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Tytuł pozycji:

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)

Tytuł:
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)
Autorzy:
Farajzadeh, S.
Khaleghi, M. R.
Data publikacji:
2020
Słowa kluczowe:
rainfall simulator
erosion
multivariate linear regression
regional model
symulator opadów deszczu
erozja
wielowymiarowa regresja liniowa
model regionalny
Język:
angielski
Dostawca treści:
BazTech
Artykuł
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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.

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