气候变化领域集成服务门户(CCPortal): Quantifying uncertainty in groundwater depth from sparse well data in the California Central Valley

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DOI	10.1088/1748-9326/ab88fb
	Quantifying uncertainty in groundwater depth from sparse well data in the California Central Valley
	Quay A.N.; Hering A.S.; Mauter M.S.
发表日期	2020
ISSN	17489318
卷号	15 期号:8
英文摘要	Groundwater is a critical freshwater resource for irrigation in the California Central Valley, particularly in times of drought. Groundwater depth has dropped rapidly in California's overdrafted basins, but irregular monitoring across space and time limits the accuracy of the groundwater depth projections in the Groundwater Sustainability Plans required by the California Sustainable Groundwater Management Act (SGMA). This work constructs a Bayesian hierarchical model for predicting groundwater depth from sparse monitoring data in three Central Valley counties. We apply this model to generate 300 m resolution monthly groundwater depth estimates for drought years 2013-2015, and compare our smoothed groundwater depth map to smoothed rasterized maps published by the CA Department of Water Resources. Finally, we quantify uncertainty in groundwater depth predictions that are made by imputing missing well data and interpolating predictions across the study domain, which is helpful in directing future sampling efforts towards areas with high uncertainty. The BHM model accurately captures the spatiotemporal pattern in groundwater depth, as evidenced by 94.54% of withheld test samples' true depth being covered by the 95% prediction interval drawn from the BHM posterior distribution. The model converged despite a very sparse dataset, demonstrating broad applicability for evaluating changes in regional groundwater depth as required by SGMA. Depth prediction intervals can also help prioritize future groundwater depth sampling activity and increase the utility of groundwater depth maps in total storage predictions by enabling sensitivity analysis. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	Bayesian hierarchical model; environmental policy; groundwater; uncertainty
语种	英语
scopus关键词	Digital storage; Drought; Forecasting; Groundwater; Hierarchical systems; Landforms; Sensitivity analysis; Uncertainty analysis; Water management; Bayesian hierarchical model; Department of Water Resources; Fresh water resources; Posterior distributions; Prediction interval; Regional groundwater; Spatiotemporal patterns; Sustainable groundwater management; Groundwater resources; data set; groundwater; quantitative analysis; uncertainty analysis; water depth; water resource; California; Central Valley [California]; United States
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153867
作者单位	Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, United States; Department of Statistical Science, Baylor University, Waco, TX 76798, United States
推荐引用方式 GB/T 7714	Quay A.N.,Hering A.S.,Mauter M.S.. Quantifying uncertainty in groundwater depth from sparse well data in the California Central Valley[J],2020,15(8).
APA	Quay A.N.,Hering A.S.,&Mauter M.S..(2020).Quantifying uncertainty in groundwater depth from sparse well data in the California Central Valley.Environmental Research Letters,15(8).
MLA	Quay A.N.,et al."Quantifying uncertainty in groundwater depth from sparse well data in the California Central Valley".Environmental Research Letters 15.8(2020).