不同城市化程度对杨洼闸排水区产汇流影响研究
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摘要
不同城市化程度背景下,城市区域下垫面差异所引发的产汇流机理改变,进一步加剧了城市区域洪涝灾害。本文针对城市化进程较快的北京市通州区,选取区域内杨洼闸排水区为研究对象,根据Landsat卫星影像数据,采用多主题指数组合技术分别提取研究区2010年和2015年城镇用地数据,基于两种不同的城市化程度设置模型不透水率,构建SWMM模型。通过设置不同重现期暴雨情景,定量分析不同城市化程度洪水特征的变化。研究结果表明:研究区在两种城市化程度背景下一年一遇设计暴雨时的径流系数差值为0.13,从五十年一遇设计暴雨开始2010年和2015年径流系数较接近,由城市化引起的不透水率改变对研究区径流系数的影响逐步减弱。洪峰流量与城市化程度则始终表现出较强的正相关性。一年一遇设计暴雨时2015年峰现时间比2010年提前了3 h,从五十年一遇设计暴雨开始峰现时间不变,原因是强降雨条件下流域内流速较快,减小了城市区域因汇流路径复杂导致汇流时间延长的影响。
Under the background with different degrees of urbanization, changes in the mechanism of urban rainfall-runoff processes caused by the differences in underlying surface of urban areas have further increased the flood risk in urban areas. Considering the rapid development of urbanization in Tongzhou District of Beijing, the Yangwazha Drainage Area is selected as the case study in this paper. According to Landsat satellite image data, a multi-thematic index combination technique is used to extract the urban landuse data in 2010 and 2015, respectively. Based on two different degrees of urbanization, percentage of the impervious area is identified, and then the Storm Water Management Model(SWMM) is developed. Precipitation with different return periods is taken as model input to analyze the changes in flood characteristics due to urbanization. The study results show that the difference in runoff coefficient between the two urbanization scenarios in the study area is 0.13 in one-year return period. From the 50-year return-period on, the runoff coefficients begins to gradually approach each other in 2010 and 2015, indicating that when the return period continues to increase, the impact of changes in percentage of impervious area due to urbanization on the runoff coefficient of the study area gradually weakens. Peak flow and degrees of urbanization always show a strong positive correlation.In one-year return period,the 2015 peak time is 3 hours ahead of that in 2010. The flood peak time is the same in 50-year and 100-year return period. The reason is that under heavy precipitation conditions,the flow velocity in the river basin is faster and hence the influence caused by the delayed time for overland flow concentration become smaller due to the complicated path for overland flow concentration.
Under the background with different degrees of urbanization, changes in the mechanism of urban rainfall-runoff processes caused by the differences in underlying surface of urban areas have further increased the flood risk in urban areas. Considering the rapid development of urbanization in Tongzhou District of Beijing, the Yangwazha Drainage Area is selected as the case study in this paper. According to Landsat satellite image data, a multi-thematic index combination technique is used to extract the urban landuse data in 2010 and 2015, respectively. Based on two different degrees of urbanization, percentage of the impervious area is identified, and then the Storm Water Management Model(SWMM) is developed. Precipitation with different return periods is taken as model input to analyze the changes in flood characteristics due to urbanization. The study results show that the difference in runoff coefficient between the two urbanization scenarios in the study area is 0.13 in one-year return period. From the 50-year return-period on, the runoff coefficients begins to gradually approach each other in 2010 and 2015, indicating that when the return period continues to increase, the impact of changes in percentage of impervious area due to urbanization on the runoff coefficient of the study area gradually weakens. Peak flow and degrees of urbanization always show a strong positive correlation.In one-year return period,the 2015 peak time is 3 hours ahead of that in 2010. The flood peak time is the same in 50-year and 100-year return period. The reason is that under heavy precipitation conditions,the flow velocity in the river basin is faster and hence the influence caused by the delayed time for overland flow concentration become smaller due to the complicated path for overland flow concentration.
引文
[1] 宋晓猛,张建云,王国庆,等. 变化环境下城市水文学的发展与挑战——II.城市雨洪模拟与管理[J]. 水科学进展,2014,25(5):752-764.
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[5] 周毅,余明辉,陈永祥. SWMM子汇水区域宽度参数的估算方法介绍[J]. 中国给水排水,2014,(22):61-64.
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[7] 班玉龙,孔繁花,尹海伟,等. 土地利用格局对SWMM模型汇流模式选择及相应产流特征的影响[J]. 生态学报,2016,36(14):4 317-4 326.
[8] 杨海波,李云飞,王宗敏. 不同暴雨与城市化程度情景下城区内涝SWMM模拟分析[J]. 水利水电技术,2014,45(11):15.
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[17] 徐宗学. 水文模型[M]. 北京:科学出版社, 2009.
[18] 梅超,刘家宏,王浩,等. SWMM原理解析与应用展望[J]. 水利水电技术,2017,48(5):33-42.
