气候变化领域集成服务门户(CCPortal): Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario; Canada

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DOI	10.5194/hess-24-4887-2020
	Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario; Canada
	King F.; Erler A.R.; Frey S.K.; Fletcher C.G.
发表日期	2020
ISSN	1027-5606
起始页码	4887
结束页码	4902
卷号	24 期号:10
英文摘要	Snow is a critical contributor to Ontario's waterenergy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n = 383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50% (16mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5° N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes. © 2020 Now Publishers Inc. All rights reserved.
语种	英语
scopus关键词	Budget control; Decision trees; Flood control; Floods; Machine learning; Snow melting systems; Surveys; Water management; Data assimilation systems; Interannual variability; Machine learning methods; Machine learning techniques; Snow water equivalent; Spatio-temporal domains; Statistical techniques; Waterresource management; Snow; correction; discharge; flood forecasting; hydrograph; machine learning; nonlinearity; resource management; sampling bias; snow water equivalent; snowmelt; snowpack; Great Lakes [North America]
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159284
作者单位	King, F., Deptartment of Geography & Environmental Management, University of WaterlooON, Canada; Erler, A.R., Aquanty, Waterloo, ON, Canada; Frey, S.K., Aquanty, Waterloo, ON, Canada, Deptartment of Earth & Environmental Sciences, University of WaterlooON, Canada; Fletcher, C.G., Deptartment of Geography & Environmental Management, University of WaterlooON, Canada
推荐引用方式 GB/T 7714	King F.,Erler A.R.,Frey S.K.,et al. Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario; Canada[J],2020,24(10).
APA	King F.,Erler A.R.,Frey S.K.,&Fletcher C.G..(2020).Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario; Canada.Hydrology and Earth System Sciences,24(10).
MLA	King F.,et al."Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario; Canada".Hydrology and Earth System Sciences 24.10(2020).