Uttarakhand Himalaya in northwest India is a rural, mountain region that shares borders with Nepal and Tibet. Often referred to as “The Land of Gods” for its physical grandeur, Uttarakhand is surrounded by some of the world’s highest peaks and glaciers. However, such beauty comes at a price. The Uttarakhand area is prone to natural and glacier-related disasters, often exacerbated by the region’s topography and climate patterns. Landslides, triggered by heavy rainfall and events called glacial lake outburst floods (GLOFs), expose the high mountain communities to infrastructure, life and community losses. A recent article by Naresh Rana Poonam et al. in Geomorphology measured and mapped susceptibility in Uttarakhand to help create a template that can be applied to locations facing similar climate-related landslides.
To conduct their research, Poonam et al. relied on Landslide Susceptibility Zonation (LSZ) mapping in order to deepen understanding and response in Uttarakhand to local hazards in a manner that can also be replicated elsewhere. Landslide Susceptibility Zonation (LSZ) is a type of mapping system that organizes different variables like geological, geomorphic, meteorological and man-made factors as high-risk based on the chances of slope failure. A slope failure occurs whenever a mountain slope collapses due to gravitational stresses, often triggering a destructive local landslide. Mapping these vulnerabilities is critical to understanding the dynamics and potential force of future landslides in the Himalayas and elsewhere.
Many of Uttarakhand’s peaks have year-round snowpack with glaciers and glacial lakes that can be disturbed by shifting rainfall patterns and changes in the onset of monsoon season. These disruptions can cause a destabilization deep within the ground, causing the initial movement needed to produce a landslide. Additionally, Uttarakhand’s proximity to the Indian Plate, a large tectonic plate where movement occurs along the boundaries, makes it especially vulnerable to frequent earthquakes. According to the United States Geological Survey, the last earthquake in Uttarakhand occurred on December 1, 2016, with a 5.2 magnitude. The energy released during an earthquake of that magnitude has the potential to trigger multiple, large-scale landslides.
Given the high-altitude location of Uttarakhand, earthquakes can also cause glacial lake outburst floods (GLOFs), a type of flood that occurs when the terminal moraine dam located at the maximum edge of a glacier collapses, releasing a large volume of water. These events can be especially destructive to rural mountain communities that are hard to access, making recovery efforts challenging and untimely. Additionally, these villages are often settled in areas where landslides naturally funnel. Preparing mountain communities to understand the risks they face is critical to minimizing damage associated with natural disasters. As a recent article in GlacierHub points out, “Educating and adapting ensures resilience to risks associated not only with glacial outburst flood risks, but also other risks associated with changing climates.” In an attempt to lower the risk of a landslide disaster triggered by a glacial lake outburst flood or rainfall event, Poonam et al. looked at ways to increase accuracy of floodplain mapping. The hope is to help increase the resiliency of communities by encouraging smart expansion with higher predictability of slide prone areas.
LSZ mapping is created using the Weights of Evidence method, a statistical procedure for calculating risk assessment using training data, like an established inventory of previous landslides. This statistical approach allows for information retrieved from a geographic information system (GIS) and remotely sensed data to be integrated regionally. LSV maps can also be derived from a knowledge-driven method that involves more human interpretation; however, this method is based on expert evaluations of a location. According to the article, the statistical approach is used more frequently because it lacks the subjective nature of the knowledge-driven method. When a location is evaluated by an expert, risks and interpretation of potential risks will differ based on the expert, leaving the risk of human error. The statistical approach provides consistency and confidence of regional LSZ maps because they can be interpreted using a common baseline.
The researchers hope that more precise mapping will help communities prepare for disasters such as the one that occurred in Uttarakhand in 2013. In a normal year, the monsoon rains soak Uttarakhand during the second week of July; however, in 2013, those rains arrived in June, a month earlier than expected, catching Uttarakhand off guard. During the spring months, water levels are high with snowmelt from rivers and glacial lakes. Combining monsoon rains with snowmelt during the spring can lead to devastating floods and landslides. As a result, 7,000 people and hundreds of animals lost their lives in a rainfall event on June 15th that took place in the Mandakini Valley, east of Nanda Devi National Park, according to BBC News. Adding to the devastating losses, the Manadkini Valley is also home to the Kedarnath Temple, where Hindu pilgrims travel between the months of May to October. The high volumes of people paired with the early-activated monsoon resulted in increased losses.
After experiencing the devastation of the landslides resulting from the June 2013 monsoon, many people thought the risk of staying in Uttarakhand was too high, so they relocated to the plains. The outmigration left 3,600 villages mostly deserted, as reported by Poonam et al. Outmigration due to climate-related disasters places mountain communities at additional risk for economic stagnation that may lead to increased forced migration to other areas.
Educating communities in both a scientific and social capacity on the risks associated with the natural interaction of weather and a geography allows for increased awareness among local populations which can help lead to better preparedness for future events. According to a recent GlacierHub article, the state of Jammu and Kashmir, located nearby, held a workshop to communicate risk to small mountain communities to help them understand and raise awareness into the unique risks associated with their location. Like with Uttarakhand, it’s not a question of if these events will happen, but when. Providing communities with detailed maps highlighting certain areas that are more prone to landslides and GLOFs will not eliminate the risk, but it may lower it. Combining LSV mapping with education programs on how to use the mapping information will provide small mountain villages with the future tools to build more sustainable and resilient communities. Since LSV mapping efforts are still being integrated, success may not be immediate. However, LSV mapping shows tremendous potential to enable people to continue residing in the world’s richly historic and picturesque locations.