Framework for implementation of a landslide early warning forecast model in developing countries : challenges and lessons from SHEAR

Budimir, Mirianna; Mondini, Alessandro; Rossi, Mauro; Arnhardt, Christian; Robbins, Joanne. 2022 Framework for implementation of a landslide early warning forecast model in developing countries : challenges and lessons from SHEAR. SHEAR, 26pp. (Unpublished)

Before downloading, please read NORA policies.

Preview

Text Framework for landslide forecasting_final.pdf Download (2MB) | Preview

Abstract/Summary

The Science for Humanitarian Emergencies and Resilience (SHEAR) programme supports world-leading research to enhance the quality, availability and use of risk and forecast information. Forecasting rainfall-induced landslides is a difficult yet important task that can provide time to take action to save lives, reduce economic losses and help to mitigate the impacts of landslides. The type, quality and accessibility of data directly constrain the choice of approach used for the landslide forecasting and its skill. However, in many landslide-prone countries, limited resources, and lack of investment lead to limited data availability and/or insufficient quality data for informed forecasts. This paper collates understanding from SHEAR consortium members on key considerations for developing territorial (‘regional-scale’) landslide forecasts, particularly in developing country contexts.

Item Type:	Publication - Report
Funders/Sponsors:	British Geological Survey, NERC, SHEAR, Department for International Development (DfID)
Additional Information. Not used in RCUK Gateway to Research.:	This item has been internally reviewed, but not externally peer-reviewed.
Date made live:	27 Jul 2022 08:58 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532989

Actions (login required)

View Item

Document Downloads

More statistics for this item...