Precipitation pattern has changed over many regions in recent decades. There are evidences of increased heavy precipitation and decreased light precipitation in widespread parts of the globe due to global warming. Many studies over Indian region focus on heavy precipitation and risk of floods. But few works discuss the changes in light precipitation and risk of droughts. In this study, changes in total dry days, prolonged dry spells, light precipitation, and risk of drought as indicated by Modified Palmer Index (MPI) over India during six decades (1951–2010) are examined quantitatively in the context of global warming. It is found that there are increases of 49% ± 21% and 33% ± 17% in prolonged dry spells and total dry days, respectively, over India for each degree Kelvin (K) increase in global mean temperature. There is an increase of 51% ± 24% K⁻¹ in drought index MPI (<= − 2.0). There is also a reduction of 31 ± 14% K⁻¹in light precipitation days over India. These changes are more severe over northeastern and western part of India. Increases in prolonged dry spells, total dry days, and decreases in light precipitation relate well with the increases in drought index MPI (<= − 2.0). These results suggest that there is an increased risk of drought due to increased prolonged dry spells, total dry days, and decreased light precipitation days over India as a result of global warming.

Rainfall is an extremely variable parameter in both space and time. Rain gauge density is very crucial in order to quantify the rainfall amount over a region. The level of rainfall accuracy is highly dependent on density and distribution of rain gauge stations over a region. Indian Space Research Organisation (ISRO) have installed a number of Automatic Weather Station (AWS) rain gauges over Indian region to study rainfall. In this paper, the effect of rain gauge density over daily accumulated rainfall is analyzed using ISRO AWS gauge observations. A region of 50 km × 50 km box over southern part of Indian region (Bangalore) with good density of rain gauges is identified for this purpose. Rain gauge numbers are varied from 1–8 in 50 km box to study the variation in the daily accumulated rainfall. Rainfall rates from the neighbouring stations are also compared in this study. Change in the rainfall as a function of gauge spacing is studied. Use of gauge calibrated satellite observations to fill the gauge station value is also studied. It is found that correlation coefficients (CC) decrease from 82% to 21% as gauge spacing increases from 5 km to 40 km while root mean square error (RMSE) increases from 8.29 mm to 51.27 mm with increase in gauge spacing from 5 km to 40 km. Considering 8 rain gauges as a standard representative of rainfall over the region, absolute error increases from 15% to 64% as gauge numbers are decreased from 7 to 1. Small errors are reported while considering 4 to 7 rain gauges to represent 50 km area. However, reduction to 3 or less rain gauges resulted in significant error. It is also observed that use of gauge calibrated satellite observations significantly improved the rainfall estimation over the region with very few rain gauge observations.

Groundwater quality of Chennai, Tamil Nadu (India) has been assessed during different seasons of year 2012. Three physical (pH, EC, and TDS) and four chemical parameters (Ca²⁺, Cl⁻, TH, Mg²⁺ and SO₄²⁻) from 18 bore wells were assessed. The results showed that pH of majority of groundwater samples indicates a slightly basic condition (7.99_post-monsoon and 8.35_pre-monsoon). TH was slightly hard [322.11 mg/l_pre-monsoon, 299.37 mg/l_post-monsoon but lies under World Health Organization (WHO) upper limit]. EC, TDS, Ca²⁺ and Mg²⁺ concentrations were under WHO permissible limit during post-monsoon (1503.42 μS/cm, 1009.37, 66.58 and 32.42 mg/l respectively) and pre-monsoon (1371.58 μS/cm, 946.84, 71.79 and 34.79 mg/l, respectively). EC shows a good correlation with SO₄²⁻ (R² = 0.59_pre-monsoon, 0.77_post-monsoon) which indicates that SO₄²⁻ plays a major role in EC of ground water of bore wells. SO₄²⁻ has also showed positive correlations with TDS (R² = 0.84_pre-monsoon, 0.95_post-monsoon) and TH (R² = 0.70_pre-monsoon, 0.75_post-monsoon). The principal component analysis (PCA)/factor analysis (FA) was carried out; Factor₁ explains 59.154 and 69.278 % of the total variance during pre- and post-monsoon, respectively, with a strong positive loading on Ca²⁺, Mg²⁺, SO₄²⁻, TDS and a negative loading on pH. Factor₂ accounts for 13.94 and 14.22 % of the total variance during pre- and post-monsoon, respectively, and was characterized by strong positive loading of only pH and poor/negative loading of EC, Ca²⁺, Mg²⁺, SO₄²⁻, TDS and TH during pre- and post-monsoon. We recommend routine monitoring and thorough treatment before consumption. Further, this study has demonstrated the effectiveness of PCA/FA to assess the hydrogeochemical processes governing the groundwater chemistry in the area.