مدل‎سازی استوکستیک و پیش‏ بینی رفتار بلندمدت خصوصیات مختلف بار رسوبی معلق رودخانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی منابع آب، دانشگاه ارومیه

2 استاد گروه مهندسی آب، دانشگاه ارومیه.

3 دکتری مهندسی منابع آب، دانشگاه ارومیه.

چکیده

تغییر رفتار جریان رودخانه‌ها به دلیل وقوع پدیده تغییر اقلیم در سال‌های گذشته منجر به افزایش رخدادهای هیدرولوژیکی شدید از جمله سیل‌های بزرگ به همراه رسوبات زیاد شده است که ضرورت شناخت مشخصات مختلف رسوبات رودخانه‌‏ای را برای برنامه‌ریزی و طراحی سازه‌های آبی را بسیار پر اهمیت کرده است. در این مطالعه به‌صورت نوآورانه، با توسعه شاخص جدید رسوب استاندارد (SSI)، مشخصات مختلف داده‌‏های رسوب از جمله تابع چگالی احتمال، بزرگی، شدت رسوب و غیره به ازای یک فرآیند شبیه‌‏سازی مونت‏کارلو در رودخانه‌‏های غرب حوضه دریاچه ارومیه تعیین گردید. بدین منظور، ابتدا سری داده‌های رسوب جریان رودخانه به ازای مدل‌های مختلف منحنی دبی- رسوب، تعیین و سپس سری داده‏‌های مصنوعی رسوب (به تعداد 1000 سری) با استفاده از مدل استوکستیک مناسب تولید و برای پایش و تعیین خصوصیات مختلف رسوب استفاده شد. نتایج نشان داد که در اکثر ایستگاه‌ها روش محاسبه و برآورد رابطه دبی- رسوب به ازای روش نوآورانه این مطالعه در اولویت اول دارای عملکرد مناسب نسبت به سایر روش‌های پیشنهادی در مطالعات مختلف بوده است. ضمناً بر اساس نتایج، تابع چگالی احتمال داده‌های رسوب کاملاً از توزیع نرمال تبعیت نموده است که بیانگر تطابق کامل احتمالات مذکور به عنوان رخدادهای مورد انتظار از یک فرآیند طبیعی نرمالیزه بوده و دارای رفتار نظام‏مند با ضرایب چولگی داده‌‏ها است. نهایتاً نتایج این مطالعه به‌عنوان یک راهنمای جامع در استنباط دقیق و واقعی از پدیده رسوب رودخانه به ازای شاخص SSI است و می‏‌تواند تأثیر قابل توجهی در کاهش خسارات ناشی از رسوبات داشته باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Stochastic Modeling and Long-Term Forecasting of Suspended River Sediment

نویسندگان [English]

  • Nader Jannatdoust 1
  • Majid Montaseri 2
  • Babak Amirataee 3
1 Ph.D. Candidate in Water Resources Engineering, Urmia University, Urmia, Iran.
2 Professor, Department of Water Engineering, Urmia University, Urmia, Iran
3 Ph.D. in Water Resources Engineering, Urmia University, Urmia, Iran.
چکیده [English]

In the past years the climate change has altered the behavior of river flow and led to more frequent extreme hydrological events such as flash floods with high sediments. This has made it very important to know the different characteristics of river sediments when planning for and designing water structures. In this study, a new Standardized Sediment Index (SSI) was developed and different characteristics of sediment, including probability density function (PDF), magnitude, intensity, etc., were determined for the western of Lake Urmia basin using a Monte Carlo simulation process. For this purpose, first, the sediment data was determined according to different models of rating curve, and then the synthetic data series of sediment (1000 series) were generated using a suitable stochastic model and were used to determine different characteristics of sediment. The results showed that in most stations, the method of estimating the rating relationship according to the innovative method of this study, i.e. using the flow discharge index method to classify the relationship between flow and sediment, has the higher performance compared to other methods proposed in different studies. Also, the PDF of sediment data completely follows the normal distribution, as expected from a normalized natural process, and has a systematic behavior with skewness data. Finally, the results of this study are a comprehensive guide for accurate and real inference of river sediment phenomenon according to the SSI index and can significantly reduce the damages caused by sediments.

