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An Approach for Estimating Monthly Curve Number Based on Remotely-Sensed MODIS Leaf Area Index Products

Author

Listed:
  • Zahra Parisay

    (Gorgan University of Agricultural Sciences and Natural Resources)

  • Vahedberdi Sheikh

    (Gorgan University of Agricultural Sciences and Natural Resources)

  • Abdolreza Bahremand

    (Gorgan University of Agricultural Sciences and Natural Resources)

  • Chooghi Bairam Komaki

    (Gorgan University of Agricultural Sciences and Natural Resources)

  • Khodayar Abdollahi

    (Shahrekord University)

Abstract

Curve number (CN) is a principal factor which is widely used in hydrology, specifically in the rainfall-runoff modelling. Its value varies based on soil moisture condition; soil hydrologic group, land use, and vegetation cover type. Remote sensing technology provides a tool to investigate spatiotemporal variations of land-cover. This may lead to generation of some continuous spatiotemporal CN datasets required for many hydrological applications. The purpose of this research is to develop an approach for estimating a monthly-distributed curve number via MODIS leaf area index (LAI) products. For calculating monthly CN, we investigated the relationship between monthly LAI, rainfall and CN under different computational scenarios of arithmetic mean, median, and geometric mean for estimating monthly LAI and rainfall. For this purpose, rainfall data for the period 2002–2016 were collected. Further, LAI data were obtained from MODIS for the same period. The performance of the modelled CN (correlation coefficients and Nash-Sutcliffe coefficient) was compared against obtained values of monthly rainfall and runoff in the SCS-CN method (observation-based CN). The findings of this study suggested that CN values obtained from the arithmetic mean of LAI and the geometric mean of rainfall yielded the best scenario (R2 = 0.916 and NS = 0.903 for calibration set; R2 = 0.926 and NS = 0.892 for validation set). Therefore, we suggest a simple appropriate method to generate monthly spatially distributed CN for hydrological applications.

Suggested Citation

  • Zahra Parisay & Vahedberdi Sheikh & Abdolreza Bahremand & Chooghi Bairam Komaki & Khodayar Abdollahi, 2019. "An Approach for Estimating Monthly Curve Number Based on Remotely-Sensed MODIS Leaf Area Index Products," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2955-2972, June.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:8:d:10.1007_s11269-019-02279-8
    DOI: 10.1007/s11269-019-02279-8
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    References listed on IDEAS

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    1. Cao, XiaoLei & Zhou, ZuHao & Chen, XiangDong & Shao, WeiWei & Wang, ZiRu, 2015. "Improving leaf area index simulation of IBIS model and its effect on water carbon and energy—A case study in Changbai Mountain broadleaved forest of China," Ecological Modelling, Elsevier, vol. 303(C), pages 97-104.
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    Cited by:

    1. Akbar Norouzi-Shokrlu & Mehdi Pajouhesh & Khodayar Abdollahi, 2020. "Relating Sediment Yield Estimations to the Wet Front Term Using Rainfall Simulator Field Experiments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4181-4196, October.

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