Evaluation of probable maximum snow accumulation: Development of a methodology for climate change studies

被引:16
作者
Klein, Iris M. [1 ]
Rousseau, Alain N. [1 ]
Frigon, Anne [2 ]
Freudiger, Daphne [1 ]
Gagnon, Patrick [1 ]
机构
[1] INRS, Ctr Eau Terre Environm, ETE, 490 Rue Couronne, Quebec City, PQ G1K 9A9, Canada
[2] Consortium Ouranos, Montreal, PQ, Canada
关键词
Snow; Canadian Regional Climate Model; Precipitable water; Moisture maximization; Non-stationary frequency analysis; Climate change; COUPLED MODEL;
D O I
10.1016/j.jhydrol.2016.03.031
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Probable maximum snow accumulation (PMSA) is one of the key variables used to estimate the spring probable maximum flood (PMF). A robust methodology for evaluating the PMSA is imperative so the ensuing spring PMF is a reasonable estimation, This is of particular importance in times of climate change (CC) since it is known that solid precipitation in Nordic landscapes will in all likelihood change over the next century. In this paper, a PMSA methodology based on simulated data from regional climate models is developed. Moisture maximization represents the core concept of the proposed methodology; precipitable water being the key variable. Results of stationarity tests indicate that CC will affect the monthly maximum precipitable water and, thus, the ensuing ratio to maximize important snowfall events. Therefore, a non-stationary approach is used to describe the monthly maximum precipitable water. Outputs from three simulations produced by the Canadian Regional Climate Model were used to give first estimates of potential PMSA changes for southern Quebec, Canada. A sensitivity analysis of the computed PMSA was performed with respect to the number of time-steps used (so-called snowstorm duration) and the threshold for a snowstorm to be maximized or not. The developed methodology is robust and a powerful tool to estimate the relative change of the PMSA. Absolute results are in the same order of magnitude as those obtained with the traditional method and observed data; but are also found to depend strongly on the climate projection used and show spatial variability. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:74 / 85
页数:12
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