Speaker
Description
Landslides are rainfall induced geo-hydrological hazards that frequently occur in the mountains of Rwanda, a densely-inhabited region of the African tropics. In May 2023 alone, a heavy rainfall event triggered in a few hours a cluster of hundreds of landslides, which led to more than 100 fatalities and significant economic losses in the impacted communities. Although quite common, natural hazard disasters associated with the rapid occurrence of rainfall-triggered landslides are difficult to predict, especially in a data-scarce context such as that of Rwanda. The lack of accurate rainfall data plays an important role in this problematic situation, and a such prevents the issuance of timely and contextualized early warning systems (EWS). To tackle this issue, and considering the dense network of mobile phone antennas in Rwanda, the project entitled “Supporting Early-Warning Systems and Nature-based Solutions using Opportunistic Rainfall monitoring in Rwanda” (SENSOR2) has been initiated. This research, part of SENSOR2 project, aims to update the susceptibility-based hydro-climatic landslide thresholds that have been developed over Rwanda by integrating real-time rainfall monitoring, hydro-climatic data, and geospatial analysis. Specifically, this study will focus on the development of empirical landslide susceptibility models using updated hydro-climatic thresholds based on calibrated Commercial Microwave Link (CML) rainfall data from the mobile phone network of Rwanda. It is foreseen that CML-based thresholds will lead to improved landslide prediction models and thus enhance the performance of existing EWS for effective risk communication and preparedness for climate-induced geo-hydrological hazards in Rwanda.
Key words: Landslides, Hydro-climatic thresholds, Early Warning Systems (EWS), Commercial Microwave Links (CML)
| Are you an Early Career Scientist ? | Yes |
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