Quantifying and understanding how global change, such as climate change and extremes, land use change and socio-economic developments affects clean water availability across space and time is essential. This knowledge is key to ensuring sufficient water of suitable quality to meet both human and ecosystem needs at present day and in the future. Recent work has highlighted the importance of considering water quality as a key factor in limiting water supply for sectoral uses. Hence, there is an urgent need for tools such as models that span a gradient from purely statistical (e.g., machine learning) to process-based approaches, anticipating the combined impacts of climate and socio-economic changes on water quality and addressing the resulting environmental and societal consequences. Some of these tools, within both Bayesian and frequentist paradigms, enable consideration of prediction reliability, relating uncertainties to a decision makers’ attitudes and preferences towards risks, all while accounting for the uncertainty related to our system understanding, data and random processes. We seek contributions that apply modeling and data-analytic approaches to:
• investigate the combined impacts on water quality and quantity from climate change and/or extremes across local to global scales, including climate impact attribution studies;
• investigate the impacts of present and future socio-economic developments on surface and/or groundwater quality;
• investigate the implications of compound and cascading extreme climate events (e.g., wildfire and floods, drought and heatwaves) on water quality;
• quantify and couple supply and demand in support of water quality management including vulnerability assessment, scenario analysis, indicators, and the water footprint;
• project future water scarcity (combining water quality & quantity) supply and demand in the context of a changing climate;
• quantify the uncertainty of water quality models under drivers of global change;
• interpret and characterize uncertainties in machine-learning, AI and data-mining approaches that are trained on large data sets;
• address the problem of temporal and spatial scaling in water quality modelling;
• test transferability and generalizability of water quality predictions;
• involve stakeholders in water quality model development to inform risk analysis and decision support;
• application of remote sensing and/or citizen science in water quality estimates at multiple scales.