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DOI | 10.1007/s10584-020-02878-0 |
Why predict climate hazards if we need to understand impacts? Putting humans back into the drought equation | |
Enenkel M.; Brown M.E.; Vogt J.V.; McCarty J.L.; Reid Bell A.; Guha-Sapir D.; Dorigo W.; Vasilaky K.; Svoboda M.; Bonifacio R.; Anderson M.; Funk C.; Osgood D.; Hain C.; Vinck P. | |
发表日期 | 2020 |
ISSN | 0165-0009 |
起始页码 | 1161 |
结束页码 | 1176 |
卷号 | 162期号:3 |
英文摘要 | Virtually all climate monitoring and forecasting efforts concentrate on hazards rather than on impacts, while the latter are a priority for planning emergency activities and for the evaluation of mitigation strategies. Effective disaster risk management strategies need to consider the prevailing “human terrain” to predict who is at risk and how communities will be affected. There has been little effort to align the spatiotemporal granularity of socioeconomic assessments with the granularity of weather or climate monitoring. The lack of a high-resolution socioeconomic baseline leaves methodical approaches like machine learning virtually untapped for pattern recognition of extreme climate impacts on livelihood conditions. While the request for “better” socioeconomic data is not new, we highlight the need to collect and analyze environmental and socioeconomic data together and discuss novel strategies for coordinated data collection via mobile technologies from a drought risk management perspective. A better temporal, spatial, and contextual understanding of socioeconomic impacts of extreme climate conditions will help to establish complex causal pathways and quantitative proof about climate-attributable livelihood impacts. Such considerations are particularly important in the context of the latest big data-driven initiatives, such as the World Bank’s Famine Action Mechanism (FAM). © 2020, Springer Nature B.V. |
英文关键词 | Decision-support; Disaster resilience; Drought; Impact assessment; Mobile technologies |
语种 | 英语 |
scopus关键词 | Drought; Environmental technology; Food supply; Hazards; Pattern recognition; Risk management; Climate monitoring; Contextual understanding; Methodical approach; Mitigation strategy; Risk management strategies; Socio-economic assessments; Socio-economic data; Socio-economic impacts; Forecasting; climate conditions; disaster management; drought stress; extreme event; livelihood; machine learning; mitigation; pattern recognition; strategic approach; virtual reality |
来源期刊 | Climatic Change |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/146989 |
作者单位 | Harvard Humanitarian Initiative, Harvard University, Cambridge, MA, United States; World Bank Disaster Risk Financing and Insurance (DRFI) Program, Washington, DC, United States; Department of Geographical Sciences, University of Maryland, College Park, MD, United States; European Commission, Joint Research Center, Ispra, VA, Italy; Department of Geography, Miami University, Oxford, OH, United States; Department of Environmental Studies, New York University, New York, United States; Centre for Research on the Epidemiology of Disasters, Institute for Health and Society, Université Catholique de Louvain, Brussels, Belgium; Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria; Department of Economics, Orfalea College of Business, California Polytechnic University, San Luis Obispo, CA, United States; US National Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, United States; United Nations World Food Programme, Rome, Italy; U.S. Department of Agricultur... |
推荐引用方式 GB/T 7714 | Enenkel M.,Brown M.E.,Vogt J.V.,et al. Why predict climate hazards if we need to understand impacts? Putting humans back into the drought equation[J],2020,162(3). |
APA | Enenkel M..,Brown M.E..,Vogt J.V..,McCarty J.L..,Reid Bell A..,...&Vinck P..(2020).Why predict climate hazards if we need to understand impacts? Putting humans back into the drought equation.Climatic Change,162(3). |
MLA | Enenkel M.,et al."Why predict climate hazards if we need to understand impacts? Putting humans back into the drought equation".Climatic Change 162.3(2020). |
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