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Investigation the Joint Probabilistic Behavior of River Flow Discharge-Suspended Sediment Load in Nazlochai Basin, Urmia | ||
| Water Harvesting Research | ||
| دوره 8، شماره 2، 2025، صفحه 310-327 اصل مقاله (1.22 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2026.10879.1199 | ||
| نویسندگان | ||
| Soraya Tavasoli1؛ Keivan Khalili* 2؛ Mohammad Nazeri Tahroudi3 | ||
| 1M.Sc. Student, Department of Water Engineering, Saba Higher Education Institute, Urmia, Iran. | ||
| 2Associate Professor, Urmia Lake Research Institute, Urmia University, Urmia, Iran. | ||
| 3Assistant Professor, Department of Water Engineering, Lorestan University, Khorramabad, Iran. | ||
| چکیده | ||
| Accurately determining the relationship between river flow discharge and suspended sediment load in watersheds is challenging due to the influence of natural and human-induced variables on river estimations. Therefore, it is essential to employ modern methods to improve existing models. In this context, multivariate methods and copula-based modeling and simulation, given their ability to analyze data distributions, can be suitable options. In this study, joint frequency analysis of river flow discharge and suspended sediment load was conducted at the Abajalo and Tapik stations in the Nazlochai sub-basin, Lake Urmia, Iran using copula functions and marginal distributions. First, the correlation between variables was examined using Kendall’s tau coefficient, indicating a strong positive relationship between river flow discharge and suspended sediment load. For modeling marginal distributions, the Log-Normal and GEV distributions with NSE=0.99 at the Abajalo station were selected as the best distributions for flow discharge and suspended sediment load, respectively, while the GEV and Generalized Pareto distributions with NSE=0.99 at the Tapik station were chosen for river flow discharge and suspended sediment load, respectively. In the joint analysis, the Galambos and Gumbel-Hougaard copula functions demonstrated the best performance based on evaluation criteria. Bivariate analysis revealed that at the Abajalo station, with a 90% probability and river flow discharge exceeding 40 m³/s, the suspended sediment load reaches over 3,000 tons/day, while at the Tapik station, the same probability with a river flow discharge of 25 m³/s indicates a suspended sediment load exceeding 500 tons/day. Finally, based on the conditional density of copula functions and considering various probabilities, equations were proposed for simulating suspended sediment load conditioned on river flow discharge at both stations. The proposed equations were suggested for probability levels of 80–90%, 90–95%, and 95–99% and evaluated using various statistical metrics. The proposed equations for conditional estimation of suspended sediment load demonstrated high performance at the Abajalo station (NSE>0.95 and 386.2<RMSE<642.8 tons/day) and the Tapik station (NSE>0.84 and 26.8<RMSE<836.8 tons/day). This study emphasizes that multivariate methods based on copula functions are effective tools for modeling nonlinear hydrological relationships. | ||
| کلیدواژهها | ||
| Conditional density؛ Conditional simulation؛ Copula function؛ Joint probability | ||
| مراجع | ||
|
Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and economics, 44(2), 182-198.
Abdi, A., Hassanzadeh, Y., Talatahari, S., Fakheri-Fard, A., & Mirabbasi, R. (2017). Parameter estimation of copula functions using an optimization-based method. Theoretical and Applied Climatology, 129, 21-32.
Bedford, T., & Cooke, R. (2001). Probabilistic risk analysis: foundations and methods. Cambridge University Press.
Beersma, J. J., & Buishand, T. A. (2004). The joint probability of rainfall and runoff deficits in the Netherlands. In Critical Transitions in Water and Environmental Resources Management (pp. 1-10).
Bevacqua, E., Maraun, D., HobækHaff, I., Widmann, M., Vrac, M. J. H., & Sciences, E. S. (2017). Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy). 21(6), 2701-2723.
Bezak, N., Rusjan, S., KramarFijavž, M., Mikoš, M., &Šraj, M. J. W. (2017). Estimation of suspended sediment loads using copula functions. 9(8), 628.
