Groundwater storage loss in the central valley analysis using a novel method based on in situ data compared to GRACE-derived data

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Michael D. Stevens , Saul G. Ramirez , Eva-Marie H. Martin , Norman L. Jones , Gustavious P. Williams , Kyra H. Adams , Daniel P. Ames , Sarva T. Pulla
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引用次数: 0

Abstract

We estimate long-term groundwater storage loss in California's Central Valley (CV) using a novel data imputation method that combines in situ data with Earth Observations to generate temporally and spatially interpolated groundwater elevations. We combine these data with storage coefficient maps to produce time series of groundwater volume changes which compare well with previously published groundwater storage change estimates for the valley. We also compare our results to groundwater storage changes we calculated using Gravity Recovery and Climate Experiment (GRACE) mission data and show that the two storage estimates are well correlated, but the GRACE volume estimates are lower due the well-known “leakage” effect. While other researchers have accounted for leakage by scaling the GRACE results using various factors and assumptions, our method demonstrates a direct method for calibrating GRACE estimated groundwater change, which can then be applied to future GRACE results in the CV with confidence.
用一种基于原位数据与grace数据相比较的新方法分析中央谷地地下水储存损失
本文采用一种新颖的数据代入方法,将现场数据与地球观测数据相结合,生成时间和空间内插的地下水高度,估算了加州中央山谷(CV)地下水的长期储存损失。我们将这些数据与储存系数图结合起来,得出地下水体积变化的时间序列,与之前公布的山谷地下水储存变化估计值进行了很好的比较。我们还将我们的结果与使用重力恢复和气候实验(GRACE)任务数据计算的地下水储存量变化进行了比较,结果表明,这两个储存量估算值具有良好的相关性,但由于众所周知的“泄漏”效应,GRACE体积估算值较低。虽然其他研究人员通过使用各种因素和假设缩放GRACE结果来解释泄漏,但我们的方法展示了一种校准GRACE估计地下水变化的直接方法,然后可以放心地将其应用于CV中的未来GRACE结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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