Keywords

rainfall runoff; model structure; catchment behaviour

Start Date

27-6-2018 9:00 AM

End Date

27-6-2018 10:20 AM

Abstract

Currently, there are many rainfall-runoff models available, but no single model can account for the uniqueness and variability of all catchments. While there has been great progress in developing frameworks for optimal model selection, the process currently selects a range of model structures a priori rather than starting from catchment behaviour and hydrological processes. In this study, six hydrological signatures and two catchment characteristics from 108 catchments were extracted for two 7-year time periods: (1) wet and; (2) dry. The data was modelled using the GR4J model to explore the relationship between model performance, catchment features and identified parameters. The assumption is that the hydrological signatures reflect catchment behaviour, and therefore will lead to distinct parameters. Results show that during the wet period, smaller catchment areas, a greater high flow value and greater autocorrelation in the flow data were related to better calibration performance, while smaller area, greater mid flow values and peak distribution determined better performance in the dry period. Catchments also performed better in the wet period compared to the dry period. This resulted in variability in model parameters between the periods, with the soil moisture accounting parameter greatly varying in the dry period, and greater losses of groundwater in the dry period. This study is provides a foundation to optimise and improve model selection in catchments based on their unique characteristics. It suggest that the specific model structure of GR4J is more suited to modelling wet catchments with smoother flow signals.

Stream and Session

E3: Complexity, Sensitivity, and Uncertainty Issues in Integrated Environmental Models

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Jun 27th, 9:00 AM Jun 27th, 10:20 AM

Identifying Model Structure using Catchment Characteristics

Currently, there are many rainfall-runoff models available, but no single model can account for the uniqueness and variability of all catchments. While there has been great progress in developing frameworks for optimal model selection, the process currently selects a range of model structures a priori rather than starting from catchment behaviour and hydrological processes. In this study, six hydrological signatures and two catchment characteristics from 108 catchments were extracted for two 7-year time periods: (1) wet and; (2) dry. The data was modelled using the GR4J model to explore the relationship between model performance, catchment features and identified parameters. The assumption is that the hydrological signatures reflect catchment behaviour, and therefore will lead to distinct parameters. Results show that during the wet period, smaller catchment areas, a greater high flow value and greater autocorrelation in the flow data were related to better calibration performance, while smaller area, greater mid flow values and peak distribution determined better performance in the dry period. Catchments also performed better in the wet period compared to the dry period. This resulted in variability in model parameters between the periods, with the soil moisture accounting parameter greatly varying in the dry period, and greater losses of groundwater in the dry period. This study is provides a foundation to optimise and improve model selection in catchments based on their unique characteristics. It suggest that the specific model structure of GR4J is more suited to modelling wet catchments with smoother flow signals.