The growing availability of large-sample and large-scale water quality datasets offers new opportunities to understand spatial and temporal dynamics of water quality across diverse natural and human influence settings. While challenges in homogenizing heterogeneous datasets from various sources, such datasets enable researchers to move beyond site-specific studies and perform comparative, multi-catchment analyses, revealing emergent patterns in water quality responses. They also help identify drivers of water quality patterns including climate, geology, soil, land-use and human activities, along with their interactions and temporal evolutions across hydrological settings. Large-sample or large-scale water quality analysis support robust statistical models development, facilitate machine learning applications, and improve transferability and generalization of findings across sites, aquatic ecosystem types, and regions, and thus increase the understanding of water quality controls and management options to support decision-making under changing environmental conditions.
This session invites scientists focused on the compilation and exploration of large water quality datasets, aiming to unravel natural and anthropogenic drivers shaping water quality patterns. We welcome studies on any solutes, such as major ions, nutrients, metals, and pollutants, in inland waters ecosystems (streams, lakes, groundwater) and soils.
Topics of interest include but are not limited to:
1.Development and improvement of large datasets:
•Dataset compilation, harmonization across regions and data platforms development
•QA&QC, addressing data gaps and uncertainty
•Emerging opportunities and challenges in large-scale research
2.Innovative approaches (e.g., machine learning) to handle big datasets:
•Application of diverse statistics and machine learning methods
•Empirical and mechanistic analyses of concentration–discharge (C–Q) relationships
3.Spatial patterns, temporal trends and controls:
•Regional contrasts and patterns, multi-scale temporal trend analysis
•Driver-response relationships including lagged responses to changes in management and pressures
•Influence of physical processes, land cover, climate variability, hydrological regimes, soil/geology and human activity
4.Hot spots and hot moments:
•Event-based or threshold-triggered changes during extremes (floods and droughts)
•Spatial clustering of high-impact areas or sources
•Seasonal timing of pollution loading or retention
Elizabeth Boyer