Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/55329 |
Resumo: | Estimates of future climate change impacts using numerical impact models are commonly based on a limited selection of projections of climate and other key drivers. However, the availability of large ensembles of such projections offers an opportunity to estimate impact responses probabilistically. This study demonstrates an approach that combines model-based impact response surfaces (IRSs) with probabilistic projections of climate change and population to estimate the likelihood of exceeding pre-specified thresholds of impact. The changing likelihood of exceeding impact thresholds during the 21st century was estimated for selected indicators in three European case study regions (Iberian Peninsula, Scotland and Hungary), comparing simulations that incorporate adaptation to those without adaptation. The results showed high likelihoods of increases in heat-related human mortality and of yield decreases for some crops, whereas a decrease of NPP was estimated to be exceptionally unlikely. For a water reservoir in a Portuguese catchment, increased likelihoods of severe water scarce conditions were estimated for the current rice cultivation. Switching from rice to other crops with lower irrigation demand changes production risks, allowing for expansion of the irrigated areas but introducing a stronger sensitivity to changes in rainfall. The IRS-based risk assessment shown in this paper is of relevance for policy making by addressing the relative sensitivity of impacts to key climate and socio-economic drivers, and the urgency for action expressed as a time series of the likelihood of crossing critical impact thresholds. It also examines options to respond by incorporating alternative adaptation actions in the analysis framework, which may be useful for exploring the types, choice and timing of adaptation responses. |
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Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfacesImpact modelSensitivity analysisTemperaturePrecipitationPopulationEstimates of future climate change impacts using numerical impact models are commonly based on a limited selection of projections of climate and other key drivers. However, the availability of large ensembles of such projections offers an opportunity to estimate impact responses probabilistically. This study demonstrates an approach that combines model-based impact response surfaces (IRSs) with probabilistic projections of climate change and population to estimate the likelihood of exceeding pre-specified thresholds of impact. The changing likelihood of exceeding impact thresholds during the 21st century was estimated for selected indicators in three European case study regions (Iberian Peninsula, Scotland and Hungary), comparing simulations that incorporate adaptation to those without adaptation. The results showed high likelihoods of increases in heat-related human mortality and of yield decreases for some crops, whereas a decrease of NPP was estimated to be exceptionally unlikely. For a water reservoir in a Portuguese catchment, increased likelihoods of severe water scarce conditions were estimated for the current rice cultivation. Switching from rice to other crops with lower irrigation demand changes production risks, allowing for expansion of the irrigated areas but introducing a stronger sensitivity to changes in rainfall. The IRS-based risk assessment shown in this paper is of relevance for policy making by addressing the relative sensitivity of impacts to key climate and socio-economic drivers, and the urgency for action expressed as a time series of the likelihood of crossing critical impact thresholds. It also examines options to respond by incorporating alternative adaptation actions in the analysis framework, which may be useful for exploring the types, choice and timing of adaptation responses.ElsevierRepositório da Universidade de LisboaFronzek, StefanHonda, YasushiIto, AkihikoNunes, João PedroPirttioja, NinaRäisänen, JouniTakahashi, KiyoshiTerämä, EmmaYoshikawa, MinoruCarter, Timothy R.2022-12-02T15:25:22Z2022-112022-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/55329eng10.1016/j.crm.2022.100466info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:02:11Zoai:repositorio.ul.pt:10451/55329Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:05:59.239790Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces |
title |
Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces |
spellingShingle |
Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces Fronzek, Stefan Impact model Sensitivity analysis Temperature Precipitation Population |
title_short |
Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces |
title_full |
Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces |
title_fullStr |
Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces |
title_full_unstemmed |
Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces |
title_sort |
Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces |
author |
Fronzek, Stefan |
author_facet |
Fronzek, Stefan Honda, Yasushi Ito, Akihiko Nunes, João Pedro Pirttioja, Nina Räisänen, Jouni Takahashi, Kiyoshi Terämä, Emma Yoshikawa, Minoru Carter, Timothy R. |
author_role |
author |
author2 |
Honda, Yasushi Ito, Akihiko Nunes, João Pedro Pirttioja, Nina Räisänen, Jouni Takahashi, Kiyoshi Terämä, Emma Yoshikawa, Minoru Carter, Timothy R. |
author2_role |
author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Fronzek, Stefan Honda, Yasushi Ito, Akihiko Nunes, João Pedro Pirttioja, Nina Räisänen, Jouni Takahashi, Kiyoshi Terämä, Emma Yoshikawa, Minoru Carter, Timothy R. |
dc.subject.por.fl_str_mv |
Impact model Sensitivity analysis Temperature Precipitation Population |
topic |
Impact model Sensitivity analysis Temperature Precipitation Population |
description |
Estimates of future climate change impacts using numerical impact models are commonly based on a limited selection of projections of climate and other key drivers. However, the availability of large ensembles of such projections offers an opportunity to estimate impact responses probabilistically. This study demonstrates an approach that combines model-based impact response surfaces (IRSs) with probabilistic projections of climate change and population to estimate the likelihood of exceeding pre-specified thresholds of impact. The changing likelihood of exceeding impact thresholds during the 21st century was estimated for selected indicators in three European case study regions (Iberian Peninsula, Scotland and Hungary), comparing simulations that incorporate adaptation to those without adaptation. The results showed high likelihoods of increases in heat-related human mortality and of yield decreases for some crops, whereas a decrease of NPP was estimated to be exceptionally unlikely. For a water reservoir in a Portuguese catchment, increased likelihoods of severe water scarce conditions were estimated for the current rice cultivation. Switching from rice to other crops with lower irrigation demand changes production risks, allowing for expansion of the irrigated areas but introducing a stronger sensitivity to changes in rainfall. The IRS-based risk assessment shown in this paper is of relevance for policy making by addressing the relative sensitivity of impacts to key climate and socio-economic drivers, and the urgency for action expressed as a time series of the likelihood of crossing critical impact thresholds. It also examines options to respond by incorporating alternative adaptation actions in the analysis framework, which may be useful for exploring the types, choice and timing of adaptation responses. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-02T15:25:22Z 2022-11 2022-11-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/55329 |
url |
http://hdl.handle.net/10451/55329 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.crm.2022.100466 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134612120666112 |