Statistical generation of fine-resolution precipitation data in Ganga and Godavari River basins of India using limited training datasets

The Ganga and Godavari are major rivers of India and are known for satisfying the agricultural needs of most part of the country. In the past few decades, these basins have seen increased geohazard scenarios such as floods, flash floods, landslides, etc. The availability of fine-scale precipitation data is a necessity for accurate monitoring and routine issuance of flood warnings. Downscaling of precipitation is a challenging task due to the complex topography of the basin, seasonality of the Indian rainfall, and large-scale influence of meteorological variables. In this study, we set up a Multiple-Point Statistics (MPS) based statistical downscaling approach using available precipitation data of the previous years to generate precipitation data for future at a finer resolution. The MPS approach uses the Training Image (TI) as input, hence we investigate into the adequate length of the past data record required for setting up the statistical model. We also investigate whether the length of data used as a TI in one River basin has any similarity in the other River basin. Further, what is the minimum year of data required to set up the statistical model. This is done by diving the datasets into five sets of TIs with each succeeding set larger than the previous one. This study uses datasets from High Asia Refined Analysis (HAR) (30×30 km) and the Integrated Multi-satellitE Retrievals for GPM (IMERG) (10×10 km) as the reanalysis and observation data respectively for a time period of 2001 to 2014. The idea is to explore if MPS is able to reproduce proper spatial features even with smaller TI data. The work is significant as it will benefit the hydrologists and water resource managers. The work is in progress and the results of the study will be presented at the conference.