Water quality modelling of the Mekong River basin: Climate change and socioeconomics drive flow and nutrient flux changes to the Mekong Delta

The Mekong delta is recognised as one of the world's most vulnerable mega-deltas, being subject to a range of environmental pressures including sea level rise, increasing population, and changes in flows and nutrients from its upland catchment. With changing climate and socioeconomics there is a need to assess how the Mekong catchment will be affected in terms of the delivery of water and nutrients into the delta system. Here we apply the Integrated Catchment model (INCA) to the whole Mekong River Basin to simulate flow and water quality, including nitrate, ammonia, total phosphorus and soluble reactive phosphorus. The impacts of climate change on all these variables have been assessed across 24 river reaches ranging from the Himalayas down to the delta in Vietnam. We used the UK Met Office PRECIS regionally coupled climate model to downscale precipitation and temperature to the Mekong catchment. This was accomplished using the Global Circulation Model GFDL-CM to provide the boundary conditions under two carbon control strategies, namely representative concentration pathways (RCP) 4.5 and a RCP 8.5 scenario. The RCP 4.5 scenario represents the carbon strategy required to meet the Paris Accord, which aims to limit peak global temperatures to below a 2 °C rise whilst seeking to pursue options that limit temperature rise to 1.5 °C. The RCP 8.5 scenario is associated with a larger 3-4 °C rise. In addition, we also constructed a range of socio-economic scenarios to investigate the potential impacts of changing population, atmospheric pollution, economic growth and land use change up to the 2050s. Results of INCA simulations indicate increases in mean flows of up to 24%, with flood flows in the monsoon period increasing by up to 27%, but with increasing periods of drought up to 2050. A shift in the timing of the monsoon is also simulated, with a 4 week advance in the onset of monsoon flows on average. Decreases in nitrogen and phosphorus concentrations occur primarily due to flow dilution, but fluxes of these nutrients also increase by 5%, which reflects the changing flow, land use change and population changes.

Impacts of climate change and socio-economic scenarios on flow and water quality of the Ganges, Brahmaputra and Meghna (GBM) river systems: low flow and flood statistics

The potential impacts of climate change and socio-economic change on flow and water quality in rivers worldwide is a key area of interest. The Ganges-Brahmaputra-Meghna (GBM) is one of the largest river basins in the world serving a population of over 650 million, and is of vital concern to India and Bangladesh as it provides fresh water for people, agriculture, industry, conservation and for the delta system downstream. This paper seeks to assess future changes in flow and water quality utilising a modelling approach as a means of assessment in a very complex system. The INCA-N model has been applied to the Ganges, Brahmaputra and Meghna river systems to simulate flow and water quality along the rivers under a range of future climate conditions. Three model realisations of the Met Office Hadley Centre global and regional climate models were selected from 17 perturbed model runs to evaluate a range of potential futures in climate. In addition, the models have also been evaluated using socio-economic scenarios, comprising (1) a business as usual future, (2) a more sustainable future, and (3) a less sustainable future. Model results for the 2050s and the 2090s indicate a significant increase in monsoon flows under the future climates, with enhanced flood potential. Low flows are predicted to fall with extended drought periods, which could have impacts on water and sediment supply, irrigated agriculture and saline intrusion. In contrast, the socio-economic changes had relatively little impact on flows, except under the low flow regimes where increased irrigation could further reduce water availability. However, should large scale water transfers upstream of Bangladesh be constructed, these have the potential to reduce flows and divert water away from the delta region depending on the volume and timing of the transfers. This could have significant implications for the delta in terms of saline intrusion, water supply, agriculture and maintaining crucial ecosystems such as the mangrove forests, with serious implications for people's livelihoods in the area. The socio-economic scenarios have a significant impact on water quality, altering nutrient fluxes being transported into the delta region.

Rainfall runoff modelling of the Upper Ganga and Brahmaputra basins using PERSiST

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.