Analyses of groundwater level in a data-scarce region based on assessed precipitation products and machine learning

• Published in: Groundwater for sustainable development Vol. 26; p. 101299
• Main Authors: El-Azhari, Ahmed, Karaoui, Ismail, Ait Brahim, Yassine, Azhar, Mohamed, Chehbouni, Abdelghani, Bouchaou, Lhoussaine
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024
• Subjects: Data-scarce region; Groundwater level estimation; Machine learning; Morocco; Precipitation products; Precipitation products; Groundwater level estimation; Data-scarce region; Morocco; Machine learning
• ISSN: 2352-801X; 2352-801X
• DOI: 10.1016/j.gsd.2024.101299

Groundwater, a vital natural resource globally, faces challenges related to data scarcity, particularly in data-limited regions. To address this issue in the region of Bahira in Morocco, we developed an innovative approach for estimating groundwater levels by utilizing precipitation products and employed a random forest (RF) machine learning (ML) model to fill data gaps. Our study involved a comprehensive assessment of ten precipitation products, comprising six Reanalysis Precipitation Products (RPPs) and four Satellite-based Precipitation Products (SPPs). We evaluated their performance across various time scales, including daily, monthly, and seasonal, across different topographical classes. The outcomes highlighted the consistency of ERA5-based datasets, boasting daily correlation to values higher than 0.6, whereas monthly and seasonal correlations exceed 0.8, except during summer. GPM-IMERG, MERRA2, and CPC-UPP also demonstrated commendable accuracy, particularly in plain and mountainous areas. Nonetheless, CFSR, CHIRPS, PERSIANN CDR, and TRMM datasets exhibited limitations, particularly in high mountain areas. To address data gaps, we initially explored correlations between RPPs, SPPs, and groundwater data. However, these correlations failed to meet the accuracy standards required for precise predictions. Notably, the strongest correlations were observed in monitoring stations located in mountainous regions, indicating significant aquifer recharge activities in these areas. In the subsequent phase, the Multiple Imputation by Chained Equations (MICE) machine learning-based imputation method served as a valuable tool for estimating groundwater levels in regions where ground observations were insufficient. Our trend analysis yielded significant insights, with approximately 95% of groundwater points displaying negative trends, with a maximum rate of −0.91 m. In contrast, 69% of precipitation stations exhibited negative trends, with a maximum rate of −0.06 mm. Our approach offers a promising potential to address the challenges associated with the scarcity of groundwater and precipitation data, making it a valuable tool for the assessment, monitoring, and management of groundwater resources. [Display omitted] •A thorough analysis to identify the most reliable precipitation data products.•Machine learning to estimate groundwater level and variability.•A promising approach to fill gaps in groundwater data.•A useful tool for groundwater research in data-scarce regions.