The growing availability of large-sample and large-scale water quality datasets creates new opportunities to understand how water quality varies in space and time across diverse natural and human-influenced settings. Although homogenizing heterogeneous data remains challenging, such datasets move research beyond site-specific studies towards comparative, multi-catchment analyses that reveal emergent patterns in water quality responses. They also help identify the drivers of these patterns, including climate, geology, soil, land use and human activities, together with their interactions and evolution over time. Large-sample analyses support robust statistical and machine learning models and improve the transferability of findings across sites, ecosystem types and regions, thereby deepening our understanding of water quality controls and informing management under changing environmental conditions.
We invite contributions from scientists compiling and exploring large water quality datasets to unravel the natural and anthropogenic drivers shaping water quality patterns. Studies on any solute, such as major ions, nutrients, carbon, metals, emerging contaminants and other pollutants, in inland water ecosystems (streams, rivers, lakes, groundwater) and soils are welcome. Early career scientists are especially welcome. Topics include, but are not limited to:
1. Development and improvement of large water quality datasets
•Dataset compilation and harmonization across regions; data platform development
•QA/QC, treatment of data gaps, censored values and uncertainty
•Integration of high-frequency sensor, remote sensing and citizen science data
2. Innovative approaches to handling big datasets
•Machine learning and deep learning methods, explainable AI methods
•Process-based modelling and statistical approaches beyond machine learning
•Empirical and mechanistic analyses of concentration-discharge (C-Q) relationships
3. Spatial patterns, temporal trends and controls
•Regional contrasts and multi-scale trend analysis
•Driver-response relationships, including lagged responses to changing management and pressures
•Influence of hydrological regimes, climate variability, land cover, soil and geology, and human activity
4. Hot spots and hot moments
•Event-based or threshold-triggered changes during extremes (floods, droughts, heatwaves)
•Spatial clustering of high-impact areas and sources
•Seasonal timing of pollutant loading and retention