پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,473 |
| تعداد مقالات | 69,970 |
| تعداد مشاهده مقاله | 122,759,391 |
| تعداد دریافت فایل اصل مقاله | 95,907,740 |
مقایسه چهار روش تحلیل حساسیت پارامترهای مدل مفهومی HBV در حوضه آبریز کرخه و زیرحوضههای آن | ||
| فیزیک زمین و فضا | ||
| مقاله 7، دوره 45، شماره 1، فروردین 1398، صفحه 89-105 اصل مقاله (691.5 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2019.253304.1006979 | ||
| نویسندگان | ||
| مریم شفیعی1؛ جواد بذرافشان* 2؛ پرویز ایران نژاد3 | ||
| 1دانش‌آموخته دکتری، گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2دانشیار، گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 3دانشیار، گروه فیزیک فضا، مؤسسه ژئوفیزیک، دانشگاه تهران، تهران، ایران | ||
| چکیده | ||
| مدل HBV (Hydrologiska Byråns Vattenbalansavedlning) یک مدل مفهومی است که به‌طور گستردهای برای پیشبینیهای آب‌شناسی و مطالعات منابع آب به‌کار میرود. در این مطالعه تحلیل حساسیت پارامترهای مدل HBV برای زیرحوضههای کرخه و کل حوضه کرخه در چهار بازه زمانی مختلف 1، 5، 10 و 25 سال با چهار روش FAST (Fourier Amplitude Sensitivity Test)، (Regional Sensitivity Analysis) RSA،Sobol و رگرسیون بررسی شده است. پس از تعیین حساسترین پارامترها مدل با روش الگوریتم ژنتیک با مرتبسازی نامغلوب، NSGA (Nondominated Sorting Genetic Algorithm) واسنجی شده است. توابع هدف برای بررسی عملکرد مدل شامل NSE، RMSE، RSR و BIAS میباشند. نتایج تحلیل حساسیت پارامترها نشان میدهد که روشهای Sobol و RSA به‌علت تغییرپذیری در بازههای زمانی و زیرحوضههای مختلفروشهای قابل اطمینانتری هستند. حساسترین پارامترهای مدل HBV برای زیرحوضهها و حوضه کرخه در روال خاک پارامتر بیشینه ذخیره رطوبت خاک (Fcap) و در روال پاسخ پارامتر بیشینه ذخیره رطوبت لایه سطحی خاک (hl1) هستند، این پارامترها در دبیهای کمینه بیشترین حساسیت را نشان دادهاند. پارامترهای روال برف مخصوصاً پارامتر دمای آستانه برای یخزدگی (ttlim) در زیرحوضههای قرهسو و کشکان و در بازههای زمانی کوتاهمدت (1 و 5 سال) حساسیت نشان دادهاند. مدل HBV توانایی شبیهسازی رواناب در حوضه کرخه و زیرحوضههای آن با دقت بالا را دارد. این مطالعه نشان میدهد انتخاب بازههای زمانی کوتاهتر واسنجی، نتایج شبیهسازی بهتری ارائه میدهد. در بازه زمانی یک سال بهترین ضریب NSE، RSR و RMSE مربوط به زیرحوضه گاماسیاب به‌ترتیب به‌مقدار 95/0، 21/0 و 4/1 و بهترین BIAS مربوط به زیرحوضه کشکان و حوضه کرخه به‌مقدار 13/0 است. | ||
| کلیدواژه‌ها | ||
| مدل مفهومی HBV؛ تحلیل حساسیت؛ واسنجی؛ حوضه آبریز کرخه | ||
| عنوان مقاله [English] | ||
| Comparison of four Sensitivity Analysis Methods of HBV Conceptual Model Parameters in Karkheh Basin and its Sub-basins | ||
| نویسندگان [English] | ||
| Maryam Shafiei1؛ Javad Bazrafshan2؛ Parviz Irannejad3 | ||
| 1Ph.D. Graduated, Department of Irrigation and Reclamation Engineering, Natural Resources and Agricultural Campus, University of Tehran, Karaj, Iran | ||
| 2Associate Professor, Department of Irrigation and Reclamation Engineering, Natural Resources and Agricultural Campus, University of Tehran, Karaj, Iran | ||
| 3Associate Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| The HBV (Hydrologiska Byråns Vattenbalansavedlning) is a conceptual model widely used for hydrological forecasting and water resource studies. In this study, sensitivity analysis of parameters of the HBV model is investigated for Karkhe basin and its sub-basins for four different periods 1, 5, 10 and 25 years with four methods including FAST (Fourier Amplitude Sensitivity Test), RSA (Regional Sensitivity Analysis), Sobol and regression. After determining