With an aim to enhance the accuracy of rainfall predictions in Indian River basins, researchers from the Indian Institute of Science Education and Research Bhopal (IISER Bhopal) in collaboration with Australian universities have developed a statistical technique that will reduce the challenges posed by the erratic and irregular rainfall.
The unpredictable nature of the rainfall in the country has frequently put the agricultural productivity and overall sustenance at risk. The early and accurate prediction of the rains will help the country to keep a check on the adverse impacts that it experiences during extremely heavy or low rainfall. The pattern of rainfall in India shows considerable variation in intensity, frequency, and distribution. The prediction of accurate rainfall is crucial here as it depends heavily on rainfall for carrying agricultural operations. The early and accurate prediction of rainfall will also help prepare and tackle the adverse consequences of disasters such as flood and drought. The research will also help in judiciously planning of managing water resources during the erratic monsoon months of June, July, August, and September.
What does the research include?
The findings of the research have been published in journals, the Hydrological Sciences, the International Journal of River Basin Management and the Journal of Hydrology: Regional Studies
The diverse statistical approach implemented by the researchers this year is called the Seasonally Coherent Calibration (SCC) model. The model is designed to improve rainfall forecasts in the Narmada and Godavari River basins. The SCC model significantly improved the skill of forecasts over a five-day lead time. The calibrated precipitation forecasts were further applied to generate streamflow predictions using a Soil and Water Assessment Tool.
In yet another branch of research, the researchers focused on the Ganga, Mahanadi, Godavari, Narmada, and Tapti River basin. This research aims at refining Indian summer-monsoon precipitation forecasts from the NCMRWF. They used a statistical approach called the Bayesian Joint Probability (BJP), originally used in Australia, to evaluate the approach's effectiveness in the context of India's monsoon-dominated climate. The study indicated that the BJP-based post-processing approach could substantially enhance forecast skills, particularly when considering only monsoonal precipitation forecasts.
