ارزیابی مدل های تعیین پاسخ آب‌شناختی در آبخیز آزمایشگاهی

نوع مقاله : پژوهشی

نویسندگان

1 گروه مهندسی عمران، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران

2 گروه مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

3 گروه مهندسی عمران، واحد اسلام‌شهر، دانشگاه آزاد اسلامی، اسلام‌شهر، ایران

چکیده

مقدمه و هدف
با توجه به کاربرد پاسخ آب‌شناختی آبخیز، روش ­های مختلفی برای تعیین این پاسخ انجام شده است و نتایج آن با توجه به داده ­های استفاده شده تنوع دقت و صحت زیادی داشته است. با بررسی و جمع ­بندی نتایج پژوهش‌های انجام شده در بحث مدل­ سازی بارش-رواناب و به‌ویژه روش زمان-مساحت، مشخص شد که در اغلب این پژوهش‌ها، از مفهوم زمان تمرکز آبخیز بهره گرفته شده است که در اکثر رابطه‌های تعیین اندازه‌ی آن، از خصوصیات فیزیکی آبخیز استفاده شده است و وابستگی زمان تمرکز به شرایط بارش بررسی نشده است. از این رو، این پژوهش با هدف ارزیابی این روش ­ها با بهره‌گیری از روش موج جنبشی در بستر GIS، روش HEC-1 و روش بهینه­ سازی با استفاده از دستورالعمل ژنتیک، در یک آبخیز آزمایشگاهی وی-شکل انجام شد.
مواد و روش‌ها
در این پژوهش به منظور مدل­ سازی بارش-رواناب از داده ­های مشاهده ­ای موجود در آبخیز آزمایشگاهی وی-شکل آزمایشگاه دانشگاه ایلینویز استفاده شد. آبخیز مطالعه شده، با سطح نفوذناپذیر از جنس آلومینیوم و دو صفحه‌ی جانبی همسان با شیب یک‌ طرفه به سمت کانال با اندازه‌ی ثابت 1% بود. افزون بر این، یک کانال میانی هم با شیب یک‌طرفه به سمت خروجی آبخیز با اندازه‌ی ثابت 1% داشت. اندازه‌ی زبری مانینگ در این آبخیز، بر پایه‌ی سعی و خطا 0/014 تعیین شد.
نتایج و بحث
پس از تهیه نمودار زمان-مساحت آبخیز با استفاده از هریک از روش ­های مزبور، آب‌نگار خروجی آبخیز متناظر با این روش ­ها تعیین شد. سپس نتایج به‌دست آمده با داده ­های مشاهده‌ای مقایسه شد و اجزای مختلف آب‌نگار­های محاسبه‌ای نیز بررسی شد. نتایج نشان داد که عملکرد دستورالعمل ژنتیک در تعیین زمان اوج آب‌نگار با 15% خطای نسبی، از عملکرد مدل‌های موج جنبشی و HEC-1 بهتر بود. همچنین مدل دستورالعمل ژنتیک با میانگین شاخص نش-ساتکلیف 0/968 و میانگین شاخص همبستگی 0/983 بیشترین تطابق را با آب‌نگار­های مشاهده‌ای داشت. همچنین با برازش منحنی با نتایج مدل­ سازی، معادله‌ی تعیین زمان تعادل آبخیز نسبت به شدت بارش به‌دست آمد که ضریب تعیین آن 0/999 بود. این معادله بیان‌گر رابطه‌ی عکس زمان تعادل با شدت بارش (با توان 0/33) است یعنی با دو برابر شدن شدت بارش، زمان تعادل 20% کاهش می­ یابد. درنهایت ضریب معادله‌ی تعیین زمان تعادل برای این آبخیز 495/2 به‌دست آمد که به ازای هر شدت بارش، زمان تعادل متناظر آن با دقت زیاد قابل محاسبه است.
نتیجه­ گیری و پیشنهادها
در این پژوهش در شرایط آبخیز آزمایشگاهی مدل‌سازی بارش-رواناب با سه دسته رخداد و سه دسته مدت بارش گوناگون که هر دسته رخداد با چهار اندازه‌ی مختلف شدت بارش بود انجام شد. نمودارهای آب‌نگار متناظر با هر رخداد به‌دست آمد. با بررسی نمودارهای محاسبه‌ای و مقایسه با نمودارهای مشاهده‌ای، مشخص شد عملکرد سه مدل در تعیین حداکثر آب‌دهی، خطای نسبی یک تا دو درصد بود ولی در تعیین زمان رسیدن به اوج آب‌نگار، مدل­ های موج جنبشی و HEC-1، با خطای نسبی 44% ، عملکرد متوسط داشت. درنهایت با بهره ­گیری از دو شاخص همبستگی و نش-ساتکلیف، مشخص شد که در روش دستورالعمل ژنتیک، آب‌نگار محاسبه‌ای به اندازه‌های مشاهده‌ای نزدیک­تر بودند و تطابق بیشتری با داده ­های مشاهده‌ای داشتند. شایان ذکر است در این آبخیز آزمایشگاهی وابستگی زمان تعادل به شدت بارش در شرایط نفوذناپذیری تأیید شد. پیشنهاد می‌شود در صورت اعمال شرایط نفوذ عمقی و یا تغییر در اندازه‌ی زبری سطح و تعیین اندازه‌ی وابستگی تعیین و بررسی شود.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of Hydrological Response Extraction Models in Laboratory Watershed

