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Temporal and spatial correlation between impervious surface and surface runoff： A case study of the main urban area of Hangzhou city

  • role： First author第一作者
  • Affiliation：

    School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China

  • Email：2001160310@cugb.edu.cn
  • Introduction：要志鑫，1993年生，女，硕士研究生，研究方向为遥感图像处理，城市生态环境。E-mail: 2001160310@cugb.edu.cn
YAO Zhixin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China

    Sanya Institute of Remote Sensing, Sanya 572029, China

  • Email：mengqy@radi.ac.cn
  • Introduction：孟庆岩，1971年生，男，研究员，研究方向为城市路表环境遥感。E-mail: mengqy@radi.ac.cn
MENG Qingyan23*,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China

SUN Zhenhui2,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China

LIU Shufu2,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China

ZHANG Linlin2

résumé

In the process of urbanization, the expansion of impervious surface has a significant impact on the urban surface hydrdogical cycle, studying the temporal and spatial correlation between impervious surface and surface runoff can alleviate urban waterlogging, which can provide some suggestions for the construction of "sponge city" and scientific basis for the improvement of urban drainage system. In this work, the combination of linear spectral decomposition method with BCI index and SCS—CN model are used to simulate the surface runoff changes caused by impervious surface in the main urban area of ​​Hangzhou in 1990—2017. Moreover, the landscape pattern index is calculated to explore the relationship between impervious surface and surface runoff.Results show that: (1) In the main urban area of ​​Hangzhou, the impervious surface is high and increasing during the past 30 years, and the growth rate tends to be stable. The impervious surface area of ​​Shangcheng District and Xiacheng District has the highest proportion. Jianggan District, Xihu District, and Gongshu District have a large growth rate and intensity. (2) In the past 30 years, the surface runoff of Hangzhou’s main urban area continued to increase, and the 220—240 mm runoff radiated from the center of the city to the surrounding area. The surface runoff of Shangcheng District and Xiacheng District was the highest, which was roughly consistent with the evolution of impervious surface. (3) The spatial pattern of impervious surface is highly related to surface runoff. The number and fragmentation degree of impervious surface are negatively correlated with surface runoff, and the proportion of patch occupied landscape, patch density, and patch aggregation are positively correlated with surface runoff. Surface runoff can be controlled by optimizing the space patterns of impervious surface, such as reducing large areas of impervious surfaces and increasing the number of impervious surface patches.

mots-clés

remote sensing;hydrological model;impervious surface;spatial pattern;surface runoff;regressive analysis;Hangzhou

