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Abstract
Abstract The water supply in the Gaza Strip substantially depends on the groundwater resource of the Gaza coastal aquifer. The climate changes and the over-exploiting processes negatively impact the recovery of the groundwater balance. The climate variability is characterized by the decline in the precipitation of −5.2% and an increase in temperature of +1 °C in the timeframe of 2020–2040. The potential evaporation and the sunshine period are expected to increase by about 111 mm and 5 hours, respectively, during the next 20 years. However, the atmosphere is predicted to be drier where the relative humidity will fall by a trend of −8% in 20 years. The groundwater abstraction is predicted to increase by 55% by 2040. The response of the groundwater level to climate change and groundwater pumping was evaluated using a model of a 20-neuron ANN with a performance of the correlation coefficient (r)=0.95–0.99 and the root mean square error (RMSE)=0.09–0.21. Nowadays, the model reveals that the groundwater level ranges between −0.38 and −18.5 m and by 2040 it is expected to reach −1.13 and −28 m below MSL at the northern and southern governorates of the Gaza Strip, respectively.
Highlights
Groundwater is the dominant water resource supplier for more than half of the domestic and agricultural needs on Earth (Anderson 2017)
The rainfall model reveals that the rainfall declines by a yearly average trend of about À0.26%, the average yearly rainfall for the Gaza Strip over the 20 years is assigned to 370 mm
The period 2020–2040 is critical for climate and water security in the Gaza Strip, as monthly average precipitation will be assigned to about 21–33 mm by 2040, as well the temperature is expected to increase by þ1 °C by 2040
Summary
Groundwater is the dominant water resource supplier for more than half of the domestic and agricultural needs on Earth (Anderson 2017). The abuse of the groundwater through high over-pumping processes causes severe and subnational depression in the groundwater table to levels below the mean sea level (MSL) and this, in turn, causes prolonged salinization, irreversible economic losses, and a serious threat to food security (Zekri et al 2017). In this scope, groundwater modeling-based management becomes crucial to evaluate the groundwater level variability to develop effective mitigation strategies and efficient management policies in order to preserve the groundwater resources sustainably (Gladden & Park 2016; Karimi et al 2019). The artificial neural networks (ANNs) are the most promising and competitive algorithms among the AI algorithms which are widely utilized in the applications of groundwater modeling
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