The COVID-19 pandemic's footprint in India: An assessment on the district-level susceptibility and vulnerability

In this nationwide study, we trace the COVID-19 global pandemic's footprint across India's districts. We identify its primary epicentres, which are the major international airports of Mumbai and Delhi. We then track the outbreak into India's hinterlands in four separate time-steps that encapsulate the different lockdown stages implemented. Using a detailed district-level database that encompasses climatic, demographic and socioeconomic parameters, we identify hotspots and significant clusters of COVID-19 cases, which are examined to discern temporal changes and predict areas where the pandemic can next spread into. Of prime concern are the significant clusters in the country's western and northern parts and the threat of rising numbers in the east. Encouraging insights emerge from Kerala in South India, where virus hotspots have been eradicated through effective contact-tracing, mass testing and accessible treatment. Allied with this, we perform epidemiological and socioeconomic susceptibility and vulnerability analyses. The former elicits areas whose resident populations are likely to be physiologically weaker in combating the virus and therein we expect a high incidence of cases. The latter shows regions that can report high fatalities due to ambient poor demographic and health-related factors. Correlations derived from the generalised additive model show that a high share of urban population and high population density (1500-2500 people/km2), particularly in slum areas, elevate the COVID-19 risk. Aspirational districts have a higher magnitude of transmission (susceptibility) as well as fatality (vulnerability). Discerning such locations can allow targeted resource allocation by governments to combat the next phase of this pandemic in India

Inventory and GLOF hazard assessment of glacial Lakes in the Sikkim Himalayas, India

Glacial Lake Outburst Floods (GLOFs) are a recurring hazard in the Himalayas, posing significant threat to downstream communities. The North Sikkim district of India, comprising the upper reaches of the Teesta River in the Eastern Himalayas, has experienced past GLOF events. The identification of lakes susceptible to this phenomenon is therefore paramount. Using multi-temporal satellite images, this study tracks lake growth in the region, revealing that 203 new lakes had developed herein during the observation period (2000–2018). Of these, 82 lakes had formed during 2011–2018 alone; indicating marked glacial retreat and consequent lake area growth, alongside a rising temperature trend. Using various weighted geometric and geomorphic parameters, the 36 most hazardous lakes were identified, from which the 10 lakes posing the greatest GLOF threat were discerned. These lakes are mostly situated along the main snowline and Great Himalayan water-divide in the north-eastern part of Sikkim and should be monitored continuously

Ecosystem services value assessment and forecasting using integrated machine learning algorithm and CA-Markov model: an empirical investigation of an Asian megacity

The ecosystem services in an area are quite dependent on its ambient land use and land cover (LULC) attributes. Here we assess the spatio-temporal distribution of Ecosystem Services Value (ESV) for the years 1990, 2000, 2010, 2020, based on the then existing LULC aspects of the Kolkata Urban Agglomeration in eastern India. Further, these are simulated for 2030, 2040 and 2050 to determine the future potential ESV. The respective LULC layers were extracted from Landsat images using the support vector machine method and future projections were done using Markov Chain-Cellular Automata models. Results reveal that all LULC aspects are likely to significantly decrease except built-up tracts. The available ESV shall concomitantly decline by 29.7%, especially due to wetland loss. The ESV patterns also showed strong spatial correlation/clustering, with higher ESV patches locating along rivers/wetlands. These results can better inform management of high-value EVS components for sustaining/improving the urban environmental quality