دوره 8، شماره 15 - ( بهار و تابستان 1396 )                   جلد 8 شماره 15 صفحات 235-249 | برگشت به فهرست نسخه ها

XML English Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Uncertainty Estimation of HEC-HMS Flood Simulation Model using Markov Chain Monte Carlo Algorithm. jwmr. 2017; 8 (15) :235-249
URL: http://jwmr.sanru.ac.ir/article-1-859-fa.html
نورعلی مه روز، قهرمان بیژن، پوررضا بیلندی محسن، داوری کامران. تخمین عدم قطعیت مدل شبیه سازی سیلاب HEC-HMS با استفاده از الگوریتم مونت کارلو زنجیره مارکوف. پ‍‍ژوهشنامه مديريت حوزه آبخيز. 1396; 8 (15) :235-249

URL: http://jwmr.sanru.ac.ir/article-1-859-fa.html

چکیده:   (2595 مشاهده)
مدل­های هیدرولوژیکی اغلب شامل پارامترهایی هستند که به­طور مستقیم نمی­توانند اندازه­گیری شوند. تخمین پارامترها توسط روش­ها و الگوریتم­های مختلف بهینه­سازی هم با خطا همراه است. بنابراین تجزیه و تحلیل عدم قطعیت امری ضروری به­شمار
می­آید. در تحقیق حاضر از الگوریتم
DREAM-ZS (از الگوریتم­های مبتنی بر مونت کارلو زنجیره مارکوف) به­منظور بررسی عدم قطعیت پارامترهای مدل­هیدرولوژیکی HEC-HMS در حوزه­ آبخیز تمر به مساحت 1530 کیلومتر­مربع واقع در استان گلستان استفاده شد. به منظور ارزیابی عدم قطعیت 24 پارامتر بکار رفته درمدل HEC-HMS، سه رویداد سیل برای واسنجی و یک رویداد سیل در اعتباریابی استفاده گردید. نتایج حاصل از واسنجی نشان داد که بازه­های 95 درصد عدم قطعیت کل، بیشتر داده­های مشاهده­ای بویژه دبی اوج را در برگرفتند. همچنین علاوه بر عدم قطعیت ناشی از پارامترهای مدل بارش رواناب، منابع دیگر عدم قطعیت مانند ساختار مدل و داده­های ورودی هم سهم مهمی در خطای شبیه­سازی دارند. با مشاهده مقادیر پایین ضریب تغییرات برای پارامتر CN (شماره منحنی) در تمامی سیلاب­ها، این پارامتر به­عنوان حساس­ترین پارامتر به­حساب آمد. هیستوگرام­های پسین پارامترها نشان داد که بیشتر پارامترها به­خوبی تعیین شده­اند و ناحیه کوچکی از توزیع­های یکنواخت پیشین را اشغال می­کنند. همچنین بهترین شبیه­سازی حاصل از اجرای الگوریتم عدم قطعیت DREAM-ZS آشکارا بر شبیه سازی حاصل از الگوریتم جستجوی خودکار نلدر و مید  برتری داشت.
متن کامل [PDF 6329 kb]   (2462 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: تخصصي
دریافت: 1396/6/28 | ویرایش نهایی: 1396/7/3 | پذیرش: 1396/6/28 | انتشار: 1396/6/28

