Rapid urbanization and associated impacts on land surface temperature changes over Bhubaneswar Urban District, India.

Urban settlements are the socio-economic drivers of human community. However, rapid urban expansion during recent times has a profound effect on regional as well as global rise in temperatures. Remote sensing is a powerful tool to monitor and quantify the expansion of urban built-up area and assess its effects on regional heating. Landsat-derived Land Use Land Cover (LULC) maps have shown a very rapid urbanization in Bhubaneswar, a tier-2 city and the capital of Odisha in the eastern part of India, almost doubling over the last 15 years. Our study on the effects of urbanization reveals that during the period of 2003-2017, the built-up area in the urban district of Bhubaneswar has increased by about 77% at the cost of natural vegetative cover and agricultural/fallow land. This affects the behavior of several climate variables over the urban centers with a clear distinction in these variables observed between the urban and surrounding rural settlements. Land surface temperature (LST) derived from the thermal bands of Landsat satellites at a spatial resolution of 30 m is used for the analysis over the area of interest. The Bhubaneswar airport (urban reference) has shown an increase in mean summer LST by 8% over the study period of 15 years, and 9.84% in LST in the last 5 years from 2013 to 2017 alone. From the present study, it is inferred that it is essential to adopt a sustainable approach for any urban growth to restrain its effect on urban temperature rise.

A systematic method of estimation of alongshore windstress and Ekman transport associated with coastal upwelling.

A standard method for estimating alongshore windstress (AWS) and related cross-shore Ekman transport (ET) is proposed. In the absence of standard methodologies for estimating coastal angles required for AWS estimation, an estimation is typically derived on a case-to-case basis, often approximating entire coastlines with a single line segment. A novel standard parametric method for estimating this coastal angle is developed in this manuscript consisting:•Computation of Coastal Angle from Global Self-consistent Hierarchical High-resolution Shoreline (GSHHS) coastline data through line simplification•Estimation of Windstress components at coastal points from Copernicus Marine Environment Monitoring Service (CMEMS) wind data•Estimation of AWS and associated Ekman TransportThe method developed was demonstrated for the eastern and western coasts of India. While, a single line approximation was adequate for representing the west coast of India (a nearly linear coastline), an ET variation greater than 20% was introduced by oversimplification of the coastline for the east coast, which has significant curvature. This demonstrated the relevance of our systematic approach for coastline simplification, emphasizing the significance of accurate determination of coastal angles for computing ET simultaneously.

COVID-19 lockdowns induced land surface temperature variability in mega urban agglomerations in India.

The COVID-19 pandemic forced a nationwide lockdown in India for months when close to 1.3 billion people were confined to their homes. An abrupt halt in the majority of the urban activities reduced the generation of anthropogenic heat which often exacerbates the Urban Heat Island (UHI) effect in the urban pockets of the country. We studied the lockdown impact on seven highly populated and polluted mega urban agglomerations across India, namely Delhi, Ahmedabad, Hyderabad, Kolkata, Mumbai, Bengaluru and Chennai, using near-anniversary Landsat 8 data. The results revealed that the lockdowns have improved the air quality and reduced the Land Surface Temperature (LST) and hence the UHI effect over these cities. Each of the cities experienced an improved Air Quality Index (AQI) ranging from 18 to 151 units except Chennai (with a marginal 8 units increase in AQI), a decrease in mean LST in the range of 0.27 °C to 7.06 °C except Kolkata which showed an increment by ∼4 °C, and a reduction in daily averaged air temperature ranging from 0.3 °C to 10.88 °C except Hyderabad which witnessed an increase of 0.09 °C during the lockdown (April 2020) compared to the previous years (April 2019 and 2018). Delhi exhibited the maximum positive impact of the lockdown in all aspects with two-fold improved air quality, and Ahmedabad showed the least improvement. In addition to the variations in regional land use and land cover and proportion of essential industries that remained operational throughout the lockdown, the geographic location, topography, local meteorology and climate were some of the other factors also responsible for either aiding or overcompensating the large scale LST variabilities observed in these cities. These results hint at an unprecedented opportunity to evaluate the effectiveness of periodic planned lockdowns as a possible mitigating measure to reduce LST spikes and degraded air quality in urban areas in the future.