the most sensitive parameters, the model is calibrated using Nondominated Sorting Genetic Algorithm (NSGA) method. In all statistical periods, one year has been used for warm-up to eliminate the effects of initial conditions. In this study, the MOUSE Toolbox is used to analyze the sensitivity of the HBV model parameters. This software is based on Java programming language. To analyze the sensitivity of the HBV model parameters based on the Monte Carlo sampling method and the Halton sequence method for each of the samples (time periods) in each sub-basin separately, 1000 samples are taken for the set of input parameters with a specified range for each parameter taken. Objective functions for evaluating performance of model are NSE, RMSE, RSR and BIAS. The results of sensitivity analysis of the parameters show that Sobol and RSA are more reliable methods because of variability in time intervals and different sub-basins. Fast and regression methods in the Karkheh basin and its sub-basins for different time periods show similar results that considering the change in hydroclimate conditions in this basin, isn't practical and the results of these methods can not be used for investigating sensitivity of parameters and their identification in the studied basin. The most sensitive parameters of HBV model for Karkheh basin and its sub-basins in soil routine is maximum soil moisture content (Fcap) and in the response routine is the storage of soil surface moisture content (hl1). These parameters have shown the most sensitive factor in minimum fluxes. The snow routine parameters, especially the threshold temperature for ice freezing (ttlim), are sensitive in the sub-basins of Ghare Sou and Kashkan in short periods (1 and 5 years). For a specific sub-basin, the sensitivity of the parameters in different time periods is not completely stable and a little variability has been observed in different periods. But the most sensitive parameters (hl1 and fcap) have maintained their sustainability almost in all periods. Parameters of response and soil routines are more sensitive to the parameters of snow and routing routines. The results of the interaction between the parameters using the Sobol method in different sub-basins indicate that the strongest interactions are between the soil routine parameters, especially Fcap, with the response routine parameters and also the response routine parameters with each other. The time variability of parameters indicates that the soil routine and response parameters in the minimum discharge show the most sensitivity. Other parameters are more sensitive in the dry season of the basin (summer and autumn). The HBV model has the ability to simulate runoff in the Karkhe basin and its sub-basins with high precision. This study shows that selection of shorter period of calibration gives better simulation results. For one year's period the best NSE, RSR and RMSE are in Gamasyab sub-basin respectively 0.95, 0.21 and 1.4 and the best BIAS is in Kashkan sub-basin and Karkhe basin with 0.13. | ||
| کلیدواژه‌ها [English] | ||
| HBV conceptual model, Sensitivity analysis, calibration, Karkhe basin | ||
| مراجع | ||
|
یعقوبی، م. و مساح بوانی، ع.، 1393، تحلیل حساسیت و مقایسه عملکرد سه مدل مفهومی HBV، IHARCES و HEC-HMS در شبیهسازی بارش-رواناب پیوسته در حوضه‌‌های نیمه‌خشک (بررسی موردی: حوضه اعظم هرات-یزد)، مجله فیزیک زمین و فضا، 40 (2)، 172-153.
