بررسی اثر رفتار نواحی ماندابی در مدل‌های یک بعدی هیدرودینامیک و انتقال مواد محلول در رودخانه‌ها

نوع مقاله : مقالات پژوهشی

نویسندگان

1 گروه مهندسی و مدیریت آب، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

2 گروه مهندسی و مدیریت آب، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایراندانشگاه تربیت مدرس

چکیده

بیان رودخانه به عنوان مسئلۀ یک بعدی باعث شده بسیاری از عوامل دخیل در انتقال آلاینده در رودخانه ساده شوند و یا در نظر گرفته نشوند. در رودخانه‌ها نواحی نگهداشت عاملی اثرگذار در کیفیت آب می‌باشند. عموماً محدودیت داده‌های مشاهداتی که یکی از عوامل محدود کننده استفاده از مدل‌های دوبعدی و سه‌بعدی است باعث استفاده گسترده‌تر از مدل‌های یک‌بعدی شده است. اکثر مدل‌های تجاری موجود بر اساس معادله انتقال– انتشار کلاسیک توسعه یافته‌اند و نواحی نگهداشت را در نظر نمی‌گیرند. به این منظور برای مدل‌سازی تأثیرات نواحی ماندابی از سرعت و ضریب پراکندگی مؤثر به عنوان روشی برای بهبود عملکرد معادلۀ انتقال- انتشار پیشنهاد شده است. سرعت و ضریب پراکندگی مؤثر، از طریق اصلاح سرعت متوسط جریان و ضریب پراکندگی طولی، با نسبت مساحت سطح مقطع ناحیۀ ماندابی به سطح مقطع عرضی رودخانه تعریف می‌شوند. با جای‌گذاری این پارامترها در حل معادلات سنت– ونانت و انتقال– انتشار در مدل‌های یک‌بعدی می‌توان نتایج را بهبود بخشید. در این مطالعه برای اثبات بهبود عملکرد مدل از MIKE11 به عنوان نماینده‌ای از مدل‌های یک بعدی اعمال و رودخانۀ اروند با آن مورد بررسی قرار گرفت. برای واسنجی و صحت‌سنجی مدل از داده‌های مشاهداتی غلظت در طول رودخانه استفاده شد. پارامتر‌های فاکتور پراکندگی و توان پراکندگی همراه با نسبت مساحت سطح مقطع ناحیۀ ماندابی به سطح مقطع عرضی رودخانه برای ایستگاه sehan در 123 کیلومتری از القرنه، با الگوریتم بهینه سازی کلونی مورچگان برآورد شد و برای ایستگاه‌های Faw و Dweeb با داده‌های غلظت مورد صحت‌سنجی قرار گرفت. در ایستگاه Sehan، Dweeb و Faw سه پارامتر خطای ضریب همبستگی، نش- ساتکلیف و جذر میانگین مربعات در مدل اصلاح شده کاهش یافته و به عنوان مثال پارامتر آماری خطای جذر میانگین مربعات به ترتیب به 78/1، 27/1 و 84/0 کاهش می‌یابد؛ بنابراین اصلاحات حاکی از بهبود نتایج پیش‌بینی طبق مدل مفهومی جدید است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigate the Effect of Stagnant Zone Behavior in One-Dimensional Hydrodynamic Models and Solute Transport in Rivers

نویسندگان [English]

  • A.H. Montazeri 1
  • S. Khodambashi Emami 1
  • M. Mazaheri 2
1 Department of Water Management and Engineering, College of Agriculture, Tarbiat Modares University, Tehran, Iran
2 Depatment of Water Management and Engineering, College of Agriculture, Tarbiat Modares University, Tehran, Iran
چکیده [English]

