Reliability of fire danger forecasts for Czech agricultural and forestry landscapes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F25%3A00619059" target="_blank" >RIV/86652079:_____/25:00619059 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43210/25:43926880 RIV/00020702:_____/25:N0000092 RIV/00020699:_____/25:N0000072

Výsledek na webu

<a href="https://fireecology.springeropen.com/articles/10.1186/s42408-025-00362-7" target="_blank" >https://fireecology.springeropen.com/articles/10.1186/s42408-025-00362-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s42408-025-00362-7" target="_blank" >10.1186/s42408-025-00362-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reliability of fire danger forecasts for Czech agricultural and forestry landscapes

Popis výsledku v původním jazyce

BackgroundThe increasing threat of fire caused by ongoing climate change requires accurate and timely prediction for the effective management of extreme fire situations. The limited research on the connection between fire danger metrics and the occurrence of wildfires in the forested and agricultural landscapes of the Czech Republic underscores the need to better understand how to properly quantify fire danger in the context of Central Europe. This study focused on assessing the accuracy of fire danger prediction with respect to the number of wildfires in different geographic regions of the Czech Republic and provided new insights into central European fire ecology.ResultsWe found that the fire season in the Czech Republic has two peaks, in spring and summer, with regional differences in the total number of wildfires. Analyses of fire danger via the Canadian Fire Weather Index (FWI) and Australian Forest Fire Danger Index (FFDI) for the years 2018-2022 revealed that the IFS numerical weather prediction model is the most suitable for conditions in the Czech Republic. A linear regression model showed a high predictive capability for the total number of wildfires in the Czech Republic, with an observed R-squared value of 0.81 and a mean absolute error (MAE) of 5.19 wildfires with a 95% confidence interval (CI) of 4.94-5.44. Additionally, the second model, which utilized a linear model with random effects to account for regional variability, had an R-squared value of 0.34 and an MAE of 1 wildfire (95% CI +/- 3), indicating that the inclusion of regional correction coefficients (random effects) enhanced the prediction accuracy.ConclusionsThis study provides key insights into fire danger prediction in relation to the number of wildfires. With this model, it is possible to predict how many wildfires may occur at specific values of the FWI and FFDI in individual regions (NUTS 3) of the Czech Republic. This information can be used for more effective readiness planning for human resources and fire equipment while also contributing to the enhancement of general knowledge in the field of fire science in the context of central Europe.

Název v anglickém jazyce

Reliability of fire danger forecasts for Czech agricultural and forestry landscapes

Popis výsledku anglicky

BackgroundThe increasing threat of fire caused by ongoing climate change requires accurate and timely prediction for the effective management of extreme fire situations. The limited research on the connection between fire danger metrics and the occurrence of wildfires in the forested and agricultural landscapes of the Czech Republic underscores the need to better understand how to properly quantify fire danger in the context of Central Europe. This study focused on assessing the accuracy of fire danger prediction with respect to the number of wildfires in different geographic regions of the Czech Republic and provided new insights into central European fire ecology.ResultsWe found that the fire season in the Czech Republic has two peaks, in spring and summer, with regional differences in the total number of wildfires. Analyses of fire danger via the Canadian Fire Weather Index (FWI) and Australian Forest Fire Danger Index (FFDI) for the years 2018-2022 revealed that the IFS numerical weather prediction model is the most suitable for conditions in the Czech Republic. A linear regression model showed a high predictive capability for the total number of wildfires in the Czech Republic, with an observed R-squared value of 0.81 and a mean absolute error (MAE) of 5.19 wildfires with a 95% confidence interval (CI) of 4.94-5.44. Additionally, the second model, which utilized a linear model with random effects to account for regional variability, had an R-squared value of 0.34 and an MAE of 1 wildfire (95% CI +/- 3), indicating that the inclusion of regional correction coefficients (random effects) enhanced the prediction accuracy.ConclusionsThis study provides key insights into fire danger prediction in relation to the number of wildfires. With this model, it is possible to predict how many wildfires may occur at specific values of the FWI and FFDI in individual regions (NUTS 3) of the Czech Republic. This information can be used for more effective readiness planning for human resources and fire equipment while also contributing to the enhancement of general knowledge in the field of fire science in the context of central Europe.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

<a href="/cs/project/EH22_008%2F0004635" target="_blank" >EH22_008/0004635: AdAgriF - Pokročilé metody redukce emisí a sekvestrace skleníkových plynů v zemědělské a lesní krajině pro mitigaci změny klimatu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

FIRE ECOLOGY

ISSN

1933-9747

e-ISSN

1933-9747

Svazek periodika

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

20

Kód UT WoS článku

001462059700001

EID výsledku v databázi Scopus

2-s2.0-105002885682