فهرست منابع
1. Abbaspour, K.C. 2011. User manual for SWAT-CUP4, SWAT calibration and uncertainty Programs, Swiss Federal Institute of Aquatic Science and Technology, Eawag, Duebendorf, Switzerland, 103 pp.
2. Alazzy, A.A., H. Lü and Y. Zhu. 2015. Assessing the uncertainty of the Xinanjiang rainfall-runoff model: effect of the likelihood function choice on the GLUE method, Journal of Hydrologic Engineering, 20: 04015016. [DOI:10.1061/(ASCE)HE.1943-5584.0001174]
3. Asadi H, H. Moradi, A. Telvari and S. Sadeghi. 2010. Evaluating methods of storage coefficient of Clark's instantaneous unit hydrograph in simulation of flood unit hydrograph, Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science (JWSS), Isfahan University of Technology, 14: 41-50 (In Persian).
4. Baltas, E.A., N.A. Dervos and M.A. Mimikou. 2007. Technical note: Determination of SCS initial abstraction ratio in an experimental watershed in Greece, Hydrology and Earth System Sciences, 11: 1825-1829. [DOI:10.5194/hess-11-1825-2007]
5. Bates, B.C. and E.P. Campbell. 2001. A Markov Chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall - runoff modeling, Water Resources Research, 37: 937-947. [DOI:10.1029/2000WR900363]
6. Beven, K.J. and A.M. Binley. 1992. The future of distributed models: Model calibration and uncertainty prediction, Hydrological Processes, 6: 279-298. [DOI:10.1002/hyp.3360060305]
7. Beven, K.J. 2006. A manifesto for the enquiringly thesis, Journal of Hydrology, 320: 18-36.
8. Blasone, R.S. 2007. Parameter estimation and uncertainty assessment in hydrological modelling, Ph.D. Thesis, Institute of Environment & Resources, Technical University of Denmark, 55 pp.
9. Blazkova, S. and K. Beven. 2009. A limits of acceptability approach to model evaluation and uncertainty estimation in flood frequency estimation by continuous simulation: Skalka catchment, Czech Republic, Water Resources Research, 45: W00B16. [DOI:10.1029/2007WR006726]
10. Boyle, D.P., H.V. Gupta and S. Sorooshian. 2000. Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resources Research, 36: 3663-3674. [DOI:10.1029/2000WR900207]
11. Dotto, C.B.S., M. Kleidorfer, A. Deletic, W. Rauch, D.T. McCarthy and T.D. Fletcher. 2011. Performance and sensitivity analysis of storm water models using a Bayesian approach and long-term high resolution data, Environmental Modelling & Software, 26: 1225-1239. [DOI:10.1016/j.envsoft.2011.03.013]
12. Gao, G.Y., B.J. Fu, Y.H. Lu, Y. Liu, S. Wang and J. Zhou. 2012. Coupling the modified SCS-CN and RUSLE models to simulate hydrological effects of restoring vegetation in the Loess Plateau of China, Hydrology and Earth System Sciences, 16: 2347-2364. [DOI:10.5194/hess-16-2347-2012]
13. Gelman, A. and D.B. Rubin. 1992. Inference from iterative simulation using multiple sequences, Statistical Science, 7: 457-472. [DOI:10.1214/ss/1177011136]
14. Gupta, H.V., S. Sorooshian and P.Q. Yapo. 1999. Status of automatic calibration for hydrologic models, Comparison with multi-level expert calibration, Journal of Hydrologic Engineering, 4: 135-143. [DOI:10.1061/(ASCE)1084-0699(1999)4:2(135)]
15. Gupta, H.V., H. Kling, K.K. Yilmaz and G.F. Martinez. 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling, Journal of Hydrology, 377: 80-91 [DOI:10.1016/j.jhydrol.2009.08.003]
16. Hastings, W.K. 1970. Monte Carlo sampling methods using Markov Chains and their applications, Biometrika, 57: 97-109. [DOI:10.1093/biomet/57.1.97]
17. Heidari, A., B. Saghafian and R. Maknoon. 2006. Assessment of flood forecasting lead time based on generalized likelihood uncertainty estimation, Stochastic Environmental Research and Risk Assessment, 20: 363-380. [DOI:10.1007/s00477-006-0032-y]
18. Iran Water Research Institute, Water Resources Department (IWRI). 2008. Report on hydrologic model calibration: Gorganroud flood warning system project, Tehran, Iran (In Persian).
19. Kaatz, J.A. 2014. Development of a HEC-HMS model to inform river gauge placement for a flood early warning system in Uganda, M.Sc. Thesis, Massachusetts Institute of Technology, 67 pp.