Introduction
Showing the rivers as a one-dimensional problem has simplified or eliminated many processes affecting salinity transfer in the river. Storage processes are one of the factors affecting water quality in rivers. Generally, as a substantial factor, the limitation of observational data confines the use of two-dimensional and three-dimensional models, leading to the use of more widely employed one-dimensional models. Most existing computer models are developed based on the Advection-Dispersion Equation (ADE) and do not consider the storage zone. For this purpose, Modified Advection-Dispersion Equation (MADE) is proposed to consider the stagnant area by defining effective velocity and dispersion coefficient. In this study, a solution has been proposed to apply the effect of the Stagnant zone in water quality simulation in one-dimensional models. The river simulation is closer to the natural conditions. In this model, to prove the improvement of the proposed method, the average stagnant zone fraction expressed as the fraction of the average cross-sectional area of the river (η) and employed in a one-dimensional model through the definition of the effective velocity and the dispersion coefficient. This model is considered representative of the one-dimensional models developed only by the Advection /Dispersion relation, and the proposed method was investigated for the Arvand River. Observational data along the river were used to calibrate and validate the model.
Materials and Methods
Since the available and well-known one-dimensional computer programs are generally developed based on the 1D Advection-Dispersion model, they do not consider factors affecting salinity transport such as topography and river morphology heterogeneities known as storage areas. In such a way, these processes are not expressed by presenting the problem as a one-dimensional equation. In this research, in order to increase the accuracy of the simulation with well-known and available one-dimensional models a corrective solution is proposed. To compare the proposed modified method and the base ADE, at the first, the tidal and transboundary arvand river is modeled as a study area, which is a well-mixed river. The river's upstream and downstream boundary conditions were defined according to the available data in 2014. Manning's roughness parameters ranged from 0.017 to 0.033, and the dispersion coefficient was 285 m3/s according to previous studies. In order to apply the effect of stagnant areas in the modified equations, it is essential to determine the value of η for the river. This study uses three parameters of dispersion factor (a), dispersion exponent (b), and η by ant colony algorithm with the definition of 5 initial ants and 100 repetitions in Sehan station in the study area, Arvand river was optimized. The values of the estimated parameters are respectively η = 0.168, a = 273.4, b = 0.94. Therefore, in the modified model, corrections were made using the speed and effective dispersion coefficient as the modified Advection - Dispersion (MADE) method and considering variable dispersion coefficient depending on the flow's speed in the one-dimensional model. These changes were validated in the other two stations (Faw and Dweeb).
Results and Discussion
Based on this study results, increasing the parameter η caused the peak of the time series to rise and the river's travel time to decrease. The shortening of the water travel time in the river, although increases the dispersion coefficient due to the influence of the stagnant zone, the effect of this parameter on the time series of the simulated concentration is reduced. Like the observational data, the slope of falling and rising limbs is increased. By comparing the one-dimensional model in the two cases of using the effective dispersion coefficient and velocity and without it, the increase in accuracy in the simulation was determined at Sehan station - 123 km from the river formation site - after optimizing the coefficients with three statistical errors parameters. In addition, these changes at two other stations along the river with distances of 180 and 150 km from the river's source confirm this accuracy. For instance, the simulated and measured concentration in 12 months of the year by applying the optimized coefficients reaches the correlation coefficient (r) of 0.86 to 0.97 at a distance of 150 km from the upstream, and the root means square error (RMSE) improves 1.27 ppt. The remaining difference in the concentration estimation may be caused by the effect of other parameters or even the entry of agricultural runoff from the lands along the river.
Conclusion
Accurate estimation and simulation of concentration in river engineering have always been one of the environmental challenges. This research aimed to improve water quality simulation using one-dimensional model in well-mixed rivers. In order to increase the accuracy of the modeling and become closer to the actual conditions, correction factors such as considering the dead zones along the river have been suggested. Analysis showed that, on average, 16% of the surface of the Arvand River's cross-sections are stagnant areas, and the dispersion coefficient depends on the river's speed. These areas include bed dunes and meanderings of the river. The point that attracts attention is the tidal irrigation channels on the sides of the river. The results showed that in Sehan, Dweeb, and Faw stations, the root means square error decreases to 1.78, 1.27, and 0.84, respectively. Therefore, the modified 1D model estimated the concentration (in this study salinity) closer to the measurement data. In Dweeb and Sehan stations, the effect of dead zones such as river meandering is evident. Still, in Faw station, no significant improvement in the impact of stagnant zones was observed due to its proximity to the river mouth. The results of this research can be used for higher accuracy in one-dimensional water quality simulations and bringing the models closer to the natural conditions in rivers.

