Developing a landslide early warning system for Vietnam

Vietnam is one of the most disaster-prone countries in Southeast Asia, facing increasing pressure from natural hazards. In September 2024, the north of Vietnam was hit by an extraordinary strong typhoon Yagi causing significant losses in lives and an economic damage of approx. USD 3.3 billion. Approximately 70% of Vietnam’s land area is mountainous or hilly, with complex geological formations. Weak soil structures, combined with steep slopes of 25–40 degrees, make these areas highly susceptible to landslides. 

Early warning systems (EWS) are central component to the Sendai Framework for Disaster Risk Reduction 2015–2030 Framework’s architecture for disaster risk reduction and vital for disaster preparedness and risk mitigation. According to the United Nations Office for Disaster Risk Reduction (UNDRR), EWS constitute “an integrated system of hazard monitoring, forecasting and prediction, disaster risk assessment, communication and preparedness activities systems and processes that enables individuals, communities, governments, businesses and others to take timely action to reduce disaster risks in advance of hazardous events.” (UNDRR, 2015).

In 2022, the United Nations launched the] Early Warnings for All (EW4All) initiative to ensure every person on Earth is protected by timely disaster alerts by 2027. One of the pillars of this effort is strengthening disaster risk knowledge” as the foundation of effective early warning. Landslides stand out as a gap in risk knowledge. A recent global status report on Multi-Hazard Early Warning Systems (MHEWS) in 2024 (UNDRR, WMO, 2024) found that while many countries have improved hazard forecasting for floods and storms, secondary hazards like landslides remain difficult to forecast with the needed precision. Indeed, worldwide surveys indicate that nearly half of the people who experienced a landslide had no warning at all; the worst gap among hazards surveyed. This underscores an urgent need: better data on past landslides and their impacts can drive better models and warnings for the future.