Shedding light on the ‘invisible’ water crisis: Modelling past and future global surface water quality

Clean water is essential for supporting human livelihoods and maintaining ecosystem health. However, our knowledge of water quality is severely impaired by a lack of quantitative information. Being under-monitored and often imperceptible to the human eye, water pollution has been branded an “invisible crisis”. Protecting and improving the quality of surface waters globally is contingent upon an improved understanding of the problem and its drivers. Process-based models are tools that can supplement our knowledge of water quality beyond what is possible using in situ measurements alone. This thesis introduces and applies the Dynamical Surface Water Quality (DynQual) model, a high-resolution global surface water quality model for simulating water temperature and concentrations of salinity (total dissolved solids; TDS), organic (biological oxygen demand; BOD) and pathogen (fecal coliform; FC) pollution. DynQual was used to provide a global assessment of past and current surface water quality. Modelled results demonstrate that surface water quality issues are globally relevant, with exceedances of key concentration thresholds for TDS, BOD and FC pollution occurring across all world regions albeit with different frequencies and magnitudes. Current year-round and multi-pollutant hotspots are located across northern India and eastern China, whereas trends towards surface water quality deterioration in the last ~40 years are most profound in Sub-Saharan Africa and southern Asia. Process-based models provide unique opportunities to quantitatively assess the impact of future change on the availability and quality of water resources. This includes exploring the effectiveness of management strategies for improving water quality. In this thesis, DynQual was applied to assess the effectiveness of halving the proportion of untreated wastewater entering the environment by 2030 for improving ambient surface water quality. While substantial reductions in organic (BO