نویسندگان [English]

  • Mohammad Mohammadi Hashemi 1
  • Bahram Saghafian 2
  • Mahmoud Zakeri Niri 3
  • Mohsen Najarchi 1
1 Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran
2 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 Department of Civil Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
چکیده [English]

Introduction and Objective
Given the application of hydrological response in a watershed, various methods have been used to determine this response and the results have shown a high degree of accuracy and accuracy variability depending on the data used. By reviewing and summarizing the results of research carried out in the modeling of rainfall-runoff, particularly the time-area method, it was found that in most of these studies, the concept of watershed time concentration was used, which in most of the formulas used, the physical properties of the watershed were used and the dependence of time concentration on rainfall conditions was not studied. Therefore, this study was conducted to evaluate these methods using the kinematic wave method in the GIS environment, the HEC-1 method, and optimization methods using genetic algorithms in a V-shaped experimental watershed.
Materials and Methods
Observational data available in the V-shaped experimental watershed of the University of Illinois was used for rainfall-runoff modeling. The studied watershed had an impermeable aluminum surface and two uniform side sheets with a one-sided slope towards the channel with a constant value of 1%. In addition, a central channel with a one-sided slope towards the outlet of the watershed with a constant value of 1% was present. The roughness coefficient in this watershed was determined based on trial and error at 0.014.
Results and Discussion
After preparing the time-area histogram of the watershed using each of the mentioned methods, the corresponding outflow hydrographs of the watershed were determined. Then, the results were compared with observational data, and various components of the computational hydrographs were also examined. The results showed that the performance of the genetic algorithm in determining the peak time of the hydrograph with a 15% relative error was better than the performance of the kinematic wave and HEC-1 models. Additionally, the genetic algorithm model had the highest correlation coefficient with observational hydrographs with an average Nash-Sutcliffe Index of 0.968 and an average correlation coefficient of 0.983. Furthermore, by fitting the curve to the modeling results, an equation was obtained to determine the equilibrium time of the watershed relative to rainfall intensity, with a determination coefficient of 0.999. This equation expresses the inverse relationship between equilibrium time and rainfall intensity (with a power of 0.33), i.e., doubling the rainfall intensity reduces the equilibrium time by 20%. Finally, the coefficient of the equation determining the equilibrium time for this watershed was found to be 495.2, and for each rainfall intensity, its corresponding equilibrium time can be calculated with high accuracy.
Conclusion and Suggestions
In this study, under experimental watershed conditions, rainfall-runoff modeling was performed with three categories of events and three categories of different rainfall durations, each event category having four different rainfall intensity sizes. Corresponding hydrographs were obtained for each event by examining the computational hydrographs and comparing them with observational hydrographs, and it was found that the three models had one to two percent relative error in determining the maximum runoff, but in determining the time to reach the hydrograph peak, the kinematic wave and HEC-1 models had an average error of 44%. Finally, using the correlation and Nash-Sutcliffe coefficients, it was determined that the computational hydrographs produced by the genetic algorithm method were closer to the observational hydrographs and had a higher degree of correlation. It is worth mentioning that in this experimental watershed, the dependence of equilibrium time on rainfall intensity in impermeable conditions was confirmed. It is recommended that the effect of infiltration or changes in surface roughness and the determination of the dependence size be investigated.

