| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شماره‌ها | 3,272 |
| تعداد مقالات | 36,149 |
| تعداد مشاهده مقاله | 55,713,738 |
| تعداد دریافت فایل اصل مقاله | 94,492,331 |
ارزیابی کارایی مدلهای برآورد زمان تمرکز در برخی زیرحوضههای شمال غرب ایران | ||
| مهندسی و مدیریت آبخیز | ||
| مقاله 7، دوره 16، شماره 3، مهر 1403، صفحه 432-449 اصل مقاله (912.71 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijwmse.2024.361229.2012 | ||
| نویسندگان | ||
| علی رضا واعظی1؛ اولدوز بخشی راد* 2 | ||
| 1استاد گروه علوم خاک، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران | ||
| 2دانش‌آموخته دکترای تخصصی فیزیک و حفاظت خاک، دانشکده کشاورزی، دانشگاه زنجان و کارشناس آبخیزداری اداره کل منابع طبیعی و آبخیزداری آذربایجان‌شرقی | ||
| چکیده | ||
| مقدمه زمان تمرکز حوزههای آبخیز، یکی از مهم‌ترین و متداول‌ترین ویژگیهای مؤثر در مطالعات هیدرولوژیکی بهویژه تعیین دبی جریان برای طرح‌های آبخیزداری است. اغلب حوزههای آبخیز دنیا و همچنین ایران، فاقد ایستگاه‌های اندازه‌گیری جریان هستند و مجریان طرح‌ها ناگزیر به استفاده از مدلهای تجربی برآورد زمان تمرکز هستند. بررسی مطالعات پیشین نشان می‌‌دهد که مدلهای تجربی برآورد زمان تمرکز به‌دلیل تغییر شرایط محیطی در خارج از محل ارائه مدل نتایج نامطلوبی دارند. از سوی دیگر اطلاعات کافی در مورد کارایی مدلهای تجربی برآورد زمان تمرکز در بسیاری از حوزههای آبخیز در ایران و بهویژه در مناطق نیمه‌‌خشک وجود ندارد. هدف از این مطالعه ارزیابی دقت برخی مدلهای تجربی برآورد زمان تمرکز در زیرحوضههای منطقه نیمه‌‌خشک شمال غرب کشور و شناسایی عوامل تعیین‌کننده آن است. مواد و روش‌ها این پژوهش در هشت زیرحوضه شامل آلانق، اردکلو، شکرعلی‌چای، شیرامین، کرجان، کلاله و لیوار از حوضه‌های دریاچه ارومیه و رود ارس در شمال غرب ایران انجام گرفت. داده‌های هواشناسی و هیدرومتری از اداره کل منابع طبیعی آذربایجان شرقی و ایستگاه‌های متعلق به وزارت نیرو اخذ شد. ویژگیهای حوضه مانند مساحت، طول، شیب، ارتفاع و شکل از راه مطالعات میدانی و رسم نقشه‌‌ها در بستر GIS تعیین شد و زمان تمرکز با استفاده از هیدروگراف جریان‌‌ها در دوره آماری 30 سال (از سال 1367 تا 1397) محاسبه شد و از طریق شش مدل تجربی شامل کرپیچ (1940)، کربای (1959)، چاو (1962)، سازمان هوانوردی امریکا (1972)، برانسبی-ویلیامز (1980) و ونتورا (2007) برآورد شد. بررسی رابطه میان زمان تمرکز و ویژگیهای حوزه آبخیز به روش ماتریس همبستگی با استفاده از معیار پیرسون انجام گرفت. برای ارزیابی دقت مدل‌ها از ضریب کارایی نش-ساتکلیف، میانگین خطا و ریشه میانگین مربعات خطا استفاده شد. نتایج و بحث بر اساس نتایج مشاهده‌ای حاصل از روش هیدروگراف، زیرحوضه شکرعلی‌چای کوتاه‌‌ترین (66 دقیقه) و کلاله طولانی‌‌ترین زمان تمرکز (132 دقیقه) را دارد. مدل برانسبی-ویلیامز کمترین خطا (6.8 درصد) و بیشترین ضریب کارایی (73) را داشت؛ درحالی‌که بیشترین خطای برآورد (2/36 درصد) و کمترین ضریب کارایی (14.4-) در مدل سازمان هوانوردی آمریکا بود. شیب، مهم‌ترین عامل مؤثر بر زمان تمرکز برآوردی در مدل کرپیج (0.83-=r)، چاو (0.82-=r) و برانسبی-ویلیامز (0.73-=r) بود. مدل سازمان هوانوردی امریکا (1972) و مدل ونتورا (2007) در زیرحوضههای با شیب زیاد، برآورد ضعیف‌‌تری دارند و برای مناطق کوهستانی مناسب نیستند. نتیجه‌گیری نتایج نشان داد که از میان ویژگیهای فیزیکی حوضه، مساحت، شیب و طول زیرحوضه در تغییرات زمان تمرکز نقش مهم‌تری دارند. این مطالعه نشان داد درصد شیب حوضه مهم‌ترین عامل کاهش زمان تمرکز و دبی اوج و افزایش سرعت سیلابی شدن در زیرحوضههای مورد بررسی است. بنابراین پیشنهاد می‌شود، برای زیرحوضه‌هایی که درصد شیب بالاتری دارند، از طرح‌های حفاظت خاک به‌منظور افزایش زمان تمرکز استفاده شود. ارزیابی مدلهای برآورد زمان تمرکز در هشت زیرحوضه آبخیز نشان داد که مدل برانسبی-ویلیامز (1980) با میانگین خطا 6.80 درصد و ضریب کارایی نش- ساتکلیف 73 درصد مطلوب‌ترین برآورد را ارائه می‌کند، لذا، استفاده از این مدل در حوضه‌های مشابه که فاقد ایستگاه‌های اندازه‌گیری هستند، پیشنهاد می‌شود. | ||
| کلیدواژه‌ها | ||
| دبی اوج؛ درجه شیب؛ شرایط محیطی؛ مدل برانسبی-ویلیامز؛ مساحت | ||
| عنوان مقاله [English] | ||
| Evaluating the efficiency of concentration time estimation models in some sub-basins in northwest of Iran | ||
| نویسندگان [English] | ||
| Ali Reza Vaezi1؛ Ouldouz Bakhshi Rad2 | ||
