中文版 | English
题名

绿色基础设施环境效益识别及多目标优化决策

其他题名
ENVIRONMENTAL PERFORMANCE IDENTIFICATION AND MULTI-OBJECTIVE DECISION MAKING ON GREEN INFRASTRUCTURE
姓名
姓名拼音
LIU Qianhui
学号
12032743
学位类型
硕士
学位专业
0801 力学
学科门类/专业学位类别
08 工学
导师
刘俊国
导师单位
环境科学与工程学院
论文答辩日期
2023-05-09
论文提交日期
2023-06-29
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

在气候变化和快速城市化进程的影响下,内涝灾害、热岛效应等诸多城市环境问题日益突显。绿色基础设施因其具有滞、渗、蓄、用、排等海绵城市功能,且增加了植被覆盖和反照率,在削减径流(水文效益)、降低城市地表温度(降温效益)方面发挥着重要作用。因此,在有限的建设面积和成本投入下,如何综合量化不同绿色基础设施的水文和降温效益,并针对不同未来气候变化情景提出绿色基础设施的最优组合方案,是亟需回答的科学问题。

本研究在南方科技大学校园这一小流域进行了连续的基础数据监测,构建了基于实测气象数据和水力数据的SWMM水文水动力模型,并结合卫星遥感产品Landsat8/9对地表温度的反演,综合量化了生物滞留、绿色屋顶和透水铺装三种绿色基础设施水文效益和降温效益的差异。以全生命周期成本、水文效益和降温效益为目标,基于NSGA-II多目标优化算法和熵权法,提出了研究区在不同情景下布设绿色基础设施的最优组合方案。

结果表明:从水文效益来看,生物滞留、绿色屋顶和透水铺装分别在“蓄”、“排”、“渗”方面表现最佳。中小雨时,雨水下渗占比约75%,径流量占比仅约10%,此时,绿色屋顶削减径流和洪峰效果最佳;大暴雨时,雨水下渗量只占约42%-56%,径流量占比约40%,此时,生物滞留削减径流最佳,透水铺装削减洪峰效果最佳。从降温效益来看,生物滞留和绿色屋顶对周围环境存在降温效益,透水铺装对周围环境的降温效益十分微弱;同时,三者对适宜布设区域均有潜在降温效益,绿色屋顶、透水铺装和生物滞留的降温平均值分别为1.45 ℃0.66 ℃0.3 ℃。针对研究区2031-2050年所有未来情景,最优方案为:透水铺装、绿色屋顶和生物滞留的总建设面积分别为8194.6平方米、13225.71平方米和7960.31平方米。

本研究从海绵城市建设功能角度量化了绿色基础设施的水文效益,提出了一种适用于数据有限的小尺度绿色基础设施降温效益的计算方法,为绿色基础设施环境效益评估提供了新思路。同时,提出南方科技大学绿色基础设施建设方案,为海绵校园乃至海绵城市建设提供了理论依据。

关键词
语种
中文
培养类别
独立培养
入学年份
2020
学位授予年份
2023-07
参考文献列表

[1] JONES, R. N. An environmental risk assessment/management framework for climate change impact assessments[J]. Natural Hazards. 2001; 23 (2–3): 197-230.
[2] WANG JN, QIN NX, JIANG T, et al. Interpretation of IPCC AR6: impacts and adaptations of climate change on cities, settlements and key infrastructure[J]. Climate Change Research. 2022; 18(4): 433-441.
[3] CRANDON T, SCOTT J, CHARLSON F, et al. A social–ecological perspective on climate anxiety in children and adolescents[J]. Nature Climate Change. 2022; 12(2): 123-131.
[4] JIANG L, O’Neill B. Global urbanization projections for the Shared Socioeconomic Pathways[J]. Global Environment Change. 2017; 42: 193-199.
[5] ÁGOSTON C, CSABA B, NAGY B, et al. Identifying types of eco-anxiety, eco-guilt, eco-grief, and eco-coping in a climate-sensitive population: a qualitative study[J]. International Journal of Environmental Research and Public Health. 2022; 19(4): 2461.
[6] FANNING A, O’NEILL D, HICKEL J, et al. The social shortfall and ecological overshoot of nations[J]. Nature Sustainability. 2022; 5(1): 26-36.
