دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 A systematic review of performance assessment in canal irrigation systems: Integrating socio-technical, remote sensing, and AI-driven approaches for a climate-resilient future A systematic review of performance assessment in canal irrigation systems: Integrating socio-technical, remote sensing, and AI-driven approaches for a climate-resilient future 254 276 4115 10.22098/mmws.2025.18343.1683 FA Mohansing Rajaput Ph.D. Scholar, Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru-575025, Karnataka, India Abhilash Ramadasa Scientist ‘C’, National Institute of Hydrology, Hard Rock Regional Centre, Visvesvaraya Nagar, Belagavi – 590019, Karnataka, India 0000-0002-7505-5263 Basavanand M. Dodamani Professor, Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru-575025, Karnataka, India Journal Article 2025 09 17 This systematic review investigates the evolution of performance assessment in canal irrigation systems globally, drawing evidence from Asia, Africa, and Latin America. Adhering to PRISMA guidelines, it synthesized 98 peer-reviewed studies and key organizational reports published between 1990 and 2025, primarily from Scopus and Web of Science. The analysis reveals a clear methodological progression from direct measurements to remote sensing (RS) and agro-hydrological modeling, with Artificial Intelligence (AI) now evidenced as an applied tool in some assessments, not merely a future prospect. A critical insight, however, is that despite these technical advancements, persistent underperformance is primarily rooted in deep-seated non-technical (financial, institutional, social) barriers. The current review highlights a significant gap: the absence of a unified framework systematically integrating these technical and socio-institutional dimensions with forward-looking climate resilience. Our primary contribution is a novel, integrated socio-technical assessment framework designed to bridge this divide. Distinct from previous reviews, the proposed framework explicitly combines the methodological triad, comprehensive socio-institutional analysis, quantifiable climate resilience metrics, and mechanisms to ensure social equity in AI-driven management. This adaptable, multi-scale diagnostic tool offers an actionable blueprint, applicable from local canal management to national policy levels, that accounts for diverse regional data limitations. By enabling more effective problem diagnosis and intervention design, proposed framework provides significant analytical value and actionable lessons for enhancing the productivity, equity, and climate resilience of canal irrigation systems, thereby directly advancing Sustainable Development Goals 2 and 6. This systematic review investigates the evolution of performance assessment in canal irrigation systems globally, drawing evidence from Asia, Africa, and Latin America. Adhering to PRISMA guidelines, it synthesized 98 peer-reviewed studies and key organizational reports published between 1990 and 2025, primarily from Scopus and Web of Science. The analysis reveals a clear methodological progression from direct measurements to remote sensing (RS) and agro-hydrological modeling, with Artificial Intelligence (AI) now evidenced as an applied tool in some assessments, not merely a future prospect. A critical insight, however, is that despite these technical advancements, persistent underperformance is primarily rooted in deep-seated non-technical (financial, institutional, social) barriers. The current review highlights a significant gap: the absence of a unified framework systematically integrating these technical and socio-institutional dimensions with forward-looking climate resilience. Our primary contribution is a novel, integrated socio-technical assessment framework designed to bridge this divide. Distinct from previous reviews, the proposed framework explicitly combines the methodological triad, comprehensive socio-institutional analysis, quantifiable climate resilience metrics, and mechanisms to ensure social equity in AI-driven management. This adaptable, multi-scale diagnostic tool offers an actionable blueprint, applicable from local canal management to national policy levels, that accounts for diverse regional data limitations. By enabling more effective problem diagnosis and intervention design, proposed framework provides significant analytical value and actionable lessons for enhancing the productivity, equity, and climate resilience of canal irrigation systems, thereby directly advancing Sustainable Development Goals 2 and 6. https://mmws.uma.ac.ir/article_4115_ef6b3e4656b31edad8a06309db4f7f36.pdf