Our research adopts a learning framework methodology to study transport processes. This approach integrates field-based monitoring of systems with model development, analysis and experimentation. When modelling is undertaken in parallel with field studies there is an opportunity to generate multi-directional synergistic feedback between the approaches.
A perceptual understanding of a catchment system is normally developed at the outset, based on a mix of pre-existing data and visual field interpretation. This is used to set the framework for initial sampling strategies and model structure.

Our models (e.g. DIY, NIRAMS, STREAM) are based on conceptualised processes that incorporate fundamental principles, such as the water balance, into relatively simple combinations of stores and flows. These are designed to mimic the principle transport processes operational within a catchment. The models are semi-distributed, and incorporate spatially varying catchment characteristics such as topography, stream networks, soils and land use.

Models are commonly applied in an experimental mode that examines sensitivities of the system and the behaviour of the model, in order to improve understanding of the hydrological functioning of a system. Modification to model structures may be tested, as well as different parameterisations. The results from such experiments are used to develop further hypotheses of catchment behaviour which can subsequently inform field campaigns targeted to validate the hypotheses.

Our research in this area focuses on:

• Developing conceptual models tailored to specific catchments and issues
• Integrating the models with field-based monitoring in an iterative approach
• Using the models to develop hypotheses of key transport processes and catchment functioning

Who is working in this area?

• Sarah Dunn

Keywords

• catchment, conceptual model, monitoring, learning, experiment