济南市城区增长背景下洪涝模拟研究
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摘要
为准确规划评估未来发展条件下城市抗洪能力,以济南市城区为例,基于城市增长SLEUTH模型,预测研究区域到2035年城区增长状态。并在城市增长背景下建立济南城区二维洪涝模拟Caflood模型,针对济南市"0718"特大暴雨过程,通过模拟结果对比2007年与预测的2035年洪涝情景,进行济南市城区增长对暴雨洪水的影响分析。结果显示:SLEUTH模型校准结果较好,并准确预测城市增长状态; Caflood模型验证结果平均误差为0.135 m;预测2035年淹没情景与2007年模拟淹没情景基本类似,但2035年淹没水深峰值提前,高危险性区域面积增加,且"南水北淹"情景更为严重。研究结果既验证了两个模型的有效性及适用性又能在一定程度上反映出城市增长对城市洪涝的影响,能为济南市城区规划以及防洪预警工作提供理论指导依据。
In order to accurately plan and assess a city's flood resilience capacity under future underlying surface conditions,based on the urban growth SLEUTH model,this paper chooses Jinan City as a study area to predict its growth state by 2035 and has established Caflood model,a two-dimensional rain flooding simulation model. In addition,based on the torrential rain of 718 event in Jinan,flooding scenario in2007 is compared with that in 2035 through model results and the impact of urban growth on storm floods is analyzed. The results show that the SLEUTH model has a good calibration and accurately predicts the urban growth state; the average error of the Caflood model verification is 0.135 m and that the predicted flooding situation in 2035 is basically similar to that in 2007,but the peak of flooding depth is advanced in2035,the high-risk area is expanding and the flood risk level map indicates that the risk of flooding is more serious in 2035. The research results not only verify the validity and applicability of the two models,but also reflect the impact of urban growth on flood flooding to some extent,so they can provide theoretical guidance for urban planning and flood control early warning work in Jinan.
In order to accurately plan and assess a city's flood resilience capacity under future underlying surface conditions,based on the urban growth SLEUTH model,this paper chooses Jinan City as a study area to predict its growth state by 2035 and has established Caflood model,a two-dimensional rain flooding simulation model. In addition,based on the torrential rain of 718 event in Jinan,flooding scenario in2007 is compared with that in 2035 through model results and the impact of urban growth on storm floods is analyzed. The results show that the SLEUTH model has a good calibration and accurately predicts the urban growth state; the average error of the Caflood model verification is 0.135 m and that the predicted flooding situation in 2035 is basically similar to that in 2007,but the peak of flooding depth is advanced in2035,the high-risk area is expanding and the flood risk level map indicates that the risk of flooding is more serious in 2035. The research results not only verify the validity and applicability of the two models,but also reflect the impact of urban growth on flood flooding to some extent,so they can provide theoretical guidance for urban planning and flood control early warning work in Jinan.
引文
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[12]朱飞鸽,胡瀚文,沈兴华,等.基于SLEUTH模型的上海城市增长预测[J].生态学杂志,2011,30(9):2 107-2 114.
[13]徐杰,罗震东,尹海伟,等.基于SLEUTH模型的昆山市城市扩展模拟研究[J].地理与地理信息科学,2016,32(5):59-64.
[14]GHIMIRE B,CHEN A S,GUIDOLIN M,et al.Formulation of fast2D urban pluvial flood model using cellular automata approach[J].Journal of Hydroinformatics,2013,15(3):676-686.
[2]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.
[3]王好芳,刘阳.济南城市化发展对降水时空演变的影响[J].水电能源科学,2017(7):6-9.
[4]王育奎,徐帮树,李术才.济南城市防洪汛情预警等级判定方法[J].山东大学学报(工学版),2010,40(4):88-91.
[5]KHAN D M,TAHER I,VEERBEEK W,et al.Back to the future:assessing the impact of the 2004 flood in Dhaka in 2050[C]∥Proceedings of the International Conference on Flood Resilience,Experiences in Asia and Europe,2013:5-7.
[6]CLARKE K C.A self-modifying cellular automaton model of historical urbanization in the San Francisco Bayarea,Environment and Planning B[J].Planning&Design,1997,24.
[7]SOLECKI W D,Oliveri C.Downscaling climate change scenarios in an urban land use change model[J].Journal of Environmental Management,2004,72(1-2):105.
[8]付玲,胡业翠,郑新奇.基于BP神经网络的城市增长边界预测——以北京市为例[J].中国土地科学,2016,30(2):22-30.
[9]MOGHADAS S,LEONHARDT G,MARSALEK J,et al.Modeling urban runofffrom rain-on-snow events with the U.S.EPA SWMMmodel for current and future climate scenarios[J].Journal of Cold Regions Engineering,2018,32(1).
[10]SONINBAYAR J,AUDRA P.Dam Break Simulation Using DHI-MIKE21 in the Eg Hydropower Plant,Mongolia[M].Advances in Hydroinformatics.Springer,Singapore,2018:415-422.
[11]刘佩瑶,郝振纯,王国庆,等.新安江模型和改进BP神经网络模型在闽江水文预报中的应用[J].水资源与水工程学报,2017,28(1):40-44.
[12]朱飞鸽,胡瀚文,沈兴华,等.基于SLEUTH模型的上海城市增长预测[J].生态学杂志,2011,30(9):2 107-2 114.
[13]徐杰,罗震东,尹海伟,等.基于SLEUTH模型的昆山市城市扩展模拟研究[J].地理与地理信息科学,2016,32(5):59-64.
[14]GHIMIRE B,CHEN A S,GUIDOLIN M,et al.Formulation of fast2D urban pluvial flood model using cellular automata approach[J].Journal of Hydroinformatics,2013,15(3):676-686.
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