期刊论文详细信息
JOURNAL OF HYDROLOGY 卷:564
Targeting high robustness in snowpack modeling for Nordic hydrological applications in limited data conditions
Article
Mas, Alexandre1  Baraer, Michel1  Arsenault, Richard1  Poulin, Annie1  Prefontaine, Jonathan1 
[1] Univ Quebec, Dept Construct Engn, Ecole Technol Super, 1100 Notre Dame St West, Montreal, PQ H3C 1K3, Canada
关键词: Snowpack modeling;    Hydrological model;    Snow;    Energy balance;    Robustness;   
DOI  :  10.1016/j.jhydrol.2018.07.071
来源: Elsevier
PDF
【 摘 要 】

Most hydrological models simulate snowmelt using a degree day or simplified energy balance method, which usually requires a calibration of snow-related parameters using discharge data. Despite its apparent efficiency, this method leads to empirical relations which are not proven to remain valid in a changing climate. The direct application of robust physically-based snow models in hydrological modeling is difficult due to the high number of not easily available input variables this model type requires. The objective of this study is to test the robustness of a physically-based snowpack model that requires only a limited number of common meteorological parameters. The MASiN model computes the energy and mass balance of multiple layers of the snowpack using hourly air temperature, relative humidity and wind speeds, as well as daily precipitations. MASiN was tested at 23 sites across Canada and Sweden, using a unique set of parameters fixed at a single site. At each site, the snow depth simulated by MASiN was compared against measurements. Robustness was challenged by comparing MASiN's performance to that of three other models on three different criteria. MASiN showed the highest robustness among the tested models. With a unique set of parameters, it showed better results than the three reference models when used in similar conditions and matched their performances when reference models were calibrated at each site. The results prove non-data intensive physically based models to be promising tools for hydrological and other snow cover-related studies.

【 授权许可】

Free   

【 预 览 】
附件列表
Files Size Format View
10_1016_j_jhydrol_2018_07_071.pdf 2354KB PDF download
  文献评价指标  
  下载次数:6次 浏览次数:1次