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Landslide early warning systems, an essential part of disaster management, became operational in India just two weeks before the tragic Wayanad landslide. The National Landslide Forecasting Centre, initiated by the Geological Survey of India (GSI), was launched in July 2024. Currently, the centre provides daily forecasts for Kalimpong, Darjeeling, and the Nilgiris, with plans to extend coverage to other vulnerable areas by 2030.
While experimental forecasts are being shared with state authorities, public dissemination is on hold as models are being refined.
Early warning systems (EWS) involve predicting landslide likelihood, disseminating information and enabling timely response. These can be region-specific or target individual slopes.
An EWS generates and shares timely information to mitigate the impacts of disasters like landslides, involving risk knowledge, monitoring and warning, dissemination and communication and response capability. For example, EWS can forecast or predict landslides regionally or on specific slopes.
GSI is working to operationalise a rainfall-induced landslide early warning system (LEWS) prototype, which was developed between 2016 and 2021 by nine partners from India, the United Kingdom and Italy as part of the LANDSLIP project.
While the GSI is focusing on regional systems, several Indian researchers are working on a more local warning system, slope-specific LEWS.
Regional LEWS has a major drawback — it cannot point the slope in a region that might fail. “One challenge that GSI will continue to face is that local people in India want to know forecasts house by house. We had to explain to the district collectors and sub-district collectors that we were giving them forecasts over a region,” Bruce Malamud, professor at Durham University, England, who co-led the LANDSLIP project with the British Geological Survey while at King’s College London, told Down To Earth (DTE).
This approach would require spying on individual hill slopes using sensors installed on the ground. These gadgets can detect movement in the soil along with the moisture levels in it, humidity and rainfall.
In India, researchers from Indian Institute of Technology (IIT) Mandi, IIT Roorkee, IIT Indore and Amrita Vishwa Vidyapeetham are either conducting experiments or have already deployed such systems in landslide-prone areas.
IIT Mandi has so far installed 60 sensors to measure rainfall, soil moisture and humidity in Mandi, Kinnaur and Kangra regions in Himachal Pradesh. Of them, only 45 are functioning after a few malfunctioned or were vandalised.
The team has founded a startup, iIoTs, incubated at IIT Mandi. “Our system can forecast a landslide three hours in advance with an accuracy of 99 per cent. The accuracy drops to 90-92 per cent for the next 24 hours,” said Kala Venkata Uday, associate professor at the Indian Institute of Science, Mandi and a co-founder of the startup, told DTE.
The team tracks rainfall and issues alerts on the likelihood of landslides in the next 24 hours. They also track land movements and warn vehicular traffic via traffic signals at the landslide site. It also advises people living in the vicinity via text messages.
“Before landslides occur, there is intense movement on the ground. Landslides are not a single-day phenomenon. They accumulate over time — anything between weeks to months to years, unless it is a sudden rockfall due to blasting,” Uday explained.
The startup is working on a goldmine of information, which could help improve predictions. The sensors have collected more than five years of data.
Uday knows what sort of movements are being generated in the installed locations and which slopes are moving faster. He can integrate huge amounts of data from a particular area on the temperature, rainfall, soil movement and moisture to improve prediction.
Amrita Vishwa Vidyapeetham has designed, developed and deployed a real-time wireless sensor network for landslide detection. The network is deployed in Munnar, Idukki district in Kerala, covering seven acres of the mountain, according to information provided on the website. In July 2009, this system successfully warned of a possible landslide after monitoring data on soil movement during the torrential rains in Munnar.
However, such slope-specific LEWS can be expensive. “The number of active landslides in the country is huge (Inventory is over 87,000), therefore, installing site-specific movement sensors is extremely cost-prohibitive,” stressed Saibal Ghosh, deputy director general, GSI. Malamud also agreed, explaining that India needs to go regional, except for certain areas with higher population densities and higher susceptibility to landslides.
The success of regional or slope-specific landslide early warning systems rests on communication and awareness. Hong Kong, for instance, has effectively communicated its information. “They conducted door-to-door advertising campaigns,” Fausto Guzzetti, current research director of the National Research Council in Italy, told DTE. Guzzetti is also one of the authors of a 2020 paper that found links between rainfall and landslide occurrence.
For now, GSI is conducting landslide awareness programs involving people in Gram Panchayats in collaboration with the state and district administrations.
The nodal agency is disseminating landslip information through WhatsApp and social media groups involving local, public and non-governmental organisations, according to Ghosh. The foremost challenge is to involve citizens from the very beginning and develop a true people-centric landslide early warning system to achieve the maximum results, he added.
In 2024, scientists from IIT Roorkee published a study in journal Natural Hazards and Earth System Sciences on a new model to forecast debris flow — a type of landslide that is particularly dangerous to life and property. Debris flows can travel at speeds exceeding 35 metres per hour, according to the United States Geological Survey. The Wayanad landslide in July 2024 and floods in North India in 2023 and 2013 are a few examples of large-scale debris flows.
Though other countries, such as Italy, the US and Japan, have developed early warning systems for debris flows, India has yet to fully determine the intensity-duration thresholds for rainfall, particularly debris flows, which is a critical step in forecasting such events. This is because historical records of the rainfall intensity that initiated debris flows in the past are limited.
To overcome this limitation, the IIT Roorkee team created synthetic landslides. This means simulating landslide occurrences under various magnitudes of precipitation to learn more about conditions that could trigger this disaster, Srikrishnan Siva Subramanian, faculty at the Centre of Excellence in Disaster Mitigation and Management at IIT Roorkee, told DTE.
Furthermore, hourly rainfall data is required for forecasting debris flows. Subramanian explained that the implications for hillslope behaviour will differ dramatically between 50 millimetres per hour (mm/hr) rainfall for one hour within a day and 2.08 mm/hr rainfall for 24 hours within a day, despite the fact that both accumulate approximately 50 mm/day rainfall.
Going forward, the researchers plan to use their model for other catchments of Uttarakhand before expanding to the entire state. “Later, once offered an opportunity, we may extend it to the Western Ghats and other landslide-prone areas within India,” the expert added.
The team used a computer model to produce hourly rainfall at finer scales of 1.8 kilometres by 1.8 km. This was then fed to another model, which determines the intensity-duration rainfall thresholds.
Going forward, the researchers plan to use their model for other catchments of Uttarakhand before expanding to the entire state. “Later, once offered an opportunity, we may extend it to the Western Ghats and other landslide-prone areas within India,” the expert added.
This is the final part of a series on early warning systems for landslides. Read the first part here and the second here.