| 1Professor at Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
| 2Ph.D. Graduated, Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran and Watershed Management Expert in East Azerbaijan Department of Natural Resources and Watershed Management | ||
| چکیده [English] | ||
| Introduction The concentration time of catchments is one of the most important and common effective features in hydrological studies, particularly in determining the flow discharge for designing watershed management projects. Most of the catchments in the world especially in Iran were not equipped with hydrometric stations, and project managers are forced to use traditional empirical models to estimate concentration time and peak flow. The review of previous studies shows that experimental models for estimating concentration time have unfavorable results due to the change of environmental conditions outside the place where the model is presented. On the other hand, there is not enough information about the effectiveness of experimental models for estimating concentration time in many catchments in Iran, especially in semi-arid areas. The purpose of this study is to evaluate the accuracy of some experimental models for estimating concentration time in the sub-basins of the semi-arid region of the northwest of the country and to identify its determining factors. Materials and methods This study was conducted in eight sub-basins including Alanagh, Ordakloo, Shekaralichay, Shiramin, Kurjan, Kalaleh and Livar from Urmia Lake and Araz River basins in Northwest Iran. Meteorological and hydrometric data were obtained from the Natural Resources of East Azerbaijan and stations belonging to the Ministry of Energy. The characteristics of the basin such as area, length, slope, height and shape were determined through field studies and drawing maps in the GIS platform. The concentration time was calculated using the hydrograph of the flows in the statistical period of 30 years (from 1367 to 1397) and it was estimated through six experimental models including Kirpich (1940), Kerby (1959), Chow (1962), Federal Aviation Administration (1972), Bransby-Williams (1980) and Ventura (2007). The relationship between concentration time and catchment characteristics was investigated by correlation matrix, Pearson's method. Nash-Sutcliffe efficiency coefficient, average error and root mean square error were used to evaluate the accuracy of the models. Results and discussion According to the results, Shekaralichay sub basin has the shortest (66 minutes) and the Kalaleh sub basin has the longest concentration time (132 minutes). Bransby-Williams model had the lowest error (6.8 %) and the highest efficiency coefficient (73%); while the estimation error (36.2 %) and the Nash-Sutcliffe efficiency of Federal Aviation Administration model were 36.2% and-14.4% respectively. The slope was the most important main factor on the estimation of concentration time of the assessment in the Kirpich model (r= 0.83), Chow (r= 0.82) and Bransby-Williams (r= 0.73). Federal Aviation Administration model (1972) and Ventura model (2007) have a weak estimate in sub-basins with low slope and length. Conclusions The results showed that among the physical characteristics of the basin, the area, slope and length of the sub-basin play a more important role in changes in concentration time. This study showed that the slope percentage of the basin is the most important factor in reducing concentration time, peak discharge and increasing the speed of flooding in the studied sub-basins, so it is suggested to use soil protection plans in order to increase the concentration time for sub-basins that have a higher slope percentage. The evaluation of concentration time estimation models in eight catchments showed that the Bransby-Williams (1980) model with an average error of 6.80% and Nash-Sutcliffe efficiency coefficient of 73% provides the best estimation among others, so the use of this model in similar basins which do not have measuring stations, it is suggested. | ||
