SEDIMENT FLOW PATTERNS AND WATER QUALITY IN RAMGANGA RIVER, GANGA BASIN, INDIA

Abstract

Rivers are the most important geological agents on the surface of the continents and are the major pathways for transfer of continental materials to the ocean, in the form of water, dissolved and suspended loads. A river system is a complex system of intertwining channels with an ongoing interaction of water flow and sediment transport processes. Water flow and its level form an important part of the dynamics of this system. The prediction of water level, water flow and sediment transport is important to form an understanding of the river system comprising hydraulics, irrigation, hydropower, geomorphology and management of water resources. However, determining these parameters in difficult terrains and rugged topography is a challenging task. Mathematical models, such as Artificial Neural Network (ANN) and Neuro-fuzzy models are in use for the analysis, prediction and design of river system related projects. The varying physical and geomorphological parameters make it a challenging task to model such a complex system. These models should therefore be user friendly, enabling quick simulations and predictions that generate minimum error. ANN is a modeling paradigm that possesses these features of parallel link, error correction, and nonlinear transfer. ANN enables a user to configure and improve the model at every step based upon model performance and analysis of new data points. It therefore presents a flexible model, with more understanding of the underlying procedure, which otherwise is a complex process to comprehend. The highlights of the research work carried out in this doctoral thesis are summarized below. In the first phase, an ANN model has been developed for Ramganga River catchment of the Ganga basin. The modeled network was trained, validated and tested using daily water discharge and water level data pertaining to 4 years (2010-2013). The network has been optimized using enumeration technique and a network topology of 4-10-2 with a learning rate set at 0.06, which was found optimum for predicting discharge and water levels for the Ramganga River. The Mean Square Error (MSE) values obtained for discharge and water level for the tested data was found to be 0.046 and 0.012 respectively. Thus, monsoon flow patterns can be estimated with an accuracy of about 93.42 %. The information on the total suspended sediments discharge of the river is considered to be crucial for issues concerning water management and environment. The abrupt quantity and Sediment flow patterns and water quality in Ramganga River iv nature of sediment loads can be best studied by simultaneously considering the governing variables towards this physical phenomenon. However, it is almost impossible to account for all the contributing variables towards any physical phenomenon. ANN is one of the suitable data mining techniques which help in carrying out modeling of this phenomenon. In the second phase, ANNs were employed to approximate the monthly mean suspended sediment load for Ramganga River. Simulations run with rainfall and water discharge data were carried out to predict the suspended sediment load. In terms of the selected performance criteria, three algorithms namely Feed Forward Back Propagation (FFBP), Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN) were evaluated and the results so obtained were presented. It has been observed that only rainfall values were not sufficient to correctly predict the suspended sediment load. However, considering water discharge as an input significantly improves the performance of all the three considered algorithms. In the third phase, the temporal and spatial variations of water discharge and sediment flux of the Ramganga River were evaluated and the factors which control these variations were identified. 78 samples were collected from different locations over the 642 km stretch of the river and its major tributaries to observe the temporal and spatial variation of suspended matter in river water. In addition, daily water flow and sediment concentration data of two locations, viz., Bareilly and Dabri, for duration of 10 years (2003-2012) were used to understand the variation in those parameters over an extended time period. An attempt was also made to relate meandering to the change in water discharge and sediment flux in the Ganga Flood Plains (GFP). Human activities also contribute to the sediment concentration. The results showed that a significant amount of water flow and sediment flux (>75%) were attributed to the monsoon months. However, in 2009, the results were not similar to other years, probably because of low rainfall due to the occurrence of an El-Nino. In the fourth phase, physicochemical and heavy metal pollution in the Ramganga River was assessed. The chemical data analysis was processed and analyzed by application of multivariate statistical techniques such as, cluster analysis (CA), principal component analysis (PCA) and one way ANOVA on yearly dataset of 26 sampling stations along the 642 km stretch of the river. Spatial variability of the physicochemical and heavy metal pollution in the Abstract v river was analyzed by hierarchal cluster analysis. The results obtained was used to divide the whole stretch of the river into three clusters; from elevation 4278 ft to 850 ft as less polluted, from 680 ft to 506 ft as moderately polluted and from elevation 506 ft to 455ft as highly polluted. PCA of the seasonal dataset resulted in three significant principal components (PC) in each season explaining 72% to 8% of total variance with strong loadings (>0.75) of PC1on Fluoride (F-), Chloride (Cl-), Sodium (Na+), Calcium (Ca2+), Magnesium (Mg2+), Bicarbonate (HCO3 -), Total Dissolved Solids (TDS) and Electrical Conductivity (EC) representing mineral portion of the river with common source as dissolution of rock containing the related minerals and gypsum soil from the catchment to the river. PCA of heavy metals yielded one PC in summer and monsoon season and three PCs in winter season explaining 65% to 21% variation of the dataset. PC1 of winter season had strong loadings on zinc (Zn), iron (Fe) and manganese (Mn). Temporal variation by one way ANOVA showed significant seasonal variation was in the pH, Chemical oxygen demand (COD), Biochemical oxygen demand (BOD), turbidity, HCO3 -, F-, Zn, cadmium (Cd) and manganese (Mn) (p<0.05). Turbidity showed approximately a two fold increase in monsoon season due to high rainfall resulting in higher suspensions and chemical constituents in the catchment area and subsequent flow of runoff into the river. HCO3 - concentration, F- and pH also showed similar increase in monsoon. The concentration of Zn, Cd and Mn showed an increasing trend in summer compared to monsoon and winter season due to dilution effect in the monsoon season and its lasting effect in winters.