[7] 张丽霞,陈晓龙,辛晓歌. CMIP6 情景模式比较计划:ScenarioMIP概况与评述[J]. 气候变化研究进展. 2019; 15(5): 519-525.
[8] RIZWAN A, DENNIS L, LIU C. A review on the generation, determination and mitigation of Urban Heat Island[J]. Journal of Environmental Sciences. 2008; 20(1): 1208
[9] HESTER ET, BAUMAN KS. Stream and retention pond thermal response to heated summer runoff from urban impervious surfaces[J]. Journal of the American Water Resources Association. 2013; 49(2): 328-342.
[10] XU LY, YIN H, XIE XD. Health risk assessment of inhalable particulate matter in Beijing based on the thermal environment[J]. International Journal of Environmental Research and Public Health. 2014; 11(12): 12368-388.
[11] Online Database. EM-DAT. Emergency Management Database. https://www.emdat.be/
[12] LIANG C, ZHANG RC, ZENG J, et al. A land-use decision approach integrating thermal regulation, stormwater management, and economic benefits based on urbanization stage identification[J]. Science of Total Environment. 2021; 779: 146-154.
[13] 百度星图. 近20年来中国大陆重大的洪涝灾害. https://baike.baidu.com/starmap/view?nodeId=a079f3de56f8e820d6a44b41&lemma
[14] FRY T, MAXWELL R. Evaluation of distributed BMPs in an urban watershed—High resolution modeling for stormwater management[J]. Hydrological Processes. 2017; 31(15): 2700-2712.
[15] MCFARLAND A, LARSEN L, YESHITELA K, et al. Guide for using green infrastructure in urban environments for stormwater management[J]. Environmental Science: Water Research and Technology. 2019; 5(4): 643-659.
[16] HOU X, GUO H, WANG F, et al. Is the sponge city construction sufficiently adaptable for the future stormwater management under climate change? [J]. Journal of Hydrology. 2020; 588: 125055.
[17] LIU H, JIA Y, NIU C. “Sponge city” concept helps solve China’s urban water problems[J]. Environ Earth Science. 2017; 76(14): 1-5.
[18] WANG Y, SUN M, SONG B. Public perceptions of and willingness to pay for sponge city initiatives in China[J]. Resources, Conservation and Recycling. 2017; 122: 11-20.
[19] ZENG Q, XIE Y, LIU K. Assessment of the patterns of urban land covers and impervious surface areas: A case study of Shenzhen, China[J]. Physics and Chemistry of the Earth. 2019; 110: 1-7.
[20] ZHANG T, HUANG X. Monitoring of urban impervious surfaces using time series of high-resolution remote sensing images in rapidly urbanized areas: a case study of Shenzhen[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11(8): 2692-2708.
[21] HAYAT M, XIANG J, YAN C, et al. Environmental control on transpiration and its cooling effect of Ficus cincinnal in a subtropical city Shenzhen, southern China[J]. Agricultural and Forest Meteorology. 2022; 312: 108715.
[22] MEI C, LIU J, WANG H, et al. Integrated assessments of green infrastructure for flood mitigation to support robust decision-making for sponge city construction in an urbanized watershed[J]. Science of Total Environment. 2018; 639: 1394-1407.
[23] KRISTVIK E, KLEIVEN GH, LOHNE J, et al. Assessing the robustness of raingardens under climate change using SDSM and temporal downscaling[J]. Water Science and Technology. 2018; 77(6): 1640-1650.
[24] FANG M, WANG X, LIU J, et al. Design, application and performance improvement of Eco-Permeable pavement materials (Eco-PPMs): A review[J]. Construction and Building Materials. 2022; 360: 129558.
[25] SANTANA T, GUISELINI C, CAVALCANTI S, et al. Quality of rainwater drained by a green roof in the metropolitan region of Recife, Brazil[J]. Journal of Water Process Engineering. 2022; 49: 102953.
[26] YANG B, LI S. Green infrastructure design for stormwater runoff and water quality: Empirical evidence from large watershed-scale community developments[J]. Water, 2013, 5(4): 2038-2057.
[27] FELDMAN A, FOTI R, MONTALTO F. Green infrastructure implementation in urban parks for stormwater management[J]. Journal of Sustainable Water in the Built Environment, 2019, 5(3): 05019003.
