Navigating the Overlap of Wildfires and Flood Events

By Matt Larson, Lead Account Executive 

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For most regions, the days of isolated disasters are over. In parts of Australia and the U.S., communities are facing a harsh new reality: wildfire and flood seasons are increasingly overlapping. Seasoned emergency response teams are managing two high-risk disasters unfolding in rapid succession. 

Wildfire-damaged landscapes behave very differently during rainfall, prioritizing rapid runoff rather than absorption. At the same time, research shows that smoke aerosols from mega-fires can influence weather systems and even global climate patterns, shaping the very rainfall events that follow. To effectively protect communities in this complex environment, real-time, adaptive flood models powered by AI and machine learning are becoming essential. 

Wildfires Change Flood Risk, Quickly 

When rain falls onto a catchment area, some of it will run off, contributing to rising streams and river levels. Normally, some of the rainfall will be ‘lost’ to the catchment.  

Some water is absorbed into the ground and vegetation. This is known as infiltration. 

Catchments also have the capacity to absorb water into the soil, where it is stored in soil voids.  They also lose water through evaporation from the ground and transpiration, where plants lose water from their leaves. This combined process is known as evapotranspiration. 

After a fire, soils often become hydrophobic for a period, repelling water instead of absorbing it. This reduces infiltration into the soil and increases the volume of water running off. With vegetation burned away and fewer plants to soak up water, catchments lose capacity for evapotranspiration losses, further contributing to intense and rapid runoff. 

These changes can trigger flash floods from storms that normally wouldn’t be a concern, creating life-threatening conditions and forcing rapid response.  

Losing vegetation to fires (particularly trees and their root systems) can lead to instabilities, particularly on steep hills or slopes. This can exacerbate erosion and lead to landslides or debris flow, sending mud and debris downstream that block roads, damage bridges, and complicate evacuation or rescue operations. Standard flood models struggle to account for these altered conditions, making real-time analysis more challenging. 

Forecasting Floods After a Fire Isn’t Simple 

Flood risk after wildfires can evolve daily. AI and machine learning help: 

• Integrate shifting inputs, like the most up-to-date geographical data and incoming rainfall forecasts. 

• Adjust model behaviors quickly as data is ingested throughout the event. 

FloodMapp’s ForeCast and NowCast tools adapt to known changes, offering flood predictions aligned with current catchment conditions. These adaptive insights give emergency teams situational awareness and confidence to make faster, more informed decisions when minutes matter. 

In Australia, bushfire season typically peaks between August and January. Following closely is the La Niña-influenced wet season, which brings heavy rainfall and flooding. Similarly, in Colorado and California, the window for burn-scar floods often arrives in late summer to early fall, not long after major wildfires. 

These overlapping hazard windows force emergency teams to pivot rapidly, from fighting fires to preparing for floods, emphasizing the need for real-time flood intelligence that seamlessly adapts to fire-induced changes. 

Atmospheric Challenges: Smoke & Satellite Blind Spots 

Thick wildfire smoke, like during the 2019-20 Australian bushfires, can blind satellite imagery, leaving many flood monitoring systems in the dark when emergency response teams need them most. With smoke-heavy skies, FloodMapp’s dynamic models, fed by gauge data, forecasting models, and terrain analysis, remain unaffected. This resilience ensures consistent flood awareness even when satellites fail to deliver. 

The impact of wildfires isn’t limited to local catchment changes. New research has shown that smoke from Australia’s 2019–20 “Black Summer” bushfires contributed to the rare triple-dip La Niña event that lasted from 2020 to 2022. Massive amounts of smoke aerosols brightened clouds, cooled the Earth’s surface, and set off a chain of atmospheric and oceanic changes that helped trigger prolonged La Niña conditions, which, in turn, drove record-breaking floods across eastern Australia. 

This is the first documented example of wildfire emissions influencing global-scale weather patterns in such a profound way. It uncovers a stark reality: disasters do not occur in isolation, but as part of interconnected ecological and climate systems. Fires can directly amplify the risk of flooding, both locally through hydrology and globally through atmospheric feedback loops. 

Cross-Hazard Resilience 

As disasters increasingly collide, resilience must be built across hazards. Wildfire recovery must include flood preparation, especially when the landscape and skies are changing. 

With AI-powered flood modeling, agencies can maintain situational awareness and safeguard communities more effectively, no matter which hazard comes first. 

Disasters don’t wait. Equip your team with real-time flood intelligence to make faster, more informed decisions during flood response and recovery in wildfire-prone regions. Request a demo today to explore tools built for evolving conditions and real-time resilience.