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کالیبراسیون خودکار مدل شبیهسازی آبهای زیرزمینی (MODFLOW) با الگوریتم غیرقطعی SUFI-II | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 16، دوره 53، شماره 4، تیر 1400، صفحه 1507-1524 اصل مقاله (2.11 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2019.16990.6426 | ||
| نویسندگان | ||
| فریبرز معصومی* 1؛ سعید نجار قابل2؛ اکبر صفرزاده1 | ||
| 1دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 2دانشکده عمران، دانشگاه تبریز، تبریز، ایران. | ||
| چکیده | ||
| شبیهسازی ریاضی سیستم منابع آب زیرزمینی یکی از ابزارهای ضروری در مدیریت این منابع ارزشمند بهحساب میآید و کالیبراسیون این مدلهای شبیهساز یکی از مراحل وقتگیر و پیچیده در این فرآیند است. کالیبراسیون خودکار که در سالهای اخیر توسط محققان با الگوریتمهای مختلفی توسعه داده شدهاست، یکی از روشهای مؤثر در غلبه بر این مشکلات محاسباتی است. از طرف دیگر، کمبود دادههای صحرایی از لحاظ زمانی و مکانی و پیچیدگیهای هیدرولوژیکی و هیدروژئولوژیکی، عدم قطعیتهای زیادی را به نتایج کالیبراسیون وارد میکند. الگوریتم SUFI-II یک روش کالیبراسیون خودکار مبتنی بر عدم قطعیت است که توانایی کالیبراسیون و تحلیل عدم قطعیت مدلهای شبیهسازی عددی را دارد. در این مقاله، برای اولین بار، از این الگوریتم برای کالیبراسیون و تحلیل عدم قطعیت پارامترهای هیدرودینامیکی (هدایت هیدرولیکی و آبدهی ویژه) مدل MODFLOW استفاده شدهاست. نتایج اجرای مدل برای آبهای زیرزمینی دشت اردبیل (شمال غربی ایران)، نشان دهنده قرار گرفتن بهطور متوسط 62 درصد مقادیر مشاهداتی سطح ایستایی در محدوده بازه اطمینان 95 درصد است. درنهایت، با رویکرد پیشنهادی، مناسبترین مقدار برای بازه پارامترهای هدایت هیدرولیکی و آبدهی ویژه تعیین شدهاست. همچنین کالیبراسیون مدل شبیهسازی آب زیرزمینی با استفاده از PEST نیز صورت گرفته است. مطابق نتایج، مقدار مجذور میانگین مربعات خطا (RMSE) در این حالت (3/37 =RMSE) بیشتر از مقدار به دست آمده از روش SUFI-II ( RMSE=1/86) است که نشاندهندهی عملکرد بهتر الگوریتم SUFI-II نسبت به مدل PEST است. | ||
| کلیدواژهها | ||
| کالیبراسیون خودکار؛ MODFLOW؛ تحلیل عدم قطعیت؛ الگوریتم SUFI-II؛ آبخوان دشت اردبیل | ||
| موضوعات | ||
| آب های زیر زمینی؛ مدیریت منابع آب | ||
| عنوان مقاله [English] | ||
| Automatic Calibration of Groundwater Simulation Model (MODFLOW) by Indeterministic SUFI-II Algorithm | ||
| نویسندگان [English] | ||
| fariborz masoumi1؛ Saeid Najjar-Ghabel2؛ Akbar Safarzadeh1 | ||
| 1Department of civil engineering, faculty of engineering, university of mohaghegh ardabili, Ardabil, Iran | ||
| 2Water Resources Engineering Department, Faculty of Civil Engineering, Tabriz University Tabriz, Iran. | ||
| چکیده [English] | ||
| Mathematical simulation of groundwater resource systems is one of the essential tools in managing these valuable resources and calibration of the groundwater simulation models is the time-consuming, and complicated step of these systems. Automated calibration, developed in recent years by researchers with different algorithms, is one of the effective methods to overcome these computational problems. On the other hand, lack of field data in terms of time and space and the hydrological and hydrogeological complexities leads to many uncertainties in the calibration results. The SUFI-II algorithm is an uncertainty-based automatic calibration method that is capable of calibration and uncertainty analysis of numerical simulation models. In this paper, for the first time, this algorithm is used to calibrate and analyze the uncertainty of hydrodynamic parameters (hydraulic conductivity and specific yield) of the MODFLOW model. The results of model implementation for the Ardabil plain groundwater model (Northwestern Iran), indicate an average of 62 percent of the observation data within the 95 percent confidence interval. Finally, the best intervals of parameters are determined for the hydraulic conductivity and specific yield by the proposed approach. Also, the calibration of the groundwater model has been carried out using PEST. According to the results, the root-mean-squared error (RMSE) value in this case (RMSE = 3.37) is higher than the SUFI-II method (RMSE = 1.86), which indicates better performance of the SUFI-II algorithm than the PEST model. | ||
| کلیدواژهها [English] | ||
| Automatic Calibration, MODFLOW, Uncertainty Analysis, SUFI-II Algorithm, Ardabil Plain Aquifer | ||
| مراجع | ||