[19] 赵刚,庞博,徐宗学,等. 基于SWMM模型的北京大红门排水片区雨洪模拟研究[J]. 北京师范大学学报(自然科学版),2014,(5):452-455.
[20] Di P F, Khu . From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms[J]. Water Science & Technology A Journal of the International Association on Water Pollution Research, 2006,54(6-7):57.
[21] Gironás J, Roesner L A, Rossman L A, et al. A new applications manual for the Storm Water Management Model (SWMM)[J]. Environmental Modelling & Software, 2010,25(6):813-814.
[22] Rossman L A. Storm Water Management Model User's Manual[Z]. 2009.
[23] 常晓栋,徐宗学,赵刚,等. 基于SWMM模型的城市雨洪模拟与LID效果评价——以北京市清河流域为例[J]. 水力发电学报,2016,35(11):84-93.
[24] 史蓉,赵刚,庞博,等. 基于GLUE方法的城市雨洪模型参数不确定性分析[J]. 水文,2016,36(2):1-6.
[25] 赵刚,史蓉,庞博,等. 快速城市化对产汇流影响的研究:以凉水河流域为例[J]. 水力发电学报,2016,35(5):55-64.
[2] 宋晓猛,张建云,占车生,等. 气候变化和人类活动对水文循环影响研究进展[J]. 水利学报,2013,44(7):779-790.
[3] 张建云,宋晓猛,王国庆,等. 变化环境下城市水文学的发展与挑战——I.城市水文效应[J]. 水科学进展,2014,25(4):594-605.
[4] 徐宗学,赵刚,程涛. “城市看海”:城市水文学面临的挑战与机遇[J]. 中国防汛抗旱,2016,(5):54-55.
[5] 周毅,余明辉,陈永祥. SWMM子汇水区域宽度参数的估算方法介绍[J]. 中国给水排水,2014,(22):61-64.
[6] 李阳,何俊仕. 基于SWMM模型的不透水率与产汇流关系研究[J]. 水电能源科学,2017,(2):34-37.
[7] 班玉龙,孔繁花,尹海伟,等. 土地利用格局对SWMM模型汇流模式选择及相应产流特征的影响[J]. 生态学报,2016,36(14):4 317-4 326.
[8] 杨海波,李云飞,王宗敏. 不同暴雨与城市化程度情景下城区内涝SWMM模拟分析[J]. 水利水电技术,2014,45(11):15.
[9] Suriya S, Mudgal B V. Impact of urbanization on flooding: The Thirusoolam sub watershed-A case study[J]. Journal of Hydrology, 2012,412(1):210-219.
[10] 沈聪. 北京向东:走近首都城市副中心[J]. 前线,2012,(11):30-33.
[11] 贾梁,赵恒谦,尹政然,等. 北京市城市副中心区域土地覆盖变化研究[J]. 科技经济导刊,2017,(34).
[12] Xu H. Extraction of Urban Built-up Land Features from Landsat Imagery Using a Thematicoriented Index Combination Technique[J]. Photogrammetric Engineering & Remote Sensing, 2007,73(12):1 381-1 391.
[13] Huete A R. Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment[J]. Remote Sensing of Environment, 1988,25(3):295-309.
[14] Xu H Q. A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI)[J]. Journal of Remote Sensing, 2005.
[15] Gao B C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space: Imaging Spectrometry[Z]. 1995:257-266.
[16] Wu H, Jiang J, Zhou J, et al. Dynamics of urban expansion in Xi'an city using Landsat TM/ETM+ data[J]. Acta Geographica Sinica, 2005.
[17] 徐宗学. 水文模型[M]. 北京:科学出版社, 2009.
[18] 梅超,刘家宏,王浩,等. SWMM原理解析与应用展望[J]. 水利水电技术,2017,48(5):33-42.
[19] 赵刚,庞博,徐宗学,等. 基于SWMM模型的北京大红门排水片区雨洪模拟研究[J]. 北京师范大学学报(自然科学版),2014,(5):452-455.
[20] Di P F, Khu . From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms[J]. Water Science & Technology A Journal of the International Association on Water Pollution Research, 2006,54(6-7):57.
[21] Gironás J, Roesner L A, Rossman L A, et al. A new applications manual for the Storm Water Management Model (SWMM)[J]. Environmental Modelling & Software, 2010,25(6):813-814.
[22] Rossman L A. Storm Water Management Model User's Manual[Z]. 2009.
[23] 常晓栋,徐宗学,赵刚,等. 基于SWMM模型的城市雨洪模拟与LID效果评价——以北京市清河流域为例[J]. 水力发电学报,2016,35(11):84-93.
[24] 史蓉,赵刚,庞博,等. 基于GLUE方法的城市雨洪模型参数不确定性分析[J]. 水文,2016,36(2):1-6.
[25] 赵刚,史蓉,庞博,等. 快速城市化对产汇流影响的研究:以凉水河流域为例[J]. 水力发电学报,2016,35(5):55-64.