Abebe, N. A., Ogden, F. L. and Pradhan, N. R., 2010, Sensitivity and uncertainty analysis of the conceptual HBV rainfall–runoff model: Implications for parameter estimation, Journal of Hydrology, 389(3), 301-310. Akhtar, M., Ahmad, N. and Booij, M., 2008, The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios, Journal of hydrology, 355(1), 148-163. Ascough II, J. C., Green, T. R., Fischer, C., Kralisch, S., Lighthart, N. and David, O., 2015, The Model Optimization, Uncertainty, and Sensitivity analysis (MOUSE) toolbox: overview and application Annual Hydrology Days Conference Proceedings, March 23-25, Fort Collins, Colorado, Colorado State University, 17-28. Baroni, G. and Tarantola, S., 2014, A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study. Environmental Modelling & Software, 51, 26-34. Bennett, D. A., Wade, G. A. and Armstrong, M. P., 1999, Exploring the solution space of semi-structured geographical problems using genetic algorithms, Transactions in GIS, 3(1), 51-71. Bergstrom, S., 1995, The HBV model. Computer models of watershed hydrology. Beven, K. and Binley, A., 1992, The future of distributed models: model calibration and uncertainty prediction, Hydrological processes, 6(3), 279-298. Bronstert, A., Niehoff, D. and Bürger, G., 2002, Effects of climate and land‐use change on storm runoff generation: present knowledge and modelling capabilities, Hydrological processes, 16(2), 509-529. Coello, C. C., Lamont, G. B. and Van Veldhuizen, D. A., 2007, Evolutionary algorithms for solving multi-objective problems, Springer Science & Business Media. Driessen, T. L. A., Hurkmans, R. T. W. L., Terink, W., Hazenberg, P., Torfs, P. J. J. F. and Uijlenhoet, R., 2010, The hydrological response of the Ourthe catchment to climate change as modelled by the HBV model. Hydrology and Earth System Sciences, 14: 651-665. Fischer, C., Kralisch, S. and Flügel, W., 2012, An integrated, fast and easily useable software toolbox which allows comparative and complementary application of various parameter sensitivity analysis methods, Proc. International Congress on Environ, Modell. & Soft., Sixth Biennial Meeting. Leipzig, Germany. Gan, Y., Duan, Q., Gong, W., Tong, C., Sun, Y., Chu, W., Ye, A., Miao C. and Di, Z. 2014, "A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model." Environmental modelling & software. 51, 269-285. Gupta, H. V., Sorooshian, S. and Yapo, P. O., 1999, Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration, Journal of Hydrologic Engineering, 4(2), 135-143. Hamby, D., 1994, A review of techniques for parameter sensitivity analysis of environmental models, Environmental monitoring and assessment, 32(2), 135-154. Helton, J.C. and Davis, F., 2002, Illustration of sampling-based methods for uncertainty and sensitivity analysis, Risk analysis, 22(3), 591-622. Herman, J., Reed, P. and Wagener, T., 2013, Time-varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior, Water Resources Research, 49(3), 1400-1414. Hornberger, G. M. and Spear, R. C., 1981, Approach to the preliminary analysis of environmental systems, J. Environ. Mgmt., 12(1), 7-18. Massmann, C. and Holzmann, H., 2012, Analysis of the behavior of a rainfall–runoff model using three global sensitivity analysis methods evaluated at different temporal scales, Journal of Hydrology, 475, 97-110. Merz, R. and Blöschl, G., 2004, Regionalisation of catchment model parameters, Journal of hydrology, 287(1), 95-123. Moradkhani, H. and Sorooshian, S., 2008, General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis, Hydrological modelling and the water cycle, 1-24. Moriasi, D. N., Arnold, G. J., Van Liew, M. W., Bingner, R. L., Harmel, R. D. and Veith, T. L., 2007, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900. Muleta, M. K. and Nicklow, J. W., 2005, Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model, Journal of Hydrology, 306(1), 127-145. Nützmann, G. and Mey, S., 2007, Model-based estimation of runoff changes in a small lowland watershed of north-eastern Germany, Journal