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.

For catchment-scale streamflow estimation, recently, the preference is shifting towards the use of innovative remote sensing (RS)-based approaches at the remote gauging stations. In this context, this study evaluates the performances of three RS-based models designed with the near-infrared (NIR) bands of Landsat images at the Jenapur outlet of the Brahmani River basin in eastern India. These RS-based models are designed using the spectral behavior of land (C) and water (M) pixels in the NIR region of the electromagnetic spectrum in the presence and absence of water surrounding the streamflow gauging station. Further, the computed pixel ratio (C/M) is used as a parameter for the discharge estimation, in which four years (2009-2013) of Landsat images are used during calibration and three years (2014-2016) of these images are used during validation. Model-I uses the C/M method in which a box-matrix is conceptualized to analyze the optimal location of the land pixel (C0); and subsequently, the time series of C/M is calibrated with the in-situ discharge (Q) time series. The best pixel ratio (C0/M) time series is preprocessed with an exponential smoothing filter to derive the best filtered-pixel ratio (C0/M*) time series, which is used in the regression model to estimate the river discharge. Model-II corresponds to the multi-pixel ratio (MPR) method, where a 3×3 window is used to calculate the average reflectance of both the C and M pixels within the box-matrix, and subsequently, to obtain the best pixel (C0ʹ/Mʹ) ratio as in the case of Model-I to develop the spectral relationship between C0ʹ/Mʹ and Q time series. Model-III uses both the C/M and water width-based function to estimate the streamflow. The performance evaluation of the models is carried out using the Nash-Sutcliffe efficiency, Percentage bias, and Mean absolute error, which reveals that the model performance varies in the order: Model-III > Model-II > Model-I. This proposed RS-based discharge estimation model framework has the potential to be used in many world- rivers with varying cross-sections.

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.

Nowadays, many of the world cities and its peripheral areas are under constant threat of flooding due to high rainfall events. Increased rate of severe flood hazards world-wide have created a demand for understanding the flooding behaviour across various urban and peri-urban catchments. However, simulating the flooding scenarios under limited hydrological data condition is a great challenge for the urban water planners and managers, especially in developing countries. To this, in this study, a combined modelling approach is proposed with the physically based Variable Parameter Muskingum Stage (VPMS) routing model to develop a local rating curve for generating the discharge data from the available stream stage data, which was subsequently used as an input to the Storm Water Management Model (SWMM). The coupled methodology is applied in a typical ungauged urban and peri-urban catchment of eastern India. The proposed SWMM-VPMS model is calibrated for the monsoon (rainy) season of the year 2009, and validated for the monsoon seasons of 2011 and 2014. The performance of the model is satisfactory with the Nash-Sutcliffe efficiency (NSE) estimates of 0.82, 0.89, and 0.89 for the years 2009, 2011, and 2014, respectively. The proposed SWMM-VPMS model can be used for a catchment where the tributary is ungauged while the main river is gauged at a downstream location of the confluence point. This methodology can also be adopted in catchments with missing rating curves. Additionally, the information regarding spatial distribution of channel flow depth at different time can be utilised to have an initial assessment of the flood-vulnerable areas. Keywords: VPMS; SWMM; Urban and peri-urban catchment; Ungauged catchment

Water Balance Components (WBCs), the most fundamental processes of a river basin, are getting disturbed by climate change and land-use alterations in the present scenario. These two factors are the major driving force behind the spatio-temporal variation of the WBCs that creates an alarming concern for fulfilling water demand by different sectors. The popular hydrological model Soil and Water Assessment Tool (SWAT) is applied to the Baitarani River basin (12,095 km2) of eastern India for evaluating the dominance of the above factors on the WBCs deviations at a decadal scale (1980-1989, 1990-1999, and 2000-2009). A quantile regression-based stochastic approach has been used to analyze the uncertainties resulting from different simulations accounting the combined responses of climate change and land-use alterations as well as model parameters. However, such analysis has not been explored more for the present study area. The model performance results reveal that the statistical performance indices (NSE and R2) are within the acceptable limit. The WBCs in terms of evapotranspiration, surface runoff, lateral flow, water yield, soil moisture storage, and deep aquifer recharge has been quantified at a decadal scale from the simulated model outcomes. The result shows that the water yield component is more (680.36 mm) for mid-decade (1990-1999) as compared to other decades, which is favourable to fulfill sectoral water demand. Further, the uncertainty analysis explains that climate change impact plays a vital role in WBCs variation. The developed approach can portray the variability of WBCs of other river basins that are vulnerable to climate change. The outcomes of this study can be used to maintain an appropriate balance between water availability and demand to avoid water scarcity. Keywords: Water balance components; Climate change; Land-use Alterations, Brahmani; Baitarani

Catchment-scale streamflow assessment in the paddy-dominated catchments using the available hydrological models is highly intricate due to the presence of significant ponding water during the rainy season and irrigation events. While the Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) model could be some suitable options for this, the model conceptualization, discretization of spatial domain, involved computational time, and these integrated effects on streamflow simulation are yet to be evaluated. Hence, this study evaluates the performances of the semi-distributed hydrologic response unit (HRU) based SWAT-pothole and grid-based VIC model for catchment-scale streamflow simulation; and subsequently, assesses the effect of the spatial domain representation of the catchment on the model computational requirement. The selected study is the 12,014 km2 Kangsabati River basin (KRB) of eastern India that consists of about 46% paddy land use with the tropical monsoon type climate having complex paddy field dynamics. SWAT is set up for the whole KRB using the add-in reservoir module; whereas the VIC model, due to the absence of reservoir module, is set up individually for the upstream river catchment and the downstream reservoir-catchment command. The SWAT setup consisted of 44 and 191 discrete sub-basins and HRUs, respectively. Conversely, the VIC setup resulted in 31 grids with 0.25°0.25° spatial discretization. The model simulation results reveal that the SWAT-pothole approach performed better with the Nash-Sutcliffe Efficiency (NSE) estimate of 0.79 than the standalone VIC model (NSE=0.68). However, the other water balance components, viz. evapotranspiration, baseflow, and percolation are far away from reality in both the models which could be attributed to the non-accountability of irrigation return flow by the SWAT-pothole model. The inferior water balance simulation in VIC could be aggravated due to the absence of crop management module and ignorance of explicit paddy land use class in a VIC grid. These findings highlight the future scope of including more dynamic spatial representation of paddy land use in the SWAT and VIC model domain for their application in other similar world river basins.