Assessing the Influence of Socio-Economic and Environmental Drivers on Water Availability Ecosystem Services: A Geographically Weighted Regression approach

Addressing water availability ecosystem service in human water resources is fundamental for the development of strategies that encompass sustainable pathways in a climate changing era. The total amount of water available for human activities is essential to economic development, influencing food production, power generation, human well-being, and healthy environments. Although key drivers of water availability, such as precipitation and land use, are well established in the literature, other potentially influential factors remain underexplored, including population density, GDP per capita, the human development index (HDI), water governance indicators, and total water demand. In this work, we developed a new concept for water management through the lens of ecosystem services approach. This framework emphasizes understanding the socio-economic and environmental drivers that influence water yield, aiming to enhance human well-being by promoting best practices in water management. This perspective enables a deeper understanding of the mechanisms influencing water availability beyond conventional assessment methods, while prioritizing management and restoration strategies. In this context, hydroinformatics enables advanced spatial analysis for examining water availability and ecosystem services. By integrating data analytics and machine learning (Random Forest) with traditional modeling approaches, it is possible to uncover complex relationships between socio-economic and environmental drivers and their spatial influence on water resources. At the same time, combining with Geographically Weighted Regression (GWR) tool, it is possible to analyze how socio-economic and environmental factors influence water availability ecosystem services across different geographic regions. This is possible because GWR captures spatial variability by estimating local rather than global relationships. Finally, this methodology will be applied globally using Level 5 basins from HydroATLAS, allowing the identification of regional heterogeneities, cross-scale patterns, and dominant local drivers of water availability. This global application provides a robust basis for comparing basins and supporting targeted management and policy interventions. The results are expected to provide a global understanding of how human and environmental factors jointly regulate water availability, supporting the design of more adaptive, equitable, and resilient water management strategies.