Surface water quality deterioration is typically assessed and managed at the catchment scale. Management decisions rely on process knowledge and understanding of cause-effect relationships to be effective. However, the dynamics of solute and particulate concentrations integrate a multitude of hydrological and biogeochemical processes interacting at different temporal and spatial scales, which are difficult to assess using local field experiments. Hence, time series of water quality observed at the outlet of catchments can be highly beneficial to understand these processes. Long-term, high-frequency as well as multiple-site datasets can be used to inform experimental and modelling studies and formulate hypotheses on dominant ecohydrological and geochemical processes moving “from pattern to process”. Recent advances in this field have used concentration-discharge relationships to infer the interplay between hydrological and biogeochemical controls, both in the terrestrial part of catchments and in the river network. Long-term time series of nutrient input-output relationships help understand nutrients legacy effects and catchments response times. High-frequency observations allow understanding the fine structure of concentration dynamics, including flowpath contributions during runoff events and ecological controls on diel cycles. When multiple catchments are monitored, it is possible to relate metrics from concentration time series to catchment descriptors.
This session aims to bring together studies using data-driven analysis of river concentration time series to infer solute and particulate export mechanisms. We strongly encourage studies that use findings from data-driven analysis to build conceptual and process-based models. Presentations of the following topics are invited:
- Interpretation of C-Q relationships
- Long-term changes of nutrient inputs, outputs and apparent nutrient travel times
- Co-variance of solute and particulate concentrations and their ecohydrological controls
- Instream processes and river network effects on solute concentrations
- Utilizing time series of compound-specific isotopic fingerprints
- Time series analysis of emerging contaminants such as pesticides or micropollutants