A document by Amina Khatun. Click on the document to view its contents.

A document by Amina Khatun. Click on the document to view its contents.

A document by Amina Khatun. Click on the document to view its contents.

Compound flooding, characterized by the co-occurrence of multiple drivers is a cause of concern in many world river basins. High river flows accompanied by high rain events are responsible for severe flooding events. Many studies in the recent past have analysed rain and river-flow dominated compound floods. In this study, we assessed the rain and river-flow induced compound flooding scenarios in a large tropical river basin, the Mahanadi River basin, located in eastern India. Since, many high floods have occurred in the past decade in the Mahanadi River basin, we analysed how the historical flood (in the year 2000) will unfold in a projected climate change scenario (in the year 2080) under RCP8.5. We used the bias-corrected hydro-meteorological data of nine Global Climate Models (GCMs) as input forcings to a hydrologic model MIKE11 NAM-HD to simulate the river flows at the head of the Mahanadi delta. The mean areal rainfall for the delta region and the river-flow simulations are forced into a calibrated and validated MIKE FLOOD model to simulate the historical and projected flood inundations. Comparison of the compound floods with the rain-induced and streamflow-induced floods indicate increased compound flooding in the projected scenario. Both historical and projected floods are found to be predominantly influenced by the river flows. Also, both the areal extent and flooding depth are found to be increasing the projected period as compared to the historical counterpart.

Use of ensemble rainfall forecasts has gained popularity in providing detailed uncertainty information and improving hydrologic prediction skill. India Meteorological Department (IMD) provides medium-range multi-model ensemble (MME) forecasts for all over India with 1- to 5-day lead time. In this study, we bias correct the IMD MME rainfall forecasts using a modified version of Kohonen Self-Organizing Maps (KSOM) and analyse the effect of bias correction on the streamflow prediction skill of MIKE 11 Hydrodynamic (HD) model for the years 2012-2014. We have selected the upper region of the Mahanadi River basin as the test bed. The results indicate improvement of rainfall forecasts after bias correction. Subsequently, use of bias corrected rainfall forecasts as input forcing to the MIKE 11 HD model provides better streamflow forecasts at all the lead times (Nash Sutcliffe Efficiency, NSE ranging from 0.89 to 0.41) compared to the use of raw forecasts (NSE ranging from 0.89 to -0.51). To further improve the streamflow forecasts, we have applied the recently developed robust wavelet-based non-linear autoregressive with exogenous inputs dynamic neural network model, WNARX. This post-processing operation tends to improve the streamflow forecast quality to an acceptable range (NSE = 0.92-0.71). The results encourage us to conclude that the IMD MME forecasts has great potential to improve streamflow prediction skill of a hydrodynamic model.