GIS-Based Modeling of Shallow Groundwater Potential in Arid Regions under changing Climate and Future Water Demands: a case study of Al-Madinah, Saudi Arabia.
Date
2024-06-14
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Publisher
University of York
Abstract
Investigating water resources in arid regions is essential for managing water scarcity's unique challenges in these environments. GIS and remote sensing approaches have been applied here to model and analyse three main aspects: mapping potential groundwater zones, assessing climate change impacts, and examining future water needs under socio-economic scenarios.
A fuzzy-frequency ratio model and a logistic regression model successfully delineated the potential groundwater zones. An ensemble of models performed well (Best model AUC = 0.943). Soil type was the most important factor in driving both models. The spatial distribution of very high potential groundwater areas in Al-Madinah is primarily compatible with volcanic lava areas with Lithosols and Calcic Yermosols soils.
Assessing climate change under IPCC RCPs scenarios (2021-2100, RCP4.5 and RCP8.5) revealed that the temperature and Reference Evapotranspiration (ET0) rate of Al-Madinah is expected to continue to increase although rainfall may also increase by around 18.74% or 22.81 mm (2081-2100, RCP8.5) compared to 1970-2018. Such an increase might not have a pronounced effect on enhancing groundwater availability due to raising temperature (2°C) and ET0 (359.70 mm) with a higher probability of drought events indicated by the Standardised Precipitation Evapotranspiration Index (SPEI). Increases with higher water accumulation opportunities are predicted at 2081-2100 (RCP8.5). However, changes in potential groundwater zones using the Topographic Wetness Index (TWI) weighted by Rainfall are expected to show a small quantitative increase with the greatest addition of suitable potential zones also estimated for 2081-2100 under RCP8.5 (logistic regression = 19296km²)
Analysing water needs in Al-Madinah city under the Impact of Population, Affluence, and Technology (IPAT) model confirmed that population was the most important factor in explaining water consumption trends. Water demand is projected to increase by up to 28% under IPCC_ SSP scenarios. These findings should aid in developing water resources management strategies and sustainable decision-making.
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Keywords
GIS, Groundwater, Fuzzy logic, Frequency ratio, Logistic regression, LULC, Random Forest, IPAT model, Water demand