کلیدواژه‌ها [English]

  • Advection-Dispersion equation
  • Ant colony optimization
  • Arvand river
  • Stagnant zone
  1. Abarca, E., Carrera, J., Voss, C.I., & Sánchez-Vila, X. (2002). Effect of aquifer bottom morphology on seawater intrusion. 17th Salt Water Intrusion Meeting (SWIM).
  2. Abdullah, A.D., Gisen, J.I.A., Van Der Zaag, P., Savenije, H.H.G., Karim, U.F.A., Masih, I., & Popescu, I. (2016). Predicting the salt water intrusion in the Shatt al-Arab estuary using an analytical approach. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-20-4031-2016.
  3. Abdullah, A.D., Masih, I., van der Zaag, P., Karim, U.F.A., Popescu, I., & Al Suhail, Q. (2015). Shatt al Arab River system under escalating pressure: a preliminary exploration of the issues and options for mitigation. International Journal of River Basin Management 13(2): 215–227.
  4. Abdullah, A.D., Popescu, I., Dastgheib, A., van der Zaag, P., Masih, I., & Karim, U.F.A. (2017). Analysis of possible actions to manage the longitudinal changes of water salinity in a tidal river. Water Resources Management 31(7): 2157–2171.
  5. Al-Aesawi, Q., Al-Nasrawi, A.K.M., Jones, B.G., & Yang, S.-Q. (2021). Geomatic freshwater discharge estimations and their effect on saltwater intrusion in alluvial systems: a case study in Shatt Al-Arab estuary. Environmental Earth Sciences 80(18): 1–15.
  6. Al-Asadi, S.A.R. (2017). The future of freshwater in Shatt Al-Arab River (Southern Iraq). Journal Geography Geology 9(2): 24–38.
  7. Al-Battat, M.Q. (2019). Empirical prediction model of salt intrusion along Shatt Al-Arab River, southern Iraq. Mesopotamian Journal of Marine Sciences 34(1): 1–12.
  8. Al-Taei, S.A., Alfartusi, A.J., & Abdulhussein, I.A. (2019). Determination of hydrodynamic resistance coefficient (Manning’s coefficient) in Shatt Al Arab River, southern of Iraq-Basrah. Journal of Engineering and Sustainable Development 23(03).
  9. Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews 2(4): 353–373.
  10. Chapra, S.C. (2008). Surface water-quality modeling. Waveland press.
  11. Cheme, E.K., Mazaheri, M., Karami Cheme, E., & Mazaheri, M. (2021). The effect of neglecting spatial variations of the parameters in pollutant transport modeling in rivers. Environmental Fluid Mechanics 21(3): 587–603.
  12. Choi, S.Y., Seo, I.W., & Kim, Y.-O. (2020). Parameter uncertainty estimation of transient storage model using Bayesian inference with formal likelihood based on breakthrough curve segmentation. Environmental Modelling & Software 123: 104558.
  13. (2016). ‘Mike 11 – User Guide’. Danish Hydraulic Institute, p. 512.
  14. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine 1(4): 28–39.
  15. Dorigo, M., & Stützle, T. (2019). Ant colony optimization: overview and recent advances. Handbook of Metaheuristics 311–351.
  16. Eslami, S., Hoekstra, P., Nguyen Trung, N., Ahmed Kantoush, S., Van Binh, D., Do Dung, D., Tran Quang, T., & van der Vegt, M. (2019). Tidal amplification and salt intrusion in the Mekong Delta driven by anthropogenic sediment starvation. Scientific Reports 9(1): 1–10.
  17. Etemad-Shahidi, A., Parsa, J., & Hajiani, M. (2011). Salinity intrusion length: comparison of different approaches. Proceedings of the Institution of Civil Engineers-Maritime Engineering 164(1): 33–42.
