Parameter transferability across spatial resolutions in urban hydrological modelling: a case study in Beijing, China

Xiaoshu HOU , Lei CHEN , Xiang LIU , Miao LI , Zhenyao SHEN

Front. Earth Sci. ›› 2019, Vol. 13 ›› Issue (1) : 18 -32.

PDF (4252KB)
Front. Earth Sci. ›› 2019, Vol. 13 ›› Issue (1) : 18 -32. DOI: 10.1007/s11707-018-0710-3
RESEARCH ARTICLE
RESEARCH ARTICLE

Parameter transferability across spatial resolutions in urban hydrological modelling: a case study in Beijing, China

Author information +
History +
PDF (4252KB)

Abstract

This study examined the influence of spatial resolution on model parameterization, output, and the parameter transferability between different resolutions using the Storm Water Management Model. High-resolution models, in which most subcatchments were homogeneous, and high-resolution-based low-resolution models (in 3 scenarios) were constructed for a highly urbanized catchment in Beijing. The results indicated that the parameterization and simulation results were affected by both spatial resolution and rainfall characteristics. The simulated peak inflow and total runoff volume were sensitive to the spatial resolution, but did not show a consistent tendency. High-resolution models performed very well for both calibration and validation events in terms of three indexes: 1) the Nash-Sutcliffe efficiency, 2) the peak flow error, and 3) the volume error; indication of the advantage of using these models. The parameters obtained from high-resolution models could be directly used in the low-resolution models and performed well in the simulation of heavy rain and torrential rain and in the study area where sub-area routing is insignificant. Alternatively, sub-area routing should be considered and estimated approximately. The successful scale conversion from high spatial resolution to low spatial resolution is of great significance for the hydrological simulation of ungauged large areas.

Keywords

SWMM / high resolution / low resolution / rainfall characteristics / parameter transferability

Cite this article

Download citation ▾
Xiaoshu HOU, Lei CHEN, Xiang LIU, Miao LI, Zhenyao SHEN. Parameter transferability across spatial resolutions in urban hydrological modelling: a case study in Beijing, China. Front. Earth Sci., 2019, 13(1): 18-32 DOI:10.1007/s11707-018-0710-3

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Baek S S, Choi D H, Jung J W, Lee H J, Lee H, Yoon K S, Cho K H (2015). Optimizing low impact development (LID) for stormwater runoff treatment in urban area, Korea: experimental and modeling approach. Water Res, 86: 122–131

[2]

Barco J, Wong K M, Stenstrom M K (2008). Automatic calibration of the U.S. EPA SWMM model for a large urban catchment. J Hydraul Eng, 134(4): 466–474

[3]

Bedient P B, Huber W C (2002). Hydrology and Flood Plain Analysis. New Jersey: Prentice-Hall

[4]

Blöschl G, Sivapalan M (1995). Scale issues in hydrological modelling: a review. Hydrol Processes, 9(3–4): 251–290

[5]

Borris M, Viklander M, Gustafsson A M, Marsalek J (2014). Modelling the effects of changes in rainfall event characteristics on TSS loads in urban runoff. Hydrol Processes, 28(4): 1787–1796

[6]

Chen A S, Evans B, Djordjević S, Savić D A (2012). A coarse-grid approach to representing building blockage effects in 2D urban flood modelling. J Hydrol (Amst), 426–427(6): 1–16

[7]

Chow M F, Yusop Z, Toriman M E (2012). Modelling runoff quantity and quality in tropical urban catchments using storm water management model. Int J Environ Sci Technol, 9(4): 737–748

[8]

di Pierro F, Khu S T, Savi D (2006). From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms. Water Sci Technol, 54(6–7): 57–64

[9]

Elliott A H, Trowsdale S A, Wadhwa S (2009). Effect of aggregation of on-site storm-water control devices in an urban catchment model. J Hydrol Eng, 14(9): 975–983

[10]

Ghosh I, Hellweger F L (2012). Effects of spatial resolution in urban hydrologic simulations. J Hydrol Eng, 17(1): 129–137 doi:10.1061/(ASCE)HE.1943-5584.0000405

[11]

Gooré Bi E, Monette F, Gachon P, Gaspéri J, Perrodin Y (2015). Quantitative and qualitative assessment of the impact of climate change on a combined sewer overflow and its receiving water body. Environ Sci Pollut Res Int, 22(15): 11905–11921

[12]

Huber W C, Dickinson R E, Barnwell T O Jr, Branch A (1988). Storm water management model; version 4. Environmental Protection Agency, United States

[13]

James W, Huber W, Dickinson R, Pitt R, Roesner L, Aldrich J (2003). User’s Guide to PCSWMM. Computational Hydraulics International: Guelph, Ontario, Canada

[14]

