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Published
2023-06-30
Section
Articles
How to Cite
Data-Driven Strategies for Sustainable Water Resource Management under Climate Change
Sara Khalid
Department of Environmental Sciences, National University of Sciences & Technology (NUST), Islamabad, Pakistan
Hamza Rafiq
Department of Environmental Sciences, National University of Sciences & Technology (NUST), Islamabad, Pakistan
Keywords: Climate change adaptation, water security, hydrological modeling, remote sensing, machine learning
Abstract
Climate change is intensifying hydrological variability, increasing the frequency of droughts, floods, heatwaves, and glacier- and monsoon-driven extremes that threaten water security. In water-stressed countries such as Pakistan, sustainable water resource management increasingly depends on data-driven decision-making that improves forecasting, allocation, efficiency, and risk governance. This article synthesizes strategies that combine remote sensing, IoT sensing, hydrological and climate modeling, machine learning, and decision support systems to strengthen water planning from basin to farm to city. It proposes an integrated framework: (i) build a high-quality data foundation (monitoring, interoperability, and governance), (ii) convert data into actionable intelligence (early warning, scenario planning, and adaptive rules), and (iii) translate intelligence into equitable operations (smart allocation, demand management, and resilient infrastructure). The paper highlights practical use-cases—reservoir operations, irrigation scheduling, groundwater management, and flood risk mapping—while addressing implementation constraints such as data gaps, institutional fragmentation, and capacity limitations. The article concludes that a “data-to-decision” approach can reduce losses, improve reliability, and increase climate resilience when paired with transparent governance, stakeholder participation, and sustained investment in monitoring and analytics.
