气候变化领域集成服务门户(CCPortal): Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

CCPortal

DOI	10.5194/tc-12-1579-2018
	Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan
	Bair E.H.; Calfa A.A.; Rittger K.; Dozier J.
发表日期	2018
ISSN	19940416
卷号	12 期号:5
英文摘要	In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques - bagged regression trees and feed-forward neural networks - produced similar mean results, with 0-14% bias and 46-48mm RMSE on average. Nash-Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource. © Author(s) 2018.
学科领域	accuracy assessment; AMSR-E; artificial neural network; brightness temperature; estimation method; machine learning; mountain environment; real time; reconstruction; remote sensing; sensor; snow water equivalent; snowmelt; spatial resolution; watershed; Afghanistan; California; Sierra Nevada [California]; United States
语种	英语
scopus关键词	accuracy assessment; AMSR-E; artificial neural network; brightness temperature; estimation method; machine learning; mountain environment; real time; reconstruction; remote sensing; sensor; snow water equivalent; snowmelt; spatial resolution; watershed; Afghanistan; California; Sierra Nevada [California]; United States
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/119156
作者单位	Earth Research Institute, University of California, Santa Barbara, CA 93106-3060, United States; Department of Computer Science, University of California, Santa Barbara, CA 93106-5110, United States; National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309-0449, United States; Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106-5131, United States; Arista Networks, Santa Clara, CA 95054, United States
推荐引用方式 GB/T 7714	Bair E.H.,Calfa A.A.,Rittger K.,et al. Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan[J],2018,12(5).
APA	Bair E.H.,Calfa A.A.,Rittger K.,&Dozier J..(2018).Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan.Cryosphere,12(5).
MLA	Bair E.H.,et al."Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan".Cryosphere 12.5(2018).