Quantifying and understanding the impacts of global change (climate change and extremes, land use change and socio-economic developments) on clean water availability across space and time is critically important for ensuring that there is enough water of suitable quality to meet 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. Thus, 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 address 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 other 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;
• 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 or water security (combining water quality & quantity) supply and demand in the context of a changing climate and other global change drivers;
• quantify the uncertainty of water quality model under drivers of global change;
• interpret and characterize uncertainties in machine-learning, AI and data-mining approaches that are trained on large, possibly high-resolution data sets;
• address the problem of temporal and spatial scaling (e.g. disparity of scales between processes, observations, model resolution and predictions) in water quality modelling;
• test transferability and generalizability of water quality findings;
• involve stakeholders in water quality model development to inform risk analysis and decision support;
• application of remote sensing in water quality estimates at multiple scales.
• 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;
• 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 or water security (combining water quality & quantity) supply and demand in the context of a changing climate and other global change drivers;
• quantify the uncertainty of water quality model under drivers of global change;
• interpret and characterize uncertainties in machine-learning, AI and data-mining approaches that are trained on large, possibly high-resolution data sets;
• address the problem of temporal and spatial scaling (e.g. disparity of scales between processes, observations, model resolution and predictions) in water quality modelling;
• test transferability and generalizability of water quality findings;
• involve stakeholders in water quality model development to inform risk analysis and decision support;
• application of remote sensing in water quality estimates at multiple scales.