RAPID: Streamflow and water quality response to 2025 Los Angeles wildfire across fire-flood sequences

This RAPID project focuses on water quality dynamics after the 2025 wildfires in Los Angeles (LA). Large fires often trigger landslides and debris flows. Compounding this issue is LA’s history of intense winter rainfalls called atmospheric rivers. Together they present a significant risk of fire-flood-landslides “triple-disaster”. The project will implement a new turbidity monitoring network to record post-fire turbidity levels in surface streams to identify the direct impact of fire on water quality. The team will also estimate char cover and soil burn severity from real-time satellite images obtained during the fire events. The data will be used in AI-driven models to establish the first baseline for future post-fire risk assessments and mitigation strategies. The methodology and scientific evidence resulting from this project can be applied broadly to mitigate downstream impacts of wildfires in other fire-affected regions. It advances NSF’s mission to safeguard the nation’s water through innovative data and AI solutions. This RAPID funding will support a research team to collect critical data to understand the wildfire-induced water quality dynamics in Los Angeles. The research will fulfill three objectives: (1) conduct a 3-month turbidity data collection campaign in LA at strategically selected sites aligned with burn areas and high debris hazard probability to establish the initial post-fire baseline, followed by a 9-month data collection campaign to track turbidity under varying dry and wet conditions, (2) estimate perishable fire data, including char cover and soil burn severity, across the entire fire-affected region in LA, by analyzing high-resolution real-time remote sensing observations collected during and immediately after the fire events, and finally (3) quantify how streamflow and turbidity evolve during potential fire–flood sequences using an AI model and therefore identify drivers that may be controlling the magnitudes and recovery timescale of turbidity toward initial post-fire baseline. These detailed spatiotemporal analyses will not only secure short-lived signatures of post-fire water quality but also pave the way to the first integrated framework for mapping the triggers and timelines of cascading fire-water hazards, which is currently absent for the LA region. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.