References

  1. 1.
    Bauer M E, Heinert N J, Doyle J K and Yuan F. 2004. Impervious surface mapping and change monitoring using Landsat remote sensing//ASPRS Annual Conference Proceedings. Denver, Colorado: [s.n.]
  2. 2.
    Brabec E, Schulte S and Richards P L. 2002. Impervious surfaces and water quality: a review of current literature and its implications for watershed planning. Journal of Planning Literature, 16(4): 499-514
  3. 3.
    Chen W B, Xiao D N and Li X Z. 2002. Classification, application, and creation of landscape indices. Chinese Journal of Applied Ecology, 13(1): 121-125
  4. 4.
    Civco D L and Hurd J D. 1997. Impervious surface mapping for the state of Connecticut//Proceedings of American Society for Photogrammetry and Remote Sensing (ASPRS) Annual Conference. Seattle, WA, USA: [s.
  5. 5.
    Cronshey R G, Roberts R T and Miller N. 1985. Urban Hydrology for Small Watersheds (TR-55 REV.). American Society of Civil Engineers: 1268-1273
  6. 6.
    Deng C B and Wu C S. 2012. BCI: a biophysical composition index for remote sensing of urban environments. Remote Sensing of Environment, 127: 247-259
  7. 7.
    Dougherty M, Dymond R L, Goetz S J, Jantz C A and Goulet N. 2004. Evaluation of impervious surface estimates in a rapidly urbanizing watershed. Photogrammetric Engineering and Remote Sensing, 70(11): 1275-1284
  8. 8.
    Fan F L, Deng Y B, Hu X F and Weng Q H. 2013. Estimating composite curve number using an improved SCS-CN method with remotely sensed variables in Guangzhou, China. Remote Sensing, 5(3): 1425-1438
  9. 9.
    Fu S, Zhang G, Wang N and Luo L. 2011. Initial abstraction ratio in the SCS-CN method in the Loess Plateau of China. Transactions of the ASABE, 54(1): 163-169
  10. 10.
    Gillies R R, Box J B, Symanzik J and Rodemaker E J. 2003. Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta—a satellite perspective. Remote Sensing of Environment, 86(3): 411-422
  11. 11.
    Hodgson M E, Jensen J R, Tullis J A, Riordan K D and Archer C M. 2003. Synergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering and Remote Sensing, 69(9): 973-980
  12. 12.
    Huang M B, Gallichand J, Dong C Y, Wang Z L and Shao M A. 2007. Use of soil moisture data and curve number method for estimating runoff in the Loess Plateau of China. Hydrological Process, 21(11): 1471-1481
  13. 13.
    Huang M B, Gallichand J, Wang Z L and Goulet M. 2006. A modification to the soil conservation service curve number method for steep slopes in the Loess Plateau of China. Hydrological Process, 20(3): 579-589
  14. 14.
    Hulshoff R M. 1995. Landscape indices describing a Dutch landscape. Landscape Ecology, 10(2): 101-111
  15. 15.
    Jennings D B, Jarnagin S T and Ebert D W. 2004. A modeling approach for estimating watershed impervious surface area from national land cover data 92. Photogrammetric Engineering and Remote Sensing, 70(11): 1295-1307
  16. 16.
    Jeon J H, Lim K J and Engel B A. 2014. Regional calibration of SCS-CN L-THIA model: application for ungauged basins. Water, 6(5): 1339-1359
  17. 17.
    Kauffman G J, Belden A C, Vonck K J and Homsey A R. 2009. Link between impervious cover and base flow in the White Clay Creek Wild and Scenic Watershed in Delaware. Journal of Hydrologic Engineering, 14(4): 324-335 [DOI: )]
  18. 18.
    Ke W C, Tao C, Ma J L, Liu Y and Zou Z R. 2018. Research on unsupervised city extraction based on Landsat and DMSP-OLS. Geomatics and Spatial Information Technology, 41(7): 183-186
  19. 19.
    Kim N W, Won Y S, Lee J and Jeong J. 2011. Hydrological impacts of urban imperviousness in white rock creek watershed. Transactions of the ASABE, 54(5): 1759-1771
  20. 20.
    Li C L. 2010. The extract Impervious Surface and its Effect on Hydrological on the Qinhuai River Basin. Nanjing: Nanjing University
  21. 21.
    Li P, Wang Y J and Xing W J. 2016. Application of urban impervious surface extraction based on TM image—taking Guangzhou City as an example. Journal of Heilongjiang Institute of Technology, 30(1): 19-23
  22. 22.
    Lu D S and Weng Q H. 2006. Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment, 104(2): 157-167
  23. 23.
    Ma Y X, Zhou W B and Song Y. 2016. Land use change and its impact on surface runoff in the main city of Xi’an. South-to-North Water transfers and Water Science and Technology, 14(5): 49-54, 90
  24. 24.
    Maidment D R. 1992. Handbook of hydrology. New York: McGraw-Hill Book Company
  25. 25.
    Qiu B X. 2015. Connotation, approach and prospect of sponge city (LID). Construction Science and Technology, (1): 1-7 (仇保兴. 2015. 海绵城市(LID)的内涵、途径与展望. 建设科技, (1): 1-7) [DOI: 10.16116/j.cnki.jskj.2015.01.003]
  26. 26.
    Ridd M K. 1995. Exploring a V-I-S (Vegetation-Impervious surface-Soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16(12): 2165-2185
  27. 27.
    Shao W and Pan W B. 2012. On the relationship between urban impervious surface and rainfall-runoff. Journal of Subtropical Resources and Environment, 7(4): 20-27
  28. 28.
    Tang P, Wang W, Zhang Z, Su T X and Wang H. 2018. Quantitative research on rain-flood patterns of urban area in the construction of “Sponge Cities”. Journal of Nanjing Forestry University (Natural Sciences Edition), 42(1): 15-20
  29. 29.
    Vanhalsel M, Bfckx C, Janssens D. 2011. Measuring dissimilarity of geographically dispersed space-time paths [J]. Transportation, 38(1): 65-79.
  30. 30.
    Wang M Y and Xu H Q. 2018. Temporal and spatial changes of urban impervious surface and its influence on urban ecolo-gical quality: a comparison between Shanghai and New York. Chinese Journal of Applied Ecology, 29(11): 3735-3746
  31. 31.
    Wu C S. 2004. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93(4): 480-492
  32. 32.
    Wu C S and Murray A T. 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84(4): 493-505
  33. 33.
    Xian G. 2007. Analysis of impacts of urban land use and land cover on air quality in the Las Vegas region using remote sensing information and ground observations. International Journal of Remote Sensing, 28(24): 5427-5445
  34. 34.
    Xu H Q. 2005. A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5): 589-595
  35. 35.
    Xu H Q and Wang M Y. 2016. Remote sensing-based retrieval of ground impervious surfaces. Journal of Remote Sensing, 20(5): 1270-1289
  36. 36.
    Yang L, Tian F Q and Niyogi D. 2015. A need to revisit hydrologic responses to urbanization by incorporating the feedback on spatial rainfall patterns. Urban Climate, 12: 128-140
  37. 37.
    Yang L M, Huang C Q, Homer C G, Wylie B K and Coan M J. 2003a. An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Canadian Journal of Remote Sensing, 29(2): 230-240
  38. 38.
    Yang L M, Xian G, Klaver J M and Deal B. 2003b. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing, 69(9): 1003-1010
  39. 39.
    Yang X J. 2006. Estimating landscape imperviousness index from satellite imagery. IEEE Geoscience and Remote Sensing Letters, 3(1): 6-9
  40. 40.
    Yu K J, Li D H, Yuan H, Fu W, Qiao Q and Wang S S. 2015. “Sponge city”: theory and Practice. City Planning Review, 39(6): 26-36
  41. 41.
    Yue W Z and Wu C F. 2007. Urban impervious surface distribution estimation by spectral mixture analysis. Journal of Remote Sensing, 11(6): 914-922
  42. 42.
    Zhang W, Zhang Z Y and Yang J. 2014. Application of SCS model to estimate volume of runoff in slope field of red soil. Bulletin of Soil and Water Conservation, 34(5): 124-127
  43. 43.
    Zhang X Y, Meng F and Ding N. 2003. Application of SCS model to estimating the quantity of runoff of small watershed in semi-arid or arid region. Research of Soil and Water Conservation, 10(4): 172-174, 249
  44. 44.
    Zhao L and Zhao Z Q. 2014. Projecting the spatial variation of economic based on the specific ellipses in China. Scientia Geographica Sinica, 34(8): 979-986

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