[28] 唐双成.海绵城市建设中小型绿色基础设施对雨洪径流的调控作用研究[D].西安:西安理工大学,2016.
[29] 王强.人行道透水铺装结构雨水渗径流特性研究[D].青岛:山东科技大学,2019.
[30] 李山,仵苗,李静思,等.透水砖与垫层入渗特性对城市降雨产流的影响研究[J].自然灾害学报,2020; 29(06): 147-157.
[31] 丁永富.透水沥青路面渗流特性与雨水控制效果模拟研究[D].南京:东南大学,2019.
[32] 闫文博.生物滞留设施雨水径流调控影响因素研究[D].邯郸:河北工程大学,2019.
[33] CHUI TFM, LIU X, ZHAN W. Assessing cost-effectiveness of specific LID practice designs in response to large storm events[J]. Journal of Hydrology. 2016; 533: 353-364.
[34] YANG B, LEE D. Urban green space arrangement for an optimal landscape planning strategy for runoff reduction[J]. Land. 2021; 10(9): 1-12.
[35] ZELLNER M, MASSEY D, MINOR E, et al. Exploring the effects of green infrastructure placement on neighborhood-level flooding via spatially explicit simulations[J]. Computers, Environment and Urban Systems. 2016; 59: 116-128.
[36] BHUSAL A, PARAJULI U, REGMI S, et al. Application of machine learning and process-based models for rainfall-runoff simulation in DuPage River basin, Illinois[J]. Hydrology. 2022; 9(7): 117.
[37] GULSHAD K, WANG Y, LI N, et al. Likelihood of transformation to green infrastructure using ensemble machine learning techniques in Jinan, China[J]. Land. 2022; 11(3): 317.
[38] ARAM F, HIGUERAS GE, SOLGI E, et al. Urban green space cooling effect in cities[J]. Heliyon. 2019; 5(4): e01339.
[39] HAMADA S, OHTA T. Seasonal variations in the cooling effect of urban green areas on surrounding urban areas[J]. Urban Forestry and Urban Greening. 2010; 9(1): 15-24.
[40] NICHOL J. Remote sensing of urban heat islands by day and night[J]. Photogrammetric Engineering and Remote Sensing. 2005; 71(5): 613-621.
[41] XIAO XD, DONG L, YAN H, et al. The influence of the spatial characteristics of urban green space on the urban heat island effect in Suzhou Industrial Park[J]. Sustainable Cities and Society. 2018; 40: 428-439.
[42] YU Z, GUO X, JØRGENSEN G, et al. How can urban green spaces be planned for climate adaptation in subtropical cities? [J]. Ecological Indicators. 2017; 82: 152-162.
[43] LEE H, MAYER H, CHEN L. Contribution of trees and grasslands to the mitigation of human heat stress in a residential district of Freiburg, Southwest Germany[J]. Landscape and Urban Planning. 2016; 148: 37-50.
[44] PÉREZ G, COMA J, SOL S, et al. Green facade for energy savings in buildings: The influence of leaf area index and facade orientation on the shadow effect[J]. Applied Energy. 2017; 187: 424-437.
[45] WONG NH, TAN CL, KOLOKOTSA DD, et al. Greenery as a mitigation and adaptation strategy to urban heat[J]. Nature Reviews Earth & Environment. 2021; 2(3): 166-181.
[46] BOWLER DE, BUYUNG-ALI L, KNIGHT TM, et al. Urban greening to cool towns and cities: A systematic review of the empirical evidence[J]. Landscape and Urban Planning. 2010; 97(3): 147-155.
[47] SAARONI H, AMORIM JH, HIEMSTRA JA, et al. Urban green infrastructure as a tool for urban heat mitigation: survey of research methodologies and findings across different climatic regions[J]. Urban Climate. 2018; 24: 94-110.
[48] BARTESAGHI KOC C, OSMOND P, et al. Evaluating the cooling effects of green infrastructure: A systematic review of methods, indicators and data sources[J]. Solar Energy. 2018; 166: 486-508.
[49] JAGANMOHAN M, KNAPP S, BUCHMANN CM, et al. The bigger, the better? the influence of urban green space design on cooling effects for residential areas[J]. Journal of Environmental Quality. 2016; 45(1): 134-145.
[50] REIS C, LOPES A. Evaluating the cooling potential of urban green spaces to tackle urban climate change in Lisbon[J]. Sustainability. 2019; 11(9): 2480.