Brunner, M. I., Furrer, R., Favre, A.-C. J. H., & Sciences, E. S. (2019). Modeling the spatial dependence of floods using the Fisher copula. 23(1), 107-124.
Chow, V. T. (1955). On the determination of frequency factor in log‐probability plotting. Eos, Transactions American Geophysical Union, 36(3), 481-486.
Cooke, R. M., Kurowicka, D., & Wilson, K. J. J. o. M. A. (2015). Sampling, conditionalizing, counting, merging, searching regular vines. 138, 4-18.
Czado, C. (2019). Analyzing dependent data with vine copulas. Lecture Notes in Statistics, Springer, 222.
De Michele, C., & Salvadori, G. (2003). A generalized Pareto intensity‐duration model of storm rainfall exploiting 2‐copulas. Journal of Geophysical Research: Atmospheres, 108(D2).
Gräler, B., van den Berg, M., Vandenberghe, S., Petroselli, A., Grimaldi, S., De Baets, B., &Verhoest, N. (2013). Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrology and Earth System Sciences, 17(4), 1281-1296.
He, H., Zhou, J., Yu, Q., Tian, Y. Q., & 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.
Joe, H. (1997). Multivariate models and multivariate dependence concepts: Chapman and Hall/CRC.
Kavianpour, M., Seyedabadi, M., & Moazami, S. (2018). Spatial and temporal analysis of drought based on a combined index using copula. Environmental Earth Sciences, 77, 1-12.
Khalili, K., Tahoudi, M. N., Mirabbasi, R., & Ahmadi, F. (2016). Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stochastic Environmental Research and Risk Assessment, 30(4), 1205-1221.
Khan, F., Spöck, G., &Pilz, J. J. I. J. o. C. (2019). A novel approach for modelling pattern and spatial dependence structures between climate variables by combining mixture models with copula models.
Khashei, A., Shahidi, A., Nazeri-Tahroudi, M., & Ramezani, Y. (2022). Bivariate simulation and joint analysis of reference evapotranspiration using copula functions. Iranian Journal of Irrigation & Drainage, 16(3), 639-656.
Knighton, A. D. (1999). The gravel–sand transition in a disturbed catchment. Geomorphology, 27(3-4), 325-341.
Kondolf, G. M., Gao, Y., Annandale, G. W., Morris, G. L., Jiang, E., Zhang, J., ... & Yang, C. T. (2014). Sustainable sediment management in reservoirs and regulated rivers: Experiences from five continents. Earth's Future, 2(5), 256-280.
Kurowicka, D., & Cooke, R. M. (2007). Sampling algorithms for generating joint uniform distributions using the vine-copula method. Computational statistics & data analysis, 51(6), 2889-2906.
Landwehr, J. M., Tasker, G. D., & Jarrett, R. D. (1987). Discussion of “Relative Accuracy of Log Pearson III Procedures” by James R. Wallis and Eric F. Wood (July, 1985, Vol. 111, No. 7). Journal of Hydraulic Engineering, 113(9), 1206-1210.
Loganathan, G. V., Kuo, C. Y., & McCormick, T. C. (1985). Frequency analysis of low flows. Hydrology Research, 16(2), 105-128.
Matalas, N. C. (1967). Mathematical assessment of synthetic hydrology. Water Resources Research, 3(4), 937-945.
Nadarajah, S., & Gupta, A. K. (2006). Intensity-duration models based on bivariate gamma distributions. Hiroshima mathematical journal, 36(3), 387-395.
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282-290.
Nazeri Tahroudi, M., Khalili, K., Ahmadi, F., Mirabbasi, R., & Jhajharia, D. (2019). Development and application of a new index for analyzing temperature concentration for Iran’s climate. International Journal of Environmental Science and Technology, 16, 2693-2706.
Nazeri Tahroudi, M., Ramezani, Y., & Jhajharia, D. (2025). Integrating vector and copula-based models with conditional heteroskedasticity for enhanced suspended sediment load simulation. Modeling Earth Systems and Environment, 11(4), 1-14.
