Please wait a minute...
 Home  About the Journal Subscription Advertisement Contact us   英文  
Just Accepted  |  Current Issue  |  Archive  |  Featured Articles  |  Most Read  |  Most Download  |  Most Cited
Journal of Geographical Sciences    2018, Vol. 28 Issue (5) : 656-668     DOI: 10.1007/s11442-018-1497-6
Research Articles |
Rainfall-runoff risk characteristics of urban function zones in Beijing using the SCS-CN model
YAO Lei1(),WEI Wei2,YU Yang2,XIAO Jun2,CHEN Liding2,*()
1. College of Geography and Environment, Shandong Normal University, Jinan 250014, China
2. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, CAS, Beijing 100085, China
Download: PDF(1902 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

Urbanization significantly increases the risk of urban flooding. Therefore, quantitative study of urban rainfall-runoff processes can provide a scientific basis for urban planning and management. In this paper, the built-up region within the Fifth Ring Road of Beijing was selected as the study area. The details of land cover and urban function zones (UFZs) were identified using GIS and RS methods. On this basis, the SCS-CN model was adopted to analyze the rainfall-runoff risk characteristics of the study area. The results showed that: (1) UFZs within different levels of runoff risk varied under different rainfall conditions. The area ratio of the UFZs with high runoff risk increased from 18.90% (for rainfall return period of 1a) to 54.74% (for period of 100a). Specifically, urban commercial areas tended to have the highest runoff risk, while urban greening spaces had the lowest. (2) The spatial characteristics of the runoff risks showed an obvious circular distribution. Spatial cluster areas with high runoff risk were mainly concentrated in the center of the study area, while those with low runoff risk were mainly distributed between the fourth and fifth ring roads. The results indicated that the spatial clustering characteristic of urban runoff risk and runoff heterogeneity among different UFZs should be fully considered during urban rainwater management.

Keywords SCS-CN model      urban function zone      spatial cluster      runoff risk     
Fund:National Natural Science Foundation of China, No.41701206;The Major Program of National Natural Science Foundation of China, No.41590841
Corresponding Authors: CHEN Liding     E-mail:;
Issue Date: 31 March 2018
E-mail this article
E-mail Alert
Articles by authors
YU Yang
CHEN Liding
Cite this article:   
YAO Lei,WEI Wei,YU Yang, et al. Rainfall-runoff risk characteristics of urban function zones in Beijing using the SCS-CN model[J]. Journal of Geographical Sciences, 2018, 28(5): 656-668.
URL:     OR
Figure 1  The study area and its urban function zones
UFZ Abbreviation Area (ha) Description
residential zone
HRZ 21187.9 Services for citizens; typical residential communities in Beijing, including low-rise and high-rise buildings with a dense population.
residential zone
LRZ 450.0 Services for citizens; lower impervious fraction; mainly low-rise buildings with a sparse population.
Government zone GOZ 6119.2 Services for civil servants and students; government buildings, public organizations, research institutes, and campuses.
Industry zone INZ 9460.1 Services for production workers and laborers; city infrastructure and industrial factories, energy, and resources supply.
Commercial zone COZ 10100.6 Services for business and commercial workers; city malls, retail businesses, and public amenities such as restaurants, hotels, etc.
Recreational zone REZ 9204.5 Services for tourists and residents; urban parks, golf courses, and scenic areas with relatively high green coverage.
Preservation zone PRZ 225.1 Services for tourists and residents; open space with natural and artificial green space such as forest parks.
Agricultural zone AGZ 751.9 Services for agricultural workers; cultivated land, greenhouses, and orchards.
Public service zone PSZ 3704.6 Services for citizens, such as hospitals, libraries, museums, stadiums, and city squares.
Development zone DEZ 4302.1 Services mainly for construction workers; undeveloped open space and demolition areas.
