Reducing uncertainties

Modelling blue-green infrastructure performance – reducing uncertainties.

The calibration of urban drainage models typically involves the selection of a limited number of observed rainfall-runoff events from a larger dataset. However, this selection process can itself be undertaken in a number of ways using single- or two-step strategies.

Our research is exploring the impact of using alternative approaches to selecting data for model calibration on the performance of the high- and low-resolution Storm Water Management Model (SWMM).

Initial analysis shows that various strategies for selecting calibration events may lead in some cases to different results in the validation phase and that calibrating impervious and green-area parameters in two separate steps in two-stage strategies may increase the effectiveness of model calibration and validation by reducing the computational demand in the calibration phase and improving model performance in the validation phase.

Current research is focused on:

• the impact of four alternative urban drainage models (Urbis, SWMM, Hydrus and MIKE SHE) on predicting the performance green roofs through their application to an international green roof data set. An article has been submitted to Journal of Hydrology X.
• critical review and synthesis of swale performance data and guidelines (currently focused on hydraulic performance) to inform development of swale design guidelines to enhance their role as treatment systems

Ico Broekhuizen, Jiri Marsalek och Maria Viklander