Behaviorally informed modeling of daily urban water demand: integrating environmental drivers, mobility, and online search activity

Forecasting urban water demand requires approaches that account for both environmental variability and human behavior. Traditional models rely primarily on climatic drivers and assume stable consumption patterns, overlooking the rapid behavioral shifts triggered by social events, consumers’ attention, and environmental stress. The objective of this study is to develop and evaluate a behaviorally informed, event-aware framework for short-term urban water demand forecasting that explicitly integrates physical and social drivers. The proposed framework utilizes data from weather records, Meta mobility distributions, Google Trends search activity, and demand records from the North Okanagan Valley in British Columbia, Canada. By coupling data-driven modeling with interpretable Machine Learning (ML), the framework identifies environmental thresholds and behavioral triggers influencing consumption. The results underscore that integrating digital behavior indicators with climate ones improves forecasting performance, increasing R 2 from 0.79 to 0.85, while behavioral indicators alone achieve an R 2 of 0.70, highlighting their independent explanatory power. These findings emphasize the value of integrating real-time behavioral proxies into operational forecasting models, supporting more adaptive and socially aware water resource management. The framework is demonstrated using a real-world case study and is designed to be transferable to other urban systems where human activity strongly influences short-term demand dynamics.