کلیدواژه‌ها [English]

  • Genetic algorithm
  • HEC-1 model
  • hydrological response
  • kinematic wave
  • optimization
  • V-shaped watershed
Alizadeh, A. 2010. Principles of applied hydrology. 30. 5. University of Emam Reza. Mashhad. Iran. 991 p. (In Persian).
Barkhordari J, Vartanian T. 2014. Evaluation of a Distributed Monthly Water Balance Model to ‎Determine Catchment Runoff in Arid Region Using RS and GIS ‎‎(A Case Study in Yazd-Ardakan Basin)‎. Watershed Management Research 27(2):154–64. https://doi.org/10.22092/wmej.2014.106267. (In Persian).
Bayati F, Mirabbasi R, Fatahi Nafchi R, Radfar M. 2021. Performance Assessment of Copula Functions in Estimation of Rainfall Losses and Rainfall-Runoff Modelling (Case Study: Kasilian Watershed). Watershed Engineering and Management 13(1): 125-136. https://doi.org/10.22092/ijwmse.2020.120498.1441. (In Persian).
Chabokpour J. 2022. Operation of the Non-Linear Muskingum Model in the Prediction of the Pollution Breakthrough Curves through the River Reaches. Amirkabir Journal of Civil Engineering. 54(1):21-34. https://doi.org/10.22060/ceej.2021.17413.6556. (In Persian).
Chow VT, Maidment DR, Mays LW. 1988. Applied Hydrology. 5. McGraw Hill. 588 p.
Dong Si-Hui. 2008. Genetic Algorithm Based Parameter Estimation of Nash Model. Water Resources Management 22(4):525–33. https://doi.org/10.1007/s11269-007-9208-6.
Drisya J, Sathish Kumar D. 2017. Automated Calibration of a Two-Dimensional Overland Flow Model by Estimating Manning’s Roughness Coefficient Using Genetic Algorithm. Journal of Hydroinformatics. 20(2):440–56. https://doi.org/10.2166/hydro.2017.11.
Farzin S, Noori H, Karami H. 2018. Developing the Performance of Modern Methods Using Multi-Objective Optimization in Urban Runoff Control. Iran Water Resources Research. 14(3):45-58. (In Persian).
Her Y, Heatwole C. 2016. HYSTAR Sediment Model: Distributed Two-Dimensional Simulation of Watershed Erosion and Sediment Transport Using Time-Area Routing. JAWRA Journal of the American Water Resources Association 52(2): 376–96. https://doi.org/10.1111/1752-1688.12396.
Hydrologic Engineering Center. 1991. HEC-1 Flood Hydrograph Package. Davis, CA.
Kang K. Merwade V. 2011. Development and Application of a Storage–Release Based Distributed Hydrologic Model Using GIS. Journal of Hydrology. 403(1–2):1–13. https://doi.org/10.1016/j.jhydrol.2011.03.048.
Liang J. 2010. Evaluation of runoff response to moving rainstorms. Marquette University. Milwaukee, Wisconsin. (Dissertation).
Liang J, Melching CS. 2012. Comparison of Computed and Experimentally Assessed Times of Concentration for a V-Shaped Laboratory Watershed. Journal of Hydrologic Engineering. 17(12):1389–96. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000609.
Liang J, Melching CS.  2015. Experimental Evaluation of the Effect of Storm Movement on Peak Discharge. International Journal of Sediment Research. pp. 166–77. https://doi.org/10.1016/j.ijsrc.2015.03.004.
Madden Francis N, Godfrey Keith R, Chappell MJ, Hovorka R, Bates RA. 1996. A Comparison of Six Deconvolution Techniques. Journal of Pharmacokinetics and Biopharmaceutics. 24(3):283–99. https://doi.org/10.1007/BF02353672.
Masoumi F, Bashi-Azghadi SN, Afshar A. 2021. Application of Achieve-Based Genetic Algorithm for Consequence Management of Contaminant Entering in Water Distribution Networks. Amirkabir Journal of Civil Engineering 53(8): 3593-3604. https://doi.org/10.22060/ceej.2020.18055.6750. (In Persian).
Melesse AM, Graham WD, Jordan JD. 2003. Spatially Distributed Watershed Mapping and Modeling: GIS-Based Storm Runoff Response Snad Hydrograph Analysis: Part 2. Journal of Spatial Hydrology. 3(2):1–28.
