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Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number

Author

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  • Khurshid Jahan

    (Department of Geosciences, University of Rhode Island, South Kingston, RI 02881, USA)

  • Soni M. Pradhanang

    (Department of Geosciences, University of Rhode Island, South Kingston, RI 02881, USA)

  • Md Abul Ehsan Bhuiyan

    (Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA 02115, USA)

Abstract

Suburban growth and its impacts on surface runoff were investigated using the soil conservation service curve number (SCS-CN) model, compared with the integrated advanced remote sensing and geographic information system (GIS)-based integrated approach, over South Kingston, Rhode Island, USA. This study analyzed and employed the supervised classification method on four Landsat images from 1994, 2004, 2014, and 2020 to detect land-use pattern changes through remote sensing applications. Results showed that 68.6% urban land expansion was reported from 1994 to 2020 in this suburban area. After land-use change detection, a GIS-based SCS-CN model was developed to examine suburban growth and surface runoff estimation. The developed model demonstrated the spatial distribution of runoff for each of the studied years. The results showed an increasing spatial pattern of 2% to 10% of runoff from 1994 to 2020. The correlation between runoff co-efficient and rainfall indicated the significant impact of suburban growth in surface runoff over the last 36 years in South Kingstown, RI, USA, showing a slight change of forest (8.2% area of the total area) and agricultural land (4.8% area of the total area). Suburban growth began after 2000, and within 16 years this land-use change started to show its substantial impact on surface runoff. We concluded that the proposed integrated approach could classify land-use and land cover information to understand suburban growth and its potential impact on the area.

Suggested Citation

  • Khurshid Jahan & Soni M. Pradhanang & Md Abul Ehsan Bhuiyan, 2021. "Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number," Land, MDPI, vol. 10(5), pages 1-18, April.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:5:p:452-:d:542099
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    References listed on IDEAS

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    1. Md Abul Ehsan Bhuiyan & Feifei Yang & Nishan Kumar Biswas & Saiful Haque Rahat & Tahneen Jahan Neelam, 2020. "Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin," Forecasting, MDPI, vol. 2(3), pages 1-19, July.
    2. M. Nagarajan & George Basil, 2014. "Remote sensing- and GIS-based runoff modeling with the effect of land-use changes (a case study of Cochin corporation)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(3), pages 2023-2039, September.
    3. P. Singh & S. Mishra & R. Berndtsson & M. Jain & R. Pandey, 2015. "Development of a Modified SMA Based MSCS-CN Model for Runoff Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 4111-4127, September.
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    Cited by:

    1. Sabita Shrestha & Shenghui Cui & Lilai Xu & Lihong Wang & Bikram Manandhar & Shengping Ding, 2021. "Impact of Land Use Change Due to Urbanisation on Surface Runoff Using GIS-Based SCS–CN Method: A Case Study of Xiamen City, China," Land, MDPI, vol. 10(8), pages 1-18, August.
    2. Anna Porębska & Krzysztof Muszyński & Izabela Godyń & Kinga Racoń-Leja, 2023. "City and Water Risk: Accumulated Runoff Mapping Analysis as a Tool for Sustainable Land Use Planning," Land, MDPI, vol. 12(7), pages 1-21, July.

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