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[1] S.V. Sarath Prasanth, N.S. Magesh, K.V. Jitheshlal, N. Chandrasekar, K. Gangadhar, Evaluation of groundwater quality and its suitability for drinking and agricultural use in the coastal stretch of Alappuzha District, Kerala, India, Applied Water Science, 2(3) (2012) 165–175. [2] Y. Wada, L.P.H. van Beek, C.M. van Kempen, J.W.T.M. Reckman, S. Vasak, M.F. P. Bierkens, Global depletion of groundwater resources, Geophysical Research Letters, 37(20) (2010). [3] A. Singh, Groundwater resources management through the applications of simulation modeling: A review, Science of The Total Environment, 499 (2014) 414–423. [4] S. Zekri, C. Triki, A. Al-Maktoumi, M.R. Bazargan-Lari, An optimization-simulation approach for groundwater abstraction under recharge uncertainty, Water Resources Management, 29(10) (2015) 3681–3695. [5] Y. Yihdego, G. Reta, R. Becht, Hydrological analysis as a technical tool to support strategic and economic development: A case study of Lake Navaisha, Kenya, Water and Environment Journal, 30(1–2) (2016) 40–48. [6] A. Kamali, M.H. Niksokhan, Multi-objective optimization for sustainable groundwater management by developing of coupled quantity-quality simulation-optimization model, Journal of Hydroinformatics, 19(6) (2017) 973–992. [7] J. Carrera, S.P. Neuman, Estimation of aquifer parameters under transient and steady state conditions: 2. uniqueness, stability, and solution algorithms, Water Resources Research, 22(2) (1986) 211–227. [8] Z. Dai, J. Samper, Inverse problem of multicomponent reactive chemical transport in porous media: Formulation and applications, Water Resources Research, 40(7) (2004). [9] Z. Dai, J. Samper, Inverse modeling of water flow and multicomponent reactive transport in coastal aquifer systems, Journal of Hydrology, 327(3–4) (2006) 447–461. [10] H. Shang, W. Wang, Z. Dai, L. Duan, Y. Zhao, J. Zhang, An ecology-oriented exploitation mode of groundwater resources in the northern Tianshan Mountains, China, Journal of Hydrology, 543 (2016) 386–394. [11] P. Droogers, H.R. Salemi, A.R. Mamanpoush, Exploring basin-scale salinity problems using a simplified water accounting model: the example of Zayandeh Rud basin, Iran, Irrigation and Drainage, 50(4) (2001) 335–348. [12] J. Doherty, L. Brebber, P. Whyte, PEST: Model-Independent Parameter Estimation. Watermark Computing, Corinda, Australia, 122 (1994) 1-336. [13] E. Poeter, M. Hill, Documentation of UCODE; a computer code for universal inverse modeling, DIANE Publishing, 1998. [14] M. Zambrano-Bigiarini, R. Rojas, A model-independent Particle Swarm Optimisation software for model calibration, Environmental Modelling & Software, 43 (2013) 5–25. [15] H. Delottier, A. Pryet, A. Dupuy, Why should practitioners be concerned about predictive uncertainty of groundwater management models?, Water Resources Management, 31(1) (2017) 61–73. [16] J. Wu, X. Zeng, Review of the uncertainty analysis of groundwater numerical simulation, Chinese Science Bulletin, 58(25) (2013) 3044–3052. [17] K.C. Abbaspour, J. Yang, I. Maximov, R. Siber, K. Bogner, J. Mieleitner, J. Zobrist, R. Srinivasan, Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT, Journal of Hydrology, 333(2–4) (2007) 413–430. [18] A.E. Hassan, H.M. Bekhit, J.B. Chapman, Uncertainty assessment of a stochastic groundwater flow model using GLUE analysis, Journal of Hydrology, 362(1) (2008) 89–109. [19] R. Rojas, L. Feyen, A. Dassargues, Conceptual model uncertainty in groundwater modeling: Combining generalized likelihood uncertainty estimation and Bayesian model averaging, Water Resources Research, 44(12) (2008). [20] A.E. Hassan, H.M. Bekhit, J.B. Chapman, Using Markov Chain Monte Carlo to quantify parameter uncertainty and its effect on predictions of a groundwater flow model, Environmental Modelling & Software, 24(6) (2009) 749–763. [21] R.-S. Blasone, J.A. Vrugt, H. Madsen, D. Rosbjerg, B.A. Robinson, G.A. Zyvoloski, Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling, Advances in Water Resources, 31 (2008) 630-648. [22] J. Fu, J. Jaime Gómez-Hernández, Uncertainty assessment and data worth in groundwater flow and mass transport modeling using a blocking Markov chain Monte Carlo method, Journal of Hydrology, 364(3–4) (2009) 328–341. [23] N. Sepúlveda, J. Doherty, Uncertainty analysis of a groundwater flow model in east-central Florida, Groundwater, 53(3) (2015) 464–474. [24] X. Li, F.T.