Mohammadi Hashemi M, Saghafian B, Zakeri Niri, Najarchi M. 2021. Applicability of Rainfall–Runoff Models in Two Simplified Watersheds. Iranian Journal of Science and Technology, Transactions of Civil Engineering, September. https://doi.org/10.1007/s40996-021-00733-5.
Nagy ED, Torma P, Bene K. 2016. Comparing Methods for Computing the Time of Concentration in a Medium-Sized Hungarian Catchment. Slovak Journal of Civil Engineering. 24(4):8–14.
Nash JE, Sutcliffe JV. 1970. River Flow Forecasting through Conceptual Models Part I — A Discussion of Principles. Journal of Hydrology. 10(3):282–290. doi: 10.1016/0022-1694(70)90255-6.
Nouri H, Ildoromi A, Sepehri M, Artimani M. 2019. Comparing Three Main Methods of Artificial Intelligence in Flood Estimation in Yalphan Catchment. Geography and Environmental Planning 29(4):35–50. https://www.magiran.com/paper/1976405 LK - https://www.magiran.com/paper/1976405. (In Persian).
Saba HR, Kamalian M, Raeisizadeh I. 2018. Determining Impending Slip of Slop and Optimized Embankment Operation Volume of Earth Dams Using a Combination of Neural Networks and Genetic Algorithms (GA). Amirkabir Journal of Civil Engineering. 50(4):747–54. https://doi.org/10.22060/ceej.2017.11051.4965. (In Persian).
Sabzevari T, Ardakanian R, Shamsaee A, Talebi A. 2009. Estimation of Flood Hydrograph in No Statistical Watersheds Using HEC-HMS Model and GIS (Case Study: Kasilian Watershed). Journal of Water Engineering 4: 1–11
Sabzevari T, Noroozpour S, Pishvaei MH. 2015. Effects of Geometry on Runoff Time Characteristics and Time-Area Histogram of Hillslopes. Journal of Hydrology 531(December). pp. 638–48. https://doi.org/10.1016/j.jhydrol.2015.10.063.
Sadeghi SHR, Mostafazadeh R, Sadoddin A. 2015. Changeability of Simulated Hydrograph from a Steep Watershed Resulted from Applying Clark’s IUH and Different Time–Area Histograms. Environmental Earth Sciences. 74: 3629-3643. https://doi.org/10.1007/s12665-015-4426-3.
Sadeghi S, Samani J, Samani H. 2021. Optimal Design of Storm Sewer Network Based on Risk Analysis by Combining Genetic Algorithm and SWMM Model. Amirkabir Journal of Civil Engineering. 54(5): 1903-1924. https://doi.org/10.22060/ceej.2021.19990.7308. (In Persian).
Saghafian B, Lieshout AM, M Rajaei H. 2000. Distributed Catchment Simulation Using a Raster GIS. International Journal of Applied Earth Observation and Geoinformation. 2(3–4):199–203. https://doi.org/10.1016/S0303-2434(00)85014-X.
Saghafian B, Alireza Sh. 2006. A Corrected Time-Area Technique for One-Dimensional Flow. International Journal of Civil Engineering. 4(1):34–41.
Saghafian B, Julien PY. 1995. Time to Equilibrium for Spatially Variable Watersheds. Journal of Hydrology. 172(1–4): 231–245. doi:10.1016/0022-1694(95)02692-I.US Army Corps of Engineers. 2000. Hydrologic Modeling System HEC-HMS Technical Reference Manual.
Wong TSW. 2001. Formulas for Time of Travel in Channel with Upstream Inflow. Journal of Hydrologic Engineering. 6(5):416–22. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:5.(416)
Woolhiser DA, Liggett JA. 1967. Unsteady, One-Dimensional Flow over a Plane-The Rising Hydrograph. Water Resources Research. 3(3):753–71. https://doi.org/10.1029/WR003i003p00753.
Xiong Y, Melching CHS. 2005. Comparison of Kinematic-Wave and Nonlinear Reservoir Routing of Urban Watershed Runoff. Journal of Hydrologic Engineering. 10(1):39–49. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:1 (39)
Zakeri Niri M, Saghafian B, Golian S, Moramarco T, Shamsai A. 2012. Derivation of Travel Time Based on Diffusive Wave Approximation for the Time-Area Hydrograph Simulation. Journal of Hydrologic Engineering. 17(1):85–91. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000399
Zeraatkar Z. 2016. Simulation of Birjand Urban Flood Using HEC-RAS and ARC-GIS. Watershed Management Research Journal. 29(3):41–56. https://doi.org/10.22092/wmej.2016.112239. (In Persian).