Author
Listed:
- Youssef Kassem
(Department of Mechanical Engineering, Engineering Faculty, Near East University, via Mersin 10, Nicosia 99138, Cyprus
Department of Civil Engineering, Civil and Environmental Engineering Faculty, Near East University, via Mersin 10, Nicosia 99138, Cyprus
Energy, Environment, and Water Research Center, Near East University, via Mersin 10, Nicosia 99138, Cyprus
Research Center for Science, Technology, and Engineering (BILTEM), Near East University, via Mersin 10, Nicosia 99138, Cyprus)
- Hüseyin Gökçekuş
(Department of Civil Engineering, Civil and Environmental Engineering Faculty, Near East University, via Mersin 10, Nicosia 99138, Cyprus
Energy, Environment, and Water Research Center, Near East University, via Mersin 10, Nicosia 99138, Cyprus)
- Aşkın Kiraz
(Ataturk Faculty of Education, Near East University, via Mersin 10, Nicosia 99138, Cyprus)
- Abdalla Hamada Abdelnaby Abdelnaby
(Department of Civil Engineering, Civil and Environmental Engineering Faculty, Near East University, via Mersin 10, Nicosia 99138, Cyprus)
Abstract
The primary objective of this study is to assess the techno-economic feasibility of an innovative solar energy generation system with a rainwater collection feature to generate electrical energy and meet irrigation needs in agriculture. The proposed system is designed for an agricultural area (Gonyeli, North Cyprus) with high solar potential and limited rainfall. In the present study, global rainfall datasets are utilized to assess the potential of rainwater harvesting at the selected site. Due to the lack of the measured rainfall data at the selected site, the accuracy of rainfall of nine global reanalysis and analysis datasets (CHIRPS, CFSR, ERA5-LAND, ERA5, ERA5-AG, MERRA2, NOAA CPC CMORPH, NOAA CPC DAILY GLOBAL, and TerraClimate) are evaluated by using data from ground-based observations collected from the Meteorological Department located in Lefkoşa, Northern Cyprus from 1981 to 2023. The results demonstrate that ERA5 outperformed the other datasets, yielding a high R-squared value along with a low mean absolute error (MAE) and root mean square error (RMSE). Based on the best dataset, the potential of the rainwater harvesting system is estimated by analyzing the monthly and seasonal rainfall patterns utilizing 65 different probability distribution functions for the first time. Three goodness-of-fit tests are utilized to identify the best-fit probability distribution. The results show that the Johnson and Wakeby SB distributions outperform the other models in terms of fitting accuracy. Additionally, the results indicate that the rainwater harvesting system could supply between 31% and 38% of the building’s annual irrigation water demand (204 m 3 /year) based on average daily rainfall and between 285% and 346% based on maximum daily rainfall. Accordingly, the system might be able to collect a lot more water than is needed for irrigation, possibly producing an excess that could be stored for non-potable uses during periods of heavy rainfall. Furthermore, the techno-economic feasibility of the proposed system is evaluated using RETScreen software (version 9.1, 2023). The results show that household energy needs can be met by the proposed photovoltaic system, and the excess energy is transferred to the grid. Furthermore, the cash flow indicates that the investor can expect a return on investment from the proposed PV system within 2.4 years. Consequently, the findings demonstrate the significance of this system for promoting resource sustainability and climate change adaptation. Besides, the developed system can also help reduce environmental impact and enhance resilience in areas that rely on water and electricity.
Suggested Citation
Youssef Kassem & Hüseyin Gökçekuş & Aşkın Kiraz & Abdalla Hamada Abdelnaby Abdelnaby, 2025.
"Integrating Rainwater Harvesting and Solar Energy Systems for Sustainable Water and Energy Management in Low Rainfall Agricultural Region: A Case Study from Gönyeli, Northern Cyprus,"
Sustainability, MDPI, vol. 17(18), pages 1-42, September.
Handle:
RePEc:gam:jsusta:v:17:y:2025:i:18:p:8508-:d:1755240
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