Table 1  Standard of classification for urban functional zones in Beijing’s five-ring areas
Impervious land 98 98 98
Farmland 60 78 90
Forest 37 58 78
Grassland 40 61 80
Bare land 81 91 97
Water a 0 0 0
Table 2  Curve numbers assigned with various land cover types and antecedent moisture conditions (AMC)
Figure 2  Land-cover composition of each type of urban functional zone
Figure 3  Average runoff ratios of the study area under different rainfall return periods
Rainfall-runoff risk level Runoff ratio (α)
Lowest risk α<0.35
Lower risk 0.35≤α<0.5
Moderate risk 0.5≤α<0.6
Higher risk 0.6≤α<0.7
Highest risk 0.7≤α
Table 3  Class assignments of the runoff risk areas
Urban function
Area ratio for each level runoff risk (%)
Highest risk Higher risk Moderate risk Lower risk Lowest risk
HRZ 37.41 46.26 32.98 8.21 0.52
LRZ 0.04 0.50 0.29 4.16 0.40
GOZ 6.98 11.77 18.25 8.69 2.00
INZ 15.18 16.65 17.00 12.22 4.72
DEZ 5.17 7.27 10.68 7.30 3.74
COZ 29.88 8.87 6.83 3.97 0.68
REZ 0.27 1.30 8.38 36.58 84.17
PRZ - - - 1.39 1.90
AGZ - 0.35 1.45 6.67 1.02
PSZ 5.07 7.03 4.14 10.81 0.83
Table 4  Proportions of urban functional zones within each level runoff risk area (under the rainfall return period of 10a)
UFZ 1a 3a 5a 10a 25a 50a 100a
GOZ 24.76±4.14e 36.40±5.78de 42.02±6.46de 49.77±7.31d 60.25±8.32d 68.45±9.02d 76.70±9.66d
COZ 27.74±3.71f 40.58±5.17f 46.70±5.77f 55.06±6.52e 66.28±7.42e 74.99±8.04e 83.69±8.60e
AGZ 12.00±4.83b 21.12±6.22b 25.87±6.80b 32.66±7.52b 42.17±8.40bc 49.79±9.02c 57.57±9.59c
PSZ 25.06±5.10e 36.84±7.11de 42.51±7.94de 50.33±8.96d 60.90±10.19d 69.16±11.04d 77.45±11.81d
HRZ 24.90±3.55e 36.60±4.96de 42.24±5.54de 50.02±6.26d 60.54±7.12d 68.77±7.72d 77.03±8.27d
LRZ 17.56±5.55c 26.45±7.78c 30.93±8.70c 37.27±9.85c 46.08±11.22c 53.12±12.18c 60.31±13.05c
INZ 25.42±5.78ef 37.43±7.98ef 43.21±8.88ef 51.16±10de 61.88±11.34de 70.26±12.26de 78.66±13.09de
DEZ 22.27±7.03d 33.59±9.61d 39.10±10.68d 46.74±12d 57.12±13.57d 65.26±14.66d 73.46±15.65d
REZ 13.55±6.40b 20.83±8.94b 24.64±9.99b 30.15±11.3b 37.97±12.86b 44.33±13.94b 50.90±14.93b
PRZ 7.36±3.10a 12.39±4.48a 15.28±5.06a 19.64±5.8a 26.12±6.69a 31.54±7.31a 37.26±7.88a
Table 5  Comparison of the average runoff volume in each urban functional zone (UFZ) in different return periods of rainfall (mm)
Figure 4  Spatial distribution chart of runoff risk area under 3 types of rainfall conditions (1a, 10a, 100a)
Figure 5  LISA map for runoff risk analysis
[1] Armson D, Stringer P, Ennos A R, 2013. The effect of street trees and amenity grass on urban surface water runoff in Manchester, UK.Urban Forestry & Urban Greening, 12(3): 282-286.
doi: 10.1016/j.ufug.2013.04.001
[2] Atkinson S, 2012. A storm water runoff investigation using GIS and remote sensing [D]. Unviersity of North Texas.
