Simulating Daily Flood Inundation Forecasts for a Large Flood-Prone River Delta in Eastern India

Recurring annual floods creates havoc to the life and property of millions of people around the world. Along with damaging the natural habitat of the living beings, floods terminally affects the growth and development of crops. Accurate flood inundation forecasting plays a crucial role in analysing the risk associated with crop damage due to flooding. Keeping this in view, this study attempts to simulate the flood inundation extent and depth with a daily lead-time of up to 3 days. The Mahanadi River delta in eastern India, which is one of the highly flood prone river deltas in the world is considered as the study area. The rainfall forecasts for the river basin are first bias-corrected using a newly developed bias-correction technique employing copula functions and self-organizing maps. Forcing the hydro-meteorological inputs to a conceptual hydrological model, the discharge forecasts up to 3 days lead-time are obtained. For further improvement, the errors in the discharge forecasts are updated using the state-of-the-art deep learning model, Long-Short Term Memory. Finally, the forecasted inundation depth and extent are simulated by forcing the hydrodynamic 1D-2D MIKE FLOOD model with the improved daily discharge forecasts as the upstream inflow boundary conditions. The hydrological model-simulated discharges after performing error updation are found to be reasonably accurate with a Nash-Sutcliffe Efficiency of >0.90. More than 50% of the observed flood inundated area are found to coincide with the model simulated inundations.