In particular, two storms measured shortly after the 1 in 140 year drought exhibited unusually high peak runoff rates. There was a strong correlation (0.872) between total storm event runoff volume and total rainfall depth, but weaker correlations between peak rainfall intensity and peak runoff rate (0.234) or peak rainfall intensity and total storm event runoff volume (0.382). A total of 12 storms were deemed to have a complete data record, with rainfall depths ranging between 9.6 and 99.4 mm and peak intensities between 42 and 144 mm/hr. Based on the infiltration measurements and fitting the Horton infiltration equation to the data, the initial infiltration capacity (fc) of the soils in the park ranged between 80 and 690 mm/hr. The soils were 80-92% sand and classified mainly as sand or sandy loam. This part of the park is serviced by a tile drain and an ISCO 2150 area-velocity meter was installed near the drain outlet, together with a tipping bucket rain gauge, to monitor rainfall and runoff between 21 December 2013 and. Infiltration rates, using a double o-ring infiltrometer, were measured at 5 sites within the traditional grassed area and samples for textural analysis were collected at the same sites. Admiralty Park consists of two sections, one being a traditional urban grassed area with trees, outdoor exercise areas, and promenades and the other being a forested nature trail that includes a mangrove habitat opening to the Straits of Johor. The objective of this study was to quantify the rainfall-runoff processes for a park in Singapore and explore the efficacy of PCSWMM in estimating runoff from a nearly 100% pervious area. Singapore recently has re-branded itself from “garden city” to “city in a garden” and as such parks, urban forests, and even agricultural areas are particularly important in sustaining the liveability of this highly urbanized city-state. The results demonstrate that OSTRICH-SWMM is a promising tool for automatic calibration of SWMM models.The environmental and societal benefits of urban green space has long been recognized, but such land use often becomes a secondary consideration in urban drainage modeling, in part because it is more difficult to obtain site-specific data for model calibration. The Pareto front for the case study was obtained using a multi-objective calibration algorithm and this allowed for evaluating tradeoffs between the peak flow and total volume criteria. A catchment in Buffalo, NY was selected as a case study and was calibrated according to two competing criteria: (1) minimizing errors in simulated peak flow, and (2) minimizing errors in total flow volume. The newly developed OSTRICH-SWMM is an open-source tool with dozens of parallelized optimization algorithms.
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In this study, SWMM was integrated with the Optimization Software Tool for Research Involving Computational Heuristics (OSTRICH) to perform single- and multi-objective automatic calibration. Consequently, model calibration is a challenging task. A typical SWMM project has hundreds or thousands of sub-catchments and more than 20 parameters associated with six different physical processes for each sub-catchment. The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most widely used numerical models to simulate urban runoff and drainage.
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