title: Integrated Catchment Rainwater Harvesting Model Using GIS –Machine Learning Technique for Agricultural Use Under Climate Change
Creators: name: Al-Hasani, B
Publisher: name: Liverpool John Moores University
Year: 2025

description: The impact of global climate change on water resources is a growing concern globally therefore, the transition to the approach of water resources management and water sustainability technologies such as rainwater harvesting (RWH) presents a promising solution to mitigate water challenges. This study aims to introduce a comprehensive new methodology that leverages various technologies and data sources by integrating multiple models and two climate change scenarios to identify suitable sites for RWH and the impact on crop water requirements for the near and far future. Two locations have been studied, the first one located in a semi-arid region, Iraq and the second one located in a humid environment, the UK. The integrated mathematical models first consist of downscaling of rainfall, minimum, maximum temperatures and sunshine hours on a small scale and were developed based on historical data. Then future climate projections under two greenhouse emissions SSP2-4.5 (intermediate) and SSP5-8.5 (very high) were generated. The second step is using remote sensing (RS) and geographic information systems (GIS) to collect and process geospatial data that were incorporated into Analytical Hierarchy Process (AHP) as a decision-making tool to assess and rank potential RWH locations based on multiple criteria, helping to prioritize the most suitable sites. To account for evaporation loss from the harvested water an artificial neural network model has been developed using the historical climate variables mentioned above. For assessing the impact of RWH on selected crops (wheat, barley, and sunflower) the irrigation water requirements have been estimated using CROPWAT 8.0 software. The integrated model showed a good performance as evidenced by low probability for the downscaling of future variables (P values range from 0.745-1.0 and the assessment was a good to perfect fit for all climatic variables) and high correlation of R2 95.5% - 96.8% for evaporation models. 

The integrated model has been investigated using projections from the downscaling model and applied to the harvested rainwater, evaporation, and crop water requirements. The future outputs from these models for the periods (2031-2050, 2051-2070, 2071, 2090, and 2091-2100) were compared to the baseline period (1980-2010) to identify the climate change impacts. Based on results, the suitable site selection for RWH was divided into very high suitability, high, medium, and low. 
 For Kirkuk, the two scenarios experience some reduction comparing to historical results in RWH suitability area by 34.42, 23.5, 50.5, and 7.7% under SSP2-4.5 &amp; 37.5%, 23.9%, 27.19, and 6.5% under SSP5-8.5. The average evaporation rate was increased by 57.2% and 85.9% under SSP2-4.5, and SSP5-8.5 respectively. Due to the condition above less water becomes available to the crops and all crops require more water to avoid the wilting point. Wheat experienced steady increases in crop water requirements under both scenarios of 13-16% by the later periods. Barley showed a rise in the early periods, with up to 21% Sunflower had the most dramatic increases, reaching 29%. For Oxfordshire, the decrease in RWH suitability area ranges; very high suitability: 11.7%, high suitability:15%, moderate suitability:23% and low suitability around 25.4%. The average evaporation rate was increased by 38 - 70 % and crop water requirements for wheat increased by 8-19% across different periods, with the largest increase under SSP5-8.5. Barley: Showed moderate increases ranging from 5-17% under SSP2-4.5 and SSP5-8.5. Sunflower: had the largest crop water increase, especially under SSP5-8.5, with more than 22%. The research's findings have significant implications for sustainable water resource management in the Kirkuk and Oxfordshire study area. As climate change exacerbates water scarcity, identifying suitable RWH locations becomes crucial for ensuring water availability. This study suggests a novel method for evaluating rainwater harvesting (RWH) while taking the effects of climate change into account to solve these complicated concerns, strive toward enhanced management of water, and ensure water's future sustainability and forecasting future of rainwater patterns under the climate change scenario to avoid future water sacristy risks in the near and far future. 

The approach incorporates the use of remote sensing (RS) and the integration of Geographic Information System (GIS) technology in cooperation with LARS WG to identify suitable RWH sites under climate change scenarios and to forecast future precipitation patterns in the study region to make a better decision regarding future water resources management and to maintain suitable sustainable resolutions regarding water security and helping the managers and engineers in the region to work towards enhancing water resources management and water sustainability and implementing new water policies within the rapid effect of climate change.



 This method is affordable and convenient to address the water scarcity; by anticipating the suitable sites selection for current and future projection and it can help the water resources engineers, mangers local authorities for better planning to choose best decisions for managing water resources by choosing best suitable sites for RWH with the high suitability and avoiding the low suitability places and planning to build suitable RWH structures, like small damns, barriers and tanks.