Modeling-based Modernization of Irrigation and Drainage for Agricultural Water Productivity Enhancement under Global Change

Irrigation and drainage systems are central to global food security but face increasing pressure from water scarcity, climate variability, and rising demand for agricultural productivity. Modeling and simulation tools have become essential for analyzing, optimizing, and modernizing water management systems across scales. This review synthesizes evidence from ۴۵ peer-reviewed studies (۲۰۱۹-۲۰۲۴) to evaluate the roles of classical process-based models (HYDRUS, DRAINMOD, SWAT, and AquaCrop) and emerging digital frameworks (artificial intelligence, Internet of Things, and digital twins) in irrigation and drainage planning, modernization, and water productivity enhancement. The synthesis demonstrates that classical models remain foundational for system design and process understanding, while modern digital frameworks enable real-time monitoring, predictive control, and adaptive management. Quantitative synthesis indicates that modeling-based interventions are associated with median irrigation efficiency improvements of ۲۱% (range: ۸-۴۲%) and crop water productivity gains of ۱۸% (range: ۶-۳۵%), with higher effectiveness observed at field scale (۲۳% median efficiency gain) compared to basin scale (۱۲%). Quality assessment revealed that only ۲۷% of studies achieved high reliability ratings, with key limitations including incomplete validation reporting (۶۲%) and insufficient uncertainty analysis (۷۱%). The review identifies five modernization pathways and demonstrates that the complementary integration of classical and digital models yields greater outcomes than single-model applications. Unlike previous reviews focusing on isolated modeling domains, this study provides an integrated synthesis across spatial scales with systematic quality assessment and semi-quantitative evidence synthesis to guide model selection, technology adoption, and policy development.

کلیدواژه ها:

Irrigation Modernization ، Drainage systems ، Water productivity ، Hydrological modelling ، Digital twins ، Artificial intelligence