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

Zahra Parisay (), Vahedberdi Sheikh, Abdolreza Bahremand, Chooghi Bairam Komaki and Khodayar Abdollahi
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2019, vol. 33, issue 8, No 21, 2955-2972

Abstract: 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.

Keywords: Distributed CN; Infiltration; Averaging scenarios; Spatial CN; Bustan dam watershed (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11269-019-02279-8

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