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2023). Application of copula‐based approach as a new data‐driven model for downscaling the mean daily temperature. International Journal of Climatology, 43(1), 240-254.
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2020). 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, 4217-4231.
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., &Mirabbasi, R. (2022). Application of Copula Functions for Bivariate Analysis of Rainfall and River Flow Deficiencies in the Siminehrood River Basin, Iran. Journal of Hydrologic Engineering, 27(11), 05022015.
Nelsen, R. B. (2006). An introduction to copulas. springer.
PronoosSedighi, M., Ramezani, Y., Nazeri Tahroudi, M., &Taghian, M. (2022). Joint frequency analysis of river flow rate and suspended sediment load using conditional density of copula functions. ActaGeophysica, 1-13.
Ramezani, Y., Tahroudi, M. N., & Ahmadi, F. (2019). Analyzing the droughts in Iran and its eastern neighboring countries using copula functions. IDOJARAS, 123(4), 435-453.
Salvadori, G., & De Michele, C. (2004). Analytical calculation of storm volume statistics involving Pareto‐like intensity‐duration marginals. Geophysical Research Letters, 31(4), 1-15.
Sanikhani, H., Mirabbasi Najaf Abadi, R., & Dinpashoh, Y. (2014). Modeling of temperature and rainfall of Tabriz using copulas. Irrigation and Water Engineering, 5(1), 123-133.
Shiau, J. T., & Lien, Y. C. (2021). Copula-based infilling methods for daily suspended sediment loads. Water, 13(12), 1701.
Sklar, M. (1959). Fonctions de repartition a dimensions etleursmarges. Publ. inst. statist. univ. Paris, 8, 229-231.
Snyder, W.M. (1962). Some possibilities for multivariate analysis in hydrologic studies. Journal of geophysical research, 67(2), 721-729.
Syvitski, J. P., Morehead, M. D., Bahr, D. B., & Mulder, T. (2000). Estimating fluvial sediment transport: the rating parameters. Water resources research, 36(9), 2747-2760.
Turowski, J. M., Rickenmann, D., & Dadson, S. J. (2010). The partitioning of the total sediment load of a river into suspended load and bedload: a review of empirical data. Sedimentology, 57(4), 1126-1146.
Vahidi, M. J., Mirabbasi, R., Khashei-Siuki, A., Tahroudi, M. N., & Jafari, A. M. (2024). Modeling of daily suspended sediment load by trivariate probabilistic model (case study, Allah River Basin, Iran). Journal of Soils and Sediments, 24(1), 473-484.
Van Rijn, L. C. (1984). Sediment transport, part I: bed load transport. Journal of hydraulic engineering, 110(10), 1431-1456.
Walling, D. E., & Webb, B. W. (1996). Erosion and sediment yield: a global overview. IAHS Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, 236, 3-20.
Wang, C. (2016). A joint probability approach for coincidental flood frequency analysis at ungauged basin confluences. Natural Hazards, 82(3), 1727-1741.
Williams, G. P. (1989). Sediment concentration versus water discharge during single hydrologic events in rivers. Journal of Hydrology, 111(1-4), 89-106.
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.
Yang, X., Chen, Z., & Qin, M. (2023). Joint probability analysis of streamflow and sediment load based on hybrid copula. Environmental Science and Pollution Research, 30(16), 46489-46502.
Zhang, D., Yan, M., & Tsopanakis, A. (2018). Financial stress relationships among Euro area countries: an R-vine copula approach. The European Journal of Finance, 24(17), 1587-1608.
Zhang, L., & Singh, V. P. (2012). Bivariate rainfall and runoff analysis using entropy and copula theories. Entropy, 14(9), 1784-1812.
Zhao, F., Yi, P., Wang, Y., Wan, X., Wang, S., Song, C., & Xue, Y. (2025). Trivariate Frequency Analysis of Extreme Sediment Events of Compound Floods Based on Vine Copula: A Case Study of the Middle Yellow River in China. Journal of Hydrologic Engineering, 30(1), 05024027. | ||
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