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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)

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
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.
Czasopismo
Rocznik
Strony
1477--1488
Opis fizyczny
Bibliogr. 35 poz.
Twórcy
  • Teheran Science and Research Branch, Islamic Azad University, Tehran, Iran
  • Torbat-E-Jam Branch, Islamic Azad University, Torbat-e-Jam, Iran
Bibliografia
  • 1. Aoki AM, Sereno R (2006) Evaluation of infiltration as an indicator of the quality of the medium flowing through a micro simulator. AgriScientia 23(1):23–31. https://doi.org/10.31047/1668.298x.v23.n1.2688
  • 2. Arnaez J, Lasanta T, Ruiz-Flaño P, Ortigosa L (2007) Factors affecting runoff and erosion under simulated rainfall in Mediterranean vineyards. Soil Tillage Res 93(2):324–334. https://doi.org/10.1016/j.still.2006.05.013
  • 3. Baena JAP, Luzuriaga JS, Fernández CI (2020) Characteristics of rainfall events triggering landslides in two climatologically different areas: Southern Ecuador and Southern Spain. Hydrology 7(3):45. https://doi.org/10.3390/hydrology7030045
  • 4. Garcia PMB, Augustin CHRR, Casagrande PB (2020) Geomorphological index as support to urban planning. Mercator Fortaleza 19(1):24. https://doi.org/10.4215/rm2020.e19003
  • 5. Gholami V, Darvari Z, Mohseni Saravi M (2015) Artificial neural network technique for rainfall temporal distribution simulation (Case study: Kechik region). Casp J Environ Sci 13(1):53–60
  • 6. Gholami V, Torkaman J, Dalir P (2019) Simulation of precipitation time series using tree-rings, earlywood vessel features, and artificial neural network. Theor Appl Climatol 137(3–4):1939–1948. https://doi.org/10.1007/s00704-018-2702-3
  • 7. Gholzom EH, Gholami V (2012) A comparison between natural forests and reforested lands in terms of runoff generation potential and hydrologic response (case study: Kasilian watershed). Soil and Water Research 7(4):166–173. https://doi.org/10.17221/18/2012-SWR
  • 8. Giles PT (1998) Geomorphological signatures: classification of aggregated slope unit objects from digital elevation and remote sensing data. Earth Surf Proc Land 23(7):581–594. https://doi.org/10.1002/(SICI)1096-837(199807)23:7<581:AID-ESP863>3.0.CO;2-S
  • 9. Golian S, Saghafian B, Elmi M, Maknoon R (2011) Probabilistic rainfall thresholds for flood forecasting: evaluating different methodologies for modelling rainfall spatial correlation (or dependence). Hydrol Process 25(13):2046–2055. https://doi.org/10.1002/hyp.7956
  • 10. Hamed Y, Albergel J, Pépin Y, Asseline Y (2002) Comparison between rainfall simulator erosion and observed sedimentation in an erosion-sensitive semiarid catchment. CATENA 50(1):115. https://doi.org/10.1016/S0341-8162(02)00089-9
  • 11. Homsi R, Shiru MS, Shahid S, Ismail T, Harun SB, Al-Ansari N, Chau KW, Yaseen ZM (2020) Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria. Eng Applications Comput Fluid Mech 14(1):90–106. https://doi.org/10.1080/19942060.2019.1683076
  • 12. Hosseini SH, Khaleghi MR (2020) Application of SWAT model and SWAT-CUP software in simulation and analysis of sediment uncertainty in arid and semi-arid watersheds (case study: the Zoshk-Abardeh watershed). Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-00846-2
  • 13. Hoyos N, Waylen PR, Jaramillo A (2005) Seasonal and spatial patterns of erosivity in a tropical watershed of the Colombian Andes. J Hydrol 314:177–191. https://doi.org/10.1016/j.jhydrol.2005.03.014
  • 14. Khaleghi MR (2017) The influence of deforestation and anthropogenic activities on runoff generation. J For Sci 63:245–253. https://doi.org/10.17221/130/2016-JFS
  • 15. Khaleghi MR, Varvani J (2018) Simulation of relationship between river discharge and sediment yield in the semi-arid river watersheds. Acta Geophys 66(1):109–119. https://doi.org/10.1007/s11600-018-0110-9
  • 16. Kumar A, Srivastava SK (1991) Geomorphological units, their geohydrological characteristic and vertical electrical sounding response near Munger, Bihar. J Indian Soc Remote Sens 19:205–215. https://doi.org/10.1007/BF03030772
  • 17. Liu Y, Zhao W, Liu Y, Pereira P (2020) Global rainfall erosivity changes between 1980 and 2017 based on an erosivity model using daily precipitation data. CATENA 194:104768. https://doi.org/10.1016/j.catena.2020.104768
  • 18. Lu J, Huang X (2005) The Relationship between Sediment Yield and Catchment characteristics in the middle Yellow River Basin of China. Sediment transfer through the fluvial system—proceedings of a symposium held in Moscow, August 2004—IAHS Publication. 288: 212–219.
