Discharges and water levels are essential components of river hydrodynamics. In unreachable terrains and ungauged locations, it is quite difficult to measure these parameters due to rugged topography. In the present study an artificial neural network model has been developed for the Ramganga River catchment of the Ganga Basin. The modelled network is trained, validated and tested using daily water flow and level data pertaining to 4 years (2010-2013). The network has been optimized using an 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-level values for the considered river. The mean square error values obtained for discharge and water level for the tested data were found to be 0.046 and 0.012, respectively. Thus, monsoon flow patterns can be estimated with an accuracy of about 93.42%.

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The Ramganga basin is an important sub-catchment of the Ganga River to study the wide-scale effects of human-induced changes on geochemical processes. The basin inhabits pristine locations in the upstream and dense human establishments in the floodplain region. Furthermore, the entrapment of upstream sediments in the Kalagarh Dam aids in creating different geochemical regimes. To reveal the geochemical heterogeneity over the multi-spatial and temporal scale, controlling factors (natural and anthropogenic), and source end-members, dissolved load samples were collected during the pre-monsoon, monsoon, and post-monsoon season of the year 2014. Major cations and anions data were analyzed using principal component analysis and mass-balancing equations-based forward modeling to quantify the contribution from the atmosphere, rock weathering, and anthropogenic sources. The results show that chemical weathering predominates the dilution effect during the pre- and post-monsoon season. A high level of pollution prevails during the non-monsoon season and particularly in floodplain tributaries. Anthropogenic sources contribute up to 42% of the dissolved load composition, whereas silicate and carbonate weathering predominantly contributes 93 and 82% of the dissolved load. Further, the silicate weathering rate (4.9 t km−2 y−1) is higher than the carbonate weathering rate and efficiently uptakes an average of 3.5 × 105 mol km−2 y−1 of CO2. The findings revealed the extent of geochemical heterogeneity and controlling factors influencing the element flux, weathering rates, and chemical transportation over multi-spatial and temporal scales.