Impacts of urbanization on land use /cover changes and its probable implications on local climate and groundwater level

Given that urbanization is considered as one of the most significant anthropogenic alterations of the environmental framework, the present study attempts to understand spatiotemporal characteristics of urban growth and its implications for the hydro-meteorological parameters in the Howrah Municipal Corporation (HMC) of the Indian state of West Bengal. The empirical approach of the paper is based on land use/land cover (LULC) changes and normalized difference built-up index (NDBI) computed using remote sensing and GIS techniques. Spatiotemporal satellite images and conventional data are used to characterize the urban growth process, whereas K-Means based unsupervised classification technique is used for LULC changes. Inverse distance weighting (IDW) interpolation method is applied for the spatial distribution of rainfall, temperature and groundwater level analysis. In order to examine whether hydro-meteorological (e.g., rainfall, temperature) parameters have any relationship with hydrological components (e.g., groundwater level) the Kendalls Tau test was performed. It is found that the maximum urban built-up area has increased during the last two decades with fluctuations in depth to groundwater level in northern, north-western and south-western side of the city. Notably, built-up expansions have taken place from the north-eastern to the south-eastern part. There are evidences of urban sprawl or shrinkage indicating expansion of built-up area and thus causing environmental degradation in the city area. While the methodology used in the paper has the potential for understanding the urbanization process, the findings have important implications for designing necessary policies and regulations. • The spatiotemporal characterization of urban growth and its probable implications on hydro-meteorological parameters. • K-Means based unsupervised classification technique is used for LULC changes. • Used geographical weighted regression (GWR) to examine the relationships across geographical space.

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