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 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 À1 in drought index MPI (<= À 2.0). There is also a reduction of 31 ± 14% K À1 in 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 (<= À 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.

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[1] In the present study, an attempt was made to estimate rainfall by synergistically analyzing collocated thermal infrared (TIR) brightness temperatures from Meteosat along with rainfall estimates from active microwave precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) over Indian land and oceanic regions. In this study, we used broad and frequent TIR measurements from a geostationary satellite for rainfall estimation, calibrating them with sparse but more accurate PR rain rates. To make the algorithm robust, we used a two-step procedure. First, a cloud classification scheme was applied to TIR measurements using the 6.7 mm water vapor channel and TIR radiances to delineate the rain-bearing clouds. Next, the concurrent TIR and PR observations were used to establish a regression relation between them. The relationship thus established was used to estimate rainfall from TIR measurements by applying it to rain-producing systems during southwest and northeast monsoons and tropical cyclones. Comparisons were made with TRMM-merged (3B42 V6) data, Global Precipitation Climatology Project (GPCP) monthly rain rate data, ground-based rain gauge observations from automatic weather stations, and Doppler weather radar over India. The results from combined infrared and microwave sensors were in very good agreement with the ground-based measurements, TRMM-3B42 V6, as well as GPCP.Citation: Mishra, A., R. M. Gairola, A. K. Varma, and V. K. Agarwal (2010), Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multisatellite sensors,

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During the first week of September 2014, the Jammu and Kashmir region witnessed devastating floods across the majority of its districts, caused by multi-day heavy rainfall events. According to data provided by the Home Ministry of India, several thousand villages across the state were hit and 390 villages completely submerged. The preliminary assessment of property damage was estimated between INR 50,000 million to INR 60,000 million. Approximately 277 people died. In this study, an effort was made to analyze the heavy rainfall events over Jammu and Kashmir using hourly data at the fine spatial scale from satellite remote sensing. Data over Jammu and Kashmir reveal strong diurnal variation in rainfall over the severely affected districts. Most of these districts experienced continuous frequent heavy rainfall rates in the range of 15-22 mm/h during the first week of September 2014. The results show that the cumulative rainfall during 2-6 September 2014 may have contributed to the flood events.

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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)