Urbanization modifies the natural water cycle particularly by reducing the water storage capacity. We analysed the storage capacity of three stormwater management designs in south-western Finland to demonstrate how an urban catchment releases stormwater and how storage contributes to flood resilience. The analysis relies on EPA SWMM5.1 simulations of water balance for a seven-month period including two extreme rain events during the summer and autumn. The enhanced storage capacity provided by the designs increased resilience against flooding and released stormwater with slower rates leading to reduced peak flows. Even the design with the least storage (10% LID coverage) was efficient at regulating floods due to controlled flow in a vegetated swale, whereas the design with the highest storage capacity (60% LID coverage) demonstrated the possibility of restoring nearly natural water cycle in urban catchments. The study suggests storage capacity can act as a flood resilience indicator directly linked with the physical catchment characteristics.
Nature-based solutions and similar natural water retention measures to manage urban runoff are often implemented by cities in order to reduce runoff peaks, catch pollutants, and improve sustainability. However, the performance of these stormwater management solutions is relatively rarely assessed in detail prior to their construction, or monitored and evaluated following implementation. The objective of this study was to investigate the field-scale performance of road runoff filters with respect to the management of stormwater quantity and quality. This study synthesizes data from two intensive measurement surveys after the construction of sand and biochar-amended road runoff filters. The filters were able to strongly control the runoff volume and shape of the hydrograph. The long-term retention was about half that of the water inflow, and a hydrographic analysis showed the significant but strong event-size-dependent detention of runoff in both the sand and the sand–biochar filters. The biochar amendment in the filter showed no clear hydrological impact. The pollutant attenuation of the implemented road runoff filters was modest in comparison with that observed under controlled conditions. The impact of the biochar layer on the effluent water quality was observed as the levels of phosphorous, organic carbon, K, Ca and Mg in the sand–biochar filter effluent increased in comparison with the sand filter.
Constructing hydrological models for large urban areas is time consuming and laborious due to the requirements for high-resolution data and fine model detail. An open-source algorithm using adaptive subcatchments is proposed to automate Storm Water Management Model (SWMM) construction. The algorithm merges areas with homogeneous land cover and a common outlet into larger subcatchments, while retaining small-scale details where land cover or topography is more heterogeneous. The method was tested on an 85-ha urban catchment in Helsinki, Finland. A model with adaptive subcatchments reproduced the observed discharge at the catchment outlet with high model-performance indices emphasizing the strength of the proposed method. Computation times of the adaptive model were substantially lower than those of a corresponding model with uniformly sized high-resolution subcatchments. Given that high-resolution land cover and topography data are available, the proposed algorithm provides an advanced method for implementing SWMM models automatically even for large urban catchments without a substantial manual workload. Simultaneously, the high-resolution land cover details of the catchments can be maintained where they matter the most.