[51] CAI Y, CHEN Y, TONG C. Spatiotemporal evolution of urban green space and its impact on the urban thermal environment based on remote sensing data: A case study of Fuzhou City, China[J]. Urban Forestry and Urban Greening. 2019; 41: 333-343.
[52] ZHOU Y, SHEPHERD JM. Atlanta’s urban heat island under extreme heat conditions and potential mitigation strategies[J]. Nature Hazards. 2010; 52(3): 639-668.
[53] ARAMBURU J, ANTÓN R, RODRÍGUEZ-FRAILE M, et al. Computational fluid dynamics modeling of liver radioembolization: a review[J]. CardioVascular and Interventional Radiology. 2022; 45: 12-20.
[54] OUYANG W, SINSEL T, SIMON H, et al. Evaluating the thermal-radiative performance of ENVI-met model for green infrastructure typologies: Experience from a subtropical climate[J]. Build Environment. 2022; 207: 108427.
[55] ZINZI M, AGNOLI S. Cool and green roofs. An energy and comfort comparison between passive cooling and mitigation urban heat island techniques for residential buildings in the Mediterranean region[J]. Energy Buildings. 2012; 55: 66-76.
[56] LI J, ZHAO R, LI Y, CHEN L. Modeling the effects of parameter optimization on three bioretention tanks using the HYDRUS-1D model[J]. Journal of Environmental Management. 2018; 217: 38-46.
[57] ZHANG Z, GU J, ZHANG G, et al. Design of urban runoff pollution control based on the sponge city concept in a large-scale high-plateau mountainous watershed: A case study in Yunnan, China[J]. Journal of Water and Climate Change. 2021; 12(1): 201-222.
[58] KULLER M, BACH PM, ROBERTS S, et al. A planning-support tool for spatial suitability assessment of green urban stormwater infrastructure[J]. Science of the Total Environment. 2019; 686: 856-868.
[59] IMTEAZ MA. STORMKIT: A decision support tool for stormwater system analysis and design[J]. International Journal of Computer Aided Engineering and Technology. 2015; 7(3): 305-320.
[60] LI Q, WANG F, YU Y, et al. Comprehensive performance evaluation of LID practices for the sponge city construction: A case study in Guangxi, China[J]. Journal of Environmental Management. 2019; 231: 10-20.
[61] ZENG Z, YUAN X, LIANG J, et al. Designing and implementing an SWMM-based web service framework to provide decision support for real-time urban stormwater management[J]. Environmental Modelling and Software. 2021; 135: 104887.
[62] FU X, LIU J, SHAO W, et al. Evaluation of permeable brick pavement on the reduction of stormwater runoff using a coupled hydrological model[J]. Water (Switzerland). 2020; 12(10): 2821.
[63] LI J, ZHAO R, LI Y, et al. Simulation and optimization of layered bioretention facilities by HYDRUS-1D model and response surface methodology[J]. Journal of Hydrology. 2020; 586: 124813.
[64] 涂安国,李英,莫明浩,等.基于RECARGA模型生物滞留池设计参数的水文效应[J].水土保持学报,2020; 34: 149-153.
[65] LUAN Q, ZHANG K, LIU J, et al. The application of MIKE URBAN model in drainage and waterlogging in Lincheng county, China[J]. Proceedings of the International Association of Hydrological Sciences. 2018; 379: 381-386.
[66] GAO J, WANG R, HUANG J, et al. Application of BMP to urban runoff control using SUSTAIN model: Case study in an industrial area[J]. Ecological Modelling. 2015; 318: 177-183.
[67] LI S, LIU Y, HER Y, et al. Improvement of simulating sub-daily hydrological impacts of rainwater harvesting for landscape irrigation with rain barrels/cisterns in the SWAT model[J]. Science of the Total Environment. 2021; 798: 149336.
[68] LIU R, ZHANG P, WANG X, et al. Cost-effectiveness and cost-benefit analysis of BMPs in controlling agricultural nonpoint source pollution in China based on the SWAT model[J]. Environmental Monitoring and Assessment. 2014; 186: 9011-9022.
[69] QIAO XJ, LIU L, KRISTOFFERSSON A, et al. Governance factors of sustainable stormwater management: A study of case cities in China and Sweden[J]. Journal of Environmental Management. 2019; 248: 109249.
