Leveraging Advanced Remote Sensing with Machine Learning and Geospatial Techniques for Spatio-Temporal Monitoring of Lake Water Dynamics in Inaccessible and Data-Scarce Catchments

Lakes are the essential asset for the inhabitants of our planet since these are vital sources of water. It is understood that these lakes become more crucial in the regions where water is not easily available such as in Himalayas, drought-prone, and arid regions. However, it has been noticed that the dual problems have arisen at the same time due to climate change, i.e., water scarcity in the arid or drought-prone regions due to rapid extinction of some of the lakes and flood devastations in Himalayan due to overtopping of water from the vulnerable lakes. Climate Change extremes cannot be blamed alone for the extinction of these lakes while overexploitation, improper maintenance and non-civic senses have also exaggerated the process. While the catastrophic events due to these lakes called Glacier Lake Outburst Floods (GLOFs) are mostly occurring due to extremes rainfall events causing regular expansion and contraction of the lakes. However, these extreme events are more intense and frequent due to climate change and tends to increase in the future, making these lakes more vulnerable and responsible for such events.  It is essential to monitor the lake water dynamics not only for sustainable water resources management but also for mitigating future catastrophic event risk arising due to these lakes. While the monitoring of lakes is not always easy either due to data-scarcity in the catchments or impossible in-situ measurements due to inaccessible catchment terrain like in Himalayas. The availability and accessibility of advanced remote satellite sensing data such as altimeter, and space-borne Light Detection and Ranging (LiDAR) have been enabled us lake monitoring, however, their processing demands modern approaches. Hence, the present study aims to develop a machine learning model integrated with geospatial approach to process these advance remote sensing data for the spatio and temporal monitoring of water dynamics of lakes. The present study utilizes Icesat-2 as space-borne LiDAR and Surface Water and Ocean Topography (SWOT) as wide swath altimeter data. The study provides a reliable and precise remote sensing derived Water Surface Elevation (WSE) for the lakes at spatial and temporal scales. The derived WSE for lakes would help us to identify the vulnerable lakes and to evolve robust policies to solve dual lake problems at greater extent, i.e., water scarcity in drought or arid-prone regions as well as in the regions like Himalayas for mitigating catastrophic events due to glacier lakes. Further, the developed model would be easily applicable to any lake while the finer adjustment may be required due to different topographic conditions. 

Keywords: Lake water dynamics, Space-borne LiDAR, Altimeter, Machine Learning, and Geospatial.