|
|
Parameter transferability across spatial resolutions in urban hydrological modelling: a case study in Beijing, China |
Xiaoshu HOU1,2, Lei CHEN1, Xiang LIU2, Miao LI2, Zhenyao SHEN1() |
1. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China 2. School of Environment, Tsinghua University, Beijing 100084, China |
|
|
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
|
Corresponding Author(s):
Zhenyao SHEN
|
Just Accepted Date: 14 May 2018
Online First Date: 26 June 2018
Issue Date: 25 January 2019
|
|
1 |
S SBaek, D H Choi, J W Jung, H J Lee, H Lee, K SYoon, K HCho (2015). Optimizing low impact development (LID) for stormwater runoff treatment in urban area, Korea: experimental and modeling approach. Water Res, 86: 122–131
https://doi.org/10.1016/j.watres.2015.08.038
|
2 |
JBarco, K M Wong, M K Stenstrom (2008). Automatic calibration of the U.S. EPA SWMM model for a large urban catchment. J Hydraul Eng, 134(4): 466–474
https://doi.org/10.1061/(ASCE)0733-9429(2008)134:4(466)
|
3 |
P BBedient, W C Huber (2002). Hydrology and Flood Plain Analysis. New Jersey: Prentice-Hall
|
4 |
GBlöschl, M Sivapalan (1995). Scale issues in hydrological modelling: a review. Hydrol Processes, 9(3–4): 251–290
https://doi.org/10.1002/hyp.3360090305
|
5 |
MBorris, M Viklander, A MGustafsson, JMarsalek (2014). Modelling the effects of changes in rainfall event characteristics on TSS loads in urban runoff. Hydrol Processes, 28(4): 1787–1796
https://doi.org/10.1002/hyp.9729
|
6 |
A SChen, B Evans, SDjordjević, D ASavić (2012). A coarse-grid approach to representing building blockage effects in 2D urban flood modelling. J Hydrol (Amst), 426–427(6): 1–16
|
7 |
M FChow, Z Yusop, M EToriman (2012). Modelling runoff quantity and quality in tropical urban catchments using storm water management model. Int J Environ Sci Technol, 9(4): 737–748
https://doi.org/10.1007/s13762-012-0092-0
|
8 |
Fdi Pierro , S TKhu, DSavi (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
https://doi.org/10.2166/wst.2006.609
|
9 |
A HElliott, S A Trowsdale, S Wadhwa (2009). Effect of aggregation of on-site storm-water control devices in an urban catchment model. J Hydrol Eng, 14(9): 975–983
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000064
|
10 |
IGhosh, F L Hellweger (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 |
Bi EGooré , FMonette, PGachon, JGaspéri, YPerrodin (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
https://doi.org/10.1007/s11356-015-4411-0
|
12 |
W CHuber, R E Dickinson, T O Jr Barnwell, A Branch (1988). Storm water management model; version 4. Environmental Protection Agency, United States
|
13 |
WJames, W Huber, RDickinson, RPitt, L Roesner, JAldrich (2003). User’s Guide to PCSWMM. Computational Hydraulics International: Guelph, Ontario, Canada
|
14 |
JKnighton, E White, ELennon, RRajan (2014). Development of probability distributions for urban hydrologicmodel parameters and a Monte Carlo analysis of model sensitivity. Hydrol Processes, 28(19): 5131–5139
https://doi.org/10.1002/hyp.10009
|
15 |
GKrebs, T Kokkonen, MValtanen, HKoivusalo, HSetälä (2013). A high resolution application of a stormwater management model (SWMM) using genetic parameter optimization. Urban Water J, 10(6): 394–410
https://doi.org/10.1080/1573062X.2012.739631
|
16 |
GKrebs, T Kokkonen, MValtanen, HSetälä, HKoivusalo (2014). Spatial resolution considerations for urban hydrological modelling. J Hydrol (Amst), 512: 482–497
https://doi.org/10.1016/j.jhydrol.2014.03.013
|
17 |
JLeandro, A Schumann, APfister (2016). A step towards considering the spatial heterogeneity of urban key features in urban hydrology flood modelling. J Hydrol (Amst), 535: 356–365
https://doi.org/10.1016/j.jhydrol.2016.01.060
|
18 |
