Urban areas are vulnerable to flooding as a result of climate change and rapid urbanization and thus flood losses are becoming increasingly severe. Low impact development (LID) measures are a storm management technique designed for controlling runoff in urban areas, which is critical for solving urban flood hazard. Therefore, this study developed an exploratory simulation–optimization framework for the spatial arrangement of LID measures. The proposed framework begins by applying a numerical model to simulate hydrological and hydrodynamic processes during a storm event, and the urban flood model coupled with the source tracking method was then used to identify the flood source areas. Next, based on source tracking data, the LID investment in each catchment was determined using the inundation volume contribution ratio of the flood source area (where most of the investment is required) to the flood hazard area (where most of the flooding occurs). Finally, the resiliency and sustainability of different LID scenarios were evaluated using several different storm events in order to provide suggestions for flooding prediction and the decision-making process. The results of this study emphasized the importance of flood source control. Furthermore, to quantitatively evaluate the impact of inundation volume transport between catchments on the effectiveness of LID measures, a regional relevance index (RI) was proposed to analyze the spatial connectivity between different regions. The simulation–optimization framework was applied to Haikou City, China, wherein the results indicated that LID measures in a spatial arrangement based on the source tracking method are a robust and resilient solution to flood mitigation. This study demonstrates the novelty of combining the source tracking method and highlights the spatial connectivity between flood source areas and flood hazard areas.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price includes VAT (USA)
Tax calculation will be finalised during checkout.
Availability of Data and Materials
The data and code that support the study are available from the corresponding author upon reasonable request.
Ahiablame L, Shakya R (2016) Modeling flood reduction effects of low impact development at a watershed scale. J Environ Manage 171:81–91. https://doi.org/10.1016/j.jenvman.2016.01.036
Akhter MS, Hewa GA (2016) The Use of PCSWMM for Assessing the Impacts of Land Use Changes on Hydrological Responses and Performance of WSUD in Managing the Impacts at Myponga Catchment, South Australia. Water 8: 511. https://doi.org/10.3390/w8110511
Becker P (2018) Dependence, trust, and influence of external actors on municipal urban flood risk mitigation: the case of Lomma Municipality, Sweden. Int J Disast Risk Re 31:1004–1012. https://doi.org/10.1016/j.ijdrr.2018.09.005
Birkel C, Soulsby C (2015) Advancing tracer-aided rainfall-runoff modelling: a review of progress, problems and unrealised potential. Hydrol Process 29:5227–5240. https://doi.org/10.1002/hyp.10594
Cano OM, Barkdoll BD (2017) Multiobjective, Socioeconomic, Boundary-Emanating, Nearest Distance Algorithm for Stormwater Low-Impact BMP Selection and Placement. J Water Resour Plan Man 143: 05016013. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000726
Chen WJ, Huang GR, Zhang H, Wang WQ (2018) Urban inundation response to rainstorm patterns with a coupled hydrodynamic model: A case study in Haidian Island, China. J Hydrol 564:1022–1035. https://doi.org/10.1016/j.jhydrol.2018.07.069
CHI (Computational Hydraulics Int) (2014). PCSWMM- Advanced Modeling of Stormwater, Wastewater and Watershed Systems Since 1984. Available at: http://www.pcswmm.com/
Cristiano E, Ten Veldhuis MC, Wright DB, Smith JA, van de Giesen N (2019) The influence of rainfall and catchment critical scales on urban hydrological response sensitivity. Water Resour Res 55:3375–3390. https://doi.org/10.1029/2018WR024143
Duan HF, Li F, Yan HX (2016) Multi-Objective Optimal Design of Detention Tanks in the Urban Stormwater Drainage System: LID Implementation and Analysis. Water Resour Manag 30:4635–4648. https://doi.org/10.1007/s11269-016-1444-1
Fletcher TD, Shuster W, Hunt WF, Ashley R, et al (2014) SUDS, LID, BMPs, WSUD and more–the evolution and application of terminology surrounding urban drainage. Urban Water J 12:525–542.https://doi.org/10.1080/1573062X.2014.916314
Gilroy KL, McCuen RH (2009) Spatio-temporal effects of low impact development practices. J Hydrol 367:228–236. https://doi.org/10.1016/j.jhydrol.2009.01.008
Kim JH, Kim HY, Demarie F (2017) Facilitators and Barriers of Applying Low Impact Development Practices in Urban Development. Water Resour Manag 31:3795–3808. https://doi.org/10.1007/s11269-017-1707-5
Li JK, Deng CN, Li Y, Li YJ, Song, JX (2017) Comprehensive Benefit Evaluation System for Low-Impact Development of Urban Stormwater Management Measures. Water Resour Manag 31:4745–4758. https://doi.org/10.1007/s11269-017-1776-5
Men H, Lu H, Jiang WJ, Xu D, (2020) Mathematical Optimization Method of Low-Impact Development Layout in the Sponge City. Math Probl Eng 2020: 6734081. https://doi.org/10.1155/2020/6734081
Soulsby C, Birkel C, Geris J, Dick J, Tunaley C, Tetzlaff D (2015) Stream water age distributions controlled by storage dynamics and nonlinear hydrologic connectivity: modeling with high-resolution isotope data. Water Resour Res 51, 7759–7776. https://doi.org/10.1002/2015WR017888.
Urich C, Rauch W (2014) Exploring critical pathways for urban water management to identify robust strategies under deep uncertainties. Water Res 66:374–389. https://doi.org/10.1016/j.watres.2014.08.020
Van Huijgevoort MHJ, Tetzlaff D, Sutanudjaja EH, Soulsby C, (2016) Using high resolution tracer data to constrain water storage, flux and age estimates in a spatially distributed rainfall-runoff model. Hydrol Process 30, 4761–4778. https://doi.org/10.1002/hyp.10902.
Willems P, Arnbjerg-Nielsen K, Olsson J, Nguyen VTV (2012) Climate change impact assessment on urban rainfall extremes and urban drainage: methods and shortcomings. Atmos Res 103:106–118. https://doi.org/10.1016/j.atmosres.2011.04.003
Winsemius HC, Aerts JCJH, van Beek, LPH, Bierkens MFP, et al (2016) Global drivers of future river flood risk. Nat Clim Change 6:381–385. https://doi.org/10.1038/NCLIMATE2893
Xu HS, Ma C, Lian JJ, Xu K, Chaima E (2018) Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China. J Hydrol 563:975–986. https://doi.org/10.1016/j.jhydrol.2018.06.060
Xu T, Jia HF, Wang Z, Mao XH, Xu CQ (2017) SWMM-based methodology for block-scale LID-BMPs planning based on site-scale multi-objective optimization: a case study in Tianjin. Front Env Sci Eng 11:1. https://doi.org/10.1007/s11783-017-0934-6
This study is supported by the National Natural Science Foundation of China (No. 51679156).
Consent to Publish
The authors are indeed informed and agree to publish.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Qi, W., Ma, C., Xu, H. et al. Low Impact Development Measures Spatial Arrangement for Urban Flood Mitigation: An Exploratory Optimal Framework based on Source Tracking. Water Resour Manage 35, 3755–3770 (2021). https://doi.org/10.1007/s11269-021-02915-2
- Low impact development
- Regional relevance
- Source tracking
- Spatial arrangement
- Urban flood model