The role of local knowledge in enhancing climate change risk assessments in rural Northern Ireland
Climate risk modelling provides valuable quantitative data on potential risks at different spatiotemporal scales, but it is essential that these models are evaluated appropriately. In some cases, it may be useful to merge quantitative datasets with qualitative data and local knowledge, to better inf...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Climate Risk Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212096325000166 |
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| author | Alan T. Kennedy-Asser Oliver D. Andrews Jill Montgomery Katie L. Jenkins Ben A.H. Smith Elizabeth Lewis Stephen J. Birkinshaw Helen He Richard F. Pywell Matt J. Brown John W. Redhead Rachel Warren Craig Robson Adam J.P. Smith Robert J. Nicholls Donal Mullan Ryan McGuire |
| author_facet | Alan T. Kennedy-Asser Oliver D. Andrews Jill Montgomery Katie L. Jenkins Ben A.H. Smith Elizabeth Lewis Stephen J. Birkinshaw Helen He Richard F. Pywell Matt J. Brown John W. Redhead Rachel Warren Craig Robson Adam J.P. Smith Robert J. Nicholls Donal Mullan Ryan McGuire |
| author_sort | Alan T. Kennedy-Asser |
| collection | DOAJ |
| description | Climate risk modelling provides valuable quantitative data on potential risks at different spatiotemporal scales, but it is essential that these models are evaluated appropriately. In some cases, it may be useful to merge quantitative datasets with qualitative data and local knowledge, to better inform and evaluate climate risk assessments. This interdisciplinary study maps climatic risks relating to health and agriculture that are facing rural Northern Ireland. A large range of quantitative national climate risk modelling results from the OpenCLIM project are scrutinised using local qualitative insights identified during workshops and interviews with farmers and rural care providers. In some cases, the qualitative local knowledge supported the quantitative modelling results, such as (1) highlighting that heat risk can be an issue for health in rural areas as well as urban centres, and (2) precipitation is changing, with increased variability posing challenges to agriculture. In other cases, the local knowledge challenged the national quantitative results. For example, models suggested that (1) potential heat stress impacts will be low, and (2) grass growing conditions will be more favourable, with higher yields as a result of future climatic conditions. In both cases, local knowledge challenged these conclusions, with discomfort and workplace heat stress reported by care staff and recent experience of variable weather having significant impacts on grass growth on farms across the country. Hence, merging even a small amount of qualitative local knowledge with quantitative national modelling projects results in a more holistic understanding of the local climate risk. |
| format | Article |
| id | doaj-art-268cd4a213254a0cb37b47db1da8eb3e |
| institution | DOAJ |
| issn | 2212-0963 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Climate Risk Management |
| spelling | doaj-art-268cd4a213254a0cb37b47db1da8eb3e2025-08-20T03:05:54ZengElsevierClimate Risk Management2212-09632025-01-014810070210.1016/j.crm.2025.100702The role of local knowledge in enhancing climate change risk assessments in rural Northern IrelandAlan T. Kennedy-Asser0Oliver D. Andrews1Jill Montgomery2Katie L. Jenkins3Ben A.H. Smith4Elizabeth Lewis5Stephen J. Birkinshaw6Helen He7Richard F. Pywell8Matt J. Brown9John W. Redhead10Rachel Warren11Craig Robson12Adam J.P. Smith13Robert J. Nicholls14Donal Mullan15Ryan McGuire16School of Geographical Sciences, University of Bristol, Bristol, UK; Corresponding author.Department of Environment and Geography, University of York, York, UKNorthern Ireland Environment Link, Belfast, UKTyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UKSchool of Engineering, Newcastle University, Newcastle-upon-Tyne, UKSchool of Engineering, University of Manchester, Manchester, UKSchool of Engineering, Newcastle University, Newcastle-upon-Tyne, UKTyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UKUK Centre for Ecology and Hydrology, Wallingford, UKUK Centre for Ecology and Hydrology, Wallingford, UKUK Centre for Ecology and Hydrology, Wallingford, UKTyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UKSchool of Engineering, Newcastle University, Newcastle-upon-Tyne, UKTyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UKTyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UKSchool of Natural and Built Environment, Queen’s University Belfast, Belfast, UKSchool of Biological Sciences, Queen’s University Belfast, Belfast, UKClimate risk modelling provides valuable quantitative data on potential risks at different spatiotemporal scales, but it is essential that these models are evaluated appropriately. In some cases, it may be useful to merge quantitative datasets with qualitative data and local knowledge, to better inform and evaluate climate risk assessments. This interdisciplinary study maps climatic risks relating to health and agriculture that are facing rural Northern Ireland. A large range of quantitative national climate risk modelling results from the OpenCLIM project are scrutinised using local qualitative insights identified during workshops and interviews with farmers and rural care providers. In some cases, the qualitative local knowledge supported the quantitative modelling results, such as (1) highlighting that heat risk can be an issue for health in rural areas as well as urban centres, and (2) precipitation is changing, with increased variability posing challenges to agriculture. In other cases, the local knowledge challenged the national quantitative results. For example, models suggested that (1) potential heat stress impacts will be low, and (2) grass growing conditions will be more favourable, with higher yields as a result of future climatic conditions. In both cases, local knowledge challenged these conclusions, with discomfort and workplace heat stress reported by care staff and recent experience of variable weather having significant impacts on grass growth on farms across the country. Hence, merging even a small amount of qualitative local knowledge with quantitative national modelling projects results in a more holistic understanding of the local climate risk.http://www.sciencedirect.com/science/article/pii/S2212096325000166Climate changeRiskLocal knowledgeNorthern IrelandRural |
| spellingShingle | Alan T. Kennedy-Asser Oliver D. Andrews Jill Montgomery Katie L. Jenkins Ben A.H. Smith Elizabeth Lewis Stephen J. Birkinshaw Helen He Richard F. Pywell Matt J. Brown John W. Redhead Rachel Warren Craig Robson Adam J.P. Smith Robert J. Nicholls Donal Mullan Ryan McGuire The role of local knowledge in enhancing climate change risk assessments in rural Northern Ireland Climate Risk Management Climate change Risk Local knowledge Northern Ireland Rural |
| title | The role of local knowledge in enhancing climate change risk assessments in rural Northern Ireland |
| title_full | The role of local knowledge in enhancing climate change risk assessments in rural Northern Ireland |
| title_fullStr | The role of local knowledge in enhancing climate change risk assessments in rural Northern Ireland |
| title_full_unstemmed | The role of local knowledge in enhancing climate change risk assessments in rural Northern Ireland |
| title_short | The role of local knowledge in enhancing climate change risk assessments in rural Northern Ireland |
| title_sort | role of local knowledge in enhancing climate change risk assessments in rural northern ireland |
| topic | Climate change Risk Local knowledge Northern Ireland Rural |
| url | http://www.sciencedirect.com/science/article/pii/S2212096325000166 |
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