[70] MA Y, JIANG Y. Mainstreaming the framework of ecosystem services to enhance China’s policy implementation for sponge city development[J]. Sustainable Development. 2023; 2023: 1-16.
[71] TANG S, JIANG J, ZHENG Y, et al. Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring[J]. Science of the Total Environment. 2021; 753: 142007.
[72] TANG S, JIANG J, SHAMSELDIN AY, et al. Comprehensive optimization framework for low impact development facility layout design with cost-benefit analysis: a case study in Shenzhen city, China[J]. ACS Environmental Science and Technology Water. 2022; 2: 63-74.
[73] BHATT A, BRADFORD A, ABBASSI BE. Cradle-to-grave life cycle assessment (LCA) of low-impact-development (LID) technologies in southern Ontario[J]. Journal of Environmental Management. 2019; 231: 98-109.
[74] LIU Y, AHIABLAME LM, BRALTS VF, et al. Enhancing a rainfall-runoff model to assess the impacts of BMPs and LID practices on storm runoff[J]. Journal of Environmental Management. 2015; 147: 12-23.
[75] ZENG J, HUANG G, MAI Y, et al. Optimizing the cost-effectiveness of low impact development (LID) practices using an analytical probabilistic approach[J]. Urban Water Journal. 2020; 17: 136-143.
[76] RAEI E, REZA AM, REZA NM, et al. Multi-objective decision-making for green infrastructure planning (LID-BMPs) in urban storm water management under uncertainty[J]. Journal of Hydrology. 2019; 579: 124091.
[77] TORRES MN, FONTECHA JE, WALTEROS JL, et al. City-scale optimal location planning of Green Infrastructure using piece-wise linear interpolation and exact optimization methods[J]. Journal of Hydrology. 2021; 601: 126540.
[78] GOGATE NG, KALBAR PP, RAVAL PM. Assessment of stormwater management options in urban contexts using Multiple Attribute Decision-Making[J]. Journal of Cleaner Production. 2017; 142: 2046-2059.
[79] INAMDAR PM, SHARMA AK, COOK S, et al. Evaluation of stormwater harvesting sites using multi criteria decision methodology[J]. Journal of Hydrology. 2018; 562: 181-192.
[80] ALHUMAID M, GHUMMAN AR, HAIDER H, et al. Sustainability evaluation framework of urban stormwater drainage options for arid environments using hydraulic modeling and multicriteria decision-making[J]. Water (Switzerland). 2018;10(5): 581.
[81] LUAN B, YIN R, XU P, et al. Evaluating green stormwater infrastructure strategies efficiencies in a rapidly urbanizing catchment using SWMM-based TOPSIS[J]. Journal of Cleaner Production. 2019; 223: 680-691.
[82] PISCOPO AN, WEAVER CP, DETENBECK NE. Using multi-objective optimization to inform green infrastructure decisions as part of robust integrated water resources management plans[J]. Journal of Water Resources Planning and Management. 2021; 147(6): 1-12.
[83] BENDOR TK, SHANDAS V, MILES B, et al. Ecosystem services and U.S. stormwater planning: An approach for improving urban stormwater decisions[J]. Environmental Science and Policy. 2018; 88: 92-103.
[84] LI J, DENG C, LI Y, et al. Comprehensive benefit evaluation system for low-impact development of urban stormwater management measures[J]. Water Resource Management. 2017; 31(15): 4745-4758.
[85] KWAKKEL JH, HAASNOOT M, WALKER WE. Comparing robust decision-making and dynamic adaptive policy pathways for model-based decision support under deep uncertainty[J]. Environmental Science and Policy. 2016; 86: 168-183.
[86] GONZÁLEZ XI, BERT F, PODESTÁ G. Many objective robust decision-making model for agriculture decisions (MORDMAgro) [J]. International Transactions in Operational Research. 2020; 0: 1-30.
[87] DIAS LF, APARÍCIO BA, NUNES JP, et al. Integrating a hydrological model into regional water policies: Co-creation of climate change dynamic adaptive policy pathways for water resources in southern Portugal[J]. Environmental Modelling and Software. 2020; 114: 519-532.
[88] MOALLEMI EA, ELSAWAH S, TURAN HH, et al. Multi-objective decision making in multi-period acquisition planning under deep uncertainty[C]. Proceedings - Winter Simulation Conference. 2019: 1334-1345.