Knighton J, White E, Lennon E, Rajan R (2014). Development of probability distributions for urban hydrologicmodel parameters and a Monte Carlo analysis of model sensitivity. Hydrol Processes, 28(19): 5131–5139

[15]

Krebs G, Kokkonen T, Valtanen M, Koivusalo H, Setälä H (2013). A high resolution application of a stormwater management model (SWMM) using genetic parameter optimization. Urban Water J, 10(6): 394–410

[16]

Krebs G, Kokkonen T, Valtanen M, Setälä H, Koivusalo H (2014). Spatial resolution considerations for urban hydrological modelling. J Hydrol (Amst), 512: 482–497

[17]

Leandro J, Schumann A, Pfister A (2016). A step towards considering the spatial heterogeneity of urban key features in urban hydrology flood modelling. J Hydrol (Amst), 535: 356–365

[18]

Liong S Y, Chan W T, Lum L H (1991). Knowledge-based system for SWMM runoff component calibration. J Water Resour Plan Manage, 117(5): 507–524

[19]

Madsen H (2003). Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv Water Resour, 26(2): 205–216

[20]

Melsen L, Teuling A, Torfs P, Zappa M, Mizukami N, Clark M, Uijlenhoet R (2016). Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-alpine basin. Hydrol Earth Syst Sci Discuss, 20: 1–38

[21]

Palla A, Gnecco I (2015). Hydrologic modeling of low impact development systems at the urban catchment scale. J Hydrol (Amst), 528: 361–368

[22]

Park S Y, Lee K W, Park I H, Ha S R (2008). Effect of the aggregation level of surface runoff fields and sewer network for a SWMM simulation. Desalination, 226(1–3): 328–337

[23]

Peel M C, Blöschl G (2011). Hydrological modelling in a changing world. Prog Phys Geogr, 35(2): 249–261

[24]

Peterson E W, Wicks C M (2006). Assessing the importance of conduit geometry and physical parameters in karst systems using the storm water management model (SWMM). J Hydrol (Amst), 329(1‒2): 294–305

[25]

Ritter A, Muñoz-Carpena R (2013). Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol (Amst), 480: 33–45

[26]

Rosa D J, Clausen J C, Dietz M E (2015). Calibration and verification of SWMM for low impact development. J Am Water Resour Assoc, 51(3): 746–757

[27]

Rossman L A (2010). Storm water management model user’s manual, version 5.0. National Risk Management Research Laboratory, Office of Research and Development, US Environmental Protection Agency

[28]

Shen Z Y, Chen L, Liao Q, Liu R M, Huang Q (2013). A comprehensive study of the effect of GIS data on hydrology and non-point source pollution modeling. Agric Water Manage, 118: 93–102

[29]

Shen Z, Hou X, Li W, Aini G (2014). Relating landscape characteristics to non-point source pollution in a typical urbanized watershed in the municipality of Beijing. Landsc Urban Plan, 123: 96–107

[30]

Sun N, Hall M, Hong B, Zhang L (2014). Impact of SWMM catchment discretization: case study in Syracuse, New York. J Hydrol Eng, 19(1): 223–234

[31]

Tian Y, Zheng Y, Wu B, Wu X, Liu L, Zheng C (2015). Modeling surface water-groundwater interaction in arid and semi-arid regions with intensive agriculture. Environ Model Softw, 63: 170–184

[32]

Tsihrintzis V A, Hamid R (1998). Runoff quality prediction from small urban catchments using SWMM. Hydrol Processes, 12(2): 311–329

[33]

Vaze J, Chiew F H (2003). Comparative evaluation of urban storm water quality models. Water Resour Res, 39(10): 1280

[34]

Vojinovic Z, Tutulic D (2009). On the use of 1D and coupled 1D-2D modelling approaches for assessment of flood damage in urban areas. Urban Water J, 6(3): 183–199

[35]

Wang K H, Altunkaynak A (2012). Comparative case study of rainfall-runoff modeling between SWMM and fuzzy logic approach. J Hydrol Eng, 17(2): 283–291

[36]

Zaghloul N A (1981). SWMM model and level of discretization. J Hydraul Div, 107(11): 1535–1545

[37]

Zhang Y, Vaze J, Chiew F H, Teng J, Li M (2014). Predicting hydrological signatures in ungauged catchments using spatial interpolation, index model, and rainfall-runoff modelling. J Hydrol (Amst), 517: 936–948

[38]

Zhao D Q, Chen J N, Wang H Z, Tong O Y, Chao S B, Sheng Z (2009). GIS-based urban rainfall-runoff modeling using an automatic catchment-discretization approach: a case study in Macau. Environ Earth Sci, 59(2): 465–472

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature

AI Summary AI Mindmap
PDF (4252KB)

1030

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/