Wildfires are increasingly reshaping hydrological processes, with profound implications for post-fire water availability and water quality. This session invites contributions that advance wildfire hydrology through innovative remote sensing and in-situ observations of fire disturbance, coupled with simulation tools leveraging statistical, physics-based, and AI models. We particularly welcome studies that integrate multi-source satellite data to capture fire behavior, vegetation recovery, and hydroclimatic variability, and translate these signals into improved predictions of streamflow, sediment transport, and water quality degradation. Emphasis will be placed on cross-scale modeling frameworks and reproducible approaches that enhance sensitivity to fire disturbance. We also encourage submissions highlighting applications that inform community recovery initiatives, including decision-support tools for water utilities, watershed managers, and policy stakeholders. By bridging observations, models, and actionable insights, this session aims to foster interdisciplinary dialogue and accelerate innovation in understanding and managing post-fire hydrological systems under a changing climate.

• GC - Global Environmental Change
• NH - Natural Hazards