Land deformation has become a crucial threat in recent decades, caused by various natural and anthropogenic activities in the environment. The seismic land dynamics, landslides activities, heavy rainfall resulting in flood events, and subsurface aquifer shrinkage due to the excessive extraction of groundwater are among the major reasons for land deformation, which may cause serious damage to the overall land surface, civil infrastructure, underground tunnels, and pipelines, etc. This study focuses on preparing a framework for estimating land deformation and analyzing the causes associated with land deformation. A time-series SAR Interferometry-based technique called PsInSAR was used to measure land deformation, using Sentinel-1 datasets from 2015 to 2021 by estimating land deformation velocities for this region. The obtained PSInSAR deformation velocity results ranged between −4 mm to +2 mm per year. Further, land use land cover (LULC) changes in the area were analyzed as an essential indicator and probable cause of land deformation. LULC products were first generated using Landsat-8 images for two time periods (2015, 2021), which were then evaluated in accordance with the deformation analysis. The results indicated an increase in the built-up areas and agricultural cover in the region at the cost of shrinkage in the vegetated lands, which are highly correlated with the land subsidence in the region, probably due to the over-extraction of groundwater. Further, the outer region of the study area consisting of undulating terrain and steep slopes also coincides with the estimated high subsidence zones, which could be related to higher instances of landslides identified in those areas from various primary and secondary information collected. One of the causes of landslides and soil erosion in the region is identified to be high-level precipitation events that loosen the surface soil that flows through the steep slopes. Furthermore, the study region lying in a high seismic zone with characteristic unstable slopes are more susceptible to land deformation due to high seismic activities. The approach developed in the study could be an useful tool for constant monitoring and estimation of land deformation and analysis of the associated causes which can be easily applied to any other region.

Urbanisation is an ever-evolving, complicated continuous process distinct from its surroundings, having the tendency to create a micro-scale system with characteristic local environmental conditions. Large-scale urbanization near the coasts has a definite impact on the coastal processes due to dynamic interactions of the coastal waters with the urban atmospheric, hydrological and anthropogenic residues. This study focuses on understanding the contribution of immediate atmospheric variations due to urbanization on surface temperature of coastal waters along the Mumbai coast. Different meteorological and air quality parameters such as Air Temperature (AT), Land Surface Temperature (LST), Precipitation (P), Relative Humidity (RH), Wind Speed (WS) and Aerosol Optical Depth (AOD) collectively were used as determinants of local urban climatic environment; to analyse and understand the impact of urbanization on Sea Surface Temperature (SST) representing coastal system. ERA5 Reanalysis meteorological data and MODIS satellite data products were used to extract information of the said parameters for a period of 20 years and time-series analysis was performed for each using Mann-Kendall method to establish their trend. Harmonic regression using Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) were used to model the existing and forecast the future trend of SST which showed an increasing trend with comparatively better representation by NNAR (RMSE 0.4 – 0.7 K). Further, a polynomial multiple regression model was built to correlate the influence of all urban climatic parameters with SST, which clearly indicated positive forcing of local climate variation on the coastal waters with an R² value of 0.93.

Kolkata, one of the oldest and largest urban centres in Asia echoes all the major characteristics of cities from developing countries experiencing rapid urbanization and unplanned development. This study focuses on understanding the impact of intra-urban variations within a morphologically complex metropolis and its capability to modify the enveloping atmospheric conditions at the meso to micro-scale. The Weather Research and Forecasting (WRF) model was configured using a 3-tier nested domain to conduct high-resolution simulations incorporating improved land surface and urban parameterization and appropriate physical parameterization. Local Climate Zone (LCZ) map representing the land use land cover (LULC) was prepared for the innermost domain covering the city and surroundings using Planet-Scope datasets for 2019, adopting Artificial Neural Network (ANN) approach and validated using ground information. This was then integrated into the model with redefined values of specific urban parameters for a better representation of the city’s morphology. It was observed that LCZ-2 (Compact Mid-rise) and LCZ-3 (Compact Low-rise) cover almost the entire core city and around 70 % of the total built-up extent of the study area, which also consists of LCZ-5 (Open Mid-Rise) and LCZ-6 (Open Low-Rise) regions. Thus, the model was calibrated according to the surface and atmospheric conditions of the region and its performance was evaluated in comparison with ground observations. Simulations were conducted at hourly intervals for a 10-day period (August 2019) during the peak summers coinciding with the south-west monsoon period receiving heavy rain spells, to analyse the impact of heterogenous urban form on the micro-climatic variations within the city as well as its surroundings. The modelled results obtained for 2m air temperature (Tair), surface temperature (Tskin), 10m Wind Speed (WS) and Rainfall (RF) indicated a significant influence of the different LCZ classes and their spatial variations over the city. The average daytime Tair and Tskin values in the LCZ-2 is about 1℃ and 1.5℃ higher than LCZ-3, which is again 0.7℃ and 1.5℃ higher compared to the other urban LULC classes where the internal variation is relatively less. Further, the average temperature differences between the compact and open built-up structures increase significantly during night (2℃ – 3.5℃), further increasing when compared to the peri-urban (around 5℃). The inter-urban heterogeneity however, has a reverse effect on the average WS even during the typical monsoon period, with lowest speed observed in the compact core (2ms-1 – 5ms-1) due to highest surface drag (d), increasing (3ms-1 – 7ms-1) along LCZ-5 and LCZ-6 with reduction in d; which further increases substantially in the peri-urban areas (10ms-1 – 15ms-1) with lowest value of d. The variations in total RF received from the complex towards peripheral urban also depicts a similar pattern, as average RF intensity is the lowest within LCZ-2 and LCZ-3 (7mm/hr – 15 mm/hr), moderate in LCZ-5 and LCZ-6 (10mm/hr – 20mm/hr) and highest along the peri-urban areas (15mm/hr – 35 mm/hr). Thus, urban structural and morphological complexity can have a substantial effect on the local-scale climatic variations even along small horizontal distances within a city.