| کلیدواژه‌ها [English] | ||
| Bransby-Williams model, Drainage area, Environmental condition, Peak discharge, Slope gradient | ||
| مراجع | ||
|
Alamri, N., Afolabi, K., Ewea, H., Elfeki, A.M., 2023. Evaluation of the time of concentration models for enhanced peak flood estimation in arid regions. Sustain. 15(3), 1987. Al Islam, M., Hasan, H., 2020. Generation of IDF equation from catchment delineation using GIS. Civil Engineer. J. 6(3), 540-547. Azadnia, F., Rostami, N., Kamalimoghadam, R., 2009. Comprehension some of the empirical equation of time of the concentration in Mimane watershed of Ilam. Iran Water Res. 3(4), 21-34 (in Persian). Azizian, A., Shokouhi Fard, M., 2015. Evaluation and analysis of the study of different time-specific methods, case: Liqvan basin. The Third International Conference on Applied Research in Civil Engineering, Architecture and Urban Management. Tehran, Iran (in Persian). Azizian, A., 2017. Assessment and uncertainty analysis of different time of concentration methods. Iranian J. Soil Water Res. 48(2), 275-288 (in Persian). Barnston, A.G., 1992. Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score. Weather Forecast. 7(4), 699-700. Beven, K.J., 2020. A history of the concept of time of concentration. Hydrol. Earth Syst. Sci. 24, 2655-2670. Chow, V.T., Maidment, D.R., Mays, L.W., 1988. Applied hydrology. New York: McGraw-Hill. 46 pages. Chow, V.T., 2010. Handbook of applied hydrology. McGraw Hill Book Co., New York. 588 pages. Dastorani, M.T., Abdollahwand, E., Asareh, M.H.,Talebi, A., 2013. The evaluation of time travel flow channel of estimation for equations time concentration empirical some of application. Watershed Manage. Res. 99, 42-52 (in Persian). Dave, T., 2019. Fundamentals of hydrology. Routedage pub. London, 3rd ed. 221 pages. de Almeida, I.K., Almeida, A.K., Steffen, J.L., 2016. Model for estimating the time of concentration in watersheds. Water Resour. Manage. 30, 4083-4096. Dingman, S.L., 2002. Physical hydrology. Prentice Hall. 646 pags. Federal Aviation Agency (FAA)., 1970. Airport drainage: department of transport advisory circular: Washington DC, USA. 83 pages. Ghaffari, Gh., Talebi, Z., 2016. Evaluation some of the empirical relations to estimate the concentration time and identify the most important physiographic. J. Watershed Manage. Res. 7(14), 188-196. Goel, M.K., 2011.Runoff coefficient. In: Singh V.P., Singh P. and Haritashya U.K. (eds) Encyclopedia of snow, ice and glaciers. Encyclopedia of Earth Sciences Series. Springer. Dordrecht. pp. 952-953. Harset, D., Jayadi, R., Legono, D., 2022. The influence of two different time of concentration equations on the GIUH-based flood hydrograph estimates of Keduang and Temon sub-watersheds, Indonesia. Current Applied Sci. Technol. 10, 55003. Horton, R.E., 1945. Erosional development of streams and their drainage basins; hydro physical approach to quantitative morphology. Geolog. Soci. America bullet. 56(3), 275-370. Jamshidian, A., Heidarnejad, M., 2018. The effect of constructed structures on time of concentration, case study: Izeh watershed. J. Water Soil Resour. Conserv. 7(3), 39-54 (in Persian). Kamath, M.A., Varun, V.M., Dwarakish, G.S., Kavyashree, B., Shwetha, H.R., 2012. Kirpich and illiams times of concentration in musle: a case study. ISH J. Hydraulic Engineer. 17(2), 1-13. Kerby, W., 1959. Time of concentration for overland flow. Civ. Eng. 29, 174. Kirpich, Z.P., 1940. Time of concentration of small agricultural watersheds. Civil Engineer. 10(6), 362-375. Kousari, M.R., Saremi Naeini, M.A., Tazeh, M., Frozeh, M.R., 2010. Sensitivity analysis of some equations for estimating of time of concentration in watersheds. J. Arid Biome. 