Evidences of localized coastal warming near major urban centres along the Indian coastline: past and future trends.

Large-scale urbanization near the coasts is reported to directly impact physical and biogeochemical characteristics of near shore waters, through hydro-meteorological forcing, developing abnormalities such as coastal warming. This study attempts to understand the impact-magnitude of urban expansion on coastal sea surface temperature (SST) rise in the vicinity of six major cities along the Indian coastline. Different parameters such as air temperature (AT), relative humidity (RH), wind speed (WS), precipitation (P), land surface temperature (LST) and aerosol optical depth (AOD) representing the climate over the cities were analysed and AT was found to have highest correlation with increasing coastal SST values, specifically, along the western coast (R2 > 0.93). Autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were employed to analyse past (1980-2019) and forecast future (2020-2029) SST trends off all urban coasts. ANN provided comparatively better prediction accuracy with RMSE values ranging from 0.40 to 0.76 K compared to the seasonal ARIMA model (RMSE: 0.60-1 K). Prediction accuracy further improved by coupling ANN with discrete wavelet transformation (DWT) which could reduce the data noise (RMSE: 0.37-0.63 K). The entire study period (1980-2029) revealed significant and consistent increase in SST values (0.5-1 K) along the western coastal cities which varied considerably along the east coast (from north to south), indicating the influence of tropical cyclones combined with increased river influx. Such unnatural interferences in the dynamic land-atmosphere-ocean circulation not only render the coastal ecosystems vulnerable to degradation but also potentially develop a feedback effect which impacts the general climatology of the region.

Analyzing urbanization induced groundwater stress and land deformation using time-series Sentinel-1 datasets applying PSInSAR approach.

Urban intensification has taken a serious toll on the groundwater reserves which is one of the primary sources of fresh water on earth. Exploitation of groundwater has exponentially increased over time, especially in urban landscapes, with ever increasing demands to cater the growing population and development processes. This emphasizes on the importance of proper monitoring of the groundwater variations, which is a difficult process for not being directly accessible for physical measurements. Therefore, it is essential to develop advanced innovative indirect methods to help long-term monitoring of groundwater reserves at a relatively higher resolution, so that local level variations and their impact could be studied in case of excessively exploited zones, like cities. Recent studies have linked land-subsidence to over-exploitation of groundwater, which can be critical for urban scenario, which requires longer duration for replenishment. Thus, this study focuses on monitoring of the groundwater variations using time-series Sentinel-1 Interferometric SAR (InSAR) datasets by retrieving land deformation by PsInSAR (Persistent Scatterer Interferometric SAR) technique; applying phase information of permanent scattering candidates. 58 and 60 images were acquired during ascending and descending passes respectively between 9/10/2014 to 2/7/2020 for the study area i.e., Lucknow city (India) and its surroundings. The field measurements of groundwater level for various seasons (pre and post monsoons) were acquired from the Central Groundwater Board, Government of India (CGWB). Besides, Landsat 5 and 8 datasets were utilized to analyze the pattern of urban growth for a 30-year period and predict the near future scenario. In-depth analysis of all the components revealed a direct relationship between land deformation, groundwater variations and urban expansion. A high correlation coefficient of 0.886 was observed between groundwater level variation and the retrieved deformation measured along the groundwater wells along the deformation zones. Therefore, the overall analysis and results indicate that PsInSAR technique has great potential for estimating the groundwater levels and surface deformation at higher resolution and could be easily applied for any other city for continuous assessment.