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The current study aims to determine the variability and trend in rainfall data. From 1989 to 2018, daily rainfall data was gathered for the Shimsha basin, a sub-basin of the Cauvery basin in Karnataka, India, and aggregated into annual, seasonal, and monthly rainfall totals as well as the number of rainy days. In this study, the time series are subjected to statistical methods to examine rainfall variability and trend. The mean, standard deviation, skewness, kurtosis, coefficient of variation (CV), and Standardized Anomaly Index (SAI) are all used in the preliminary and variability analysis. To understand the rainfall distribution characteristics, Kurtosis and skewness were used. To identify homogenous and serially independent series, homogeneity and serial correlation tests were used. Mann-Kendall (MK) and Spearman's rank correlation (SRC) tests were performed to identify the trend in the homogenous and serially independent series. Sen's slope (SS) technique is applied to quantify the amount of the trend, and the Sequential Mann-Kendall (SQMK) test was used to assess the trend change point. The statistical tests indicated in this work were performed using open-source R packages such as "trend," "modifiedmk" and "trendchange". The study area's average rainfall was 801.86 mm, and CV ranges from 43.3% to 22.27%. The basin receives the highest rainfall in southwest monsoon season (SWM) followed by post-monsoon, summer, and winter seasons, respectively.Out of 162 series, 11-time series were found to be non-homogeneous and were omitted. Then out of 151 homogeneous series, 21 series showed a significant increasing trend while 99 and 31 series showed a little rising and declining trend, respectively. The study's results will help in water resource management, agricultural planning and socioeconomic development in the area.

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Groundwater dependency has increased due to several factors like population increase, industrialisation, and climate change impact. Analysis of groundwater is very important to know the present status and also for better management of natural resources. Groundwater samples from 41 locations were collected along the Visakhapatnam coastal aquifer during the pre- and post-monsoon seasons, and samples were analysed for pH, EC, total dissolved solids (TDS), major cations, and anions like Ca2+, Mg2+, Na+, K+, Cl–, HCO3–, and SO42–. From the results of the chemical analysis, seawater intrusion is identified through qualitative approaches, i.e., Simpson ratio (Cl/(HCO3 + CO3) and Na/Cl ratio. The Simpson ratio ranges between 0.316 and 2.119 during pre-monsoon and 0.124 and 3.947 during post-monsoon, and from the Na/Cl ratio, 30 samples fell under seawater intrusion during pre-monsoon and 38 samples during post-monsoon. The Simpson ratio results also show that seawater intrusion is reducing during the post-monsoon due to increasing groundwater levels caused by rains. From the water quality index classification, 4.88% of the water samples fell under an excellent category in both seasons, and the rest of the samples were all in the remaining four classes. The spatial analysis was also done to understand the changes in groundwater quality and seawater intrusion over space.