A statistical approach of mapping drought severity using bias-corrected blended dataset over a semi-arid region

Considering the dearth of gauge-based rainfall observations at desirable resolution, it becomes immensely challenging to quantify and monitor droughts, especially over the developing countries. This can be circumvented by utilizing the high-resolution open-access rainfall products. This study is envisaged with the objective to assess the spatiotemporal variation of meteorological droughts over the Bundelkhand region, India. The multi-source weighted-ensemble precipitation (MSWEP), a blended product of global gauge-based, satellite-based and reanalysis precipitation datasets, is utilized for a period of 44 years (1980-2023). The MSWEP rainfall is bias-corrected with respect to the India Meteorological Department (IMD) gridded observation dataset for the 14 districts in the region. Using the corrected rainfall product, the droughts over each district are characterized by Standardized Precipitation Index (SPI) at three different timescales, i.e., the SPI-3, SPI-6 and SPI-12 are used to model short-term, intermediate-term and long-term droughts, respectively. A drought severity index (DSI) is proposed considering the probability of droughts in different severity classes (i.e., near-normal, moderate, severe and extreme). Further, the trend analysis of SPI at different timescales is carried out using Modified Mann-Kendall (MMK) test. The results reveal the MSWEP dataset’s problems in capturing higher quantiles, which affects the probabilistic distribution used for quantifying drought events. However, the bias-corrected MSWEP product showed an excellent match with the IMD gridded data, thereby substantiating its applicability over the Bundelkhand Region. The region is found to be prone to droughts with an increasing trend of dryness. The novel approach of DSI is found to distinguish the drought severity levels at district-scale, which can be helpful for planning and management of droughts. Overall, this study provides critical insights on the drought characterization using state-of-the-art datasets and innovative approaches, which can also be extended to other drought-prone regions of the world.

 

Keywords: Bias-correction; Bundelkhand; DSI; MSWEP; MMK; SPI