1(1), 57-69 (in Persian). Legono, D., Harset, D., Hairani, A., Ikhsan, J., Harsanto, P., 2022. Precursory characteristics of flash flood occurrence in small catchment of upper Brantas River. In IOP conference series: Earth and Environmental Science. 1105(1), 12002. Loague, K., Green, R.E., 1991. Statistical and graohical methods for evaluating solute transport models. Overview and application. J. Contam. Hydrol. 7, 51-73. Marquette, C.M., 2009. Water and development, Volume II. EOLSS Pub. Oxford. UK. PP. 167. Mata-Lima, H., Vargas, H., Carvalho, J., 2007. Comportamento hidrológico de bacias hidrográficas: integração de métodos e aplicação a um estudo de caso. Revista Escola de Minas 60(3), 40-56 (in Portuguese, summary in English). McCuen, R.H., Wong, S.L., Rawls, W.J., 1984. Estimating urban time of concentration. J. Hydraulic Engineer. 110 (7), 887-904. Mourier, B., Walter, C., Merot, P., 2008. Soil distribution in valleys according to stream order. Catena 72, 395-404. Motamed Vaziri, B., 2007. Investigating the effectiveness of the Kerpich method in estimating the time of flood concentration, case study: Karaj Watershed. The Fourth National Conference of Iran Watershed Science And Engineering, Watershed Management, Karaj, Iran (in Persian). Murillo-Bermúdez, L.F., Martim, A.L.S.S., de Abreu, A.E.S., Fais, L.M.C.F., Dalfré Filho, J.G., 2022. Estimation of the time of concentration from morphometric and hydrological monitoring parameters in São Paulo state watersheds. Ciência e Natura. 44, 24-44. Naghibi, S.A., Vafakhah, M., Moghaddam Nia, A., Eslamian, S., 2018. Evaluation of some probability distribution functions for derivation of unit hydrograph in the Bar Watershed, Iran. Int. J. Hydrol. Sci. Technol. 8(2), 134-147. Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I. A discussion of principles. J. Hydrol. 10 (3), 282-290. Parsamehr, A.H., Khosravani, Z., 2017. The use of empirical relationships in estimating the time of concentration of watersheds and investigating the sensitivity of parameters affecting it. The 12th National Conference on Watershed Science and Engineering of Iran. Malayer, Iran (in Persian). Perdikaris, J., Gharabaghi, B., Rudra, R., 2018. Reference time of concentration estimation for ungauged catchments. Earth Sci. Res. 7(2), 58-73. Razmjouyi, N., 2011. A comparison of empirical relationships in the estimation of concentration time in the 22nd district of Tehran. The 7th National Conference of Watershed Engineering Sciences of Iran, Isfahan University of Technology, Isfahan, Iran (in Persian). Roux, H., Amengual, A., Romero, R., Bladé, E., Sanz-Ramos, M., 2020. Evaluation of two hydrometeorological ensemble strategies forflash-flood forecasting over a catchment of the eastern Pyrenees. Nat. Hazards Earth Syst. Sci. 20(2), 425-450. Shahbazi, A., Khaliqi Sygarodi, S., Malekian, A., Salajegheh, A., 2014. Selection of the best empirical formula to estimate time of concentration in urban watersheds, case study: Mahdasht. J. Range Watershed Manage. 67(3), 419-435 (in Persian). Shojaei, S., Asghari, A., Alipour, H., Hasheminasab, S.N., 2016. Comparison of some empirical relationships in estimation of time concentration of the watershed. The Third International Conference on Research in Engineering, Science and Technology, Estahban. Iran (in Persian). Todd, D., Ground water hydrology. 2004. 3rd ed. Wiely and sond Inc. New York. 638 pages. Wood, P.J., Dykes, A.P., 2002. The use of salt dilution gauging technique: Ecological consideration and insights. Water Res. 36, 3054-3062. Vaezi, A.R., 2020. Water erosion (processes and models). Zanjan University, Zanjan. Iran. 453 pages (in Persian). Zolghadr, M., Rafiee, M.R., Esmaeilmanesh, F., Fathi, A., Tripathi, R.P., Rathnayake, U., Gunakala, S.R., Azamathulla, H.M., 2022. Computation of time of concentration based on two-dimensional hydraulic simulation. Water 14, 3155. | ||
|
آمار تعداد مشاهده مقاله: 777 تعداد دریافت فایل اصل مقاله: 432 |
||