Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe

Managers of wildfire-prone landscapes in the Euro-Mediterranean region would greatly benefit from fire weather predictions a few months in advance, and particularly from the reliable prediction of extreme fire seasons. However, in some cases model biases prevent from a direct application of these pr...

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Autores: Bedía Jiménez, Joaquín, Golding, Nicola, Casanueva Vicente, Ana|||0000-0002-7568-0229, Iturbide Martínez de Albéniz, Maialen|||0000-0002-5048-0941, Buontempo, Carlo, Gutiérrez Llorente, José Manuel
Tipo de documento: artigo
Data de publicação:2018
País:España
Recursos:Universidad de Cantabria (UC)
Repositório:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglês
OAI Identifier:oai:repositorio.unican.es:10902/20672
Acesso em linha:http://hdl.handle.net/10902/20672
Access Level:Acceso aberto
Palavra-chave:Climate impact indicators
Quantile mapping
Bias correction
System 4
Fire danger
Seasonal forecasting
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spelling Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean EuropeBedía Jiménez, JoaquínGolding, NicolaCasanueva Vicente, Ana|||0000-0002-7568-0229Iturbide Martínez de Albéniz, Maialen|||0000-0002-5048-0941Buontempo, CarloGutiérrez Llorente, José ManuelClimate impact indicatorsQuantile mappingBias correctionSystem 4Fire dangerSeasonal forecastingManagers of wildfire-prone landscapes in the Euro-Mediterranean region would greatly benefit from fire weather predictions a few months in advance, and particularly from the reliable prediction of extreme fire seasons. However, in some cases model biases prevent from a direct application of these predictions in an operational context. Fire risk management requires precise knowledge of the likely consequences of climate on fire risk, and the interest for decision-makers is focused on multi-variable fire danger indices, calculated through the combination of different model output variables. In this paper we consider whether the skill in dynamical seasonal predictions of one of the most widely applied of such indices (the Canadian Fire Weather Index, FWI) is sufficient to inform management decisions, and we examine various methodological aspects regarding the calibration of model outputs prior to its verification and operational applicability. We find that there is significant skill in predicting above average summer FWI in parts of SE Europe at 1 month lead time, but poor skill elsewhere. These results are largely linked to the predictability of relative humidity. Moreover, practical recommendations are given for the use of empirical quantile mapping in probabilistic seasonal FWI forecasts. Furthermore, we show how researchers, fire managers and other stakeholders can take advantage of a new open-source climate service in order to undertake all the necessary steps for data download, post-processing, analysis and verification in a straightforward and fully reproducible manner.We thank the European Union’s Seventh Framework Program [FP7/2007–2013] under Grant Agreement 308291 (EUPORIAS Project), in which this study was undertaken, and also for partially funding the ‘ECOMS User Data Gateway’ (ECOMS-UDG, http://meteo.unican.es/ecoms-udg), making available the System 4 hindcast, including derived variables from the raw model outputs required for this study (relative humidity, wind speed and deaccumulated precipitation). M. Iturbide thanks research funding from SODERCAN S.A. through the “Contrata” Programme (budget Ref. 12.04.461A.740.14). The first author thanks the FP7 Project SPECS (grant agreement 308378) for funding his current research contract and for supporting the development of the R package downscaleR for statistical downscaling and bias correction. Thanks to Jonas Bhend (MeteoSwiss), for the development of the verification routines and wrapper in R, and for fruitful discussions about the forecast verification methods. We are also grateful to two anonymous referees for their insightful comments.Elsevier B.V.Universidad de Cantabria20182018-01-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/20672Climate Services 9 (2018) 101-110reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)InglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Framework Programme Seven 308291open accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/206722026-06-02T12:39:31Z
dc.title.none.fl_str_mv Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe
title Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe
spellingShingle Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe
Bedía Jiménez, Joaquín
Climate impact indicators
Quantile mapping
Bias correction
System 4
Fire danger
Seasonal forecasting
title_short Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe
title_full Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe
title_fullStr Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe
title_full_unstemmed Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe
title_sort Seasonal predictions of fire weather index: paving the way for their operational applicability in Mediterranean Europe
dc.creator.none.fl_str_mv Bedía Jiménez, Joaquín
Golding, Nicola
Casanueva Vicente, Ana|||0000-0002-7568-0229
Iturbide Martínez de Albéniz, Maialen|||0000-0002-5048-0941
Buontempo, Carlo
Gutiérrez Llorente, José Manuel
author Bedía Jiménez, Joaquín
author_facet Bedía Jiménez, Joaquín
Golding, Nicola
Casanueva Vicente, Ana|||0000-0002-7568-0229
Iturbide Martínez de Albéniz, Maialen|||0000-0002-5048-0941
Buontempo, Carlo
Gutiérrez Llorente, José Manuel
author_role author
author2 Golding, Nicola
Casanueva Vicente, Ana|||0000-0002-7568-0229
Iturbide Martínez de Albéniz, Maialen|||0000-0002-5048-0941
Buontempo, Carlo
Gutiérrez Llorente, José Manuel
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Climate impact indicators
Quantile mapping
Bias correction
System 4
Fire danger
Seasonal forecasting
topic Climate impact indicators
Quantile mapping
Bias correction
System 4
Fire danger
Seasonal forecasting
description Managers of wildfire-prone landscapes in the Euro-Mediterranean region would greatly benefit from fire weather predictions a few months in advance, and particularly from the reliable prediction of extreme fire seasons. However, in some cases model biases prevent from a direct application of these predictions in an operational context. Fire risk management requires precise knowledge of the likely consequences of climate on fire risk, and the interest for decision-makers is focused on multi-variable fire danger indices, calculated through the combination of different model output variables. In this paper we consider whether the skill in dynamical seasonal predictions of one of the most widely applied of such indices (the Canadian Fire Weather Index, FWI) is sufficient to inform management decisions, and we examine various methodological aspects regarding the calibration of model outputs prior to its verification and operational applicability. We find that there is significant skill in predicting above average summer FWI in parts of SE Europe at 1 month lead time, but poor skill elsewhere. These results are largely linked to the predictability of relative humidity. Moreover, practical recommendations are given for the use of empirical quantile mapping in probabilistic seasonal FWI forecasts. Furthermore, we show how researchers, fire managers and other stakeholders can take advantage of a new open-source climate service in order to undertake all the necessary steps for data download, post-processing, analysis and verification in a straightforward and fully reproducible manner.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10902/20672
url http://hdl.handle.net/10902/20672
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Framework Programme Seven 308291
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Climate Services 9 (2018) 101-110
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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