The main objectives of this research with regard to upstream catchments have been to assess the magnitude and variability in the flow of the GBM rivers as a function of future changes in the climate, land use, and socio-economic conditions and to determine the flux of flows, sediments, and nutrient fluxes moving down the rivers into the Bay of Bengal, essential inputs for any analysis of the delta. Flow, sediment, and nutrient fluxes can provide critical information that can assist the Indian and Bangladesh governments to mitigate future impacts. There are several existing modelling studies on the Ganges river, most of which were funded by either government departments or international organisations, such as the World Bank (Sadoff et al. 2013). In order to undertake an assessment of the hydrology and nutrient dynamics in the GBM rivers, the semi-distributed, process-based INCA model (INtegrated Catchment model) for nitrates (N) and phosphates (P) is applied to the whole of the GBM river systems (Whitehead et al. 2015a, b). A set of climate and strategic socio-economic scenarios have then been evaluated to assess the potential impacts on both flows and water quality in the GBM river system.
3.1 The INCA N and P Model
Modelling complex river systems
such as Ganges, Brahmaputra, and Meghna requires a semi-distributed model that can account for the spatial variability across the catchment. INCA is one such model that has been applied extensively to heterogeneous catchments and has the advantage that it is dynamic, process-based, and integrates
hydrology and water quality. The INCA model has been developed over 20 years as part of the UK Research Council (NERC) and EU-funded projects (Whitehead et al. 2015b), and the model simulates hydrology flow pathways in the surface and groundwater systems and tracks fluxes of solutes/pollutants on a daily time step in both terrestrial and aquatic portions of catchments. The model allows the user to specify the spatial nature of a river basin or catchment, to alter reach lengths, rate coefficients, land use, velocity-flow
relationships and to vary input pollutant deposition loads from point sources, diffuse land sources, and diffuse atmospheric sources. INCA originally allowed simulation of a single stem of a river in a semi-distributed manner, with tributaries treated as aggregated inputs. The revised version now simulates nutrient dynamics in dendritic stream networks as in the case of the GBM system with many tributaries. The model is based on a series of interconnected differential equations that are solved using numerical integration based on the fourth-order Runge-Kutta technique. The advantage of this technique is that it allows all equations to be solved simultaneously. The INCA model has been set up for the rivers as a multi-reach model with all the sub-catchments and reach boundaries being selected based on a number of factors such as a confluence point with a tributary, a sampling or monitoring point, or an effluent input or an abstraction point associated with a major irrigation scheme or a large city. Figure 13.2 shows the sub-catchments for the Ganges. The land use data have been derived using a 1 km grid resolution DTM with land cover data generated from the moderate resolution imaging spectroradiometer (MODIS) satellite. Further details of the model setup are given in Futter et al. (2015), Jin et al. (2015), and Whitehead et al. (2015a).
3.2 Climate Drivers for the GBM Rivers
In order to run a set of hydrological simulations and climate scenarios, INCA requires a daily time series of climate data, namely, precipitation, hydrologically effective rainfall (HER), temperature, and soil moisture deficit (SMD). The model uses these data to drive the hydrological components of the model which generate the sub-catchment river flows. However, obtaining meteorological data over such a large catchment scale is difficult, especially given the wide spatial differences in topography, altitude, and land use in India, China, Bangladesh, Bhutan, and Nepal. Observational data are available from in-situ weather stations and also from satellite measurements, and these have been integrated into observational datasets which cover the region as part of the Aphrodite online data system.Footnote 1 These data have been used to calibrate the climate models both in space and time.
The large-scale general circulation models
(GCMs) have been used to simulate climate across the region and to assess the impacts of increasing greenhouse gas concentrations on the global climate system. However, GCMs typically have coarse spatial resolutions with horizontal grid boxes of a few hundred kilometres size and cannot provide the high-resolution climate information that is required for climate impact and adaptation studies. The use of regional climate model
s (RCMs), which dynamically downscale the GCM simulations through being driven using boundary conditions from GCMs, can provide higher resolution grids (typically 50 km or finer) and are better able to represent features such as local topography and coast lines and their effects on the regional climate, in particular precipitation. There have been relatively few studies focused upon the Ganges river linked to the Bangladesh region which have used RCM output. In this research, an existing set of GCM simulations were used to provide boundary conditions for a RCM for the period 1971–2099 over a South Asia domain, as described in Chap. 11 and Caesar et al. (2015). The GCM is the third climate configuration of the Met Office Unified Model (HadCM3) and is run as a 17-member perturbed physics ensemble driven by the IPCC’s Special Report on Emissions Scenarios (SRES) A1B scenario
(Nakićenović et al. 2000). SRES A1B was developed for the IPCC and still underpins much recent research into climate impacts. It is a medium-high emissions scenario and is based upon a future assumption of strong economic growth and associated increase in the rate of greenhouse gas emissions. To put this into context with the newer Representative Concentration Pathways (RCPs) used in the IPCC Fifth Assessment Report, SRES A1B lies between the RCP 6.0 and RCP 8.5 in terms of the end of twenty-first-century projected temperature increases and atmospheric carbon dioxide concentrations (see Chap. 11).
The model uses the HER, SMD, and temperature daily time series together with all the reach, land use, and catchment data to simulate flow and water quality at every reach along the whole system for the whole period of 1981–2000. The model outputs are then compared to the observed flow data for the rivers to calibrate and validate
the model. The observed flow quality data is sparse on the GBM river systems, although there are several flow gauges on the Ganges and there is a flow gauge on the Brahmaputra at Bahadurabad. In general, the calibration period of 1981–1990 and the validation period of 1991–2000 are both modelled with a good statistical agreement between simulated and observed flow. The Nash-Sutcliffe statistics for the whole period of the observed flow data for the 1981–2000 ranges is 0.55 to 0.75 which, given the complexity of the Ganges and the Brahmaputra, is reasonable. The model captures the main dynamics of the rise to the peaks in monsoon periods and the recession curves towards the dry season, as illustrated for the Brahmaputra in Fig. 13.3.
The fits to the Ganges flow data are of a similar order of magnitude (Whitehead et al. 2015a). In addition to calibrating the flow model, it is necessary to calibrate the water quality model. The water quality data is limited to infrequent observations at several monitoring points along the rivers, and Fig. 13.3 shows the simulated daily concentrations of nitrate-N and ammonium-N from the model at the lower reach of the Brahmaputra River System. The observed data for water quality along the rivers is available from the Indian Central Pollution Control Board.Footnote 2 The mean nitrate (as N) in the Brahmaputra at Dhubri is 0.12 mg/l and comparable to 0.11 from the model simulation. A key objective of the river modelling is to estimate the nutrient load flowing down the river into the Bay of Bengal, as the nutrients are crucial for agriculture, fisheries, and ecology (see Chaps. 24, 25, and 26). These are of primary concern within the project, as they relate directly to human well-being and resource availability for people in the delta region. Figure 13.4 shows the simulated and observed nitrogen load in the Ganges which suggested that the model is simulating the nutrient fluxes well.
Finally, Fig. 13.5 shows how the flows of the GBM Rivers combine to generate the total flows entering the Bay of Bengal illustrating the build-up of flows in the monsoon season from May to November. The INCA models have been used to evaluate a set of scenarios for flows and water quality, and these are described in detail in Whitehead et al. (2015a, b) and Jin et al. (2015). These scenarios have been used in the models of the study area in coastal Bangladesh (Chaps. 16 and 17) and the Bay of Bengal (Chap. 14), as well as the integrated assessment described in Chap. 28.