Anthropogenic activities have profoundly altered the hydrological cycle, particularly in heavily modified systems. Human interventions such as reservoirs, dams, drainage networks, urban expansion, infrastructure development, deforestation/afforestation, water abstraction, and wastewater discharge have reshaped natural processes and management practices. Under climate change, these alterations further shift the frequency, magnitude, and seasonality of hydroclimatic extremes, potentially amplifying risks for societies and ecosystems.

Despite advances in hydrological science and technology, our understanding of human–water interactions across scales remains limited. Challenges stem from the complexity and uncertainty in quantifying human influences, the scarcity of long-term records, and the limitations of conventional models often designed for natural catchments under assumption of stationarity. Thus, the reliability of hydrological forecasting in human-influenced systems is compromised. Given the large populations exposed to water-driven hazards, there is an urgent need for intensified research and innovation.

This session will highlight recent advances in understanding and forecasting hydroclimatic extremes in human-influenced catchments. We invite abstracts on (but not limited to):
• Development and application of statistical, process-based, machine learning, or hybrid models to forecast hydrological variables (e.g., meteorological forcings, catchment states, and responses) at multiple scales
• Advances in data acquisition capturing human activities (or proxies), including in-situ monitoring, remote sensing, and unconventional sources such as social media, with innovations in data integration and analytics
• Novel quantitative methods to assess diverse human impacts on hydrological processes and water cycle
• Coupled human-natural system modelling and scenario analysis to capture feedbacks between socio-economic drivers and hydrological processes
• Impact-based risk assessments of water-related hazards, spanning economic, health, social, and environmental dimensions
• Uncertainty quantification and risk analysis of singular and compound hydro-hazards under non-stationarity
• Enhanced visualization and communication for early warnings and short- to long-range predictions, including projections of unprecedented extremes
• Integration of nature-based solutions and adaptive management strategies into forecasting and risk reduction frameworks