Central and western part of India has been well known for severe droughts. The intensity, frequency and extent of drought have increased significantly over the years. This alarming situation in the region demands detailed analysis of drought events. In this study, Maharashtra and Madhya Pradesh have been identified to investigate meteorological, hydrological and agricultural drought events for the years 2001 to 2017. Standardized Precipitation index (SPI), Standardized water level Index (SWI) and Vegetation health index (VHI) has been used in quantifying and mapping meteorological, hydrological and agricultural drought respectively. Precipitation data which is used in computation of meteorological drought index were available at coarse scale, which makes it difficult to give holistic picture of drought events. Spatial downscaling technique has been used in addressing this problem. Downscaling methods use the concept of modelling relationship at a coarse resolution between the independent and dependent variables and this relationship is applied to finer resolution independent variables to get the precipitation data at a finer scale. In this study, downscaling of CHIRPS monthly precipitation data to 1 km was carried out with the help of Random forest machine learning algorithm. Normalised Difference Vegetation Index, Land Surface temperature and Digital elevation model were used as the independent variable for establishing relationship. The results were validated with in-situ observations. The values of downscaled CHIRPS precipitation was found to be closer to the ground observations, than the original CHIRPS data. Downscaled CHIRPS data has been used in the computation of SPI. Drought prone areas in the region are identified using the drought prone area map information. It identifies the combined aspect of different droughts. Area statistics were extratcted from the drought prone area map and analysed to understand the scale of the drought effect in terms of area.
Key Words: Drought, Indices, Fine resolution, Downscaling, Composite map