How Science Is Learning to Forecast Czech Wildfires
Once considered relatively safe from major wildfires, the Czech landscape is increasingly vulnerable to fire outbreaks as climate change alters weather patterns and vegetation dynamics. The year 2022 marked a grim milestone when the largest recorded wildfire in Czech history burned over 1,100 hectares in a national park, signaling a new era of fire risk for the Central European nation 1.
The Czech Republic experiences two distinct fire season peaks—one in spring and another in summer—unlike Mediterranean countries with a single prolonged season.
This event served as a wake-up call for scientists, fire managers, and policymakers, highlighting the urgent need for accurate and reliable fire danger forecasting systems tailored to the unique characteristics of Czech agricultural and forestry landscapes.
Fire danger represents the potential to which an area or landscape might experience a wildfire based on various contributing factors. It's not simply a measure of whether a fire will occur, but rather an assessment of how severe a fire might become if one were to start 4.
All fires require three elements: heat, fuel, and oxygen. Fire danger forecasting must account for all three components to be effective.
Two major fire danger indices have emerged as global standards: Canada's Fire Weather Index (FWI) and Australia's Forest Fire Fire Danger Index (FFDI). Both integrate weather measurements and forecasts to produce numerical ratings of fire danger, though they were developed for ecosystems quite different from Central European landscapes 1.
Despite their global use, these indices weren't originally designed for Czech conditions, which is why researchers have been working to evaluate and adapt them specifically for the Republic's agricultural and forest landscapes 1.
Since 2020, the Czech Republic has operated an advanced fire danger prediction system called FireRisk (firerisk.cz), a collaborative effort between the Global Change Research Institute of the Czech Academy of Sciences, the Institute of Forest Ecosystem Research, Mendel University in Brno, and the Czech Hydrometeorological Institute (CHMI) 1.
The system leverages not one but five different numerical weather prediction models from leading meteorological centers around the world:
This multi-model approach significantly reduces prediction errors while providing users with valuable information about forecast uncertainty.
Days ahead forecasting capability
Based on combination of FWI and FFDI
10-hour dead fuel moisture content
From 100+ monitoring stations
Temperature, wind, stability data
Between 2018 and 2022, a comprehensive research study evaluated the accuracy and reliability of fire danger predictions across the Czech Republic. The research team analyzed the relationship between fire danger metrics (FWI and FFDI) and actual wildfire occurrences across different geographic regions of the country 13.
The research yielded several important insights into fire patterns and prediction capabilities:
R-squared Value
Mean Absolute Error
| Model Type | R-squared Value | Mean Absolute Error | Confidence Interval (95%) |
|---|---|---|---|
| Linear Regression (National) | 0.81 | 5.19 fires | 4.94-5.44 |
| Linear Model with Random Effects (Regional) | 0.34 | 1 fire | ±3 |
Modern fire danger forecasting relies on an array of specialized tools, datasets, and methodologies.
Sophisticated computer models that simulate atmospheric processes to generate weather forecasts.
Field instruments that measure the water content in various types of vegetation.
Satellite observations providing information on active fires and vegetation conditions.
Comprehensive records of past fire occurrences, including location, size, and cause.
Mathematical formulas integrating weather and fuel variables to produce standardized measures.
Computer systems for capturing, storing, analyzing, and displaying geographic data.
The Czech research coincides with exciting global advances in fire prediction technology. Traditional fire danger ratings have primarily focused on weather conditions and tended to overpredict fire danger in fuel-limited regions like deserts, where extreme temperatures and low humidity might suggest high fire risk despite the absence of sufficient vegetation to carry a fire 4.
The European Centre for Medium-Range Weather Forecasts (ECMWF) has pioneered a machine learning approach that incorporates not just weather data but also information on fuel characteristics and ignition sources 4.
Global research has revealed that data quality is actually more crucial than model complexity when it comes to improving forecasts. The best predictions come from incorporating all three elements of the fire triangle 4.
The development of reliable fire danger forecasting systems represents a crucial step toward managing the growing wildfire threat in the Czech Republic.
As climate change continues to alter weather patterns and increase the frequency of extreme fire weather conditions, the ability to accurately predict fire danger becomes increasingly vital for protecting lives, property, and natural resources.
The flames may be rising, but our ability to predict and prepare for them is growing even faster.