Geographical landslide early warning systems
The design, implementation, management, and verification of landslide early warning systems (LEWSs) are gaining increasing attention in the literature and among government officials, decision makers, and the public. Based on a critical analysis of nine main assumptions that form the rationale for la...
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| Published in: | Earth-science reviews Vol. 200; p. 102973 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.01.2020
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| Subjects: | |
| ISSN: | 0012-8252, 1872-6828 |
| Online Access: | Get full text |
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| Abstract | The design, implementation, management, and verification of landslide early warning systems (LEWSs) are gaining increasing attention in the literature and among government officials, decision makers, and the public. Based on a critical analysis of nine main assumptions that form the rationale for landslide forecasting and early warning, we examine 26 regional, national, and global LEWSs worldwide from 1977 to August 2019. We find that currently only five nations, 13 regions, and four metropolitan areas benefit from LEWSs, while many areas with numerous fatal landslides, where landslide risk to the population is high, lack LEWSs. Operational LEWSs use information from rain gauge networks, meteorological models, weather radars, and satellite estimates; and most systems use two sources of rainfall information. LEWSs use one or more types of landslide forecast models, including rainfall thresholds, distributed slope stability models, and soil water balance models; and most systems use landslide susceptibility zonations. Most LEWSs have undergone some form of verification, but there is no accepted standard to check the performance and forecasting skills of a LEWS. Based on our review, and our experience in the design, implementation, management, and verification of geographical LEWSs in Italy, we conclude that operational forecast of weather-induced landslides is feasible, and it can help reduce landslide risk. We propose 30 recommendations to further develop and improve geographical LEWSs, and to increase their reliability and credibility. We encourage landslide forecasters and LEWSs managers to propose open standards for geographical LEWSs, and we expect our work to contribute to this endeavour. |
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| AbstractList | The design, implementation, management, and verification of landslide early warning systems (LEWSs) are gaining increasing attention in the literature and among government officials, decision makers, and the public. Based on a critical analysis of nine main assumptions that form the rationale for landslide forecasting and early warning, we examine 26 regional, national, and global LEWSs worldwide from 1977 to August 2019. We find that currently only five nations, 13 regions, and four metropolitan areas benefit from LEWSs, while many areas with numerous fatal landslides, where landslide risk to the population is high, lack LEWSs. Operational LEWSs use information from rain gauge networks, meteorological models, weather radars, and satellite estimates; and most systems use two sources of rainfall information. LEWSs use one or more types of landslide forecast models, including rainfall thresholds, distributed slope stability models, and soil water balance models; and most systems use landslide susceptibility zonations. Most LEWSs have undergone some form of verification, but there is no accepted standard to check the performance and forecasting skills of a LEWS. Based on our review, and our experience in the design, implementation, management, and verification of geographical LEWSs in Italy, we conclude that operational forecast of weather-induced landslides is feasible, and it can help reduce landslide risk. We propose 30 recommendations to further develop and improve geographical LEWSs, and to increase their reliability and credibility. We encourage landslide forecasters and LEWSs managers to propose open standards for geographical LEWSs, and we expect our work to contribute to this endeavour. |
| ArticleNumber | 102973 |
| Author | Gariano, Stefano Luigi Guzzetti, Fausto Rossi, Mauro Marchesini, Ivan Melillo, Massimo Peruccacci, Silvia Brunetti, Maria Teresa |
| Author_xml | – sequence: 1 givenname: Fausto surname: Guzzetti fullname: Guzzetti, Fausto email: Fausto.Guzzetti@irpi.cnr.it – sequence: 2 givenname: Stefano Luigi surname: Gariano fullname: Gariano, Stefano Luigi – sequence: 3 givenname: Silvia surname: Peruccacci fullname: Peruccacci, Silvia – sequence: 4 givenname: Maria Teresa surname: Brunetti fullname: Brunetti, Maria Teresa – sequence: 5 givenname: Ivan surname: Marchesini fullname: Marchesini, Ivan – sequence: 6 givenname: Mauro surname: Rossi fullname: Rossi, Mauro – sequence: 7 givenname: Massimo surname: Melillo fullname: Melillo, Massimo |
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