[3] Baker T J, Miller S N, 2013. Using the Soil and Water Assessment Tool (SWAT) to assess land use impact on water resources in an East African watershed.Journal of Hydrology, 486(8): 100-111.
doi: 10.1016/j.jhydrol.2013.01.041
[4] Dunnett N, Nagase A, Booth Ret al., 2008. Influence of vegetation composition on runoff in two simulated green roof experiments.Urban Ecosystems, 11(4): 385-398.
doi: 10.1007/s11252-008-0064-9
[5] Ebrahimian A, Gulliver J S, Wilson B N, 2016. Effective impervious area for runoff in urban watersheds.Hydrological Processes, 30(20): 3717-3729.
doi: 10.1002/hyp.10839
[6] Fan F L, Deng Y B, Hu X Fet al., 2013. Estimating composite curve number using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China.Remote Sensing, 5(3): 1425-1438.
doi: 10.3390/rs5031425
[7] Fu S H, Wang H Y, Wang X Let al., 2013. The runoff curve number of SCS-CN method in Beijing.Geographical Research, 32(5): 797-807. (in Chinese)
doi: 10.11821/yj2013050003
[8] Gajbhiye S, Mishra S, 2012. Application of NRSC-SCS curve number model in runoff estimation using RS & GIS,International Conference on Advances in Engineering, Science and Management, IEEE, 346-352.
[9] GB50014, 2013. Code for Design of Outdoor Wastewater Engineering. (in Chinese)
[10] Gill S, Handley J, Ennos Aet al., 2007. Adapting cities for climate change: The role of the green infrastructure.Built Environment, 33(1): 115-133.;issn=0263-7960&amp;volume=33&amp;issue=1&amp;spage=115
doi: 10.2148/benv.33.1.115
[11] Jarden K M, Jefferson A J, Grieser J M, 2015. Assessing the effects of catchment-scale urban green infrastructure retrofits on hydrograph characteristics.Hydrological Processes, 291(1): 6-14.
doi: 10.1002/hyp.10736
[12] Jiang M, Chen H, Chen Q, 2013. A method to analyze “source-sink” structure of non-point source pollution based on remote sensing technology.Environmental Pollution, 182: 135-140.
doi: 10.1016/j.envpol.2013.07.006 pmid: 23911622
[13] Kadam A K, Kale S S, Pande N Net al., 2012. Identifying potential rainwater harvesting sites of a semi-arid, basaltic region of Western India, using SCS-CN method.Water Resources Management, 26(9): 2537-2554.
doi: 10.1007/s11269-012-0031-3
[14] Lin F T, 2000. GIS-based information flow in a land-use zoning review process.Landscape and Urban Planning, 52(1): 21-32.
doi: 10.1016/S0169-2046(00)00110-9
[15] Milly P, Wetherald R, Dunne Ket al., 2002. Increasing risk of great floods in a changing climate.Nature, 415: 514-517.
doi: 10.1038/415514a
[16] NRCS, 1986. Urban hydrology for small watersheds.Technical Release, 55: 2-6.
[17] Ouyang W, Guo B, Hao Fet al., 2012. Modeling urban storm rainfall runoff from diverse underlying surfaces and application for control design in Beijing.Journal of Environmental Management, 113(1): 467-473.
doi: 10.1016/j.jenvman.2012.10.017 pmid: 23122620
[18] Pan A, Zhang S, Meng Qet al., 2009. Initial concept of stormwater and flood management in Beijing city.China Water & Wastewater, 25(22): 9-12. (in Chinese)
doi: 10.1016/S1874-8651(10)60073-7
[19] Putro B, Kjeldsen T R, Hutchins M Get al., 2016. An empirical investigation of climate and land-use effects on water quantity and quality in two urbanising catchments in the southern United Kingdom.Science of The Total Environment, 548/549: 164-172.
doi: 10.1016/j.scitotenv.2015.12.132 pmid: 26802345
[20] Shuster W D, Bonta J, Thurston Het al., 2005. Impacts of impervious surface on watershed hydrology: A review.Urban Water Journal, 2(4): 263-275.
doi: 10.1080/15730620500386529
[21] Singh P K, Yaduvanshi B K, Patel Set al., 2013. SCS-CN based quantification of potential of rooftop catchments and computation of ASRC for rainwater harvesting.Water Resources Management, 27(7): 2001-2012.
doi: 10.1007/s11269-013-0267-6
[22] Skotnicki M, Sowiński M, 2013. The influence of depression storage on runoff from impervious surface of urban catchment.Urban Water Journal, 12(3): 207-218.
