Integrated watershed management (IWM) is critical for ensuring sustainable water resource use, particularly in data-scarce regions where limited hydrological information hinders effective planning and decision-making. This study proposes a comprehensive framework for IWM tailored to such environments, leveraging machine learning techniques to reconstruct incomplete streamflow datasets. Using the completed data, a flow duration curve (FDC) is developed to characterize the watershed’s flow regime. Statistical analyses are then applied to assess water availability and variability across temporal and spatial scales. In parallel, a stakeholder mapping process is conducted to ensure inclusive decision-making, enabling local communities, policymakers, and other relevant actors to participate in strategy development, validation, and implementation. The proposed framework also includes a structured approach for monitoring and evaluating the effectiveness of implemented strategies, ensuring adaptive management over time. This integrative approach aims to bridge data gaps while fostering resilient and participatory watershed governance.
Mahinay et al. (2025) studied this question.