Development and application of an automated subcatchment generator for SWMM using open data

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.     

Parameter Calibration of SWMM Model Based on Optimization Algorithm

For the challenge of parameter calibration in the process of SWMM (storm water management model) model application, we use particle Swarm Optimization (PSO) and Sequence Quadratic Programming (SQP) in combination to calibrate the parameters and get the optimal parameter combination in this research. Then, we compare and analyze the simulation result with the other two respectively using initial parameters and parameters obtained by PSO algorithm calibration alone. The result shows that the calibration result of PSO-SQP combined algorithm has the highest accuracy and shows highly consistent with the actual situation, which provides a scientific and effective new idea for parameter calibration of SWMM model, moreover, has practical guidance for flood control and disaster mitigation.     

Urban total phosphorus loads to the St. Clair-Detroit River System

The St. Clair-Detroit River System watershed is a large, binational watershed draining into the connecting channel between lakes Huron and Erie. In addition to extensive agricultural lands, it contains large urban areas that discharge phosphorus from point source facilities, runoff of impervious surfaces, and overflows of combined sewers. To help guide actions to reduce phosphorus input to Lake Erie, we analyzed the spatial and temporal dynamics of loads from the three largest urban areas in the watershed (southeast Michigan; Windsor, Ontario; and London, Ontario), and used a previously calibrated storm water management model (SWMM) to explore options for reducing loads around metro Detroit. Point sources in these three urban areas contribute, on average, 81% of the total urban load and 19% of the Detroit River’s total phosphorus (TP) load to Lake Erie, while combined sewer overflows and runoff both contribute about 10% each to the urban load and about 2.5% each to the Detroit River’s load to Lake Erie. Most of the urban load (56%) comes from a single point source, the wastewater treatment facility in Detroit; however, TP loads from that facility have decreased by about 51% since 2008 due to improvements in wastewater treatment. Model simulations suggest that increasing pervious land area or implementing green infrastructure could help reduce combined sewer overflows in certain upper portions of the metro Detroit sewer system, but reductions were much less expressed for wet-weather discharge from the system.     

无锡市西漳片区排水管网数据标准化及初步建模分析

基于暴雨管理模型(SWMM),利用国内自主开发的数字排水平台(DigitalWater DS)软件建立了无锡市西漳片区的排水管网模型.结合建模实践,建立了利用DigitalWater DS软件对测绘数据和设计及竣工图等不同来源管网数据的标准化处理模式,并初步设定了模型计算参数,为后续的模型校准和模拟分析提供了基础.     

SWMM模型在北京市某区域的应用

以SWMM模型为基础,选取北京市某区域主要在"不同的降雨频率条件下"、"不同的不透水面积条件下"进行模拟.同时采用推理公式法计算不同降雨频率的洪峰流量,并验证SWMM模型的精确性.模拟结果显示,随着不透水面积比例的增加和降雨强度的增大,雨水的径流量增加,洪峰流量明显增大.     

Assessing the mitigation effect of deep tunnels on urban flooding: a case study in Guangzhou,China

ABSTRACT

There are growing demands of deep tunnels to mitigate the severe urban flooding by providing a large storage capacity for excess storm runoff. This study aims to assess the flooding mitigation effect of a deep tunnel system proposed in the old downtown of Guangzhou, China, and to quantify the flooding volume difference by two storm hyetographs, the Chicago curve and the Improved Huff curve. Results show that the flooding volume is significantly reduced by a minimum of 19% and a maximum of 42% under design storms of 10- and 0.5-year return periods, respectively, when the deep tunnel system is constructed. There is a distinct spatial pattern for the mitigation effect. The best mitigation is in the west and the immediate north of the main tunnel, whereas the east does not show significant mitigation. Moreover, the actual flooding risk is likely underestimated when the Chicago curve is employed to design the storms.     

SWMM模型在城市排水系统规划中的应用

对城市排水系统进行系统的模拟计算,可以为城市排水系统规划方案的调整与优化提供理论指导。以深圳河湾地区的排水系统规划为例,讨论了美国环保局开发的暴雨径流管理模型SWMM(StormWaterManagementModel)的应用情况。案例研究以典型降雨年的排污状况为参考,用SWMM分析和评价了深圳“布局规划方案”在近期(2010年)和远期(2020年)的环境影响。研究中所应用的方法和过程具有一定的通用性,可以用于其他地区排水系统的模拟。     

SWMM在保山市某区雨水管网设计中的应用

以保山市隆阳区永昌路雨水管道设计为例,讨论了SWMM的应用。采用芝加哥暴雨模型,将管段流量的模拟结果与径流系数法比较,对管道的设计管径做出调整。在较大重现期情况下,对设计管网进行模拟复核。结果显示,SWMM模型可以为计算雨水设计流量提供可靠的数据支持。该应用说明SWMM能为雨水管道的设计提供新的方法。     

基于SWMM的铁心桥实验基地内涝防治效果模拟

基于南京水科院铁心桥实验基地的地形、下垫面条件等数据构建了SWMM,模拟了实验基地象目湖观景平台和水上餐厅两个监测点在不同降雨重现期、低影响开发措施、溢流孔设置和水泵强排条件下受淹情况。结果表明,初始水位较低(25.1 m)时,象目湖遭遇500年一遇的降雨事件时水位并未抬升至水上餐厅。初始水位较高(25.6 m)时,200年一遇和500年一遇的降雨事件中水上餐厅受淹持续时间分别为7 h和11 h。采用集水池、雨水花园和生物滞留池3种LID措施对象目湖水位峰值影响模拟结果显示,在5年或10年一遇的降雨事件中雨水花园和集水池可以分别降低象目湖峰值水位0.06 m和0.02 m,生物滞留池没有削减峰值水位。在降雨量为218.5 mm的20160707次降雨事件中LID措施削减象目湖水位效果不明显。水泵抽排流量越大,象目湖水位越早回落到观景平台以下。LID在应对造成城市内涝的低频次、短历时强降雨方面作用有限,铁心桥实验基地象目湖内涝防治应以加强水文气象预测和水位管理、加大溢流孔尺寸和采用水泵强排等措施为主。     

排水管道泥沙淤积深度估算方法研究

运用C#编程语言实现了SWMM模型参数的自动提取,通过创建BP-人工神经网络实现了节点水深值与模型参数值之间的非线性拟合,基于模型参数率定的思路提出了一种排水管道泥沙淤积深度的估算方法,并且以G市某雨水排水系统为例,采用4场降雨数据对模型进行了校核与验证。结果表明,通过两场降雨数据的验证,对于管径为1.2~1.8 m的管道,淤积深度预测值与实测值之间的绝对误差均在4 cm以内;模拟结果和实测数据的水深峰现时间偏差均低于实测数据历时的5%,峰值的数值偏差均在3%以内;场次3和场次4两场降雨4个监测点的水深预测值与实测值的平均相对误差分别为3.35%、2.98%,2.75%、2.51%,7.39%、6.77%,5.53%、8.15%,说明该方法能够对排水管道淤积情况进行有效预测。