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Near-real-time drought impact assessment

  • Contemporary drought impact assessments have been constrained due to data availability, leading to an incomplete representation of impact trends. To address this, we present a novel method for the comprehensive and near-real-time monitoring of drought socio-economic impacts based on media reports. We tested its application using the case of the exceptional 2018/19 German drought. By employing text mining techniques, 4839 impact statements were identified, relating to livestock, agriculture, forestry, fires, recreation, energy and transport sectors. An accuracy of 95.6% was obtained for their automatic classification. Furthermore, high levels of performance in terms of spatial and temporal precision were found when validating our results against independent data (e.g. soil moisture, average precipitation, population interest in droughts, crop yield and forest fire statistics). The findings highlight the applicability of media data for rapidly and accurately monitoring the propagation of drought consequences over time and space. WeContemporary drought impact assessments have been constrained due to data availability, leading to an incomplete representation of impact trends. To address this, we present a novel method for the comprehensive and near-real-time monitoring of drought socio-economic impacts based on media reports. We tested its application using the case of the exceptional 2018/19 German drought. By employing text mining techniques, 4839 impact statements were identified, relating to livestock, agriculture, forestry, fires, recreation, energy and transport sectors. An accuracy of 95.6% was obtained for their automatic classification. Furthermore, high levels of performance in terms of spatial and temporal precision were found when validating our results against independent data (e.g. soil moisture, average precipitation, population interest in droughts, crop yield and forest fire statistics). The findings highlight the applicability of media data for rapidly and accurately monitoring the propagation of drought consequences over time and space. We anticipate our method to be used as a starting point for an impact-based early warning system.show moreshow less

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Metadaten
Author details:Mariana Madruga de BritoORCiD, Christian KuhlickeORCiDGND, Andreas MarxORCiD
DOI:https://doi.org/10.1088/1748-9326/aba4ca
ISSN:1748-9326
Title of parent work (English):Environmental research letters
Subtitle (English):a text mining approach on the 2018/19 drought in Germany
Publisher:IOP Publ.
Place of publishing:Bristol
Publication type:Article
Language:English
Date of first publication:2020/10/12
Publication year:2020
Release date:2022/10/05
Tag:Germany; drought impacts; newspaper; text analytics; validation
Volume:15
Issue:10
Article number:1040a9
Number of pages:11
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie
DDC classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer review:Referiert
Publishing method:Open Access / Gold Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
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