PhD defence: 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, in-stream water quality sampling and analysis can be laborious, expensive, and often require "boots on the ground". For these reasons, observations from water quality monitoring stations are often limited in space and made infrequently. Being under-monitored and often imperceptible to the human eye, water pollution has been branded an “invisible crisis”. 

Water quality models are useful tools for supplementing our knowledge of water quality dynamics. Models can also be applied for both historic and future conditions, whereas (unfortunately) it is not possible to travel backwards or forwards in time to collect a water sample. In this thesis, I developed a new global water quality model for simulating the concentrations of multiple pollutants at high spatial and temporal resolution.

We used this model to evaluate the past, present and future status of global surface water quality. Our results can help to better understand large scale spatial patterns in water pollution, as well as considering changes in surface water quality both within individual years and over longer time periods. We find that areas with high concentrations of multiple pollutants are currently prevalent across northern India and eastern China, but that surface water quality issues exist across all continents. Current trends towards surface water quality deterioration are most profound in the developing world, a pattern that is expected to continue into the future based on projections of climate and societal change. In particular, Sub-Saharan Africa will become increasingly threatened by deteriorating surface water quality – a region where vulnerability to the negative impacts of water pollution is also high.