Trend Analysis of Drought Events Over Upper Krishna Basin in Maharashtra

Abstract

Drought is a significant natural disaster which leads to food, fodder and water shortages along with destruction of vital ecological system. Drought is acknowledged as a phenomenon associated with scarcity of water due to reduction in the amount of precipitation over an extended period of time, usually a season, a year or more in length. Droughts are frequently occurring in Upper Krishna basin in Maharashtra where about 80 percent of agriculture land is rain fed. Also this region is witnessing rapid urbanization due to industrial growth where thirst for water for drinking as well as industrial use is demanding. The study of drought trends is extremely important as it is related with food security and management of scarce water resource, which becomes critical in case of drought events. In this work, the drought trend of Standardized precipitation index (SPI) at 1-, 6-, 12-, 24- and 48- month time scales, Percent of Normal Precipitation (PNP) at Annual and Water-Year time scales and Seasonal rainfall (winter, pre-monsoon, monsoon and post monsoon time scales) are computed using long time series (1960-2012) of monthly precipitation data at 59 stations in the study area. The statistical significance at 95% confidence level as per Mann-Kendall and Sen's slope estimator are used for drought trend analysis over the Upper Krishna basin in Maharashtra. The PNP drought trend analysis at both annual and water-year time scale is able to detect significant trend at only 5 stations out of all rain gauge stations, SPI based drought trend analysis is found to be more sensitive to multiple time scales. As the SPI-1 time scale is able to detect significant trend at only 4 stations, number of stations having significant trend increases as the SPI time scale increases up to SPI-48 at 50 stations. Drought trend of Seasonal rainfall time series vary as per the classes. Pre-monsoon time scale detects significant negative trend at 41 stations with no significant positive trend at any of the stations. Monsoon time scale detects only 2 positive and 1 negative significant trends at total stations. Post-monsoon time scale is not able to detect any significant trend at all stations. The results indicate that, there is negative trend of pre-monsoon rainfall at over 63 percent of area. SPI- 48 time scale is able to detect significant trend at over 84 percent of area. This analysis may help to solve problems associated with floods, droughts and allocation of water for agriculture, industry, hydro-power generation, domestic and industrial use.