INTEGRATING SELF-SUPERVISED AI AND EDGE SENSING FOR REAL-TIME ADAPTIVE WATER RESOURCE GOVERNANCE UNDER CLIMATE-INDUCED HYDROLOGICAL UNCERTAINTY IN TRANSBOUNDARY RIVER BASINS

Climate change has intensified hydrological variability, particularly in transboundary river basins, threatening water security, equity, and cooperation among nations. Traditional water governance mechanisms lack the real-time responsiveness and spatial resolution required for adaptive management in such volatile environments. This paper proposes an integrated framework combining self-supervised artificial intelligence (AI) with edge sensing technologies to deliver real-time, adaptive, and decentralized water governance. The framework aims to enhance predictive hydrological modeling, optimize water allocation, and support stakeholder decision-making across political boundaries. The study leverages recent advancements in self-supervised learning, edge-AI deployment, and distributed sensor networks. A case study application to the Ganges-Brahmaputra-Meghna (GBM) basin illustrates the system's capacity to reduce uncertainty, improve policy responsiveness, and promote sustainable cooperation.