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基于玄武岩含水层系统的降尺度处理,利用 GRACE 和 GLDAS 数据进行地下水监测。

Groundwater Monitoring Using GRACE and GLDAS Data after Downscaling Within Basaltic Aquifer System.

机构信息

Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, 440010, India.

出版信息

Ground Water. 2020 Jan;58(1):143-151. doi: 10.1111/gwat.12929. Epub 2019 Aug 20.

DOI:10.1111/gwat.12929
PMID:31359409
Abstract

Gravity Recovery and Climate Experiment (GRACE) satellite mission is ground-breaking information hotspot for the evaluation of groundwater storage. The present study aims at validating the sensitivity of GRACE data to groundwater storage variation within a basaltic aquifer system after its statistical downscaling on a regional scale. The basaltic aquifer system which covers 82.06% area of Maharashtra state in India, is selected as the study area. Five types of basaltic aquifer systems with varying groundwater storage capacities, based on hydrologic characteristics, have been identified within the study area. The spatial and seasonal trend analysis of observed in situ groundwater storage anomalies (ΔGWSano) computed from groundwater level data of 983 wells from the year 2002 to 2016, has been performed to analyze the variation in groundwater storages in the different basaltic aquifer system. The groundwater storage anomalies (ΔGWSDano) have been derived from GRACE Release 05 (RL05) after removing the soil moisture anomaly (ΔSMano) and canopy water storage anomaly (ΔCNOano) obtained from Global Land Data Assimilation System (GLDAS) land surface models (NOAH, MOSAIC, CLM and VIC). The artificial neural network technique has been used to downscale the GRACE and GLDAS data at a finer spatial resolution of 0.125°. The study shows that downscaled GRACE and GLDAS data at a finer spatial resolution is sensitive to seasonal groundwater storage variability in different basaltic aquifer systems and the regression coefficient R has been found satisfactory in the range of 0.696 to 0.818.

摘要

重力恢复和气候实验 (GRACE) 卫星任务是评估地下水储量的开创性信息热点。本研究旨在验证经过统计降尺度处理后,GRACE 数据在玄武岩含水层系统内对地下水储量变化的敏感性,该研究区域位于印度马哈拉施特拉邦,该地区的玄武岩含水层系统覆盖了 82.06%的区域。根据水文特征,在研究区域内确定了五种具有不同地下水储量的玄武岩含水层系统。对 2002 年至 2016 年期间来自 983 口井的地下水水位数据计算得出的观测到的地下水储量异常(ΔGWSano)的空间和季节趋势进行了分析,以分析不同玄武岩含水层系统中地下水储量的变化。地下水储量异常(ΔGWSDano)是从 GRACE 发布 05 版本(RL05)中提取的,在去除了从全球陆地数据同化系统(GLDAS)陆地表面模型(NOAH、MOSAIC、CLM 和 VIC)中获得的土壤湿度异常(ΔSMano)和冠层水存储异常(ΔCNOano)后得到的。本研究采用人工神经网络技术将 GRACE 和 GLDAS 数据降尺度到更精细的空间分辨率为 0.125°。研究表明,在更精细的空间分辨率下,降尺度后的 GRACE 和 GLDAS 数据对不同玄武岩含水层系统中季节性地下水储量变化敏感,回归系数 R 在 0.696 到 0.818 之间是令人满意的。

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