Simulating wind-affected snow accumulations at catchment to basin scales |
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| Institution: | 1. USDA-ARS Northwest Watershed Research Center, 800 Park Blvd., Suite 105, Boise, ID 83712, USA;2. University of Reading, ESSC, 3 Earley Gate, Reading, Berks RG6 6AL, UK;1. WSL Institute for Snow and Avalanche Research SLF, Flüelastr. 11, 7260, Davos, Switzerland;2. Professor of Hydrology and Micrometeorology, Nicholas School of the Environment, Box 90328, Duke University, Durham, NC 27708-0328, U.S.A.;1. Centre for Research in Biosciences, Department of Applied Sciences, University of the West of England, Bristol, Frenchay Campus, Coldharbour Lane, BS16 1QY, UK;2. Pendred Humidification and Water Systems, Worsley Bridge Rd, London, SE26 5BN, UK;1. School of Energy Resources, China University of Geosciences, Beijing 100083, China;2. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;3. Center for Excellence in Tibetan Plateau Earth Sciences, CAS, Beijing 100101, China;4. Institute of Low Temperature Science, Hokkaido University, Sapporo 060-0819, Japan;5. State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, CAS, Lanzhou 730000, China;1. Department of Nursing, Catholic University of Valencia San Vicente Mártir., C/Espartero, 7, Valencia 46007, Spain;2. Endocrinology Department, University Hospital la Ribera, Crtra. Corbera km 1, 46600 Alzira, Valencia, Spain;3. Department of Obstetrics and Gynecology, University Hospital la Ribera, Crtra. Corbera km 1, 46600 Alzira, Valencia, Spain;4. Pediatrics, Obstetrics and Gynecology Unit, Hospital de Riotinto, Andalusian Health Service, Avda La Esquila 5, 21660 Minas de Riotinto, Huelva, Spain;1. Faculty of Environmental Science and Engineering, Babe?-Bolyai University, Fântânele 30, 400294 Cluj-Napoca, Romania;2. Interdisciplinary Research Institute on Bio-Nano-Science of Babe?-Bolyai University, Treboniu Laurean 42, 400271 Cluj-Napoca, Romania;3. Faculty of Physics, University of Bucharest, N. B?lcescu 1, 010041 Bucharest, Romania;4. Faculty of Biology and Geology, Babe?-Bolyai University, Kog?lniceanu 1, 400084 Cluj-Napoca, Romania;1. ParaDNA, LGC Ltd, Culham Science Centre, Abingdon, OX14 3ED, UK;2. National Center for Forensic Science, PO Box 162367, Orlando, FL 32816-2367, USA;3. Department of Chemistry, University of Central Florida, PO Box 162366, Orlando, FL 32816-2366, USA;1. Office of Geomatics, National Geospatial-Intelligence Agency, USA;2. Operational Environment Analysis, Defense Intelligence Agency, USA;3. Earth System Science Interdisciplinary Center, University of Maryland at College Park, USA;4. Department of Geosciences, Pennsylvania State University, USA;5. Office of Targeting and Transnational Issues, National Geospatial-Intelligence Agency, USA;6. Geospatial Research Lab, Engineering Research and Development Center, US Army Corps of Engineers, USA;7. Office of Sciences and Methodologies, National Geospatial-Intelligence Agency, USA;8. Foreign Agricultural Service, United States Department of Agriculture, USA |
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| Abstract: | In non-forested mountain regions, wind plays a dominant role in determining snow accumulation and melt patterns. A new, computationally efficient algorithm for distributing the complex and heterogeneous effects of wind on snow distributions was developed. The distribution algorithm uses terrain structure, vegetation, and wind data to adjust commonly available precipitation data to simulate wind-affected accumulations. This research describes model development and application in three research catchments in the Reynolds Creek Experimental Watershed in southwest Idaho, USA. All three catchments feature highly variable snow distributions driven by wind. The algorithm was used to derive model forcings for Isnobal, a mass and energy balance distributed snow model. Development and initial testing took place in the Reynolds Mountain East catchment (0.36 km2) where R2 values for the wind-affected snow distributions ranged from 0.50 to 0.67 for four observation periods spanning two years. At the Upper Sheep Creek catchment (0.26 km2) R2 values for the wind-affected model were 0.66 and 0.70. These R2 values matched or exceeded previously published cross-validation results from regression-based statistical analyses of snow distributions in similar environments. In both catchments the wind-affected model accurately located large drift zones, snow-scoured slopes, and produced melt patterns consistent with observed streamflow. Models that did not account for wind effects produced relatively homogenous SWE distributions, R2 values approaching 0.0, and melt patterns inconsistent with observed streamflow. The Dobson Creek (14.0 km2) application incorporated elevation effects into the distribution routine and was conducted over a two-dimensional grid of 6.67 × 105 pixels. Comparisons with satellite-derived snow-covered-area again demonstrated that the model did an excellent job locating regions with wind-affected snow accumulations. This final application demonstrated that the computational efficiency and modest data requirements of this approach are ideally suited for large-scale operational applications. |
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