×J8. Modelling hydrological variability and change across spatio-temporal scales

Catchments are coupled human-natural system composed of components with complex behaviour and nonlinear interactions. Climate variability, change, and extremes (e.g. droughts and floods); vegetation dynamics; land use and land cover; flow regulation and other human developments all influence catchment behaviour. There are myriad unknowns about the variability and change of these components and their impacts on the catchment response (i.e. streamflow dynamics), which pose a great challenge both to explain and model the underlying processes of these components. Yet the need for reliable modelling, particularly under changing conditions, is stronger than ever, as reflected by its inclusion among the Unsolved Problems in Hydrology (https://doi.org/10.1080/02626667.2019.1620507).

In this session, we welcome contributions aimed at tackling the above problems within modelling themes such as (but not limited to)

  1. Model realism: how to modify models to improve their realism in representing catchment processes under non-stationarity, across spatio-temporal scales from catchment to global scale hydrology?
  2. Model prediction: how to provide robust hydrological predictions under changing conditions using novel approaches for model calibration/parameterisation, geospatial intelligence, or developing data-based models (e.g. machine learning and deep learning methods)?
  3. Modelling uncertainty: how to characterise and/or reduce model uncertainty under hydrological variability and change?
  4. Modelling as a learning activity: what new insights modelling experiments can provide for both model (inadequacy/improvement) diagnostics and catchment processes?

Key topics: Hydrological modelling, Non-stationarity, Uncertainty, Hydrological change and variability