Revealing anthropogenic influences on catchment surface water quality under data scarcity: case of Hindon River Basin, India

Assessing anthropogenic impacts on surface water quality is essential for developing water quality management strategies. Such assessment relies on sufficient data including anthropogenic effluent and observed concentrations along the river network. These data records  are often incomplete or unavailable in many areas. A water quality model provides means for extrapolation and integration of the available data to the full catchment extent, allowing catchment-wide quantification of pollution patterns and simulation of potential interventions. This study implements a model-based water quality assessment under data scarce conditions applied to the Hindon River Basin in India where surface water is highly polluted due to alleged contributions from industrial, domestic, and agricultural activities leading to emissions into the basin. A catchment modelling framework was implemented by linking a distributed hydrological model (wflow_sbm), which includes anthropogenic demand and allocation, with a substance-based emission model (D-Emissions) and in-stream water quality model (D-Water Quality), set up with mostly open-source datasets. The modelling tool was used to determine the sources, hot spots, and pathways of nutrients and other pollutants across the catchment and river network and assess the seasonal (pre-monsoon, monsoon, and post-monsoon) variations. To quantify the influence of data scarcity on the model output, a sensitivity analysis was conducted on the principal inputs (industrial effluent, domestic wastewater, and fertilizer use) and the resulting variabilities of simulated concentrations were compared against (limited) observations. Finally, management scenarios were simulated including changes in treatment of industrial and domestic wastewater and fertilizer application rates. The results reveal the relative contribution of each of the principal anthropogenic sectors (industries, domestic, and agriculture) on the surface water pollution and implications for catchment water quality management including pollution reduction measures and monitoring requirements for improved model predictions.