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

机译:玄武岩含水层系统缩小规模后使用GRACE和GLDAS数据进行地下水监测

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摘要

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 (Delta GWSano) 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 (Delta GWSDano) have been derived from GRACE Release 05 (RL05) after removing the soil moisture anomaly (Delta SMano) and canopy water storage anomaly (Delta CNOano) 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 degrees. 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.
机译:重力恢复和气候实验(GRACE)卫星任务是用于评估地下水存储的突破性信息热点。本研究旨在验证GRACE数据对玄武岩含水层系统内区域规模的统计缩减后的地下水储量变化的敏感性。研究区域覆盖了印度马哈拉施特拉邦82.06%的玄武岩含水层系统。在研究区域内,已经根据水文特征确定了五种类型的具有不同地下水储存能力的玄武岩含水层系统。根据2002年至2016年间983口井的地下水位数据计算的实地地下水储量异常(Delta GWSano)的空间和季节趋势分析,以分析不同玄武岩含水层系统中地下水储量的变化。去除了从全球土地数据同化系统(GLDAS)地表模型获得的土壤湿度异常(Delta SMano)和冠层蓄水异常(Delta CNOano)之后,地下水存储异常(Delta GWSDano)源自GRACE Release 05(RL05)。 (NOAH,MOSAIC,CLM和VIC)。人工神经网络技术已被用于以0.125度的更精细空间分辨率缩小GRACE和GLDAS数据。研究表明,缩小的GRACE和GLDAS数据在更精细的空间分辨率下对不同玄武质含水层系统中的季节性地下水储量变化敏感,并且回归系数R在0.696至0.818的范围内令人满意。

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  • 来源
    《Ground water 》 |2020年第1期| 143-151| 共9页
  • 作者单位

    Visvesvaraya Natl Inst Technol Dept Civil Engn Nagpur 440010 Maharashtra India;

    Visvesvaraya Natl Inst Technol Nagpur 440010 Maharashtra India;

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