doi: 10.1080/1573062X.2013.839717
[23] Su M, Zheng Y, Hao Yet al., 2017. The influence of landscape pattern on the risk of urban water-logging and flood disaster.Ecological Indicators, doi:10.1016/j.ecolind.2017.03.008
doi: 10.1016/j.ecolind.2017.03.008
[24] Sun R, Lü Y, Chen Let al., 2013. Assessing the stability of annual temperatures for different urban functional zones.Building and Environment, 65(7): 90-98.
doi: 10.1016/j.buildenv.2013.04.001
[25] Sunde M, He H S, Hubbart J Aet al., 2016. Forecasting streamflow response to increased imperviousness in an urbanizing Midwestern watershed using a coupled modeling approach.Applied Geography, 72: 14-25.
doi: 10.1016/j.apgeog.2016.05.002
[26] Tian G J, Wu J G, Yang Z F, 2010. Spatial pattern of urban functions in the Beijing metropolitan region.Habitat International, 34(2): 249-255.
doi: 10.1016/j.habitatint.2009.09.010
[27] Wang Q, Zhang X, Wei Met al., 2011. Research summary of planning and design standards for storm water system in Beijing city.Water & Wastewater Engineering, 37(10): 34-39.
[28] Yao L, Chen L, Wei W, 2017. Exploring the linkage between urban flood risk and spatial patterns in small urbanized catchments of Beijing, China.International Journal of Environmental Research and Public Health, 14(3): 239.
doi: 10.3390/ijerph14030239 pmid: 5369075
[29] Yao L, Chen L, Wei Wet al., 2015. Potential reduction in urban runoff by green spaces in Beijing: A scenario analysis.Urban Forestry & Urban Greening, 14(2): 300-308.
doi: 10.1016/j.ufug.2015.02.014
[30] Yao L, Wei W, Chen L, 2016. How does imperviousness impact the urban rainfall-runoff process under various storm cases?Ecological Indicators, 60: 893-905.
doi: 10.1016/j.ecolind.2015.08.041
[31] Zhang B, Xie G, Zhang Cet al., 2012a. The economic benefits of rainwater-runoff reduction by urban green spaces: A case study in Beijing, China.Journal of Environmental Management, 100(10): 65-71.
doi: 10.1016/j.jenvman.2012.01.015 pmid: 22366359
[32] Zhang X, Zhang X, Hu Set al., 2012b. Runoff and sediment modeling in a peri-urban artificial landscape: Case study of Olympic Forest Park in Beijing.Journal of Hydrology, 485: 126-138.
[33] Zhou F, Xu Y, Chen Yet al., 2013. Hydrological response to urbanization at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region.Journal of Hydrology, 485: 113-125.
doi: 10.1016/j.jhydrol.2012.12.040
[34] Zhu Z, Chen Z, Chen Xet al., 2016. Approach for evaluating inundation risks in urban drainage systems.Science of The Total Environment, 553: 1-12.
doi: 10.1016/j.scitotenv.2016.02.025 pmid: 26897578
[35] Zuo D, Xu Z, Yao Wet al., 2016. Assessing the effects of changes in land use and climate on runoff and sediment yields from a watershed in the Loess Plateau of China.Science of The Total Environment, 544: 238-250.
doi: 10.1016/j.scitotenv.2015.11.060 pmid: 26657370
[1] LI Yingjie,ZHANG Liwei,YAN Junping,WANG Pengtao,HU Ningke,CHENG Wei,FU Bojie. Mapping the hotspots and coldspots of ecosystem services in conservation priority setting[J]. Journal of Geographical Sciences, 2017, 27(6): 681-696.
[2] FANG Chuanglin,LIU Haimeng,LUO Kui,YU Xiaohua. Process and proposal for comprehensive regionalization of Chinese human geography[J]. Journal of Geographical Sciences, 2017, 27(10): 1155-1168.
[3] DENG Yu, LIU Shenghe, ZHANG Wenting, WANG Li, WANG Jianghao. General multidimensional cloud model and its application on spatial clustering in Zhanjiang, Guangdong[J]. Journal of Geographical Sciences, 2010, 20(5): 787-798.
Full text



Copyright © Journal of Geographical Sciences, All Rights Reserved.
Powered by Beijing Magtech Co. Ltd