Multi-Satellite Precipitation Products for Meteorological Drought Assessment and Forecasting in Bundelkhand region of Central India

Sparse ground observation networks with minimal maintenance limit spatio-temporal coverage of precipitation data in the Bundelkhand region of Uttar Pradesh state of India, which constraint the real-time drought assessment and monitoring. In this study, comparative analysis of three satellite precipitation products including Tropical Rainfall Measuring Mission (TRMM-3B43), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS version-2) with ground-measured Indian Meteorological Department (IMD) precipitation data were performed to evaluate meteorological drought in Central India. Statistical comparison was performed for nineteen years (1998-2016) at the grid level (39 grids) to assess the performance and estimate the regional differences in the spatial distribution of satellite precipitation products as compared to IMD data. The high-resolution CHIRPS showed the closest agreement with IMD precipitation and again it well captured the drought characteristics through the Standardized Precipitation Index (SPI). There are seven major droughts events were observed in the study area between the periods of 1981 to 2016. After proper calibration and validation of
the datasets, the forecasting of drought using the Auto-Regressive Integrated Moving Average (ARIMA) model in the R programming environment was also established. The best model was selected based on the ARIMA and statistical analysis such as minimum Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Forecasting result showed a reasonably good agreement with the observed datasets. The outcomes of this study have policy level implications for drought monitoring and prior preparedness in response to projected drought conditions using satellite data in this region.