排水模型不同概化方式对模拟结果的影响研究——以MIKE URBAN软件为例
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摘要
模型概化是模型构建的关键,直接影响模型模拟精度。以广泛应用的MIKE URBAN软件为例,研究排水模型不同概化方式对流量模拟结果的影响。结果表明:在排水分区尺度上,模型不同概化方式不会改变模拟趋势;不同概化方式影响峰现时间、峰值流量,且与降雨强度有关,降雨强度越大,峰值流量差异越大;整体来看,对管线进行简化合并的概化方式模拟精度最高,采用按实际情况划定子汇水区和手动指定排水路径的模型概化方式模拟精度最差。
Model generalization is the key to model construction and directly affects model simulation accuracy. Taking the widely used MIKE URBAN software as an example, this paper studies the influence of different model generalization methods on the flow simulation results. The results show that the different generalization methods of the model do not change the simulation trend at the scale of the drainage zone, and different generalization methods affect the peak time and peak flow, which related to the rainfall intensity. The greater the rainfall intensity, the greater difference in peak flow. On the whole, the generalized pipe network model has the highest simulation accuracy. While simplified model which division catchment according to the actual situation and manually specify drainage path has the worst simulation accuracy.
Model generalization is the key to model construction and directly affects model simulation accuracy. Taking the widely used MIKE URBAN software as an example, this paper studies the influence of different model generalization methods on the flow simulation results. The results show that the different generalization methods of the model do not change the simulation trend at the scale of the drainage zone, and different generalization methods affect the peak time and peak flow, which related to the rainfall intensity. The greater the rainfall intensity, the greater difference in peak flow. On the whole, the generalized pipe network model has the highest simulation accuracy. While simplified model which division catchment according to the actual situation and manually specify drainage path has the worst simulation accuracy.
引文
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[11] Zhou Q. A review of sustainable urban drainage systems considering the climate change and urbanization impacts[J]. 2014,6(4):976-992.
[12] Semadeni-Davies A, Hernebring C, Svensson G, et al. The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: Combined sewer system[J]. Journal of Hydrology, 2008,350(2):100-113.
[13] Anees M T, Abdullah K, Nauawi M N M, et al. Numerical modeling techniques for flood analysis[J]. Journal of African Earth Sciences, 2016,126:478-486.
[14] Xing W, Li P, Cao S B, et al. Layout effects and optimization of runoff storage and filtration facilities based on SWMM simulation in a demonstration area[J]. Water Science and Engineering, 2016,9(2):115-124.
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[16] Smith T, Marshall L, Sharma A. Modeling residual hydrologic errors with Bayesian inference[J]. Journal of Hydrology, 2015, 528:29-37.
[2] 王浩, 梅超, 刘家宏. 海绵城市系统构建模式[J]. 水利学报, 2017,48(9):1009-1014.
[3] 张建云, 王银堂, 贺瑞敏, 等. 海绵城市建设有关问题讨论[J]. 水科学进展, 2016,27 (6):793-799.
[4] 马洪涛, 付征垚, 王军. 大型城市排水防涝系统快速评估模型构建方法及其应用[J]. 给水排水, 2014,40(9):39-42.
[5] 马洪涛. 数学模型在城市排水防涝规划中应用的探讨[C]. 城乡治理与规划改革——2014中国城市规划年会, 2014.
[6] 宋瑞宁, 宫永伟, 李俊奇, 等. 汇水区节点选取对城市雨洪模拟结果的影响[J]. 水利水电科技进展, 2015,35(3):75-79.
[7] 赵冬泉, 陈吉宁, 佟庆远, 等. 子汇水区的划分对SWMM模拟结果的影响研究[J]. 环境保护, 2008,394(8):56-59.
[8] Semadeni-Davies A, Hernebring C, Svensso G,et al. The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: Combined sewer system[J]. Journal of Hydrology, 2008,350(1-2):100-113.
[9] 谢家强, 廖振良, 顾献勇. 基于MIKE URBAN的中心城[J]. 能源环境保护, 2016,30(5):44-49.
[10] 刘家宏, 王浩, 高学睿, 等. 城市水文学研究综述[J]. 科学通报, 2014,59(36):3581-3590.
[11] Zhou Q. A review of sustainable urban drainage systems considering the climate change and urbanization impacts[J]. 2014,6(4):976-992.
[12] Semadeni-Davies A, Hernebring C, Svensson G, et al. The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: Combined sewer system[J]. Journal of Hydrology, 2008,350(2):100-113.
[13] Anees M T, Abdullah K, Nauawi M N M, et al. Numerical modeling techniques for flood analysis[J]. Journal of African Earth Sciences, 2016,126:478-486.
[14] Xing W, Li P, Cao S B, et al. Layout effects and optimization of runoff storage and filtration facilities based on SWMM simulation in a demonstration area[J]. Water Science and Engineering, 2016,9(2):115-124.
[15] Schoups G, Vrugt J A. A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors[J]. Water Resources Research, 2010, 46(10):79-93.
[16] Smith T, Marshall L, Sharma A. Modeling residual hydrologic errors with Bayesian inference[J]. Journal of Hydrology, 2015, 528:29-37.