The RTM Event Database for Understanding Natural Hazards in Mountainous Regions: The Example of the Isere Department (France)
Databases of natural hazards play a crucial role for assessing related risks and in mitigating their impacts on the environment. In the mountainous regions of France, potentially destructive events are both numerous and diverse, however, only a limited number of databases containing information on p...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | deu |
| Published: |
Institut de Géographie Alpine
2025-01-01
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| Series: | Revue de Géographie Alpine |
| Subjects: | |
| Online Access: | https://journals.openedition.org/rga/13767 |
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| Summary: | Databases of natural hazards play a crucial role for assessing related risks and in mitigating their impacts on the environment. In the mountainous regions of France, potentially destructive events are both numerous and diverse, however, only a limited number of databases containing information on past occurrences exist. The database of the RTM service (Restoration of Mountainous Areas service, BD-RTM) consolidates such information through a multi-hazard approach over an extended timeframe, integrating systematic observations and a retrospective compilation of various sources. This article outlines the key features of this unique database. Focusing on the Isère department (38), which records the highest number of events among the 12 areas covered by the BD-RTM, we present (1) the history of the database, particularly the origins of the data and its structural framework, and (2) the richness and diversity of the recorded information. On the 31st of December 2023, the BD-RTM had documented 5,888 events in the Isère department, with nearly 70% consisting of torrential floods and landslides. Furthermore, 85% of the recorded events resulted in damages. A notable increase in the frequency of events is evident from the 2000s onwards, with an average of 85 events recorded annually since that time. All the information is accessible for further spatial and temporal analyses, including statistical and historical assessments, as well as integration with other data |
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| ISSN: | 0035-1121 1760-7426 |