کلیدواژه‌ها [English]

  • Sediment Load
  • Standard Sediment Index
  • Monte Carlo Simulation
  • Synthetic Data Generation
Amirataee B, Montaseri M, Yasi M (2013) Comparison of inherent performance of seven drought indices in drought mitigation using a Monte Carlo simulation approach. Journal of civil and environmental engineering 43(1):67-82 (In Persian)
Amorocho J, Orlob G T (1961) Non-linear analysis of hydrologic systems. Water Resources Center, University of California, Contribution 40:147pp.
Anderson O D (1977) Time series analysis and forecasting: Another look at the Box‐Jenkins Approach. Journal of the Royal Statistical Society: Series D (The Statistician) 26(4):285-303
Asselman N E M (2000) Fitting and interpretation of sediment rating curves. Journal of Hydrology 234(3-4):228-248
Azadi S, Nozari H, Godarzi E (2020) Predicting sediment load using stochastic model and rating curves in a hydrological station. Journal of Hydrologic Engineering 25(8):05020017
Barzegari F, Dastorani M T (2016) Suspended sediment prediction using time series and artificial neural networks models (Case Study: Ghazaghly Station in Gorganroud River). Journal of Watershed Management Research 6(12):216-225 (In Persian)
Barzegaribanadkoki F, Armin M (2016) Suspended sediment prediction using time Series and sediment rating curve (Case study: Ghazaghly station in Gorganroud River). Iranian Journal of Watershed Management Science and Engineering 9(31):77-88 (In Persian)
Campbell F B, Bauder H A (1940) A rating-curve method for determining silt-discharge of streams. Transactions American Geophysical Union 21(2):603–607
Chen H, Dyke P P G (1998) Multivariate time-series model for suspended sediment concentration. Continental Shelf Research 18(2-4):123-150
Chow V T, Maidment D R, Mays LW (1988) Applied hydrology. McGraw-Hill Book Co, New York
Cordova J R, Gonzalez M (1997) Sediment yield estimation in small watersheds based on streamflow and suspended sediment discharge measurements. Soil Technology 11:57-69
De Girolamo A M, Pappagallo G, Lo Porto A (2015) Temporal variability of suspended sediment transport and rating curves in a Mediterranean river basin: The Celone (SE Italy). Catena 128:135–143
Domínguez‐Castro F, Vicente‐Serrano SM, Tomás‐Burguera M, Peña‐Gallardo M, Beguería S, El Kenawy A, Luna Y, Morata A (2019) High spatial resolution climatology of drought events for Spain: 1961–2014. International Journal of Climatology 39(13):5046-5062
Efthimiou N (2019) The role of sediment rating curve development methodology on river load modeling. Environmental Monitoring and Assessment 191:108
Ferguson R I (1986) River loads underestimated by rating curves. Water Resources Research 22(1):74-76
Hadley R F (1985) Recent developments in erosion and sediment yield studies. International Hydrological Programme, United Nations Educational, Scientific and Cultural Organization
Horowitz A J (2003) An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations. Hydrological Processes 17:3387–3409
Hu B, Wang H, Yang Z, Sun X (2011) Temporal and spatial variations of sediment rating curves in the Changjiang (Yangtze River) basin and their implications. Quaternary International 230:34–43
Irvine K N, Drake J J (1987) Process‐oriented estimation of suspended sediment concentration 1. JAWRA Journal of the American Water Resources Association 23(6):1017-1025
Kalyanapu A J, Judi D R, McPherson T N, Burian S J (2012) Monte Carlo‐based flood modelling framework for estimating probability weighted flood risk. Journal of Flood Risk Management 5(1):37-48
Kao S J, Lee T Y, Milliman J D (2005) Calculating highly fluctuated suspended sediment fluxes from mountainous rivers in Taiwan. Terrestrial Atmospheric and Oceanic Sciences 16(3):653
Keihani A, Akhondali A M, Fathian H (2021) Multivariate frequency analysis of peak discharge and suspended and bed sediment load in Karaj Basin, Iran-Water Resources Research 17(1):47-67 (In Persian)
