نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشکده کشاورزی دانشگاه بیرجند
2 گروه هیدرولوژی و منابع آب دانشگاه شهید چمران اهواز
3 گروه مهندسی آب دانشگاه شهرکرد
چکیده
در این مطالعه با استفاده از رویکرد مبتنی بر مفصل به شبیه سازی دبی خروجی سیلاب در رودخانه کارون پرداخته شده است. در این خصوص از اطلاعات سیلاب مورخ سیام نوامبر 2008 رودخانه کارون استفاده شد. با استفاده از توزیع های حاشیه ای ناکاگامی و پارتو تعمیم یافته، به بررسی توابع مفصل مختلف پرداخته شد که نتایج بیانگر برتری مفصل کلایتون با مقدار 9/4= بود. با توجه به توزیع های حاشیه ای و مفصل کلایتون، تحلیل فراوانی وقوع توأم جفت متغیر هیدروگراف ورودی و خروجی سیلاب انجام شد که منجر به ارائه منحنی تیپ در خصوص تخمین مقادیر مختلف هیدروگراف خروجی سیلاب متناظر با مقادیر هیدروگراف ورودی شد. این منحنی میتواند مقادیر خروجی هیدروگراف را با احتمالات مختلف و بر اساس مقادیر ورودی شبیه سازی کند. شبیه سازی مبتنی بر مفصل نیز با توجه به چگالی شرطی توابع مفصل نشان داد که ضریب همبستگی مقادیر شبیهسازی شده 7 درصد بیشتر از مقادیر مشاهداتی است. دقت و قطعیت شبیه سازی مقادیر خروجی هیدروگراف نیز با توجه به نمودار وایولین تائید شد. نتایج این تحقیق نشان داد که مدل شبیه سازی مبتنی بر مفصل، کارایی بالایی در شبیه سازی دبی خروجی هیدروگراف سیلاب دارد. نتایج شبیه سازی مبتنی بر مفصل بر اساس آماره های جذر میانگین مربعات خطا و نش-ساتکلیف، به ترتیب میزان خطای 93/35 مترمکعب بر ثانیه و کارایی 96/0 را نشان داد. چگالی شرطی مورد استفاده در این تحقیق سبب ارائه رابطه پیشنهادی در خصوص شبیه سازی دبی هیدروگراف خروجی به شرط وقوع هیدروگراف ورودی سیلاب در منطقه مورد مطالعه با کارایی 66 درصد شد.
کلیدواژهها
عنوان مقاله [English]
The Application of Two-Dimensional Copulas in Simulation of Flood Discharge Outlet
نویسندگان [English]
- Mohammad Nazeri Tahroudi 1
- Faeshad Ahmadi 2
- Rasoul Mirabbasi 3
1 University of Birjand
2 Department of Hydrology and Water Resources,, Shahid Chamran University, Ahvaz
3 Department of Water Engineering, Shahrekord University
چکیده [English]
In this study, using the copula-based approach, the simulation of the flood discharge in the Karun River has been studied. In this regard, the flood in 11/30/2008 of the Karun River was used. Using the Nakagami and generalized Pareto marginal distributions, various copula functions were investigated, and the results showed the superiority of the Clayton copula with a parameter value of . According to Clayton copula and marginal distributions, the analysis of the frequency of occurrence of the pair variable of inflow and outflow hydrographs was carried out, and the results led to the presentation of a typical curve regarding the estimation of different values of the outflow hydrograph corresponding to the values of the inflow hydrograph. This curve can simulate the output values of the hydrograph with different probability and based on the input values. Copula-based simulation, according to the conditional density of copula functions showed that the correlation coefficient of the simulated values is 7% higher than the observed values. The accuracy and certainty of simulating the output values of the hydrograph was also confirmed according to the violin plot. The results of this research showed that the copula-based simulation model has a high efficiency in simulating the output flow of the flood hydrograph. The results of the copula-based simulation, based on the root mean square error and Nash-Sutcliffe statistics, showed an error rate of 35.93 cubic meters per second and an efficiency of 0.96, respectively. The conditional density used in this research led to the presentation of a proposed equation regarding the simulation of the flood hydrograph outlet under the condition of the occurrence of the flood hydrograph inlet in the study area with an efficiency of 66%.
کلیدواژهها [English]
- Flood Discharge
- Routing
- Copula
- Karun
- Abdi A., Hassanzadeh Y., Talatahari S., Fakheri-Fard A., and Mirabbasi, R. 2017. Regional bivariate modeling of droughts using L-comoments and copulas. Stochastic Environmental Research and Risk Assessment, 31(5):1199-1210.
- Akbarpour A., Zeynali M.J., and Nazeri Tahroudi M. 2020. Locating optimal position of pumping Wells in aquifer using meta-heuristic algorithms and finite element method. Water Resources Management, 34(1):21-34.
- Beersma J.J., and Buishand T.A. 2004. The joint probability of rainfall and runoff deficits in the Netherlands. p. 1-10. World Water and Environmental Resources Congress, June 27-July 1. 2004. Salt Lake City, Utah, United States.
