Role of Land Use Land Cover and Skilful Prediction of Rainfall Using Bias Corrected Ensemble during Extreme Rainfall Event

Extreme rainfall events (ERF) and their associated regimes are among the major catastrophes that have a worldwide impact including India which result in large-scale flooding and immense socio-economic loss. During the southwest monsoon season, these extremes are becoming a frequent norm in the hilly and mountainous regions of the country such as Assam which received one of the most historical ERFs during June 14-17, 2022. The present study investigates the ERF implementing the Weather Research and Forecasting (WRF) model using two different land use and land cover data sets (i.e. ISRO and USGS) and three different sets of physical parameterization schemes (i.e. planetary boundary layer, cumulus, and microphysics) with a lead time up to 96 hours. Further ahead, the performance of rainfall in bias-corrected ensembles (BCE) along with a suite of model ensembles is rigorously quantified up to the district level in terms of its intensity, and distribution. Results suggest that among all the ensembles (E1-E10), USGS (E6 - E10) has underestimated rainfall (140-260 mm/day) compared to ISRO (150-280 mm/day), including the heavy rainfall (HR), very heavy rainfall (VHR), and extremely heavy rainfall (EHR) categories over the upper Assam division (UAD) and lower Assam division (LAD) of Assam throughout the 96 hours. Afterward, rigorous statistical analysis in terms of frequency distribution, Taylor diagram, and benchmark skill scores is carried out to elucidate the model biases for all the ensembles. Throughout the 96 forecast hour, BCE E5(E10) is noted with the distinct realistic(underestimated) representation of model rainfall bias over all the subdivisions of Assam. The findings of the present study encapsulate the critical role of the ensembles of physical parameterization schemes, along with proper LULC, and the BCE approach is required to overcome challenges to improve the skills of ERF, particularly over complex terrains of Indian subcontinent such as Assam.