IDEAS home Printed from https://ideas.repec.org/a/taf/tjomxx/v18y2022i2p428-440.html
   My bibliography  Save this article

Estimation of rainfall-induced surface runoff for the Assam region, India, using the GIS-based NRCS-CN method

Author

Listed:
  • Laxmi Gupta
  • Jagabandhu Dixit

Abstract

The NRCS-CN method, integrated with GIS and remote sensing, can be used for estimating curve numbers (CN) and surface runoff in geohydrological systems. The study area is divided into 63 sub-basins, and the land use land cover (LULC)-hydrologic soil group (HSG) complex is identified for each sub-basin. The CN values for three antecedent soil moisture (AMC) conditions are calculated and corrected for surface slope variations. The surface runoff depth is determined using the rainfall data for 16 years (2005–2020). The average runoff depth and mean annual precipitation ranges from 444.50 to 1960.55 mm and 936.99 to 3520.55 mm, respectively. For all sub-basins, strong correlations between runoff depth and rainfall (R2 ≥ 0.8) as well as between simulated runoff and measured runoff (R2 ≥ 0.8) are observed. The Nash–Sutcliffe model efficiency coefficient (NSE) values suggest that the model's efficiency is good to satisfactory.

Suggested Citation

  • Laxmi Gupta & Jagabandhu Dixit, 2022. "Estimation of rainfall-induced surface runoff for the Assam region, India, using the GIS-based NRCS-CN method," Journal of Maps, Taylor & Francis Journals, vol. 18(2), pages 428-440, December.
    • Handle: RePEc:taf:tjomxx:v:18:y:2022:i:2:p:428-440
    DOI: 10.1080/17445647.2022.2076624
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/17445647.2022.2076624
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/17445647.2022.2076624?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saumya Arya & Arun Kumar, 2023. "AHP GIS-aided flood hazard mapping and surface runoff estimation in Gurugram, India," 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. 117(3), pages 2963-2987, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tjomxx:v:18:y:2022:i:2:p:428-440. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjom20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.