[89] 刘美辰,于守超,王吉民等.生态城市背景下的屋顶绿化设计研究[J].城市建筑,2023; 20(03): 206-209.
[90] ZHU Y, XU C, LIU Z, et al. Spatial layout optimization of green infrastructure based on life-cycle multi-objective optimization algorithm and SWMM model[J]. Resources, Conservation and Recycling. 2023; 191: 106906.
[91] 李春林,胡远满,刘淼,徐岩岩,孙凤云,陈探.SWMM模型参数局部灵敏度分析[J].生态学杂志,2014; 33(04): 1076-1081.
[92] LUGMAIR GW, MARTI K. Lunar Initial 143Nd-144Nd differential evolution of the lunar crust and mantle[J]. Earth and Planetary Science Letters. 1978; 39: 349-357.
[93] 覃志豪, 张明华, ARNON K等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001; (04): 456-466.
[94] SHAO H, KIM G. A comprehensive review of different types of green infrastructure to mitigate urban heat islands: progress, functions, and benefits[J]. Land. 2022; 11(10): 1792.
[95] CARRILLO GA, ANDRADE JL, VALDEZ JR, et al. Characterizing spatial and temporal deforestation and its effects on surface urban heat islands in a tropical city using Landsat time series[J]. Landscape and Urban Planning. 2022; 217: 19-27.
[96] BARTESAGHI C, OSMOND P, PETERS A. Innovative use of spatial regression models to predict the effects of green infrastructure on land surface temperatures[J]. Energy Buildings. 2022; 254: 111564.
[97] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation 2002; 6(2): 182-197.
[98] CHEN J, BRISSETTE FP, LECONTE R. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology[J]. Journal of Hydrology. 2011; 401(3-4): 190-202.
[99] CHOKKAVARAPU N, MANDLA VR. Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation[J]. SN Applied Sciences. 2019; 1(12): 1-15.
[100]JIA J, CUI W, LIU J. Urban catchment-scale blue-green-gray infrastructure classification with unmanned aerial vehicle images and machine learning algorithms[J]. Frontiers in Environmental Science. 2022; 9: 1-14.
[101]广东省住房和城乡建设厅.广东省海绵城市建设实施指引[S].2017
[102]中国气象局.降雨过程等级,QX/T 489-2019[S].北京:气象出版社,2019.
[103]陈卫平, 涂宏志, 彭驰, 等. 环境模型中敏感性分析方法评述[J]. 环境科学, 2017; 38(11): 4889-4897.
[104]MESGARI E, HOSSEINI SA, PARTOO LG, et al. Assessment of CMIP6 models’ performances and projection of precipitation based on SSP scenarios over the MENAP region[J]. Journal of Water and Climate Change. 2022; 13(10): 3607-3619.
[105]AHMED F, ABOD EE, RAYA M. Optimization of locations for bioswales stormwater management using BMP siting tool - case study of Sulaymaniyah city-KRG-Iraq[J]. Journal of Engineering. 2018; 2019(16): 2597-2603.
[106] CHUI TF, LIU X, ZHAN WT. Assessing cost-effectiveness of specific LID practice designs in response to large storm events[J]. Journal of Hydrology. 2016; 533: 353-364.
[107]Thompson R, Tjaden S, Tilley D. Stormwater retention of an in-series system composed of a green roof, constructed wetland, and bioretention cell for a single-family home[J]. Journal of Sustainable Water in the Built Environment. 2022; 8(1): 2379-6111.
[108] XIE J, WU C, LI H, et al. Study on storm-water management of grassed swales and permeable pavement based on SWMM[J]. Water. 2017; 9(11):840.
[109]ARJENAKI MO, SANAYEI HR, HEIDARZADEH H et al. Modeling and investigating the effect of the LID methods on collection network of urban runoff using the SWMM model (case study: Shahrekord City)[J]. Modeling Earth Systems and Environment. 2021; 7: 1–16.
[110]MALLICK K, BALDOCCHI D, JARVIS A, et al. Insights into the aerodynamic versus radiometric surface temperature debate in thermal-based evaporation modeling[J]. Geophysical Research Letters. 2022; 49(15): e2021GL097568 .
