A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications |
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| Affiliation: | 1. Marine and Freshwater Research Centre, Commercial Fisheries Research Group, Galway-Mayo Institute of Technology, Dublin Road, Galway, Ireland;2. Aarhus University, Department of Bioscience, Roskilde, Denmark;1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No.1 Fuxing Road, Haidian District, Beijing 100038, China;2. State Key Laboratory of Hydrology-Water Resource and Hydraulic Engineering, Hohai University, No.1 Xikang Road, Nanjing 210098, China;3. Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, No.219 Ningliu Road, Nanjing 210044, China;1. University of Palermo, Dipartimento di Scienze Agrarie e Forestali (SAF), V.le delle Scienze Ed. 4., 90128, Palermo, Italy;2. University of Catania, Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Via S. Sofia, 100, 95123 Catania, Italy;3. University of Palermo, Department of Civil, Environmental, Aerospace, Materials Engineering (DICAM), V.le delle Scienze Ed. 8, 90128 Palermo, Italy |
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| Abstract: | A downscaling tool was developed to provide sub-daily high spatial resolution surfaces of weather variables for distributed hydrologic modeling from NASA Modern Era Retrospective-Analysis for Research and Applications reanalysis products. The tool uses spatial interpolation and physically based relationships between the weather variables and elevation to provide inputs at the scale of a gridded hydrologic model, typically smaller (～100 m) than the scale of weather reanalysis data (～20–200 km). Nash-Sutcliffe efficiency (NSE) measures greater than 0.70 were obtained for direct tests of downscaled daily temperature and monthly precipitation at 173 SNOTEL sites. In an integrated test driving the Utah Energy Balance (UEB) snowmelt model, 80% of these sites gave NSE > 0.6 for snow water equivalent. These findings motivate use of this tool in data sparse regions where ground based observations are not available and downscaled global reanalysis products may be the only option for model inputs. |
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| Keywords: | Downscaling Reanalysis data Energy balance snowmelt model R Graphical user interface |
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