley and Sons, Ltd
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Estimating the spatial distribution of snow water equivalent in an alpine basin using binary regression tree models: The impact of digital elevation data and independent variable selection
Regression tree models have been shown to provide the most accurate estimates of distributed snow water equivalent (SWE) when intensive field observations are available. This work presents a comparison of regression tree models using different source digital elevation models (DEMs) and different combinations of independent variables. Different residual interpolation techniques are also compared. The analysis was performed in the 19.1 km2 Tokopah Basin, located in the southern Sierra Nevada of California. Snow depth, the dependent variable of the statistical models, was derived from three snow surveys (April, May and June 1997), with an average of 328 depth measurements per survey. Estimates of distributed SWE were derived from the product of the snow depth surfaces, the average snow density (54 measurements on average) and the fractional snow covered area (obtained from the Landsat Thematic Mapper and the Airborne Visible/Infrared Imaging Spectrometer). Independent variables derived from the standard US Geological Survey DEM yielded the lowest overall model deviance and lowest error in snow depth prediction. Simulations using the Shuttle Radar Topography Mission DEM and the National Elevation Dataset DEM were improved when northness was substituted for solar radiation in five of six cases. Co-kriging with maximum upwind slope and elevation proved to be the best method for distributing residuals for April and June, respectively. Inverse distance weighting was the best residual distribution method for May. Copyright © 2004 John Wiley & Sons, Ltd