DOI: 10.5194/tc-12-891-2018
Scopus记录号: 2-s2.0-85043600867
论文题名: Improving gridded snow water equivalent products in British Columbia, Canada: Multi-source data fusion by neural network models
作者: Snauffer A ; M ; , Hsieh W ; W ; , Cannon A ; J ; , Schnorbus M ; A
刊名: Cryosphere
ISSN: 19940416
出版年: 2018
卷: 12, 期: 3 起始页码: 891
结束页码: 905
语种: 英语
英文关键词: alpine environment
; artificial neural network
; data set
; hydrological modeling
; infiltration
; model validation
; regression analysis
; snow water equivalent
; snowmelt
; spatiotemporal analysis
; British Columbia
; Canada
英文摘要: Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using cross-validation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC's five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province. © Author(s) 2018. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75402
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC, Canada; Climate Research Division, Environment and Climate Change Canada, P.O. Box 1700 STN CSC, Victoria, BC, Canada; Pacific Climate Impacts Consortium, University House 1, University of Victoria, 2489 Sinclair Road, Victoria, BC, Canada
Recommended Citation:
Snauffer A,M,, Hsieh W,et al. Improving gridded snow water equivalent products in British Columbia, Canada: Multi-source data fusion by neural network models[J]. Cryosphere,2018-01-01,12(3)