Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network

Groundwater storage anomalies (GWSA) study requires the use of hydro‐climatic models coupled with Gravity Recovery and Climate Experiment climate (GRACE) and GRACE Follow‐On (GRACE‐FO) Terrestrial Water Storage experiments. Understanding groundwater storage anomalies in the research area is difficult due to the restricted hydro‐meteorological data availability, especially, groundwater levels, changes in groundwater storage, and budgetary and technical constraints. No such investigation of GWS anomalies prior to this study in this Basin was conducted. The research focuses on GWSA investigations in the basin employing Global Land Data Assimilation Systems and Artificial Neural Networks‐Back Propagation Algorithm techniques are required. The ultimate objective of this study was to reduce the GRACE GWS from 1° to 0.25° and close the discontinuity between GRACE/GRACE‐FO observations. The results illustrated that GRACE/GRACE‐FO GWS throughout the basin were overall declining. The GWS anomaly significantly decreased between 2011 and 2013, with the highest monthly depletion in March 2011 (−158 mm). In contrast, from mid‐June to mid‐October, the maximum positive value varied from 20.80 mm in the southernmost basin to 39.49 mm in the central‐southeastern basin. The basin's eastern, central‐southeastern, northern, and southern areas displayed positive monthly GWS trends. The entire dry season has been influenced by negative anomalies. Hence, the identification of GWS variations and a comprehensive understanding of their effects provided valuable scientific insights. Identifying and analyzing anomalies in groundwater storage (GWS) can provide valuable information. This data can be efficiently used to develop appropriate regulatory frameworks, which may contribute to the long‐term viability of the availability of groundwater. 摘要 地下水储存异常 (GWSA)研究需要使用水文‐气候模型、重力恢复和气候实验 (GRACE)、以及GRACE后续 (GRACE‐FO)陆地水储存实验。由于水文‐气象数据的可用性有限, 特别是地下水位、地下水储量变化以及预算和技术限制, 理解研究区域的地下水储量异常一事十分困难。在裂谷盆地进行这项研究之前, 还没有关于该盆地GWSA的此类调查。本研究采用全球陆地数据同化系统和人工神经网络‐反向传播算法技术, 对该盆地进行GWSA调查。本研究的最终目标是将 GRACE的地下水储存从1°降低到0.25°, 并消除GRACE/GRACE‐FO观测之间的不连续性。结果表明, 整个盆地的GRACE/GRACE‐FO的地下水储存总体呈下降趋势。2011年至2013年间, GWSA显著下降, 2011年3月月度损耗最高(−158毫米)。相比之下, 6月中旬至10月中旬, 最大正值变化范围从盆地最南端的20.80毫米到盆地中东南部的39.49毫米。该盆地的东部、中东南部、北部和南部地区显示出月度地下水储存的积极趋势。整个旱季都受到了负异常的影响。因此, 地下水储存差异的识别和对其影响的全面理解提供了宝贵的科学见解。识别和分析地下水储存异常能提供有价值的信息。这些数据能有效地用于制定适当的监管框架, 进而为地下水可用量的长期可行性作贡献。. Resumen El estudio de anomalías en el almacenamiento de agua subterránea (GWSA) req