  18. Gong, W., Lin, Z., Zhang, H., & Lin, H. (2022). The response of salt intrusion to changes in river discharge, tidal range, and winds, based on wavelet analysis in the Modaomen estuary, China. Ocean & Coastal Management 219: 106060.
  19. Gooseff, M.N., Wondzell, S.M., Haggerty, R., & Anderson, J. (2003). Comparing transient storage modeling and residence time distribution (RTD) analysis in geomorphically varied reaches in the Lookout Creek basin, Oregon, USA. Advances in Water Resources 26(9): 925–937. https://doi.org/10.1016/S0309-1708(03)00105-2.
  20. Guo, Y. (2022). Hydrodynamics in Estuaries and Coast: Analysis and Modeling. In Water 14(9): 1478. MDPI.
  21. Haddout, S., Priya, K.L., & Adarsh, S. (2020). A predictive model for salt intrusion in estuaries applied to the Muthupet estuary (India) and Bouregreg estuary (Morocco). ISH Journal of Hydraulic Engineering 26(4): 430–447.
  22. Hussain, M.S., Abd-Elhamid, H.F., Javadi, A.A., & Sherif, M.M. (2019). Management of seawater intrusion in coastal aquifers: a review. Water 11(12): 2467.
  23. Kanda, E.K., Kosgei, J.R., & Kipkorir, E.C. (2015). Simulation of organic carbon loading using MIKE 11 model: a case of River Nzoia, Kenya. Water Practice and Technology 10(2): 298–304.
  24. Kelleher, C., Wagener, T., McGlynn, B., Ward, A.S., Gooseff, M.N., & Payn, R.A. (2013). Identifiability of transient storage model parameters along a mountain stream. Water Resources Research 49(9): 5290–5306.
  25. Khodambashi Emami, S., & Mazaheri, M. (2022). 'Sensitivity analysis of transient storage parameters in mathematical modeling of pollution transport in rivers containing storage Zone', Irrigation Sciences and Engineering. (In Persian with English abstract). https://doi.org/10.22055/jise.2022.39365.2009.
  26. Kwon, S., Noh, H., Seo, I.W., Jung, S.H., & Baek, D. (2021). Identification framework of contaminant spill in rivers using machine learning with breakthrough curve analysis. International Journal of Environmental Research and Public Health 18(3): 1023.
  27. Lafta, A.A. (2022). Numerical assessment of Karun river influence on salinity intrusion in the Shatt Al-Arab river estuary, northwest of Arabian Gulf. Applied Water Science 12(6): 1–11.
  28. Mai, N.T.P., Kantoush, S., Sumi, T., Thang, T.D., & Binh, D.V. (2019). The influences of tidal regime and morphology change on salinity intrusion in Hau river. E-Proceedings of the 38th IAHR World Congress.
  29. Martin, J.L., McCutcheon, S.C., & Schottman, R.W. (2018). Hydrodynamics and transport for water quality modeling. CRC press.
  30. Mohamed, A.-R.M., & Abood, A.N. (2017). Compositional change in fish assemblage structure in the Shatt Al-Arab River, Iraq. Asian Journal of Applied Sciences 5(5).
  31. Montazeri, A., Mazaheri, M., & Morid, S. (2022). 'Mathematical model of salinity intrusion in the Arvand Tidal river and its effect on salinity of lands around the River', Journal of Environmental Studies 48(2): 221-248. (In Persian with English abstract). https://doi.org/10.22059/jes.2022.334989.1008258.
  32. Montazeri, A., Mazaheri, M., Morid, S., & Mosaddeghi, M.R. (2023). Effects of upstream activities of Tigris-Euphrates River Basin on water and soil resources of Shatt al-Arab Border River. Science of The Total Environment858: 159751.
  33. Nhung, T.T., Le Vo, P., Van Nghi, V., & Bang, H.Q. (2019). Salt intrusion adaptation measures for sustainable agricultural development under climate change effects: A case of Ca Mau Peninsula, Vietnam. Climate Risk Management 23: 88–100.