S YLiong, W T Chan, L H Lum (1991). Knowledge-based system for SWMM runoff component calibration. J Water Resour Plan Manage, 117(5): 507–524
https://doi.org/10.1061/(ASCE)0733-9496(1991)117:5(507)
|
19 |
HMadsen (2003). Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv Water Resour, 26(2): 205–216
https://doi.org/10.1016/S0309-1708(02)00092-1
|
20 |
LMelsen, A Teuling, PTorfs, MZappa, NMizukami, MClark, RUijlenhoet (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
https://doi.org/10.5194/hess-2015-532
|
21 |
APalla, I Gnecco (2015). Hydrologic modeling of low impact development systems at the urban catchment scale. J Hydrol (Amst), 528: 361–368
https://doi.org/10.1016/j.jhydrol.2015.06.050
|
22 |
S YPark, K W Lee, I H Park, S R Ha (2008). Effect of the aggregation level of surface runoff fields and sewer network for a SWMM simulation. Desalination, 226(1–3): 328–337
https://doi.org/10.1016/j.desal.2007.02.115
|
23 |
M CPeel, G Blöschl (2011). Hydrological modelling in a changing world. Prog Phys Geogr, 35(2): 249–261
https://doi.org/10.1177/0309133311402550
|
24 |
E WPeterson, C M Wicks (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
https://doi.org/10.1016/j.jhydrol.2006.02.017
|
25 |
ARitter, R Muñoz-Carpena (2013). Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol (Amst), 480: 33–45
https://doi.org/10.1016/j.jhydrol.2012.12.004
|
26 |
D JRosa, J C Clausen, M E Dietz (2015). Calibration and verification of SWMM for low impact development. J Am Water Resour Assoc, 51(3): 746–757
https://doi.org/10.1111/jawr.12272
|
27 |
L ARossman (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 |
Z YShen, L Chen, QLiao, R MLiu, QHuang (2013). A comprehensive study of the effect of GIS data on hydrology and non-point source pollution modeling. Agric Water Manage, 118: 93–102
https://doi.org/10.1016/j.agwat.2012.12.005
|
29 |
ZShen, X Hou, WLi, GAini (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
https://doi.org/10.1016/j.landurbplan.2013.12.007
|
30 |
NSun, M Hall, BHong, LZhang (2014). Impact of SWMM catchment discretization: case study in Syracuse, New York. J Hydrol Eng, 19(1): 223–234
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000777
|
31 |
YTian, Y Zheng, BWu, XWu, L Liu, CZheng (2015). Modeling surface water-groundwater interaction in arid and semi-arid regions with intensive agriculture. Environ Model Softw, 63: 170–184
https://doi.org/10.1016/j.envsoft.2014.10.011
|
32 |
V ATsihrintzis, RHamid (1998). Runoff quality prediction from small urban catchments using SWMM. Hydrol Processes, 12(2): 311–329
https://doi.org/10.1002/(SICI)1099-1085(199802)12:2<311::AID-HYP579>3.0.CO;2-R
|
33 |
JVaze, F H Chiew (2003). Comparative evaluation of urban storm water quality models. Water Resour Res, 39(10): 1280
https://doi.org/10.1029/2002WR001788
|
34 |
ZVojinovic, D Tutulic (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
https://doi.org/10.1080/15730620802566877
|
35 |
K HWang, A Altunkaynak (2012). Comparative case study of rainfall-runoff modeling between SWMM and fuzzy logic approach. J Hydrol Eng, 17(2): 283–291
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000419
|
36 |
N AZaghloul (1981). SWMM model and level of discretization. J Hydraul Div, 107(11): 1535–1545
|
37 |
YZhang, J Vaze, F HChiew, JTeng, M Li (2014). Predicting hydrological signatures in ungauged catchments using spatial interpolation, index model, and rainfall-runoff modelling. J Hydrol (Amst), 517: 936–948
https://doi.org/10.1016/j.jhydrol.2014.06.032
|
38 |
D QZhao, J N Chen, H Z Wang, O Y Tong, S B Chao, Z Sheng (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
https://doi.org/10.1007/s12665-009-0045-1
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|