The potential impacts of climate change and socio-economic change on flow and water quality in rivers worldwide is a key area of interest.

This study investigates the potential impacts of future climate and socio-economic change on the flow and nitrogen fluxes of the Ganga river system. This is the first basin scale water quality study for the Ganga considering climate change at 25 km resolution together with socio-economic scenarios. The revised dynamic, process-based INCA model was used to simulate hydrology and water quality within the complex multi-branched river basins. All climate realizations utilized in the study predict increases in temperature and rainfall by the 2050s with significant increase by the 2090s. These changes generate associated increases in monsoon flows and increased availability of water for groundwater recharge and irrigation, but also more frequent flooding. Decreased concentrations of nitrate and ammonia are expected due to increased dilution. Different future socio-economic scenarios were found to have a significant impact on water quality at the downstream end of the Ganga. A less sustainable future resulted in a deterioration of water quality due to the pressures from higher population growth, land use change, increased sewage treatment discharges, enhanced atmospheric nitrogen deposition, and water abstraction. However, water quality was found to improve under a more sustainable strategy as envisaged in the Ganga clean-up plan.

Anthropogenic climate change has impacted and will continue to impact the natural environment and people around the world. Increasing temperatures and altered rainfall patterns combined with socio-economic factors such as population changes, land use changes and water transfers will affect flows and nutrient fluxes in river systems. The Ganga river, one of the largest river systems in the world, supports approximately 10% global population and more than 700 cities. Changes in the Ganga river system are likely to have a significant impact on water availability, water quality, aquatic habitats and people. In order to investigate these potential changes on the flow and water quality of the Ganga river, a multi-branch version of INCA Phosphorus (INCA-P) model has been applied to the entire river system. The model is used to quantify the impacts from a changing climate, population growth, additional agricultural land, pollution control and water transfers for 2041-2060 and 2080-2099. The results provide valuable information about potential effects of different management strategies on catchment water quality.

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The Ganga-Brahmaputra-Meghna (GBM) River System, the associated Hooghly River and the Mahanadi River System represent the largest river basins in the world serving a population of over 780 million. The rivers are of vital concern to India and Bangladesh as they provide fresh water for people, agriculture, industry, conservation and support the Delta System in the Bay of Bengal. Future changes in both climate and socio-economics have been investigated to assess whether these will alter river flows and water quality. Climate datasets downscaled from three different Global Climate Models have been used to drive a daily process based flow and water quality model. The results suggest that due to climate change the flows will increase in the monsoon period and also be enhanced in the dry season. However, once socio-economic changes are also considered, increased population, irrigation, water use and industrial development reduce water availability in drought conditions, threatening water supplies and posing a threat to river and coastal ecosystems. This study, as part of the DECCMA (Deltas, vulnerability and Climate Change: Migration and Adaptation) project, also addresses water quality issues, particularly nutrients (N and P) and their transport along the rivers and discharge into the Delta System. Climate will alter flows, increasing flood flows and changing pollution dilution factors in the rivers, as well as other key processes controlling water quality. Socio-economic change will affect water quality, as water diversion strategies, increased population and industrial development alter the water balance and enhance fluxes of nutrients from agriculture, urban centers and atmospheric deposition.

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There are ongoing discussions about the appropriate level of complexity and sources of uncertainty in rainfall runoff models. Simulations for operational hydrology, flood forecasting or nutrient transport all warrant different levels of complexity in the modelling approach. More complex model structures are appropriate for simulations of land-cover dependent nutrient transport while more parsimonious model structures may be adequate for runoff simulation. The appropriate level of complexity is also dependent on data availability. Here, we use PERSiST; a simple, semi-distributed dynamic rainfall-runoff modelling toolkit to simulate flows in the Upper Ganges and Brahmaputra rivers. We present two sets of simulations driven by single time series of daily precipitation and temperature using simple (A) and complex (B) model structures based on uniform and hydrochemically relevant land covers respectively. Models were compared based on ensembles of Bayesian Information Criterion (BIC) statistics. Equifinality was observed for parameters but not for model structures. Model performance was better for the more complex (B) structural representations than for parsimonious model structures. The results show that structural uncertainty is more important than parameter uncertainty. The ensembles of BIC statistics suggested that neither structural representation was preferable in a statistical sense. Simulations presented here confirm that relatively simple models with limited data requirements can be used to credibly simulate flows and water balance components needed for nutrient flux modelling in large, data-poor basins.

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