Performance of lot-level low impact development technologies under historical and climate change scenarios

Low impact development (LID) systems have potential to make urban cities more sustainable and resilient, particularly under challenging climate conditions. To quantify performance capabilities, modeling results for an array of combinations of LIDs are described using PCSWMM at lot-level to examine performance of individual LIDs on volume and peak flow reductions. Among the four LIDs studied: rain barrel (RB), vegetative swale (VS), bioretention cell (BC), and permeable pavement (PP), PP at lot-level demonstrated the best capability for reducing surface runoff volumes and peak runoff rates under historical weather conditions, while BC showed similar capability for reduction of runoff volumes but minimal peak flow reduction. With PP as the controlling method at lot-level, the maximum percentage reduction of runoff volume for a 2-year storm is 58% whereas for a 100-year storm, the runoff volume reduction is 20%. These results mean the extent of flooding that may arise from the 100-year storm is reduced, but not eliminated. Effectively, the 100-year storm volumes with LID are devolved to have flooding equivalent to a 25-year storm. Under climate change scenarios, performance for all LIDs declined at various levels, where BC was the most resilient LID for a climate change scenario, such that projected 2-year or 5-year storms with climate change will have its impact devolved with LID in place, to result in similar volumes and peaks without LID under historical conditions. Furthermore, even with an assembly of lot-level LIDs distributed throughout the community, there is not attenuation to substantial degrees of flooding for major events, but there can be effective control for water quantity for small (2- to 5-years in particular) storm events.     

城市社区尺度降雨径流快速模拟——以福州市一排水小区为例

随着城市化进程的不断加快,我国城市正面临着越来越严峻的洪涝问题。本文在社区尺度上构建雨洪管理模型（SWMM）,使用遗传算法率定SWMM模型参数;在对研究区降雨分析的基础上,采用模糊识别法筛选出最具代表性的两种雨型;基于不同的降雨情景与SWMM模拟值组成的数据集,建立长短期记忆神经网络（LSTM）模型模拟研究区降雨径流关系,并使用不同工况评估了LSTM模型效果。结果表明,LSTM模型对降雨径流的模拟与SWMM模型基本吻合,而其对洪峰流量的拟合略有偏差。在较小降雨下,LSTM模型模拟洪峰流量较SWMM输出结果偏小;在较大降雨下,模拟结果偏大;在中等降雨时,模拟效果最好。此外,50个隐含层单元的拟合效果更好,但同时更多的隐含层单元对洪峰流量拟合效果更好。     

滇池环湖截污干渠错峰技术模拟与分析

为了实现滇池流域截污效果的最大化,基于前期建立的东岸排水管网SWMM模型,结合该区域1995-2016年间降雨资料,研究环湖截污干渠的错峰调蓄技术。根据雨水干渠液位高度执行不同控制模式:当水位低于6.76 m时执行典型污染物浓度阈值控制模式;当水位高于6.76 m时执行典型污染物阈值控制的同时执行液位-污染物通量控制模式。采用SS和TN作为干渠截流的典型污染物控制指标,其控制浓度阈值分别取12和5 mg/L。模拟重现期为0.5~1 a降雨时SS、TN浓度-负荷通量调蓄方案下干渠负荷收集情况,结果表明两种调蓄方案均能有效提高雨水干渠的负荷收集率,提高污水干渠出口浓度,降低雨水径流对污水厂的高水量和低浓度冲击负荷。随着重现期增大,干渠对污染物的高效和最大化收集效果越明显。但是TN调控下污染负荷收集效果优于SS,因此选择TN作为干渠截流的最优典型污染物控制指标。     

雨水泵站水泵机组启停优化

为解决雨水泵站运行过程中存在的水泵频繁启停问题,提出一种水泵机组启停优化方法.该方法通过分析泵站的设计参数和水泵机组运行原理,建立启泵水位优化模型,并运用暴雨洪水管理模型(storm water management model,SWMM)和粒子群优化算法(particle swarm optimization algorithm,PSO)对模型求解,最终得到水泵机组的最优启泵水位.通过实例将优化方法与传统的人工调试方法进行对比,验证了其可行性.研究结果表明:采用优化方法得到的启泵水位可使机组启停次数达到最小,实现了较好的水泵启停效果.同时优化方法也避免了复杂的人工调试过程,得出的优化结果为雨水泵站启泵水位的选取提供参考.     

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。     

基于SWMM的节制闸影响研究

针对南水北调中线长距离输水明渠,节制闸众多,难免遇到闸门启闭过程,采用SWMM模型模拟明渠在闸门启闭过程中断面各时刻的渠段内水深、流量、流速变化情况,研究了闸门不同启闭速度和闸门不同开度下断面水力参数及水面线变化规律。结果表明闸门调节速度和开度变化越快,对渠道内水位波动影响越明显,渠道响应时间会缩短。     

基于SWMM的雨水调蓄池容积研究

雨水调蓄池作为城市内涝防治的一种重要措施,其有效容积的确定在设计过程中极为重要。以云南省某市为例,采用传统公式法计算与SWMM软件模拟2种方法得到调蓄池有效容积,对2种方法的结果进行比较分析,结果表明在低重现期下公式法计算值要大于软件模拟值,而在高重现期下软件模拟值将大于公式计算值。     

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.     

LID组合措施在杭州低影响开发区项目中的应用研究——基于SWMM模型

结合杭州市低影响开发区对雨水控制的要求,运用SWMM模型模拟常规开发模式、组合LID设施、组合LID+蓄水池对雨洪控制的效果.研究结果表明,组合LID设施可以有效降低场地内洪峰流量、径流系数,并使场地在50 a重现期以下的降雨情况无溢流节点;组合LID+蓄水池可以控制杭州市100 a重现期2 h短历时降雨量的94.3％...     

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.