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Evaluation of the efciency of the rainfall simulator to achieve a regional model of erosion (case study: Toroq watershed in the east north of Iran)

Farajzadeh S., Khaleghi M. R.

Acta Geophysica

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2020

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Vol. 68, no. 5

1477--1488

EN

The purpose of this study was to obtain the regional model of erosion according to the specifc climatic, adaptive, and other conditions of the Toroq watershed located in the east north of Khorasan Razavi province. To conduct this research, frst, the homogeneous units were prepared using slope maps, lithology, land use, and erosion forms in a Geographic Information System environment. Then, to optimize the number of homogeneous units, the cluster analysis method was used in Statistical Product and Service Solutions (SPSS) software. The diagnostic analysis confrmed the accuracy of cluster analysis inho mogeneous regions. Field operations were carried out in homogeneous units with the establishment of a rainfall simulator and also the application of 30-min rainfall intensity with a return period of 10 years. Also, the collected soil samples were analyzed in the laboratory. After performing statistical analyses in the SPSS environment, the variables afecting erosion were determined and prioritized. Then, through the use of multivariate linear regression and step-by-step and interpolation methods, the equations for estimating the amount of erosion were determined. Finally, the multivariate linear model of plot erosion was prepared using the step-by-step method using two variables of plot slope and land use. The model was selected for estimating erosion after examining diferent validation methods based on less RE and less RMSE, higher R, low signifcance coefcient (Sig < 0.05), and also fewer inputs.

2

A performance evaluation of neuro‑fuzzy and regression methods in estimation of sediment load of selective rivers

Varvani J., Khaleghi M. R.

Acta Geophysica

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2019

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Vol. 67, no. 1

205--214

EN

Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.

3

Simulation of Relationship between River Discharge and Sediment Yield in the Semi-arid River Watersheds

Khaleghi M. R., Varvani J.

Acta Geophysica

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2018

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Vol. 66, no. 1

109--119

EN

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.