Adaptive Technology Transmission for Climate-Resilient Agricultural Water Management: Integrating Hydrological Processes, Field Intelligence, and Policy Innovation

Agricultural water management is increasingly challenged by climate variability, resource degradation, and the limitations of conventional data-driven and model-centric approaches. Although advances in hydrological modelling, remote sensing, and artificial intelligence have improved analytical capabilities, their applicability remains constrained in heterogeneous and data-scarce environments where field-level variability governs system behaviour. This study proposes an Adaptive Technology Transmission (ATT) framework that integrates hydrological processes, field-based experiential knowledge, and policy dimensions into a unified and operational approach. Drawing on multi-decadal field evidence across diverse agro-ecological settings—including watershed systems, forested catchments, and irrigated landscapes—the study demonstrates that water management outcomes are inherently dynamic and evolve through iterative interactions among design, implementation, and feedback mechanisms. A key contribution is the introduction of the Ground Intelligence Index (G-index), which captures field-derived knowledge as a complementary dimension to conventional data-driven metrics. The integration of ATT with emerging technologies provides a pathway for developing resilient, context-specific, and scalable water management strategies. The study highlights the need to move beyond linear “lab-to-land” paradigms toward adaptive, multi-directional systems that effectively bridge scientific analysis with ground intelligence. The proposed framework offers a robust foundation for advancing climate-resilient agricultural water management and informing policy innovation in complex and variable environments.