Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data

Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modelling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution...

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Main Authors: M. Shrestha, L. Wang, T. Koike, H. Tsutsui, Y. Xue, Y. Hirabayashi
Format: Article
Language:English
Published: Copernicus Publications 2014-02-01
Series:Hydrology and Earth System Sciences
Online Access:http://www.hydrol-earth-syst-sci.net/18/747/2014/hess-18-747-2014.pdf
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spelling doaj-d0ce056344574601b982630fce7b75562020-11-24T21:06:37ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382014-02-0118274776110.5194/hess-18-747-2014Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing dataM. Shrestha0L. Wang1T. Koike2H. Tsutsui3Y. Xue4Y. Hirabayashi5Department of Civil Engineering, The University of Tokyo, Tokyo, JapanKey Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaDepartment of Civil Engineering, The University of Tokyo, Tokyo, JapanDepartment of Civil Engineering, The University of Tokyo, Tokyo, JapanDepartment of Geography and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USAInstitute of Engineering Innovation, The University of Tokyo, Tokyo, JapanAdequate estimation of the spatial distribution of snowfall is critical in hydrologic modelling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimises an altitude-based snowfall correction factor (<i>C</i><sub>fsnow</sub>). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution–University of Arizona (SCE–UA) automatic search algorithm is used to obtain the optimal value of <i>C</i><sub>fsnow</sub> for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimised <i>C</i><sub>fsnow</sub> of 0.0007 m<sup>−1</sup>. For validation purposes, the optimised <i>C</i><sub>fsnow</sub> was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated versus observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that <i>C</i><sub>fsnow</sub> could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly gauged basins, where elevation dependence of snowfall amount is strong.http://www.hydrol-earth-syst-sci.net/18/747/2014/hess-18-747-2014.pdf
collection DOAJ
language English
format Article
sources DOAJ
author M. Shrestha
L. Wang
T. Koike
H. Tsutsui
Y. Xue
Y. Hirabayashi
spellingShingle M. Shrestha
L. Wang
T. Koike
H. Tsutsui
Y. Xue
Y. Hirabayashi
Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data
Hydrology and Earth System Sciences
author_facet M. Shrestha
L. Wang
T. Koike
H. Tsutsui
Y. Xue
Y. Hirabayashi
author_sort M. Shrestha
title Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data
title_short Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data
title_full Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data
title_fullStr Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data
title_full_unstemmed Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data
title_sort correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data
publisher Copernicus Publications
series Hydrology and Earth System Sciences
issn 1027-5606
1607-7938
publishDate 2014-02-01
description Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modelling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimises an altitude-based snowfall correction factor (<i>C</i><sub>fsnow</sub>). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution–University of Arizona (SCE–UA) automatic search algorithm is used to obtain the optimal value of <i>C</i><sub>fsnow</sub> for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimised <i>C</i><sub>fsnow</sub> of 0.0007 m<sup>−1</sup>. For validation purposes, the optimised <i>C</i><sub>fsnow</sub> was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated versus observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that <i>C</i><sub>fsnow</sub> could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly gauged basins, where elevation dependence of snowfall amount is strong.
url http://www.hydrol-earth-syst-sci.net/18/747/2014/hess-18-747-2014.pdf
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