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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

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Front. Earth Sci.    2019, Vol. 13 Issue (1) : 18-32    https://doi.org/10.1007/s11707-018-0710-3
RESEARCH ARTICLE
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
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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
 Cite this article:   
Xiaoshu HOU,Lei CHEN,Xiang LIU, et al. Parameter transferability across spatial resolutions in urban hydrological modelling: a case study in Beijing, China[J]. Front. Earth Sci., 2019, 13(1): 18-32.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0710-3
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I1/18
Fig.1  Study area map showing the detailed upstream of five outfalls. Gray areas= the upstream of outfall1, yellow areas= the upstream of outfall2, green areas= the upstream of outfall3, blue areas= the upstream of outfall4, pink areas= the upstream of outfall5.
Fig.2  The high resolution (HR) catchment discretization for the study area. Brown dots indicate the sewer inlets, wine red lines represent the storm water sewer network, and grey lines are subcatchment boundaries.
Fig.3  The low resolution (LRWA1 and LRWA2) catchment discretization for the study area. Green dots indicate the sewer inlets, blue lines represent the storm water sewer network, and grey lines are subcatchment boundaries.
Event y/mm/dd Rainfall duration
/min		 rainfall peak intensity
/(mm·h−1)		 Rainfall depth
/mm		 Rainfall type Runoff duration
/min		 Peak flow rate
/(m3·s−1)		 Runoff volume
/m3		 Time to peak
/min
Calibration events 2014/07/29 400 43.28 35.7 Heavy rain 940 0.5375 4740 170
2014/08/04 260 9.45 5.9 Light rain 595 0.06908 490.9 90
2014/08/09 120 24.08 7.2 Light rain 275 0.1401 499.9 45
2014/08/23 65 38.4 10.4 Moderate rain 235 0.5032 989.4 15
Validation events 2014/08/30 105 69.6 29 Heavy rain 200 1.022 3159 30
2014/08/31 165 86.2 70.76 Torrential rain 330 0.9653 7205 15
2014/09/01 1880 72 33.6 Heavy rain 1875 0.7034 4098 1745
2014/09/26 20 50.4 7.8 Moderate rain 160 0.3958 783 10
Tab.1  Hydrometeorological data for the selected events
Calibration Parameter Calibration interval Optimized value
Initial calibration based on SRTC Width (k value)* 0.2–5 5
Manning’s roughness pervious 0.02–0.8 0.8
Depression storage pervious/mm 3–10.2 10.2
Horton’s maximum infiltration rate/(mm·h?1) 50–200 150
Horton’s minimum infiltration rate/(mm·h?1) 0–20 20
Horton’s decay rate 2–7 2
Manning’s roughness impervious 0.011–0.033 0.012
Calibration based on genetic multi- objective optimization algorithm NSGAII Manning’s roughness conduit 0.011–0.024 0.015
Depression storage impervious/mm Asphalt area (D1) 1–2.5 1.151
Roof (D2) 1.225
Concrete block pavement (D3) 1.344
Sport I (D4) 1.77
Sport II (D5) 1.684
Mixed land (D6) 2.216
Tab.2  Parameter calibrated during parameter calibration for HR models
Fig.4  Comparison of predicted and measured hydrographs of HR model from calibration and validation runs.
Fig.5  Comparison of runoff process with different spatial resolutions.
Fig.6  Performances of three scenarios in the low resolution models and a comparison with the high resolution models.
Flow width- k value Dstore-imperv/mm N-imperv/mm N-c
LRWA/LRSR 5 1.274–1.955 0.012 0.015
LRRE 4.35 2.888 0.012 0.022
Tab.3  Comparison of key parameter values between LRWA/LRSR and LRRE
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