The aftermath of a wildfire often brings challenges that are just as dangerous as the fire itself. Once vegetation is burned away, the landscape is left exposed and unstable. Intense rainfall can trigger destructive mudslides and flash floods, while ash and debris are swept into rivers and reservoirs, threatening drinking water supplies. In some cases, extreme heat alters the soil, creating a hardened surface that repels water, dramatically increasing runoff and flood risks for years to come.
As these secondary effects become more common and severe, the need for tools that help anticipate and manage them has grown urgent. Local water managers, emergency planners, and environmental agencies are all seeking better ways to prepare for what comes after the fire. HydroFlame aims to fill that gap.
A Smarter Way to Plan for Post-Fire Water Challenges
HydroFlame was developed by a research team led by Adnan Rajib at the University of Texas at Arlington, with the goal of shifting wildfire response from reactive to proactive. The tool is built around three integrated components, each addressing a different facet of the wildfire–water connection.
The first is a Historical Fire Impact Viewer, a publicly accessible interface that lets users explore how previous wildfires have affected streamflow and sediment levels in specific watersheds. For example, the tool’s pilot site in Montana’s Clark Fork Basin allows users to visualize how past fires altered water dynamics over time.
Next is the “What-If” Scenario Builder, which helps planners and water managers simulate the possible hydrologic impacts of future wildfires in locally important areas. This forward-looking feature is especially valuable for communities aiming to prioritize risk zones and prepare infrastructure accordingly.
The third, and most advanced, component is a Near-Real-Time Forecasting Tool. It uses up-to-date satellite data to generate weekly forecasts of water flow and quality in areas impacted by ongoing fires. This allows for timely interventions when and where they’re needed most.
Together, these three components offer a layered approach: looking back at past events, modeling future possibilities, and responding to real-time conditions as fires unfold.
The Data Powering HydroFlame
What sets HydroFlame apart is its ability to integrate a wide range of Earth observation data into a unified, actionable system. At its core, the tool pulls from multiple NASA satellite missions to track key environmental conditions before, during, and after a wildfire.
Burn severity data comes from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellites. Soil moisture levels are monitored using data from the Soil Moisture Active Passive (SMAP) mission. Changes in vegetation cover are captured from additional Earth-observing instruments. These satellite observations are then combined with historical stream gauge data and run through hydrologic models to simulate how fire-affected landscapes respond to rain and runoff.
One of the tool’s standout features is its ability to compensate for sparse or missing data—an issue that often limits watershed monitoring.
To address this, HydroFlame uses a machine learning algorithm that fills in gaps where physical stream gauges don’t exist. This approach allows for reliable forecasting even in remote or ungauged areas, giving water managers a broader, more complete view of potential risks across an entire watershed up to two weeks in advance.
From Simulation to Real-World Success
Turning a model into a usable, real-world tool is no small feat, but HydroFlame has already proven itself in a high-stakes situation. During the devastating January 2025 wildfires in Los Angeles, the research team tested the tool.
As the fires spread, they used incoming satellite data to run real-time simulations, tracking how vegetation loss, changing soil moisture, and burn severity were shaping conditions on the ground. By the end of the month, HydroFlame had generated forecasts identifying specific areas like Altadena and Sierra Madre as being at high risk for mud and debris flows.
Those predictions held up. In early February, severe mudflows struck both neighborhoods following the Eaton Fire—just as the model had projected. This was a major validation of HydroFlame’s forecasting accuracy and real-world value.
Building for the Future
Building on that success, the team is now working with partners at NASA’s Jet Propulsion Laboratory, the University of California, Merced, and Los Angeles County to further refine the tool. One key effort involves fieldwork to install stream sensors during California’s rainy season. These sensors will collect direct measurements of sediment levels, helping to calibrate the satellite-based models and reduce forecast uncertainty.
This step, known as ground-truthing, is critical. By comparing satellite predictions to actual, on-the-ground data, researchers can fine-tune the system, making it more reliable and robust for future use.
For local officials like Morgan Valliant from Missoula Parks and Recreation, that kind of practical accuracy is exactly what’s needed. Knowing in advance when a stream—whether it supports a trout fishery or supplies drinking water—is likely to be overwhelmed by post-fire debris gives communities time to act. It turns emergency response into strategic preparedness.
Conclusion
HydroFlame offers a practical and much-needed tool in the face of increasing wildfire risks. By blending NASA satellite data with advanced modeling and AI, it equips decision-makers with the insights they need to protect vital water systems before disaster strikes. Its success in Los Angeles highlights its potential as a nationwide resource.
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