Maintaining good water quality is essential for preserving the ecological, recreational, and industrial functions of our water resources. This quality is mainly controlled by the catchment properties and hydro-meteorological conditions, with land use and climate change significantly altering the quantities and dynamics of particulate and solute concentrations at the catchment outlet. To address these influences, water quality is typically monitored and assessed at the catchment scale. However, effective measures to prevent or reduce water quality deterioration are still hindered by our limited understanding of the underlying processes and causal relationships resulting from the complex interplay of hydrological, biogeochemical, and temporal factors.
Data-driven statistical analysis of discharge and concentration time series observed at catchment outlets provides valuable insights into the underlying mechanisms, including process scaling and the effectiveness of measures. The growing availability of data from long-term, high-temporal and high-spatial resolution monitoring of water quality can inform experimental and modeling studies, allowing us to progress from recognizing patterns to the understanding and also modeling of processes. A profound understanding of solute and particulate mobilization, retention, and export mechanisms ultimately allow us to develop local or catchment-scale solutions to mitigate negative impacts on water quality.
This session brings together contributions focused on analyzing or modeling solute and particulate export dynamics at the catchment scale with those focused on the development of mitigation measures or other solutions to enhance or protect water quality.
Examples include solute or particulate export patterns and C-Q relationships under variable hydro-meteorological conditions or contrasting landscapes, mitigation measures based on best practices as well as innovative measures under development or tested under lab-scale conditions.
Data-driven statistical analysis of discharge and concentration time series observed at catchment outlets provides valuable insights into the underlying mechanisms, including process scaling and the effectiveness of measures. The growing availability of data from long-term, high-temporal and high-spatial resolution monitoring of water quality can inform experimental and modeling studies, allowing us to progress from recognizing patterns to the understanding and also modeling of processes. A profound understanding of solute and particulate mobilization, retention, and export mechanisms ultimately allow us to develop local or catchment-scale solutions to mitigate negative impacts on water quality.
This session brings together contributions focused on analyzing or modeling solute and particulate export dynamics at the catchment scale with those focused on the development of mitigation measures or other solutions to enhance or protect water quality.
Examples include solute or particulate export patterns and C-Q relationships under variable hydro-meteorological conditions or contrasting landscapes, mitigation measures based on best practices as well as innovative measures under development or tested under lab-scale conditions.