Tackling Climate and Water Challenges in the Himalayas using AI

Updated: Sep 20, 2020

Written and photographed by Suraj Dalal 

Pangong Lake, Ladakh


The unprecedented and existential challenges of climate change are gaining attention. However, the scale of innovation required to manage the crisis doesn’t receive as much recognition. Against this background, new technologies such as Artificial Intelligence (AI) hold promise in helping tackle the climate and water challenges most pertinent to the vulnerable populations in the Himalayas. This article explores one fundamental question: how can we deploy existing and emerging technologies to undertake ambitious climate action in the Himalayas? 


Leveraging Data in the Himalayas     


The term ‘machine learning’ is often used as a panacea to solve the pressing challenges of the world. This narrative is far from the reality. Machine learning requires human designed algorithms. Studying data involves identifying patterns from existing information and using it to predict meaningful outputs, be it in the form of solutions or problem diagnoses. The field of environmental and climate science utilizes enormous amounts of cryosphere data.  Access to credible data on the Himalayas is limited, and existing data needs to be complemented with rapidly evolving algorithms to produce meaningful results.      


Large geoscience datasets which include seismic data surveys, and supercomputer simulation outputs from global climate model projects are now available in the public domain. Using AI, open source tools and cloud computing, we can convert a large amount of data about nature into valuable information that can inform policy choices. 


Machine learning is utilized to generate insights from satellite imagery and to detect and predict changing geographic patterns. One way this technology is used is by taking satellite images in the winter, mapping them onto 3D models and comparing between two seasons. The findings help in predicting the volume of snow that will melt in the summer and help us measure the size of ice sheets, glaciers, and snow cover. Over a period of time, this data can be used to show historical snow dips and predict snow cover, delivering much more accurate information that can inform preparedness and response to potential climate-related events. 


One such use is real time monitoring of snow and ice flow. In 2019, Researchers at Columbia University and the University of Utah used newly accessible images taken by US spy satellites between 1975 and 2000 that captured a 2000 km-wide stretch of the mountains across India, China, Nepal and Bhutan. They converted them into 3D models and compared them against present-day satellite images of the same areas. The findings were startling. From 2000 to 2016, glaciers lost equivalent to more than vertical foot and half of ice each year due to rising temperatures.