  34. Pereira, H., Sousa, M.C., Vieira, L.R., Morgado, F., & Dias, J.M. (2022). Modelling salt intrusion and estuarine plumes under cimate change scenarios in two transitional ecosystems from the NW Atlantic coast. Journal of Marine Science and Engineering 10(2): 262.
  35. Rana, S.M.M., Boccelli, D.L., Scott, D.T., & Hester, E.T. (2019). Parameter uncertainty with flow variation of the one-dimensional solute transport model for small streams using Markov chain Monte Carlo. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.003.
  36. Runkel, R.L. (1998). One-dimensional transport with inflow and storage (OTIS): A solute transport model for streams and rivers (Vol. 98, Issue 4018). US Department of the Interior, US Geological Survey.
  37. Saadat, A.M., Mazaheri, M., & MV Samani, J. (2022). Backward solution (in-time) of the pollution transport equation in river using group preserving scheme. Ferdowsi Civil Engineering. https://doi.org/10.22067/jfcei.2022.77645.1165.
  38. Savenije, H.H.G. (2005). Salinity and tides in alluvial estuaries. Gulf Professional Publishing.
  39. Seo, I.W., & Cheong, T.S. (2001). Moment-based calculation of parameters for the storage zone model for river dispersion. Journal of Hydraulic Engineering 127(6): 453–465.
  40. Singh, S.K. (2003). Treatment of Stagnant zones in riverine advection-dispersion. Journal of Hydraulic Engineering 129(6). https://doi.org/10.1061/(asce)0733-9429(2003)129:6(470).
  41. Singh, S.K. (2008). Comparing three models for treatment of Stagnant zones in riverine transport. Journal of Irrigation and Drainage Engineering 134(6). https://doi.org/10.1061/(asce)0733-9437(2008)134:6(853).
  42. Tables, A.T. (2014). NP203 admiralty tide tables (ATT), vol 3 Indian Ocean and South China Sea (including Tidal Stream Tables). Hydrographer to the Navy, Admiralty Hydrography Department Place.
  43. Tong, Y., & Deng, Z. (2015). Moment-based method for identification of pollution source in rivers. Journal of Environmental Engineering 141(10): 4015026.
  44. UNEP, H. (2001). The Mesopotamian Marshlands: demise of an ecosystem. Division of Early Warning and Assessment, United Nations Environment Program (UNEP) Nairobi, Kenya, 46.
  45. Ward, A.S., Kelleher, C.A., Mason, S.J.K., Wagener, T., McIntyre, N., McGlynn, B., Runkel, R. L., & Payn, R. A. (2017). A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations. Freshwater Science. https://doi.org/10.1086/690444.
  46. Winn, K.O., Saynor, M.J., Eliot, M.J., & Elio, I. (2006). Saltwater intrusion and morphological change at the mouth of the East Alligator River, Northern Territory. Journal of Coastal Research 22(1): 137–149.
  47. Yu, Q., Wang, Y., Gao, S., & Flemming, B. (2012). Modeling the formation of a sand bar within a large funnel-shaped, tide-dominated estuary: Qiantangjiang Estuary, China. Marine Geology 299: 63–76.
  48. Zaghiyan, M.R., & Ketabchi, H. (2022). Investigating the relationship between the river flow and dissolved solids concentration. Proceedings of the Institution of Civil Engineers-Water Management 175(2): 89–97.
  49. Zaramella, M., Marion, A., Lewandowski, J., & Nützmann, G. (2016). Assessment of transient storage exchange and advection–dispersion mechanisms from concentration signatures along breakthrough curves. Journal of Hydrology 538: 794–801.

 

CAPTCHA Image