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This study is an effort to trace the spatiotemporal variation in water at Narmada estuarine region through solute concentration. A total of 72 water samples were collected and analyzed from three sampling points along with in situ measurement of tidal height at monthly basis for 2 years. Result shows that spatiotemporal variation of water quality occurs because of the following main mechanisms, i.e., carbonate weathering, dilution and seawater-freshwater mixing. Firstly, points situated toward inland showing the simple dilution effect on receiving high amount of monsoonal precipitation. Secondly, tidal fluctuation pattern has a strong influence on the water quality taken from the point located in near proximity to the coast. Finally, it can be concluded that water quality shows a different response, in accordance with the different tidal phase and the distance from the sea.

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Accurate knowledge of vertical distributions of aerosol and cloud fields and their space-time variations are required to reduce the uncertainty in estimated climate forcing. Here, multi-sensor (both passive and active) data were used to construct the climatology of 3-D cloud and aerosol fields over the Indian monsoon region. Multilayer clouds are found to persist throughout the year, among which cumulus and stratocumulus dominate the low clouds and cirrus dominates the high clouds. A combination of passive stereo-technique (MISR) and radiometric technique (ISCPP) captures the multilayer cloud structure as revealed by active sensor CALIOP. Coexistence of low clouds throughout the year with high aerosol concentration beneath and above leads to a transition from increasing to decreasing cloud fraction with an increase in aerosol optical depth. Such transition is rapid in the monsoon season due to convergence of low clouds to form high clouds facilitated by high aerosol loading. Further, the regional climate model RegCM 4.1 has been used to examine aerosol-cloud interaction. The aerosol-induced changes of low cloud amount are under-estimated by the model. The observation-based seasonal climatology of aerosol and cloud fields presented here may help in improving the model simulations of cloud variability and associated rainfall.