In early May 2024, a section of the mountainside collapsed in China’s Guangdong Province, following many days of heavy rain. A 17.9m (58ft) stretch of the Meilong expressway crumbled, leading to numerous fatalities. The catastrophe comes after 56cm (22 inches) of rain had fallen across the 4 weeks prior; an increase of more than 4x versus last year.
The incident on the Meilong expressway underscores the critical impact of increased rainfall on infrastructure stability, particularly in mountainous regions. Here’s how heightened rainfall can trigger landslips:
Soil Saturation: Prolonged and heavy rainfall saturates the soil, reducing its shear strength. Water infiltrates the soil and accumulates in pores. Negative pore pressure and the additional strength from apparent cohesion when the soil was partially saturated dissipates. The pore pressure reverses to positive when the soil becomes saturated, reducing effective stress and hence resistance to sliding.
Erosion: Continuous rainfall can erode the base of a slope, removing supporting material. This process, called undercutting, weakens the slope and can lead to its eventual collapse.
Increased Load: Water adds significant weight to soil and rock. As the ground becomes waterlogged, the increased mass can overwhelm the internal friction and cohesion that holds the slope together.
Hydrological Changes: Changes in waterflow patterns due to heavy rain can alter the natural drainage of a slope. Surface water may find new pathways, potentially leading to localised erosion and instability.
Weathering: Water can chemically weather rocks and soil, further reducing their structural integrity. Over time, repeated wetting and drying cycles can weaken the materials that constitute the slope.
Monitoring landslips and predicting potential a highway collapse can be effectively achieved using satellite remote sensing technologies, particularly Interferometric Synthetic Aperture Radar (InSAR):
Deformation Detection: InSAR can detect ground deformation with millimetre precision by analysing the phase differences between radar images taken at different times. By regularly monitoring these changes, we can identify slow-moving landslips before they become catastrophic.
Wide Area Monitoring: Satellites can cover vast and remote areas, providing comprehensive monitoring of entire regions. This is particularly useful for highways that traverse mountainous and inaccessible terrains.
Temporal Analysis: InSAR enables the monitoring of ground movement over time, allowing for the identification of trends and the potential acceleration of movements that precede landslips.
Early Warning Systems: By integrating InSAR data with meteorological forecasts and hydrological models, we can develop early warning systems. These systems can alert authorities to increased risks due to predicted heavy rainfall, enabling pre-emptive measures to mitigate damage.
Post-Event Analysis: After a landslip event, InSAR can be used to assess the extent of deformation and help in planning reconstruction and rehabilitation efforts.
To enhance the resilience of highways against increased rainfall and landslips, we propose the following steps:
Baseline Mapping: Establish a baseline map of ground stability for the entire highway network using InSAR. This will serve as a reference for detecting future changes.
Regular Monitoring: Implement a routine monitoring program with satellite revisits at regular intervals (e.g., every 12 days) to detect any ground movement or deformation.
Integration with Weather Data: Combine InSAR data with real-time weather information to correlate rainfall events with observed ground movements, improving the accuracy of risk assessments.
Risk Assessment and Prioritization: Use the data to identify high-risk areas and prioritize them for further inspection and reinforcement.
Early Warning Systems: Develop and deploy early warning systems based on InSAR data and weather forecasts to alert maintenance crews and local authorities of potential landslip threats.
By leveraging the capabilities of InSAR and integrating it with other data sources, we can significantly improve our ability to monitor, predict, and mitigate the risks of landslips, thereby safeguarding infrastructure and enhancing public safety.
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