A Data-Driven Integrated Water Resource Management Framework for Urban Demand–Supply Optimization Incorporating NRW Reduction and Sustainable Augmentation Techniques

Abstract

Urban water insecurity is one of the greatest problems that are being witnessed in the developing world in the fast growing cities. The joint pressures of population explosion, urbanization, unpredictable climatic conditions and poor infrastructures are all adding to the disparity in supply and demand of water systems in cities. This deficit is further worsened by Non-Revenue Water (NRW) losses (which in Indian towns are generally more than 25 percent), and augmentation strategies are still in disjointed and poorly data-driven forms. In this paper, it will be proposed to apply a multi-component framework of Data-Driven Integrated Water Resource Optimization Framework (IWROF) that will assist in addressing these compounding problems in an organized way. IWROF incorporates Geographic Information System (GIS) spatial analysis, population forecasting models (Arithmetic Increase, Geometric Progression, and CAGR methods), quantification of demand-supply gap, utilizing rooftop rainwater harvesting, wastewater reuse, and groundwater recharge to supplement on a sustainable basis into one decision-making framework. The model is confirmed by a case study of Butibori town, Nagpur District, Maharashtra, India where the present 9.10 MLD is supplied by a source of merely 1.78 MLD which is a severe deficit of 7.32 MLD. The IWROF depicts a projected 32 % rise in water supply efficiency in 2038 by aiming at enhanced water supply by maximizing NRW reduction, demand-side actions, and incremental augmentation. The model could be extrapolated and used in other mid-sized cities that were faced with the same situation of water stress. The paper ends with the policy level suggestions on smart water governance and the prioritization of infrastructure.