- Bezak N., Rusjan S., Kramar Fijavž M., Mikoš M., and Šraj M.J.W. 2017. Estimation of suspended sediment loads using copula functions. Water, 9(8):628.
- Chebana, F., and Ouarda, T. B. 2009. Index flood–based multivariate regional frequency analysis. Water Resources Research, 45(10):1-15.
- Chen L., Singh V.P., Shenglian G., Hao Z., and Li T. 2011. Flood coincidence risk analysis using multivariate copula functions. Journal of Hydrologic Engineering, 17(6):742-755.
- Das A. 2004. Parameter estimation of Muskingum models. Journal of Irrigation and Drain Engineering, 130(2):140–147.
- De Michele C., Salvadori G., Canossi M., Petaccia A., and Rosso R. 2005. Bivariate statistical approach to check adequacy of dam spillway. Journal of Hydrologic Engineering, 10(1):50-57.
- Durrans S.R., Eiffe M.A., Thomas W.O., and Goranflo H.M. 2003. Joint seasonal / annual flood frequency analysis. Journal of Hydrologic Engineering, 8(4):181-189.
- Favre A.C., El Adlouni S., Perreault L., Thiémonge N., and Bobée B. 2004. Multivariate hydrological frequency analysis using copulas. Water Resources Research, 40(1):1-12.
- Geem Z.W. 2006. Parameter estimation for the nonlinear Muskingum model using the BFGS technique. Journal of Irrigation and Drain Engineering, 132(5):474–478.
- Geem, Z.W. 2011. Parameter estimation of the nonlinear Muskingum model using parameter-setting-free harmony search algorithm. Journal of Hydrology Engineering, 16(8):684–688.
- Genest C., and Favre A. C. 2007. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 12(4):347-368.
- He H., Zhou J., Yu Q., Tian Y.Q., and Chen R.F. 2007. Flood frequency and routing processes at a confluence of the middle Yellow River in China. River Research and Applications, 23(4):407-427.
- Hu Y., Liang Z., and Liu Y. 2015. Quantitative assessment of climate change and human activities impact on the designed annual runoff. P. 3595-3606. EGU General Assembly Conference, 12-17 April. 2015. EGU General Assembly, Vienna, Austria.
- Hult H., and Lindskog F. 2002. Multivariate extremes, aggregation and dependence in elliptical distributions. Advances in Applied probability, 34(3):587-608.
- Jain S., and Lall U. 2000. Magnitude and timing of annual maximum floods: Trends and large‐scale climatic associations for the Blacksmith Fork River, Utah. Water Resources Research, 36(12):3641-3651.
- Joe H. 1997. Multivariate models and multivariate dependence concepts. CRC press.
- Kao S.C., and Govindaraju R.S. 2008. Trivariate statistical analysis of extreme rainfall events via the Plackett family of copulas. Water Resources Research, 44(2):1-19.
- Karahan H., Gurarslan G., and Geem Z.W. 2012. Parameter estimation of the nonlinear Muskingum flood-routing model using a hybrid harmony search algorithm. Journal of Hydrologic Engineering, 18, 352-360.
- Khozeymehnezhad H., and Nazeri-Tahroudi M. 2020. Analyzing the frequency of non-stationary hydrological series based on a modified reservoir index. Arabian Journal of Geosciences, 13(5):1-13.
- Khozeymehnezhad H., and Tahroudi M.N. 2019. Annual and seasonal distribution pattern of rainfall in Iran and neighboring regions. Arabian Journal of Geosciences, 12(8):271.
- Kim J.H., Geem Z.W., and Kim E.S. 2001. Parameter estimation of the nonlinear Muskingum model using harmony search. Journal of the American Water Resources Association, 37(5):1131–1138.
- Li B., Yu Z., Liang Z., Song K., Li H., Wang Y., and Acharya K. 2013. Effects of climate variations and human activities on runoff in the Zoige alpine wetland in the eastern edge of the Tibetan Plateau. Journal of Hydrologic Engineering, 19(5):1026-1035
- Luo J., and Xie J. 2010. Parameter estimation for the nonlinear Muskingum model based on immune clonal selection algorithm. Journal of Hydrologic Engineering, 15(10):844–851.
- Mirabbasi R., Anagnostou E.N., Fakheri-Fard A., Dinpashoh Y., and Eslamian S. 2013. Analysis of meteorological drought in northwest Iran using the Joint Deficit Index. Journal of Hydrology, 492:35-48.
- Mirakbari M., Ganji A., and Fallah S. 2010. Regional bivariate frequency analysis of meteorological droughts. Journal of Hydrologic Engineering, 15(12):985-1000.
- Mohan S. 1997. Parameter Estimation of Nonlinear Muskingum Models using Genetic Algorithm. Journal of Hydraulic Engineering, 123:137-142.
- Nadarajah S., and Gupta A.K. 2006. Intensity-duration models based on bivariate gamma distributions. Hiroshima Mathematical Journal, 36(3):387-395.
- Nash J.E., and Sutcliffe J.V. 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3):282-290.
