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Remote Sensing Estimation of Shallow and Deep Aquifer Response to Precipitation-Based Recharge Through Downscaling
Water Resources Research ( IF 4.6 ) Pub Date : 2024-12-04 , DOI: 10.1029/2024wr037360 Ikechukwu Kalu, Christopher E. Ndehedehe, Vagner G. Ferreira, Sreekanth Janardhanan, Matthew Currell, Russell S. Crosbie, Mark J. Kennard
Water Resources Research ( IF 4.6 ) Pub Date : 2024-12-04 , DOI: 10.1029/2024wr037360 Ikechukwu Kalu, Christopher E. Ndehedehe, Vagner G. Ferreira, Sreekanth Janardhanan, Matthew Currell, Russell S. Crosbie, Mark J. Kennard
The Gnangara groundwater system is a highly productive water resource in southwestern Australia. However, it is considered one of the most vulnerable groundwater systems to climate change, due to consistent declines in precipitation and recharge, and regional climate models project further declines into the future. This study introduces a new framework underpinned by machine learning techniques to provide reliable estimates of precipitation-based recharge over the whole Perth Basin (including the Gnangara system). By combining estimates of baseflow, groundwater evaporation, and extraction, groundwater recharge was estimated over the Perth (testing site) and Gnangara (calibration site) systems using downscaled Groundwater Storage Anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) mission. The random forest regression (RFR) model was used to downscale the spatial resolution of GRACE to 0.05° (approx. 5 km), providing estimable signals over the relatively small calibration site (∼2,200 km2) in order to discern any meaningful signals from the original GRACE resolution. Our study reveals that downscaled signals from GRACE can be used to provide precipitation-based recharge estimates for groundwater systems accurately. However, the growing impacts of climate change, which has led to sporadic precipitation patterns over Western Australia, can limit the efficiency of satellite remote sensing methods in estimating recharge, especially in deep and complex aquifers.
中文翻译:
通过降尺度遥感估计浅层和深层含水层对降水补给的响应
Gnangara 地下水系统是澳大利亚西南部的高效水资源。然而,由于降水和补给的持续下降,它被认为是最容易受到气候变化影响的地下水系统之一,并且区域气候模型预测未来将进一步下降。本研究引入了一个以机器学习技术为基础的新框架,以提供整个珀斯盆地(包括 Gnangara 系统)基于降水的补给的可靠估计。通过结合对基流、地下水蒸发和提取的估计,使用重力恢复和气候实验 (GRACE) 任务中缩小的地下水储存异常 (GWSA) 估计了珀斯(测试站点)和 Gnangara(校准站点)系统的地下水补给。随机森林回归 (RFR) 模型用于将 GRACE 的空间分辨率降低到 0.05°(约 5 公里),在相对较小的校准站点(∼2,200 km2)上提供可估计的信号,以便从原始 GRACE 分辨率中辨别出任何有意义的信号。我们的研究表明,来自 GRACE 的缩小信号可用于准确地为地下水系统提供基于降水的补给估计。然而,气候变化的影响越来越大,导致西澳大利亚州出现零星的降水模式,这可能会限制卫星遥感方法估算补给的效率,尤其是在深层和复杂的含水层中。
更新日期:2024-12-04
中文翻译:
通过降尺度遥感估计浅层和深层含水层对降水补给的响应
Gnangara 地下水系统是澳大利亚西南部的高效水资源。然而,由于降水和补给的持续下降,它被认为是最容易受到气候变化影响的地下水系统之一,并且区域气候模型预测未来将进一步下降。本研究引入了一个以机器学习技术为基础的新框架,以提供整个珀斯盆地(包括 Gnangara 系统)基于降水的补给的可靠估计。通过结合对基流、地下水蒸发和提取的估计,使用重力恢复和气候实验 (GRACE) 任务中缩小的地下水储存异常 (GWSA) 估计了珀斯(测试站点)和 Gnangara(校准站点)系统的地下水补给。随机森林回归 (RFR) 模型用于将 GRACE 的空间分辨率降低到 0.05°(约 5 公里),在相对较小的校准站点(∼2,200 km2)上提供可估计的信号,以便从原始 GRACE 分辨率中辨别出任何有意义的信号。我们的研究表明,来自 GRACE 的缩小信号可用于准确地为地下水系统提供基于降水的补给估计。然而,气候变化的影响越来越大,导致西澳大利亚州出现零星的降水模式,这可能会限制卫星遥感方法估算补给的效率,尤其是在深层和复杂的含水层中。