2.2.3. Water-Quality Model

Water quality is assessed using DWAQ [46,47] by combining RIBASIM discharges with pollutant load estimates. DWAQ applies the advection-diffusion equation using a numerical solution based on finite volumes derived from the RIBASIM calculation grid to obtain pollutant concentrations of, among others, BOD5 (Biological Oxygen Demand in 5 days) and coliform bacteria. For RIBASIM, links representing schematized canals without flow volumes are estimated based on length of the link and estimated maximum flow velocity in the link. Decay of BOD5 and coliforms in the Ganges and Yamuna depends on simulated residence time and kinetic rate constants adapted from [48] and adjusted by calibration against surface water quality measurements obtained by CWC and CPCB (Central Pollution Control Board) for the period 1999–2014. In DWAQ, pollutants enter surface water as net emissions representing the non-treated fraction of the total waste load generated at RIBASIM nodes. Gross emissions are the product of emission variables and specific emission factors as follows:


Sewage effluent and treatment is modelled separately, considering volumetric treatment capacity based predominantly on [40] and removal efficiency by contaminant from [60,61].

#### 2.2.4. Groundwater Model

Groundwater movement is simulated by iMOD [62], the Deltares extension of the well-known MODFLOW code [63] for solving the groundwater flow equation. iMOD uses the same calculation grid as Wflow, but is applied only to the alluvial area of the basin. It was not possible to model groundwater in the hard-rock areas because of a lack of data on surface-groundwater connectivity. iMOD simulations are transient, while recharge, abstraction and surface water level data inputs are time-dependent.

iMOD describes the alluvial aquifers using geological information. Fence diagrams were available from CGWB as well as MAP files describing the thickness of geologic layers. The result is a three-layer aquifer conceptualization of variable thickness. In the mountainous areas to the south, the shallow aquifer thins; it is thickest in the central basin with a maximum depth of approximately 400 m below sea level. Aquifer parameters (permeability, storage coefficient) were provided by CGWB based on modelling studies by Indian Institutes of Technology [14]. Groundwater recharge was obtained from Wflow (grid-based) for non-irrigated areas and from RIBASIM (lumped) for irrigated areas.

Based on RIBASIM river discharge, river water levels are derived on a 1 km scale and used to calculate fluxes between the river and groundwater. This approach was applied to the Ganga and its main tributaries. For the intermediary areas the surface water system, represented by minor streams and local drainage, is modelled using the MODFLOW Drain Package [63]. This simulates head-dependent flux boundaries, such as the exchange between the groundwater and local surface water.

For each RIBASIM node, groundwater demand for irrigation, industry and public water supply is estimated. For iMOD, all demands besides irrigation are equally distributed as abstraction wells on a 1 km scale over each node area. Irrigation abstraction is spatially distributed using additional information from the irrigation map developed by the International Water Management Institute [64] that indicates irrigation areas and irrigation source. Abstraction wells are only located in cells indicated with irrigation from groundwater.

The CGWB manages a widely-distributed network of nearly 9000 groundwater monitoring locations. Data from this network was made available for calibration. A selection of 1800 locations was used to adjust the model parameters.

#### 2.2.5. Ecological Assessment Module

The ecological assessment module translates the simulated impact of scenarios and interventions on hydrology and water quality into impacts on ecology and ecosystem services. To calculate the overall hydrological indicator, ten ecologically-relevant hydrological sub-indicators were identified to give an indication of changes in magnitude, duration, timing and frequency of both low and high discharge events compared to the pristine condition.

Ecological sub-indicators are expressed as changes in habitat suitability compared to the pristine situation for several fish species, the Ganga river dolphin, the Gharial and the Indian Flapshell turtle. Habitat suitability was calculated with response curves containing environmental thresholds for water quality and water depth.

For socio-economics, the sub-indicators are fisheries, ritual bathing and floodplain agriculture. The fisheries score depends on habitat suitability for the commercially valuable fish species; the religious bathing scope depends on water depth and BOD, coliform bacteria are less important due to the lower risk of contamination during religious bathing when compared to swimming; and the floodplain agriculture score depends on previously flooded bare areas that becomes available during the dry season.

Since the Ganga River and its tributaries differ in geomorphology, discharge and anthropogenic pressures, the system is subdivided into relatively homogeneous eco-zones. Habitat suitability of species is calculated by eco-zone with dose–effect relations for water depth, dissolved oxygen and temperature, which are extracted from the results of the other parts of the Ganga River Basin Model. Ecological scores are calculated as a percentage of agreement with the reference situation, which is the simulated pristine situation without anthropogenic pressures and with historical land use.

The ecological assessment module and its application to the analysis of different flow regimes is described in detail in [7].

#### 2.2.6. Water Information System and Dashboard

All model inputs and all relevant outputs are stored in the database of the water information system GangaWIS (Figure 6). Delft-FEWS [65] is used to run the different model components and Delft-FEWS model connectors are used to export input data from the database to different model components and to import simulation results from the model components into the database. The database consists of a PostgreSQL/PostGIS [66] geodatabase for time series and vector data and a THREDDS server [67] to store and retrieve gridded data in NetCDF format [68].

The dissemination layer has three components. A website [69] presents static information, such as the project description and reports. The Delta Data Viewer [70] is used to present data from the database on a webpage. And the dashboard presents simulation results aggregated into eleven indicators (Table 2) supported by maps and a Ganga River long-section (profile). All indicator values are calculated based on the simulation results of the meteorological time period 1985–2014.

**Figure 6.** GangaWIS (water information system) structure.



Separate pages zoom to state-level and add state-specific indicators, maps and graphs. Indicators of interest were determined through the participatory modelling process. The dashboard was designed for end-users to assess the impact of scenarios and strategies by comparing results between two model runs with different inputs.

#### **3. Results**

#### *3.1. Results of Participatory Modelling Process*

#### 3.1.1. Broad Participation

One of the results of the adopted approach was broad participation from different national-level government departments/agencies and those from the eleven Ganga states, both in the series of basin-wide workshops as well as in the different state-level workshops (Tables 3–5). Participants were particularly positive about the opportunities the approach offered counterparts in different government agencies to collaborate and share cross-sectoral information relevant to Ganga basin planning. Many reported gaining new insights into the river basin, users' needs and interests and the role modeling can play in the planning process.

**Table 3.** Participation of national-level and state-level organizations in basin-wide workshops.


**Table 4.** Participation of stakeholder organizations in state-level participatory modelling workshops, July–October 2016.


<sup>1</sup> HP = Himachal Pradesh, UK = Uttarakhand, Har = Haryana, Del = Delhi, Raj = Rajasthan, MP = Madya Pradesh, UP = Uttar Pradesh, Jhar = Jharkhand, Chh = Chhattisgarh, Bih = Bihar, WB = West Bengal.

**Table 5.** Participation of stakeholder organizations in state-level model validation workshops, April–June 2017.


#### 3.1.2. Input to the Dashboard

The interactive workshops in 2016 provided input on the most important problems in the basin and provided data for model development. The workshops paid special attention to identifying indicators relevant for stakeholders. The dashboard is based on the indicators identified through this process. Where indicators were proposed that could not be evaluated using the modeling framework, the participatory process helped to manage expectations.
