Hong Kong’s landslip warning system—40 years of progress

• Published in: Landslides 2020-06, Vol.17 (6), p.1453-1463
• Main Authors: Kong, V. W. W., Kwan, J. S. H., Pun, W. K.
• Format: Article
• Language: English
• Subjects: Agriculture; Civil Engineering; Cloud computing; Early warning systems; Earth and Environmental Science; Earth Sciences; Emergency communications systems; Emergency warning programs; Environmental risk; Extreme weather; Gauges; Geography; Geotechnical engineering; Instrumentation; Landslide warnings; Landslides; Landslides & mudslides; Mitigation; Natural Hazards; Prediction models; Public opinion; Rain; Rain gauges; Rainfall; Rainfall duration; Rainfall forecasting; Risk management; Risk perception; Risk reduction; System effectiveness; Technical Note; Warning systems; Weather; Weather conditions
• ISSN: 1612-510X; 1612-5118
• DOI: 10.1007/s10346-020-01379-6

Early warning systems have often been considered an effective risk mitigation tools for landslides. In 1977, the Geotechnical Engineering Office (GEO) of Hong Kong government established the world’s first territorial-wide early warning system for landslide disaster. The Landslip Warning System (LWS) has then been continuously enhanced and upgraded in response to the enrichment of rainfall and landslide database, advancement in instrumentation techniques and change in public perception of landslide risk over the last 40 years. This article consolidates the extensive experience of Hong Kong in using the landslip early warning system (LEWS) as a landslide risk management tool. A comprehensive review on the development process of the rainfall-landslide prediction models is presented. The landslide prediction model evolved from a rainfall duration-intensity model (late 1970) to a simple rainfall threshold model (middle 1980 to late 1990), then to a rainfall-landslide density model (early 2000) and rainfall-landslide frequency model (middle 2000s onward). Through regular review and update of the prediction model taking into account the availability of more recent data and activities that alter landslide risks, the performance of prediction model could be enhanced. The number of rain gauges expanded from 20 to 92 to support the operation of different generations of LWS. The GEO is currently adopting internet of thing (IOT) and cloud computing technology to enhance the resilience of the LWS, especially during extreme weather condition.