Multi-satellite precipitation products for meteorological drought assessment and forecasting in Central India

In this study, a comparative analysis of three satellite precipitation products including the Tropical Rainfall Measuring Mission (TRMM-3B43 V7), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS V2) with ground-measured Indian Meteorological Department (IMD) precipitation data were performed to estimate the meteorological drought in the Bundelkhand region of Central India. The high-resolution CHIRPS data showed the closest agreement with the IMD precipitation and well captured the drought characteristics. The Standardized Precipitation Index (SPI) identified seven major droughts events during the period of 1981 to 2016. Appropriate calibration and validation were performed for drought forecasting using the Auto-Regressive Integrated Moving Average (ARIMA) model. The forecasting result showed a reasonably good agreement with the observed datasets with the one-month lead time. The outcomes of this study have policy level implications for drought monitoring and preparedness in this region.

Automated summary

In this study, the authors compared three satellite precipitation products with ground-measured data to estimate meteorological drought in the Bundelkhand region of Central India. The high-resolution CHIRPS data showed the closest agreement with ground measurements and effectively captured the drought characteristics. Using the ARIMA model, the authors successfully forecasted droughts with a one-month lead time. The findings of this study have significant policy implications for drought monitoring and preparedness in the region.