Abstract
Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.
Highlights
Seasonal snowmelt in the mountainous areas affects the life of billion people around the world [1], it provides enough water for the snowmelt season in basin, and is used for soil and cropping purpose in the later stages of snowmelt [2]
The last method is the snow cover reconstruction technique proposed by Martinec and Rango [2,23], which establishes a reverse snow melt process and deduces backward the maximum snow water equivalent (SWE) accumulation on the basis of daily snow melt and area change in the snow melt period from the date when the snow cover disappears to the accumulated peak SWE period
We found that the maximum SWE accumulation increases with elevation in the area which has an elevaFtigounrebe7l.oDwa2il8y0v0amria.tHionowofevtheer,snthoewrewwataesr aeqtuuirvnailnengtp(SoWinEt )aitn2t8h0e0wmh.oSleWbEasainboanvde 2a8t 0d0iffmerednitd not increealseevactoiomnpblaentedlsy
Summary
Seasonal snowmelt in the mountainous areas affects the life of billion people around the world [1], it provides enough water for the snowmelt season in basin, and is used for soil and cropping purpose in the later stages of snowmelt [2]. The SWE is the main factor influencing river runoff, the regional water resources supply, and flood safety during the snowmelt period. The first method is the spatial interpolation method [11,12,13], which uses automatic observation data of the snow pillow at the observation point and measured data of the snow courses to carry out regional interpolation in combination with the snow area products obtained from satellite images. This method requires a high density of measured points. Compared with the interpolation method using the measured data and the data assimilation models, the SWE reconstruction model can achieve a higher resolution without any measured data [2,10,11] by using the optical snow cover data, and the amount of snow melt can be estimated by combining it with the energy balance method
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