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Estimating runoff in ungauged catchments by Nash-GIUH model using image processing and fractal analysis

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Abstract

Estimation of rainfall-runoff model parameters in ungauged catchments is of significant importance. The Nash geomorphological instantaneous unit hydrograph (NGIUH) model is widely used to predict runoff in ungauged catchments. The NGIUH model parameters are estimated based on the stream network delineation of the catchment to obtain the stream-order-law ratios. Different methods have been presented to delineate stream networks of catchments based on topographic maps and satellite images using remote sensing (RS) and geographic information system (GIS). In this study, the fractal dimension of the stream network (D) and the fractal dimension of the main river (d) were calculated by wavelet image processing of the stream network images. Shearlet transform was applied to compute the bifurcation ratio (RB). New equations were proposed to estimate the NGIUH parameters based on the fractal analysis of the river network and main river length. The proposed approach was evaluated by computing the flood hydrographs in three catchments of Kasilian, Galazchai and Heng-Chi. Based on results, coefficients of efficiency (CE) were 0.42 and 0.96. The errors in peak discharge estimation were in an acceptable range 0.93–12.91%.

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Acknowledgements

This article was excerpted from a Ph.D. thesis in Water and Hydraulic Structures, Islamic Azad University, Estahban Branch.

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Authors and Affiliations

  1. Department of Civil Engineering, Estahban Branch, Islamic Azad University, Estahban, Iran

    M. Tarahi & T. Sabzevari

  2. Department of Civil Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

    M. H. Fattahi

  3. Department of Mathematics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

    T. Derikvand

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Correspondence to T. Sabzevari.

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Tarahi, M., Sabzevari, T., Fattahi, M.H. et al. Estimating runoff in ungauged catchments by Nash-GIUH model using image processing and fractal analysis. Stoch Environ Res Risk Assess 36, 51–66 (2022). https://doi.org/10.1007/s00477-021-02068-z

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