[111]SZEICZ G, ENDRÖDI G, TAJCHMAN S. Aerodynamic and surface factors in evaporation[J]. Water Resource Research. 1969; 5(2): 380-394.
[112]PRABHAT J, JOAO PL, MAX M, et al., Not all SuDS are created equal: Impact of different approaches on combined sewer overflows[J]. Water Research, 2021;191: 116780.
[113]MUHAMMAD S, REEHO K. Green stormwater infrastructure with low impact development concept: a review of current research[J]. Desalination and Water Treatment. 2017; 83: 16-29.
[114]ROSSMAN L. Storm Water Management Model (SWMM) user’s manual version 5 .2.1[D]. US Environmental Protection Agency (EPA). 2015:1-353.
[115]ZHANG SH, GUO YP. SWMM Simulation of the Storm Water Volume Control Performance of Permeable Pavement Systems[J]. Journal of Hydrologic Engineering, 2015; 20(8): 1084-0699.
[116]关舒婧.深圳市PM2.5时空分布及与土地利用关系研究[D].重庆:西南大学,2018.
[117]孟翔晨,历华,杜永明,等.Landsat 8地表温度反演及验证—以黑河流域为例[J].遥感学报, 2018; 22(5): 857-871
[118]DANIEL L, RANDEL LD. Thermal pollution mitigation in cold water stream watersheds using bioretention [J]. Journal of the American Water Resources Association. 2014; 50(4): 977-987.
[119]WANG J, MENG Q, TAN K, et al. Experimental investigation on the influence of evaporative cooling of permeable pavements on outdoor thermal environment[J]. Building and Environment. 2018; 140: 184-193.
[120]ELNABAWI MH, SABER E. A numerical study of cool and green roof strategies on indoor energy saving and outdoor cooling impact at pedestrian level in a hot arid climate[J]. Journal of Building Performance Simulation. 2023; 16(1): 72-89.
[121]YAN C, DING J, WANG B, et al. An in-situ measurement and assessment of evaporative cooling effects of low impact development facilities in a subtropical city[J]. Agricultural and Forest Meteorology. 2023; 332: 109-123.
[122]李静,田哲.绿色建筑全生命周期增量成本与效益研究[J].工程管理学报,2011; 25(05): 487-492.
[123]广东省住房和城乡建设厅. 旧城区海绵城市改造技术规程[S]. 2021
[124]住房城乡建设部.海绵城市建设技术指南——低影响开发雨水系统构建[S]. 2014
[125]FARRUGIA S, HUDSON MD, MCCULLOCH L. An evaluation of flood control and urban cooling ecosystem services delivered by urban green infrastructure[J]. International Journal of Biodiversity Science, Ecosystem Services and Management. 2013; 9(2): 136-145.
[126]SHIH WY. The cooling effect of green infrastructure on surrounding built environments in a sub-tropical climate: a case study in Taipei metropolis[J]. Landscape Research. 2017; 42(5): 558-573.
[127]陈昆,石国桢.浮点数编码遗传算法变异概率的选取[J].武汉理工大学学报(交通科学与工程版),2001; (04): 496-499.
[128]HADDELAND I, CLARK DB, FRANSSEN W, et al. Multimodel estimate of the global terrestrial water balance: Setup and first results[J]. Journal of Hydrometeorology. 2011; 12(5): 869-884.
[129]ZHANG H, WU C, CHEN W, et al. Assessing the impact of climate change on the waterlogging risk in coastal cities: A case study of Guangzhou, South China[J]. Journal of Hydrometeorology. 2017; 18(6): 1549-1562.

所在学位评定分委会
力学
国内图书分类号
X32, K90
来源库
人工提交
成果类型学位论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/544592
专题工学院_环境科学与工程学院
推荐引用方式
GB/T 7714
刘千慧. 绿色基础设施环境效益识别及多目标优化决策[D]. 深圳. 南方科技大学,2023.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可 操作
12032743-刘千慧-环境科学与工程(6163KB)----限制开放--请求全文
个性服务
原文链接
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
导出为Excel格式
导出为Csv格式
Altmetrics Score
谷歌学术
谷歌学术中相似的文章
[刘千慧]的文章
百度学术
百度学术中相似的文章
[刘千慧]的文章
必应学术
必应学术中相似的文章
[刘千慧]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
[发表评论/异议/意见]
暂无评论

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。