title: Improving the performance of temperature index snowmelt model of SWAT by using MODIS land surface temperature data.
abstract: Simulation results of the widely used temperature index snowmelt model are greatly influenced by input air temperature data. Spatially sparse air temperature data remain the main factor inducing uncertainties and errors in that model, which limits its applications. Thus, to solve this problem, we created new air temperature data using linear regression relationships that can be formulated based on MODIS land surface temperature data. The Soil Water Assessment Tool model, which includes an improved temperature index snowmelt module, was chosen to test the newly created data. By evaluating simulation performance for daily snowmelt in three test basins of the Amur River, performance of the newly created data was assessed. The coefficient of determination (R (2)) and Nash-Sutcliffe efficiency (NSE) were used for evaluation. The results indicate that MODIS land surface temperature data can be used as a new source for air temperature data creation. This will improve snow simulation using the temperature index model in an area with sparse air temperature observations.
authors.affiliation: United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan.
authors.forename: Yan
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan.
authors.forename: Takeo
authors.initials: T
authors.lastname: Onishi
authors.affiliation: Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan.
authors.forename: Ken
authors.initials: K
authors.lastname: Hiramatsu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003625:Data Collection / Q000379:methods; D005615:Freezing; D005843:Geography; D016014:Linear Models; D008962:Models, Theoretical; D045483:Rivers; D012426:Russia; D012914:Snow; D013696:Temperature