As wildfires grow more severe and frequent, fire hazards extend beyond burned forests to altered hydrology and degraded water quality. HydroFlame integrates emerging satellite remote sensing data on droughts and fires with watershed models, providing a one-stop platform that enables end-users to predict, analyze, and visualize the effects of wildfires on water systems. With its Open Science geospatial capabilities and a proven scientific foundation, HydroFlame is the first-of-its-kind platform that makes post-fire water management and decision-making accessible, transparent, and actionable.
Explore Tools HYDRO
Climate Extremes
Altered Flow Conditions
Clean Water Availability
FIRE
Fire Behavior
Recurring Fire
Post Fire Watershed Recovery
HydroFlame
Historical & Near-Real Time Fire-Hydrology Convergence of Fire & Water Data
Watershed Management Decisions
Open Science Geospatial Capabilities
HydroFlame provides open access to its data, models, and tools, enabling researchers and practitioners to collaborate, innovate, and advance the science of fire and water systems.
Convergence of Diverse Data Sources and Types
HydroFlame integrates diverse data types, sources, formats, and resolutions—from satellite remote sensors and geospatial databases—with models of varying complexity to provide a holistic understanding of fire-hydrology interactions.
Powerful Data Visualization and Analytics
HydroFlame offers a suite of data visualization and analysis tools to help researchers and practitioners explore, analyze, and interpret complex fire and water data, fostering data-driven decision-making.
Aid to Watershed Management Decisions
HydroFlame provides end-users with the tools and information needed to predict, analyze, and visualize the effects of wildfires on water systems, enabling informed and timely watershed management decisions.
How HydroFlame Works
HydroFlame uses a process-based hydrologic model to simulate daily streamflow and water quality variations across large river networks. A built-in data-discovery tool continuously tracks potential wildfires from satellite remote sensing. When a fire is detected, the model maps burn areas within watersheds, calculating changes in vegetation, evapotranspiration, and soil moisture—sourced from multiple satellite datasets and adjusted for varying burn severity. These fire-induced alterations are then dynamically integrated into model simulations. Finally, the model is paired with a Machine Learning tool to predict post-fire streamflow and water quality in numerous unmonitored streams and rivers, potentially in near real-time and up to 14 days in advance.
Convergence in HydroFlame
HydroFlame converges three critical dimensions of post-fire watershed management: data on drought, fire, and other climate extremes, watershed modeling and analyses, and end-users’ decision-making needs. This convergence is achieved by integrating diverse data types, sources, formats, and resolutions—from a vast array of satellite remote sensors and geospatial databases—with models of varying complexity such as process-based simulations and machine learning emulations, supported by a robust data analysis and visualization tool to run use-inspired case studies. Through this harmonized approach, HydroFlame ensures that fire-hydrologic modeling and watershed management are Findable, Accessible, Interoperable, and Reusable (FAIR), thus accelerating the transition of earth science into action.