of Hydrology, 334(3), 467-476. Ouyang, S., Puhlmann, H., Wang, S., von Wilpert, K. and Sun, O. J., 2014, Parameter uncertainty and identifiability of a conceptual semi-distributed model to simulate hydrological processes in a small headwater catchment in Northwest China, Ecological Processes, 3, 14, https://doi.org/10.1186/s13717-014-0014-9. Pappenberger, F., Beven, K. J., Ratto, M. and Matgen, P., 2008, Multi-method global sensitivity analysis of flood inundation models, Advances in water resources, 31(1), 1-14. Razavi, S., Tolson, B. A. and Burn, D. H., 2012, Review of surrogate modeling in water resources, Water Resources Research, 48, W07401. Rusli, S.R., Yudianto, D. and Liu, J.-t., 2015, Effects of temporal variability on HBV model calibration, Water Science and Engineering, 8(4), 291-300. Saltelli, A., 2002, Making best use of model evaluations to compute sensitivity indices, Computer Physics Communications, 145(2), 280-297. Saltelli, A., Ratto, M., Tarantola, S. and Campolongo, F., 2012, Update 1 of: Sensitivity analysis for chemical models, Chemical Reviews, 112(5), PR1-PR21. Saltelli, A., Tarantola, S., Campolongo, F. and Ratto, M., 2004, Sensitivity analysis in practice: a guide to assessing scientific models, John Wiley & Sons. Saltelli, A., Tarantola, S. and Chan, K.-S., 1999, A quantitative model-independent method for global sensitivity analysis of model output, Technometrics, 41(1), 39-56. Seibert, J. and Vis, M., 2012, Teaching hydrological modeling with a user-friendly catchment-runoff-model software package, Hydrology and Earth System Sciences, 16(9), 3315-3325. Sobol, I. M., 2001, Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Mathematics and computers in simulation, 55(1), 271-280. Song, X., Zhan, C., Xia, J. and Zhang, Y., 2014, Methodology and application of parameter uncertainty quantification in watershed hydrological models, China Water Power Press, Beijing. Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M. and Xu, C., 2015. Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications. Journal of hydrology, 523, 739-757. Spear, R. and Hornberger, G., 1980, Eutrophication in Peel Inlet—II. Identification of critical uncertainties via generalized sensitivity analysis, Water Research, 14(1), 43-49. Tang, T., Reed, P., Wagener, T. and Van Werkhoven, K., 2007, Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrology and Earth System Sciences Discussions, 3(6), 3333-3395. Uhlenbrook, S., Seibert, J., Leibundgut, C. and Rodhe, A., 1999, Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure, Hydrological Sciences Journal, 44(5), 779-797. Van Pelt, S., Kabat, P., Ter Maat, H., Van den Hurk, B. and Weerts, A., 2009, Discharge simulations performed with a hydrological model using bias corrected regional climate model input, Hydrology and Earth System Sciences, 13(12), 2387-2397. Vis, M., Knight, R., Pool, S., Wolfe, W. and Seibert, J., 2015, Model calibration criteria for estimating ecological flow characteristics, Water, 7(5), 2358–2381. Wang, S., McGrath, R., Semmler, T., Sweeney, C. and Nolan, P., 2006, The impact of the climate change on discharge of Suir River Catchment (Ireland) under different climate scenarios, Natural Hazards and Earth System Science, 6(3), 387-395. Yang, J., 2011, Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis, Environmental Modelling & Software, 26(4), 444-457. Ye, M., Meyer, P. D. and Neuman, S. P., 2008, On model selection criteria in multimodel analysis, Water Resources Research, 44, W03428. Zhan, C.-S., Song, X.-M., Xia, J. and Tong, C., 2013, An efficient integrated approach for global sensitivity analysis of hydrological model parameters, Environmental Modelling & Software, 41, 39-52. Zhang, A., Zhang, C., Fu, G., Wang, B., Bao, Z. and Zheng, H., 2012, "Assessments of impacts of climate change and human activities on runoff with SWAT for the Huifa River Basin, Northeast China." Water resources management 26(8), 2199-2217. | ||
|
آمار تعداد مشاهده مقاله: 1,226 تعداد دریافت فایل اصل مقاله: 965 |
||