- Nazeri T.M., Ramezani Y., De Michele C., and Mirabbasi R. 2020. Estimation of the Joint Frequency of Peak Flow Discharge-Suspended Load of Zarinehrood Basin Using Two-Dimensional Analysis. Journal of Water and Soil (Agricultural Sciences and Technology), 34(2):333-347. https://doi.org/10.22067/jsw.v34i2.81812. (In Persian).
- Nazeri Tahroudi M., Ramezani Y., De Michele C., and Mirabbasi R. 2021. Flood routing via a copula-based approach. Hydrology Research, 52(6):1294-1308.
- Nelsen R.B. 2006. An introduction to copulas, ser. Lecture Notes in Statistics. New York: Springer.
- Powell R. 1985. Regression diagnostics and robust regression in geothermometer/geobarometer calibration: the garnet‐clinopyroxene geothermometer revisited. Journal of Metamorphic Geology, 3(3):231-243.
- Pronoos Sedighi M., Ramezani Y., Nazeri Tahroudi M., and Taghian M. 2022. Joint frequency analysis of river flow rate and suspended sediment load using conditional density of copula functions. Acta Geophysica, https://doi.org/10.1007/s11600-022-00894-5
- Ramezani Y., Tahroudi M. N., and Ahmadi F. 2019. Analyzing the droughts in Iran and its eastern neighboring countries using copula functions. IDŐJÁRÁS/Quarterly Journal of the Hungarian Meteorological Service, 123(4):435-453.
- Salvadori G., and De Michele C. 2007. On the use of copulas in hydrology: theory and practice. Journal of Hydrologic Engineering, 12(4):369-380.
- Salvadori G., De Michele C., Kottegoda, N. T., and Rosso R. 2007. Extremes in Nature: An approach using Copulas. Springer, Dordrecht, The Netherlands.
- Shiau J. 2003. Return period of bivariate distributed extreme hydrological events. Stochastic Environmental Research and Risk Assessment, 17(1-2):42-57.
- Shiau J.T., Wang H. Y., and Tsai C. T. 2006. Bivariate Frequency Analysis of Floods Using COPULAS1. Jawra Journal of the American Water Resources Association, 42(6):1549-1564.
- Sklar M. 1959. Fonctions de repartition an dimensions et leurs marges. Publications de l'Institut de statistique de l'Université de Paris, 8:229-231.
- Snyder W.M. 1962. Some possibilities for multivariate analysis in hydrologic studies. Journal of Geophysical Research, 67(2):721-729.
- Tahroudi M.N., Pourreza-Bilondi M., and Ramezani Y. 2019. Toward coupling hydrological and meteorological drought characteristics in Lake Urmia Basin, Iran. Theoretical and Applied Climatology, 138(3-4):1511-1523.
- Tahroudi M.N., Ramezani Y., De Michele C., and Mirabbasi R. 2020a. A New Method for Joint Frequency Analysis of Modified Precipitation Anomaly Percentage and Streamflow Drought Index Based on the Conditional Density of Copula Functions. Water Resources Management, 34(13):4217-4231.
- Tahroudi M.N., Ramezani Y., De Michele C., and Mirabbasi R. 2020b: Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions. Hydrology Research, 51(6):1348, https://doi.org/10.2166/nh.2020.036
- Vafakhah M., Dastorani A., and Moghadam Nia A. 2015. Optimal Parameter Estimation for Nonlinear Muskingum Model based on Artificial Bee Colony Algorithm. ECOPERSIA, 3(1):847-865. (In Persian).
- Vogel R.M., Yaindl C., and Walter M. 2011. Nonstationarity: flood magnification and recurrence reduction factors in the United States1. Journal of the American Water Resources Association, 47(3):464-474
- Wong S.T. 1963. A multivariate statistical model for predicting mean annual flood in New England. Annals of the Association of American Geographers, 53(3):298-311.
- Xu D.M., Qiu L., and Chen S.Y. 2011. Estimation of nonlinear Muskingum model parameter using differential evolution. Journal of Hydrologic Engineering, 17, 348-353.
- Yoon J.W., and Padmanabhan G. 1993. Parameter-estimation of linear and nonlinear Muskingum models. Journal of Water Resources Planning and Management, 119(5):600–610.
- Zeinali M.J., and Pourreza-Bilondi M. 2018. Estimation of Optimal Parameters of the Nonlinear Muskingum Model Using Continuous Ant Colony Algorithm. Journal of Irrigation and Water Engineering, 8(3): 94-108. http://www.waterjournal.ir/article_74087_en.html.(In Persian).
- Zhang D.D., Yan D.H., Lu F., Wang Y.C., and Feng J. 2015. Copula-based risk assessment of drought in Yunnan province, China. Natural Hazards, 75(3):2199-2220.
- Zhang L., and Singh V.P. 2007. Bivariate rainfall frequency distributions using Archimedean copulas. Journal of Hydrology, 332(1-2):93-109.
- Zhang Q., Li J., Singh V.P., and Xu C.Y. 2013. Copula‐based spatio‐temporal patterns of precipitation extremes in China. International Journal of Climatology, 33(5):1140-1152.