Determination of the best method of estimating the time of concentration in pasture watersheds (case study: Banadak Sadat and Siazakh Watersheds, Iran)

Paper Details

Research Paper 01/12/2013
Views (463)
current_issue_feature_image
publication_file

Determination of the best method of estimating the time of concentration in pasture watersheds (case study: Banadak Sadat and Siazakh Watersheds, Iran)

Ghorban Vahabzadeh, Iman Saleh, Atta Safari, Khabat Khosravi
J. Biodiv. & Environ. Sci. 3(12), 150-159, December 2013.
Copyright Statement: Copyright 2013; The Author(s).
License: CC BY-NC 4.0

Abstract

Temporal parameters are used in most of hydrological and hydraulic models. The most common temporal parameter in hydrology is time of concentration which is required for spillway design, flood volume estimation, producing flood hydrograph and much other hydrological analysis. Therefore, in this research, 10 estimation methods have been calculated for each sub-basin of both studied watersheds. Ultimately, equations of estimating the time of concentration were evaluated using mean deviation, mean difference, relative error percentage and mean square error tests and comparison method of mean by Tukey method and their categorization in Minitab software. The results of analysis of variance table showed that, there is a significant difference at the level of 1% between the equations. The results of analysis of variance by Tukey method for Banadak Sadat watershed showed that, Passini Model (which has the minimum amount of MD, BIAS, RE, RMSE by 0.001, 0.0031, 0.0043, 1.892 respectively) is the best approach, and after this model, Ventura model and Rational Hydrograph were respectively the best equations to estimate the time of concentration in the considered watershed. For Siazakh Watershed also, results showed that, the best method for concentration method estimation which has the minimum difference with observed values, is logistic hydrograph (also it has the minimum amount of MD, BIAS, RE and RMSE by 0.085, 0.0092, 0.068 and 5.83 respectively) and after this model, Kirpich and Chow models were respectively the best equations to estimate the time of concentration. Overall results demonstrated that, Rational Hydrograph equation is the most appropriate equation and Bransly-Williams equation is not recommended because of very much difference with observed data.

Azadnia F, Rostami N, Kamali Moghadam R. 2010. Comparison of some empirical equations to estimate time of concentration in Meymeh watershed, Ilam province. Journal of Water Research of Iran 3(4), 1-8.

Eslamian SS, Mehrabi A. 2006. Determination of empirical equations for estimating the time of concentration in mountainous watersheds. Journal of Agricultural Sciences and Natural Resources 12(5), 23-33.

Esmaeili Ori A, Samiei M. 2011. Evaluation of empirical methods of estimating the time of concentration in Tange Khosouye watershed, Fars province. Proceedings of the 7th National Conference on Watershed Sciences and Engineering.

Avarand  R,  Torabi  Poodeh  H,  Farzaei  A. 2006. Sensitivity analysis of HEC model to input parameters. 8th International Conference on River Engineering, Chamran University of Ahwaz.

Ziaei H. 2001. Principles of watershed management engineering. Astan Quds Razavi Press, University of Imam Reza (AS).

Kosari MR, Saremi Naeini MA, Taze M, Foroozeh MR. 2010. Sensitivity analysis of four equations for estimating the time of concentration in watersheds. Scientific-Research Quarterly of Dry Canvas 1(1), 57-67.

Mobaraki J. 2006. Investigation of the amount of empirical equations accuracy in estimating the time of concentration time to peak of hydrographs (Case study: Tehran province). M.Sc. Thesis of watershed management, Faculty of Natural Resources, University of Tehran, 151 p.

Alizadeh A. 2009. Principles of Applied Hydrology. Astan Quds Razavi Press, 26th edition, 634p.

Motamed Vaziri B. 2004. Investigation of some empirical equations of estimating the time of concentration in Karaj watershed. M.Sc. Thesis of watershed management, Faculty of Natural Resources, University of Tehran, 125 p.

Mahdavi M. 2007. Applied hydrology. 2nd Issue, Tehran University Press, 5th edition, 427 p.

Najmaei M. 1990. Hydrology of Engineering. Volume 1, Science and Technology University Press, 608 p.

Fang X, Thompson D, Cleveland T, Pratistha Pardhan DE, Malla R. 2008. Time of concentration estimated using watershed parameter determined by automated and manual method. Journal of Irrigation and Drainage Engineering 134 (2), 202-211.

Goitom TG. 1989. Evaluation of tc methods in a small Rural watershed, Channel flow and catchment run off: Centennial of Mannings_s Formula and Kuichling_s Rational formula B.C. Yen (Ed). University of Virginia, U.S. National Weather Service and University of Virginia.

Kang J, Kayhanian M, Stenstorm MK. 2008. Predicting the existence of storm water first flush from the time of concentration. Water Research 42(1-2), 220-228.

McCuen R. 1984. Estimating urban time of concentration. Hydraulic Engineering ASCE 100, 633-638.

Pilgrim DH. 1989. Rational methods for estimation of design floods for small to medium sized drainage basins in Australia, IAHS Publ, New direction for surface Water Modeling, Proceedings of the Baltimore Symposium, Australia, 247-259.

Sheridan J. 1994. Hydrograph time parameters for flatland watersheds. ASAE 37, 103-113.

Related Articles

Overemphasis on blue carbon leads to biodiversity loss: A case study on subsidence coastal wetlands in southwest Taiwan

Yih-Tsong Ueng, Feng-Jiau Lin, Ya-Wen Hsiao, Perng-Sheng Chen, Hsiao-Yun Chang, J. Biodiv. & Environ. Sci. 27(2), 46-57, August 2025.

An assessment of the current scenario of biodiversity in Ghana in the context of climate change

Patrick Aaniamenga Bowan, Francis Tuuli Gamuo Junior, J. Biodiv. & Environ. Sci. 27(2), 35-45, August 2025.

Entomofaunal diversity in cowpea [Vigna unguiculata (L.) Walp.] cultivation systems within the cotton-growing zone of central Benin

Lionel Zadji, Roland Bocco, Mohamed Yaya, Abdou-Abou-Bakari Lassissi, Raphael Okounou Toko, J. Biodiv. & Environ. Sci. 27(2), 21-34, August 2025.

Biogenic fabrication of biochar-functionalized iron oxide nanoparticles using Miscanthus sinensis for oxytetracycline removal and toxicological assessment

Meenakshi Sundaram Sharmila, Gurusamy, Annadurai, J. Biodiv. & Environ. Sci. 27(2), 10-20, August 2025.

Bacteriological analysis of selected fishes sold in wet markets in Tuguegarao city, Cagayan, Philippines

Lara Melissa G. Luis, Jay Andrea Vea D. Israel, Dorina D. Sabatin, Gina M. Zamora, Julius T. Capili, J. Biodiv. & Environ. Sci. 27(2), 1-9, August 2025.

Effect of different substrates on the domestication of Saba comorensis (Bojer) Pichon (Apocynaceae), a spontaneous plant used in agroforestry system

Claude Bernard Aké*1, Bi Irié Honoré Ta2, Adjo Annie Yvette Assalé1, Yao Sadaiou Sabas Barima1, J. Biodiv. & Environ. Sci. 27(1), 90-96, July 2025.

Determinants of tree resource consumption around Mont Sangbé national park in western Côte d’Ivoire

Kouamé Christophe Koffi, Serge Cherry Piba, Kouakou Hilaire Bohoussou, Naomie Ouffoue, Alex Beda, J. Biodiv. & Environ. Sci. 27(1), 71-81, July 2025.