20. Kamali, B., S.J. Mousavi and K.C. Abbaspour. 2013. Automatic calibration of HEC-HMS using single-objective and multi-objective PSO algorithms, Hydrological Processes, 27: 4028-4042. [DOI:10.1002/hyp.9510]
21. Kavetski, D., G. Kuczera and S.W. Franks. 2006b. Bayesian analysis of input uncertainty in hydrological modeling: 2. Application, Water Resources Research, 42: W03408. [DOI:10.1029/2005WR004376]
22. Koskela, J.J., B.W.F. Croke, H. Koivusalo, A.J. Jakeman and T. Kokkonen. 2012. Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment, Water Resources Research, 48: W11513. [DOI:10.1029/2011WR011773]
23. Kuczera, G. and E. Parent. 1998. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm, Journal of Hydrology, 211: 69-85. [DOI:10.1016/S0022-1694(98)00198-X]
24. Kuczera, G., D. Kavetski, B. Renard and M. Thyer. 2010. A limited-memory acceleration strategy for MCMC sampling in hierarchical Bayesian calibration of hydrological models, Water Resources Research, 46: W07602. [DOI:10.1029/2009WR008985]
25. Laloy, E. and C.L. Bielders. 2009. Modelling intercrop management impact on runoff and erosion in a continuous maize cropping system: Part II. Model Pareto multi-objective calibration and long-term scenario analysis using disaggregated rainfall, European Journal of Soil Science, 60: 1022-1037. [DOI:10.1111/j.1365-2389.2009.01190.x]
26. Laloy, E. and J.A. Vrugt. 2012. High-dimensional posterior exploration of hydrologic models using multiple-try DREAM (ZS) and high-performance computing, Water Resources Research, 48: W01526. [DOI:10.1029/2011WR010608]
27. Lee, G., Y. Tachikawa and K. Takara. 2007. Quantification of parameter uncertainty in distributed rainfall-runoff modeling, Annuals of Disaster Prevention Research Institute, Kyoto University, 50B: 44-56.
28. Mahdavi, M. 1999. Applied hydrology, Vol. 2, 2nd edn., Tehran University Press, Tehran, Iran, 401 pp (In Persian).
29. Metropolis, N., A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller and E. Teller. 1953. Equations of state calculations by fast computing machines, Journal of Chemical Physics, 21: 1087-1091. [DOI:10.1063/1.1699114]
30. Montanari, A. and G. Grossi. 2008. Estimating the uncertainty of hydrological forecasts: A statistical approach, Water Resources Research, 44: W00B08. [DOI:10.1029/2008WR006897]
31. Montanari, A. and D. Koutsoyiannis. 2012. A blueprint for process-based modeling of uncertain hydrological systems, Water Resources Research, 48: W09555. [DOI:10.1029/2011WR011412]
32. Mousavi S.J., K.C. Abbaspour, B. Kamali, M. Amini and H. Yang. 2012. Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach. Journal of Hydro informatics, 14: 286-309. [DOI:10.2166/hydro.2011.071]
33. Najafi, M.R. 2002. Hydrologic systems (rainfall-runoff modelling), Vol. 2, Tehran University Press, Tehran, Iran, 1056 pp (In Persian).
34. Natural Resources and Watershed Management Administration of Golestan. 2007. Report on: Gorganroud Watershed, Gorgan, Iran (In Persian).
35. Neal, R. 1993. Probabilistic inference using Markov Chain Monte Carlo methods, Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, Toronto, Canada, 144 pp.
36. Nelder, J.A. and R. Mead. 1965. A simple method for function minimization, The Computer Journal, 7: 308-313. [DOI:10.1093/comjnl/7.4.308]
37. Pourreza-Bilondi, M., A.M. Akhond Ali and B. Ghahraman. 2012. Parameters Uncertainty Analysis in distributed single- event rainfall-runoff model with MCMC approach, Iranian Water Research Journal, 6: 165-173 (In Persian).
38. Pourreza-Bilondi, M., K.C. Abbaspour and B. Ghahraman. 2013. Application of three different calibration-uncertainty analysis methods in a semi-distributed rainfall-runoff model application, Middle-East Journal of Scientific Research, 15: 1255-1263.
39. Pourreza-Bilondi, M., A.M. Akhond Ali, B. Ghahraman and A.R. Telvari. 2015. Uncertainty analysis of a single event distributed rainfall-runoff model by using two different Markov Chain Monte Carlo methods, Journal of Water and Soil Conservation, 21: 1-26 (In Persian).