  • 19. Lu J, Zheng F, Guifang L, Bian F (2016) The effects of raindrop impact and runoff detachment on hillslope soil erosion and soil aggregate loss in the Mollisol region of Northeast China. Soil Tillage Res 161:79–85. https://doi.org/10.1016/j.still.2016.04.002
  • 20. Martínez-Mena M, Abadía R, Castillo V, Albaladejo J (2001) Experimental design with a rainfall simulator to study the erosion changes within storm. Cuatern Geomorfol 15(1–2):31–43
  • 21. Pandey A, Himanshu SK, Mishra SK, Singh VP (2016) Physically based soil erosion and sediment yield models revisited. CATENA 147:595–620. https://doi.org/10.1016/j.catena.2016.08.002
  • 22. Parsakhoo A, Lotfalian M, Kavian A (2014) Prediction of the soil erosion in a forest and sediment yield from road network through GIS and SEDMODL. Int J Sedim Res 29(1):118–125. https://doi.org/10.1016/S1001-6279(14)60027-5
  • 23. Quansah C (1981) The effect of soil type, slope, rain intensity and their interactions on splash detachment and transport. J Soil Sci 32(2):215–224. https://doi.org/10.1111/j.1365-2389.1981.tb01701.x
  • 24. Rinaldi M, Belletti B, Comiti F, Nardi L (2015) The Geomorphic units survey and classification system (GUS), Deliverable 6.2, Part 4, of REFORM (REstoring rivers FOR effective catchment Management), a Collaborative project (large-scale integrating project) funded by the European Commission within the 7th framework programme under grant agreement 282656. Technical Report.
  • 25. Sangüesa C, Arumí J, Pizarro R, Link O (2014) A rainfall simulator for the in situ study of superficial runoff and soil erosion. Chil J Agric Res 70(1):178–182. https://doi.org/10.4067/S0718-58392010000100019
  • 26. Seutloali KE, Beckedahl HR (2015) A review of road-related soil erosion: an assessment of causes, evaluation techniques and available control measures. Earth Sci Res J 19(1):73–80. https://doi.org/10.15446/esrj.v19n1.43841
  • 27. Shamshirband S, Hashemi S, Salimi H, Samadianfard S, Asadi E, Shadkani S et al (2020) Predicting standardized streamflow index for hydrological drought using machine learning models. Eng Appl Comput Fluid Mech 14(1):339–350. https://doi.org/10.1080/19942060.2020.1715844
  • 28. Sheridan GJ, Noske P, Lane P, Sherwin C (2008) Using rainfall simulation and site measurements to predict annual interrill erodibility and phosphorus generation rates from unsealed forest roads: validation against in-situ erosion measurements. CATENA 73(1):49–62. https://doi.org/10.1016/j.catena.2007.08.006
  • 29. Summer W, Walling DE (2002) Modelling erosion, sediment transport and sediment yield. International hydrological programme, a contribution to IHP-V Projects 2.1 and 6.2. UNESCO, Paris.
  • 30. Taormina R, Chau KW (2015) ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS. Eng Appl Artif Intell 45:429–440. https://doi.org/10.1016/j.engappai.2015.07.019
  • 31. Varvani J, Khaleghi MR (2018) Investigation of application of storm runoff harvesting system using geographic information systems (GIS): a case study of the Arak watershed, Markazi (Iran). Appl Water Sci 8(6):180. https://doi.org/10.1007/s13201-018-0830-7
  • 32. Varvani J, Khaleghi MR (2019) A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers. Acta Geophys 67(1):205–214. https://doi.org/10.1007/s11600-018-0228-9
  • 33. Verbist K, Cornelis WM, Gabriels D, Alaerts K, Soto G (2009) Using an inverse modelling approach to evaluate the water retention in a simple water harvesting technique. Hydrol Earth Syst Sci 13(10):1979–1992. https://doi.org/10.5194/hessd-6-4265-2009
  • 34. Wu CL, Chau KW (2011) Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399(3–4):394–409. https://doi.org/10.1016/j.jhydrol.2011.01.017
  • 35. Wu CL, Chau KW (2013) Prediction of rainfall time series using modular soft computing methods. Eng Appl Artif Intell 26(3):997–1007. https://doi.org/10.1016/j.engappai.2012.05.023
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ddea6f1c-c365-4064-b2f6-2c0622214899
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