Orals

Global and regional water management is facing major challenges to reach targeted water quality goals. Globally major socio-economic developments are triggering a new water quality challenge, particularly in developing and transition countries. Increasing population and expanding public water supplies that fail to adequately address the treatment of wastewater flows, lead to significant water quality deterioration. Regionally the diffuse transfer of pollutants from land to water presents a major challenge, being co-dependent on changing weather patterns such as the frequency and magnitude of storms, the periodicity of droughts, land modifications and response time lags; leading to water quality degradation, risk to human and ecosystem health, food security, and the economy.

The United Nations Sustainable Development Goal 6 requires countries to monitor progress towards ‘ensuring sustainable management of water and sanitation for all' and set-up appropriate monitoring systems and indicators. SDG6 requires defining base lines, trends and targets to review the effectiveness of pollution mitigation measures. While high frequency monitoring and/or long time series have improved our process-based understanding of pollutant losses to water at catchment level, the patterns in water quality due to source management could be confounded by the effect of larger climate and weather cycles. Moreover, in many data poor locations, policy and management can only be informed by the interpretation of lower resolution data.

To this end, Bayesian approaches have become increasingly popular in water quality modelling, thanks to their ability to handle uncertainty comprehensively (data, model structure and parameter uncertainty) and as flexible statistical and data mining tools. Furthermore, graphical Bayesian Belief Networks can be powerful decision support tools that make it relatively easy for stakeholders to engage in the model building process and draw on all available information from expert knowledge to high resolution data sets.

This session focuses on global and regional water quality research and assessments concerning methods and data sets required to evaluate sustainable development measures. We invite submissions on: (i) methods to assess signals and trends in water quality, (ii) assessment of hydrological and biogeochemical processes on pollutant transfer and their relationship to climate effects, time lags and/or adaptive management changes, (iii) development of new modelling and data-driven frameworks identifying hotspots of water quality degradation posing a risk to human and ecosystem health, water and food security, and (iv) model and data based evaluations of strategies to improve water quality.

Keynote speaker:
Prof Peter Reichert: “The need for Bayesian approaches in water research and management.”
Eawag, Swiss Federal Institute of Aquatic Science and Technology; Department of Systems Analysis, Integrated Assessment and Modelling