Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation

- Application of lumped and distributed hydrological model ensemble.
- Multi-objective calibration of the models.
- Calibration includes focus on flow extremes and runoff subflows.
- Assessment of the models' predictive capacities under high rainfall intensities.

SummaryFive hydrological models with different spatial resolutions and process descriptions were applied to a medium sized catchment in Belgium in order to assess the accuracy and differences of simulated hydrological variables, including peak and low flow extremes and quick and slow runoff subflows. The models varied from the lumped conceptual NAM, PDM and VHM models over the intermediate detailed and distributed WetSpa model to the highly detailed and fully distributed MIKE SHE model. The latter model accounts for the 3D groundwater processes and interacts bi-directionally with a full hydrodynamic MIKE 11 river model. A consistent protocol to model calibration was applied to all models. This protocol uses information on the response behavior of the catchment extracted from the river flow and input time series and explicitly focuses on reproducing the quick and slow runoff subflows, and the extreme high and low flows next to testing the conventional model performance statistics. Also the model predictive capacity under high rainfall intensities, which might become more extreme under future climate change was explicitly verified for the models. The tail behavior of the extreme flow distributions was graphically evaluated as well as the changes in runoff coefficients in relation to the changing rainfall intensities.After such calibration, all tested models succeed to produce high performance for the total runoff and quick and slow runoff subflow dynamics and volumes, peak and low flow extremes and their frequency distributions. Calibration of the lumped parameter models is much less time consuming and produced higher overall model performance in comparison to the more complex distributed models.