Low Impact Development (LID) tools and green infrastructure approaches have been developed and applied to mitigate the urbanization impacts on increasing runoff and pollutant washoff. The present work is the first part of a larger effort to simulate LID scenarios for a large scale urban catchment through up-scaling of high-resolution study catchments using the Stormwater Management Model (SWMM). In this study we present the setup, calibration, validation, and the results of a parameter sensitivity analysis of a high-resolution SWMM model for a highly urbanized small catchment located in Southern Finland. The homogenous subcatchments and associated narrow parameter boundaries, which are allowed by the high spatial resolution, result in insensitivity of SWMM to the fraction of impervious cover. The model optimization, using only the two identified key parameters “depression storage” and “Manning's roughness n for conduit flow”, yielded good performance statistics for both calibration and validation of the model.

An open source subcatchment generator program was developed for the Stormwater Management Model (SWMM) to automate tedious stages in the model construction process. The generator divides the investigated area into subcatchments using a uniform computation grid and connects the grid cells together and to the underlying stormwater network. The system was tested by applying it to two small urban catchments with different fractions of impervious surfaces in Helsinki, Finland, using mostly openly available data. The simulated discharge results were compared to measured data and to results obtained from manually built models. The proposed system significantly accelerated the setup of a SWMM modelling project, as the routing between the subcatchments as well as the subcatchment slopes and flow widths were directly derived from the computation grid. Automatically generated and manually constructed SWMM models produced discharge results that differed only slightly from each other.

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.

Low impact development (LID) strategies aim to mitigate the adverse impacts of urbanization, like the increase of runoff and the decrease of evapotranspiration. Hydrological simulation is a reasonable option to evaluate the LID performance with respect to the complete water balance. The sensitivity of water balance components to LID parameters is important for the modeling and planning process of LIDs. This contribution presents the results of a global sensitivity analysis of model-based water balance components (runoff volume, evapotranspiration, groundwater recharge/storage change) using the US Environmental Protection Agency Storm Water Management Model to the parameters (e.g., soil thickness, porosity) of a green roof, an infiltration trench, and a bio-retention cell. All results are based on long-term simulations. The water balance and sensitivity analyses are evaluated for the long-term as well as single storm events. The identification of non-influential and most influential LID parameters for the water balance components is the main outcome of this work. Additionally, the influence of the storm event characteristics precipitation depth and antecedent dry period on the sensitivity of water balance components to LID parameters is shown.