A Novel Rainfall Classification for Mapping Rainwater Harvesting: A Case Study in Kalar, Iraq

Abstract

Increasing water demand driven by population growth and climate change strains water resources, especially in arid regions. The effectiveness of rainwater harvesting (RWH) as a viable solution is contingent upon the meticulous selection of appropriate sites. Contemporary efforts have increasingly utilized Geographic Information Systems (GIS) and remote sensing technologies to optimize the identification of ideal locations for implementing RWH infrastructure. However, inconsistencies in rainfall classification methodologies can compromise the accuracy of the resulted suitability maps. Consequently, a standardized approach to grading rainfall depth for mapping RWH sites becomes imperative. This study presents an innovative rainfall classification method tailored for both micro and macro catchment areas, offering a reliable and adaptable approach to rainfall analysis. By refining classification criteria, this method aims to improve the consistency and precision of RWH mapping, addressing a gap in existing methodologies and providing a more standardized approach. Through the application of FAHP and Fuzzy overlay techniques in ArcGIS 10.4, the study compares traditional rainfall classification with the proposed new classification method to assess RWH suitability in Kalar. The comparison highlights that the new rainfall classification-based map yielded higher accuracy and realism compared to traditional methods.