40. Price, K.V., R.M. Storn and J.A. Lampinen. 2005. Differential evolution, A practical approach to global optimization, Springer, Berlin, 538 pp.
41. Rafiei Sardoii, E., N. Rostami, S. Khalighi Sigaroudi and S. Taheri. 2012. Calibration of loss estimation methods in HEC-HMS for simulation of surface runoff (Case Study: Amirkabir Dam Watershed, Iran), Advances in Environmental Biology, 6: 343-348.
42. Schoups, G. and J.A. Vrugt. 2010. A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic and non-Gaussian errors, Water Resources Research, 46:W10531. [DOI:10.1029/2009WR008933]
43. Shafiei, M., B. Ghahraman, B. Saghafian, K. Davary, S. Pande and M. Vazifedous. 2014. Uncertainty assessment of the agro-hydrological SWAP model application at field scale: A case study in a dry region, Agricultural Water Management, 146: 324-334. [DOI:10.1016/j.agwat.2014.09.008]
44. Storn, R. and K. Price. 1997. Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11: 341-359. [DOI:10.1023/A:1008202821328]
45. Straub, T.D., C.S. Melching and K.E. Kocher. 2000. Equations for estimating Clark unit-hydrograph parameters for small rural watersheds in Illinois, U.S. Geological Survey, Water-Resources Investigations Report 00-4184, 36 pp.
46. Ter Braak, C.J.F. 2006. A Markov chain Monte Carlo version of the genetic algorithm differential evolution: Easy Bayesian computing for real parameter spaces, Statistics and Computing, 16: 239-249. [DOI:10.1007/s11222-006-8769-1]
47. Tolson, B.A. and C.A. Shoemaker. 2008. Efficient prediction uncertainty approximation in the calibration of environmental simulation models, Water Resources Research, 44: W04411. [DOI:10.1029/2007WR005869]
48. USACE. 2000. HEC-HMS Technical Reference Manual, US Army Corps of Engineers, Hydrologic Engineering Center, Davis, California, 158 pp.
49. USACE. 2013. HEC-HMS User's Manual, US Army Corps of Engineers, Hydrologic Engineering Center, Davis, California, 442 pp.
50. Vrugt, J.A., H.V. Gupta, W. Bouten and S. Sorooshian. 2003. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic parameter estimation, Water Resources Research, 39(8): 1201. [DOI:10.1029/2002WR001642]
51. Vrugt, J.A., C.J.F. Ter Braak, M.P. Clark, J.M. Hyman and B.A. Robinson. 2008. Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resources Research, 44: W00B09. [DOI:10.1029/2007WR006720]
52. Vrugt, J.A., C.J.F. Ter Braak, H.V. Gupta and B.A. Robinson. 2008. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environmental Research and Risk Assessment, 23: 1011-1026. [DOI:10.1007/s00477-008-0274-y]
53. Vrugt, J.A., C.J.F. Ter Braak, C.G.H. Diks, B.A. Robinson, J.M. Hyman and D. Higdon. 2009. Accelerating Markov Chain Monte Carlo simulation using self-adaptative differential evolution with randomized subspace sampling, International Journal of Nonlinear Sciences and Numerical Simulation, 10: 273-290. [DOI:10.1515/IJNSNS.2009.10.3.273]
54. Xiao, B., Q.H. Wang, J. Fan, F.P. Han and Q.H. Dai. 2011. Application of the SCS-CN Model to Runoff Estimation in a Small Watershed with High Spatial Heterogeneity, Pedosphere, 21: 738-749. [DOI:10.1016/S1002-0160(11)60177-X]
55. Yang, J., P. Reichert, K.C. Abbaspour, J. Xia and H. Yang. 2008. Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China, Journal of Hydrology, 358: 1-23. [DOI:10.1016/j.jhydrol.2008.05.012]
56. Zhang, H.L., Y.J. Wang, Y.Q. Wang, D.X. Li and X.K. Wang. 2013. The effect of watershed scale on HEC-HMS calibrated parameters: a case study in the Clear Creek watershed in Iowa, US, Hydrology and Earth System Sciences, 17: 2735-2745. [DOI:10.5194/hess-17-2735-2013]

ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:

کلیه حقوق این وب سایت متعلق به (پژوهشنامه مدیریت حوزه آبخیز (علمی-پژوهشی می باشد.

طراحی و برنامه نویسی : یکتاوب افزار شرق

© 2021 CC BY-NC 4.0 | Journal of Watershed Management Research

Designed & Developed by : Yektaweb