Khalilivavdareh S, Shahnazari A, Sarraf A (2022) Spatio-temporal variations of discharge and sediment in rivers flowing into the Anzali Lagoon. Sustainability 14(1): 507
Lobanova A, Liersch S, Nunes JP, Didovets I, Stagl J, Huang Sh, Koch H, López MR, Maule CF, Hattermann F, and Krysanova V (2018) Hydrological impacts of moderate and high-end climate change across European river basins. Journal of Hydrology: Regional Studies 18:15-30
Mao L, Carrillo R (2017) Temporal dynamics of suspended sediment transport in a glacierized Andean basin. Geomorphology 287:116–125
McMahon T A, Adeloye A J (2005) Water resources yield. Water Resources Publications, Littleton
McMahon T, Peel M, Karoly D (2015) Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation. Hydrology and Earth System Sciences 19:361-377
Melesse A M, Ahmad S, McClain M E, Wang X, Lim Y H (2011) Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management 98(5):855-866
Mimikou M (1982) An investigation of suspended sediment rating curves in western and northern Greece. Hydrological Sciences Journal 27(3):369–383
Morgan R P C (1985) Assessment of soil erosion risk in England and Wales. Soil Use and Management 1:127-131
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.
Peters-Kummerly B E (1973) Studies on composition and transport of suspended solids in some Swiss rivers. Geographica Helvetica 28:137–151 (in German)
Raeesi M, Najafinejad A, Azim Mohseni M (2019) Investigation of temporal phenomena of sediment rating curve and comparison of it with some statistical methods for estimating suspended sediment load (Case study: Gamasiab Watershed). Journal of Watershed Management Research 10(20):83-96 (In Persian)
Rice R M (1982) Sedimentation in the chaparral: How do you handle unusual events. Sediment Budgets and Routing in Forested Drainage Basins 39-49
Rovira A, Batalla R J (2006) Temporal distribution of suspended sediment transport in a Mediterranean basin: the Lower Tordera (NE Spain). Geomorphology 79:58–71
Sadeghi S H, Saeidi P, Telvari A (2018) Contribution of wash and channel sediment sources in supplying storm suspended sediment load in the Galazchai Watershed. Water Engineering 10(35):17-26 (In Persian)
Salas J D (1993) Analysis and modeling of hydrologic time series. In the McGraw Hill Handbook of Hydrology, edited by D. Maidment, Chapter 19
Shojaeezadeh S A, Nikoo M R, McNamara J P, AghaKouchak A, Sadegh M (2018) Stochastic modeling of suspended sediment load in alluvial rivers. Advances in Water Resources 119:188-196
Singer M B, Dunne T (2001) Identifying eroding and depositional reaches of valley by analysis of suspended sediment transport in the Sacramento River, California. Water Resources Research 37(12):3371-3381
Sivakumar B, Wallender W W (2004) Deriving high-resolution sediment load data using a nonlinear deterministic approach. Water Resources Research 40:W05403
Smith R E, Hebbert R H B (1979) A Monte Carlo analysis of the hydrologic effects of spatial variability of infiltration. Water Resources Research 15(2):419-429
Srikanthan R, Mcmahon T A (2001) Stochastic generation of annual, monthly and daily climate data: A review. Hydrology and Earth System Sciences Discussions, European Geosciences Union 5(4):653-670
Stedinger J R, Vogel R M (1984) Disaggregation procedures for generating serially correlated flow vectors. Water Resources Research 20(1):47-56
Walling D E (1974) Suspended sediment and solid yields from a small catchment prior to urbanization. Fluvial Processes in Instrumented Watersheds 6:169-192
Walling D E, Webb B W (1981) The reliability of suspended sediment load data. IAHS Publication 133:177–194
Yevjevich V M (1972) Structural analysis of hydrologic time series. Doctoral Dissertation, Colorado State University Libraries
Zhai H J, Hu B, Luo X Y, Qiu L, Tang W J, Jiang M (2016) Spatial and temporal changes in runoff and sediment loads of the Lancang River over the last 50 years. Agricultural Water Management 174:74-81