-C. Tsai, Bayesian model averaging for groundwater head prediction and uncertainty analysis using multimodel and multimethod, Water Resources Research, 45(9) (2009). [25] H. Yoon, D.B. Hart, S.A. McKenna, Parameter estimation and predictive uncertainty in stochastic inverse modeling of groundwater flow: Comparing null-space Monte Carlo and multiple starting point methods, Water Resources Research, 49(1) (2013) 536–553. [26] J.-C. Wu, L. Lu, T. Tang, Bayesian analysis for uncertainty and risk in a groundwater numerical model’s predictions, Human and Ecological Risk Assessment: An International Journal, 17(6) (2011) 1310–1331. [27] M. Sadat Kahe, S. Javadi, A. Roozbahani, Uncertainty assesment of hydraulic conductivity parameter in MODFLOW model using monte carlo and RPEM method (case study: AliAbad plain of Qom), Iran-Water Resources Research, 14(2) (2018) 35–53. (In Persian) [28] K. Beven, How far can we go in distributed hydrological modelling?, Hydrology and Earth System Sciences, 5(1) (2001) 1–12. [29] K. Beven, A. Binley, The future of distributed models: Model calibration and uncertainty prediction, Hydrological Processes, 6(3) (1992) 279–298. [30] J. B. Jensen, Parameter and uncertainty estimation in groundwater modelling, Aalborg University, (2003). [31] R. Rojas, S. Kahunde, L. Peeters, O. Batelaan, L. Feyen, A. Dassargues, Application of a multimodel approach to account for conceptual model and scenario uncertainties in groundwater modelling, Journal of Hydrology, 394(3–4) (2010) 416–435. [32] A. Singh, S. Mishra, G. Ruskauff, Model averaging techniques for quantifying conceptual model uncertainty, Ground Water, 48(5) (2010) 701–715. [33] A. Inam, J. Adamowski, S. Prasher, R. Albano, Parameter estimation and uncertainty analysis of the Spatial Agro Hydro Salinity Model (SAHYSMOD) in the semi-arid climate of Rechna Doab, Pakistan, Environmental Modelling & Software, 94 (2017) 186–211. [34] R.S. Blasone, D. Rosbjerg, H. Madsen, Parameter estimation and uncertainty assessment in hydrological modelling, Technical University of Denmark, (2007). [35] B.L. Barnhart, K.A. Sawicz, D.L. Ficklin, G.W. Whittaker, MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models, Transactions of the ASABE, 60(4) (2017) 1259–1269. [36] S.J. Mousavi, K.C. Abbaspour, B. Kamali, M. Amini, H. Yang, Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach, Journal of Hydroinformatics, 14(2) (2012) 286-309. [37] Y. Cao, J. Zhang, M. Yang, X. Lei, B. Guo, L. Yang, Z. Zeng, J. Qu, Application of SWAT model with CMADS data to estimate hydrological elements and parameter uncertainty based on SUFI-2 algorithm in the Lijiang river basin, China, Water, 10(6) (2018) 742. [38] J. Yang, P. Reichert, K.C. Abbaspour, J. Xia, H. Yang, Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China, Journal of Hydrology, 358(1–2) (2008) 1–23. [39] B. Kamali, K. Abbaspour, A. Lehmann, B. Wehrli, H. Yang, Uncertainty-based auto-calibration for crop yield – the EPIC+ procedure for a case study in Sub-Saharan Africa, European Journal of Agronomy, 93 (2018) 57–72. [40] K.C. Abbaspour, A. Johnson, M.T. van Genuchten, Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure, Vadose Zone Journal, 3(4) (2004) 1340-1352. [41] K.C. Abbaspour, M.T. van Genuchten, R. Schulin, E. Schläppi, A sequential uncertainty domain inverse procedure for estimating subsurface flow and transport parameters, Water Resources Research, 33(8) (1997) 1879–1892. [42] K.C. Abbaspour, Swat-Cup2: SWAT Calibration and Uncertainty Programs Manual Version 2, Duebendorf, Switzerland, (2008). [43] W. H. Press, B. P. Flannery, S. A. Teukolsky, W. T. Vetterling, Numerical Recipe, The Art of Scientific Computation, 2nd edition. Cambridge University Press, Cambridge, UK, (2007). [44] M.G. McDonald, A.W. Harbaugh, A modular three-dimensional finite-difference ground-water flow model, VA: US Geological Survey, 6 (1988). [45] D.K. Todd, L.W. Mays, Groundwater Hydrology, Third edition, John Wiley & Sons, Inc. New York, (2005). [46] M. Kord, Numerical modeling of the Ardabil plain aquifer and its management using optimization of Groundwater extraction. Natural Science, University of Tabriz, (2014). (In Persian) | ||
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