Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.

One of the most important measures taken in reducing the impact of climate change resulting from global warming is the production of energy from clean and renewable resources. Hydroelectric power plants are leading renewable energy sources. In this study, the effects of climate change on hydroelectric power plants, a renewable energy source, have been investigated. Dogancay I and II Hydroelectric Power Plants, which are built on the Sakarya River located in the Sakarya basin of Turkey, was selected as a study area. Moreover, the monthly average energy, runoff, and efficiency parameters of hydroelectric power plants and the monthly average precipitation, temperature, and humidity physical parameters of the Sakarya province, which is a working area, were considered. The length of time-series data is 48 months (2015–2018). Analysis of the data was performed with the innovative polygon trend analysis (IPTA) method, which is one of the newest trend analysis methods. When the studies in the literature are examined, the IPTA method is applied to hydroelectric energy data for the first time thanks to this study. Therefore, it is thought that this study will contribute a great deal to the literature. As a result of this study, a generally decreasing trend was observed in IPTA graphs of energy, flow, and efficiency parameters. In terms of the physical parameters of rainfall, temperature, and humidity, there was a decreasing trend in rainfall and humidity graphs and no trend in temperatures was observed.

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.