No abstract

No abstract

Low Impact Development (LID) aims to mitigate the hydrological impacts of urbanization by replication of processes in natural catchments. Green roofs covered with vegetation and pervious substrate are one alternative among a wide range of LID tools. Water retention of green roofs depends on many factors (e.g. local climate), and measurements remain crucial in evaluating their performance. The simulation of green roof retention by a hydrological model is one option to evaluate their potential benefits before implementation. In this paper, we evaluated the ability of the recently introduced LID green roof module of the stormwater management model to replicate runoff from monitored green roof test beds under Nordic climate conditions. A parameter sensitivity analysis was conducted to identify calibration parameters. The model showed an overall acceptable performance, and the results indicated the importance of accurately estimating potential evapotranspiration rates for inter‐event periods, which is essential in representing the retention capacity regeneration. Copyright © 2015 John Wiley & Sons, Ltd.

No abstract

Rainfall runoff models are frequently used for design processes for urban infrastructure. The most sensitive input for these models is precipitation data. Therefore, it is crucial to account for temporal and spatial variability of rainfall events as accurately as possible to avoid misleading simulation results. This paper aims to show the significant errors that can occur by using rainfall measurement resolutions in urban environments that are too coarse. We analyzed the spatial variability of rainfall events from two years with the validated data of 22 rain gauges spread out over an urban catchment of 125 km2. By looking at the interstation correlation of the rain gauges for different classes of rainfall intensities, we found that rainfall events with low and intermediate intensities show a good interstation correlation. However, the correlation drops significantly for heavy rainfall events suggesting higher spatial variability for more intense rainstorms. Further, we analyzed the possible deviation from the spatial rainfall interpolation that uses all available rain gauges when reducing the number of rain gauges to interpolate the spatial rainfall for 24 chosen events. With these analyses we found that reducing the available information by half results in deviations of up to 25% for events with return periods shorter than one year and 45% for events with longer return periods. Assuming uniformly distributed rainfall over the entire catchment resulted in deviations of up to 75% and 125%, respectively. These findings are supported by the work of past research projects and underline the necessity of a high spatial measurement density in order to account for spatial variability of intense rainstorms.