Fire Weather Index and Climate Change

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU137101" target="_blank" >RIV/00216305:26220/20:PU137101 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.taylorfrancis.com/chapters/fire-weather-index-climate-change-zuzana-hubnerova-sylvia-esterby-steve-taylor/e/10.1201/9781351190831-3" target="_blank" >https://www.taylorfrancis.com/chapters/fire-weather-index-climate-change-zuzana-hubnerova-sylvia-esterby-steve-taylor/e/10.1201/9781351190831-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1201/9781351190831" target="_blank" >10.1201/9781351190831</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fire Weather Index and Climate Change

Popis výsledku v původním jazyce

The Fire Weather Index (FWI), an indicator of fire potential, is calculated from weather measurements and thus expected to be responsive to climate change. The data were drawn from records of FWI within the years 1970 to 2018 and from 861 stations in British Columbia, Canada. Since high FWI increases fire risk and monthly and geographic variation in fire potential is known to exist, models of maximum FWI were fitted within month-region groups of stations. Separate for each station, parameters of the generalized extreme-value distribution with linear dependence on time in both location and scale parameters were fitted by the maximum likelihood method. To include spatial dependence, max-stable spatial processes with different distributional assumptions on the components of the spectral representation were fitted by the maximum composite likelihood method. Takeuchi's information criterion was used for model selection. Station p-values from the separate models identified tendencies for increasing or decreasing trends in location and scale parameters. May, July and August had the most stations with stronger increasing trends in location parameter of maximum FWI and this tended to occur in regions where maximum FWI was higher. In contrast, trends in the scale parameter of maximum FWI showed decrease in variability in some regions, particularly in August. Spatial modeling showed trends in some months and regions, not necessarily consistent with the separate modeling results, not unexpected since the two methods would pick up local effects and regional effects, respectively. The analyses demonstrated the usefulness of these extreme value methods for fire weather variables.

Název v anglickém jazyce

Fire Weather Index and Climate Change

Popis výsledku anglicky

The Fire Weather Index (FWI), an indicator of fire potential, is calculated from weather measurements and thus expected to be responsive to climate change. The data were drawn from records of FWI within the years 1970 to 2018 and from 861 stations in British Columbia, Canada. Since high FWI increases fire risk and monthly and geographic variation in fire potential is known to exist, models of maximum FWI were fitted within month-region groups of stations. Separate for each station, parameters of the generalized extreme-value distribution with linear dependence on time in both location and scale parameters were fitted by the maximum likelihood method. To include spatial dependence, max-stable spatial processes with different distributional assumptions on the components of the spectral representation were fitted by the maximum composite likelihood method. Takeuchi's information criterion was used for model selection. Station p-values from the separate models identified tendencies for increasing or decreasing trends in location and scale parameters. May, July and August had the most stations with stronger increasing trends in location parameter of maximum FWI and this tended to occur in regions where maximum FWI was higher. In contrast, trends in the scale parameter of maximum FWI showed decrease in variability in some regions, particularly in August. Spatial modeling showed trends in some months and regions, not necessarily consistent with the separate modeling results, not unexpected since the two methods would pick up local effects and regional effects, respectively. The analyses demonstrated the usefulness of these extreme value methods for fire weather variables.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10510 - Climatic research

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Evaluating Climate Change Impacts

ISBN

9781351190831

Počet stran výsledku

19

Strana od-do

1-19

Počet stran knihy

394

Název nakladatele

Chapman and Hall/CRC

Místo vydání

Boca Raton

Kód UT WoS kapitoly

—