Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

被引:350
作者
Sturm, Matthew [1 ]
Taras, Brian [2 ]
Liston, Glen E. [3 ]
Derksen, Chris [4 ]
Jonas, Tobias [5 ]
Lea, Jon [6 ]
机构
[1] USA, CRREL Alaska, Ft Wainwright, AK 99712 USA
[2] Alaska Dept Fish & Game, Fairbanks, AK USA
[3] Colorado State Univ, Cooperat Inst Res Atmosphere, Ft Collins, CO 80523 USA
[4] Environm Canada, Div Climate Res, Toronto, ON, Canada
[5] WSL Inst Snow & Avalanche Res SLF, Davos, Switzerland
[6] Oregon State Off, Natl Resource Conservat Serv, Portland, OR USA
基金
美国国家科学基金会;
关键词
SEASONAL SNOW; COVER DATA; SEA-ICE; MODEL; PARAMETERIZATION; VARIABILITY; SYSTEM; ALASKA;
D O I
10.1175/2010JHM1202.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes similar to 20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth-density-SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within +/- 8 cm of the measured values, with most estimates falling much closer.
引用
收藏
页码:1380 / 1394
页数:15
相关论文
共 87 条
[1]  
ADAMS WP, 1982, NORD HYDROL, V13, P139
[2]  
ANDERSON EA, 1976, NWS19 NOAA
[3]   Evaluation of spatial variability in snow water equivalent for a high mountain catchment [J].
Anderton, SP ;
White, SM ;
Alvera, B .
HYDROLOGICAL PROCESSES, 2004, 18 (03) :435-453
[4]  
[Anonymous], 2002, Model selection and multimodel inference: a practical informationtheoretic approach
[5]  
[Anonymous], SCS NATL ENG HDB
[6]  
[Anonymous], P 32 E SNOW C MANCH
[7]  
[Anonymous], 2007, AR4 CLIM CHANG 2007
[8]  
[Anonymous], HYDROL PROCESSES
[9]  
[Anonymous], CAN SNOW DAT
[10]  
[Anonymous], 2021, Bayesian data analysis