تحلیل عدم قطعیت پارامترها براي شبیه سازي جریان رودخانه با کاربرد روشGLUE
محورهای موضوعی :
مریم شفیعی
1
(دانشگاه تهران)
Javad Bazrafshan
2
(دانشگاه تهران)
پرویز ایران نژاد
3
(دانشگاه تهران)
کلید واژه: تحلیل عدم¬, قطعیت روش GLUE مدل جفت¬, شده جریان رودخانه حوضه کرخه,
چکیده مقاله :
در سال های اخیر تعیین عدم قطعیت مدل های هیدرولوژیکی در تحقیقات هیدرولوژی بسیار موردتوجه قرار گرفته است. هرچند پارامترهای مدل هیدرولوژیکی معمولا به وسیله واسنجی تعیین می شوند ولی عدم قطعیت قابل توجه در پارامترهای مدل وجود دارد. در این مطالعه با هدف واسنجی و تحلیل عدم قطعیت مدل جفت شده ALSIS-HBV در حوضه کرخه از روش GLUE استفاده شده است. تعیین منابع اصلی عدم قطعیت و شناسایی پارامترها، همچنین برآورد میزان عدم قطعیت در نتایج شبیه سازی جریان رودخانه از اهداف دیگر این پژوهش است. یافته ها نشان می دهد پارامترهای K2، Fcap، MaxBas، lp، B، degw و ttlim منابع اصلی عدم قطعیت جریان رودخانه و پارامترهای K2، Fcap،B ، lp و perc قابل شناسایی ترین پارامترها هستند. به طور کلی ارزیابی عملکرد مدل برای شبیه سازی جریان رودخانه در حوضه کرخه به ویژه در دوره واسنجی خوب و قابل قبول است و بیشتر داده های مشاهداتی جریان رودخانه در محدوده 95 درصدی فاصله اطمینان قرار دارند، بنابراین توزیع احتمال محاسبه شده از جریان رودخانه می تواند برای پیش بینی جریان رودخانه به کار رود.
In recent years, the determination of the uncertainty of hydrological models in the hydrological research has been very much taken into consideration. Although the parameters of the hydrological model are usually determined by calibration, but there is considerable uncertainty in the model parameters. In this study, with the aim of calibration and uncertainty analysis of coupled model ALSIS-HBV, GLUE method has been used in the Karkheh basin. Determining the main source of uncertainty and identification of parameters, as well as estimating uncertainty in the results of river flow simulation are other goals of this research. The findings show that K2, Fcap, MaxBas, lp, B, degw and ttlim parameters are main sources of river flow uncertainty, and the parameters K2, Fcap, B, lp and perc are the most identifiable parameters. In general, the model's performance evaluation for river flow simulation in karkheh basin, especially in calibration period is good and acceptable, and most river flow observations are in 95% confidence intervals, Therefore, the probability distribution calculated from the river flow can be used to predict the flow of the river.
1. Abbaspour, K. C., Johnson C. A. and Van Genuchten, M. T. (2004), Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J, 3(4),1340–1352;
2. Abbaspour, K.C. (2014), SWAT-CUP 2012: SWAT Calibration and Uncertainty Programs—A User Manual, Swiss Federal Institute of Aquatic Science and Technology;
3. Ajami, N. K., Duan, Q. Y. and Sorooshian, S. (2007), An integrated hydrologic Bayesian multimodal combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour Res, 43 (1), Art. No. W01403;
4. 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;
5. Alduchov, O.A. and Eskridge, R.E. (1996), Improved Magnus form approximation of saturation vapor pressure, Journal of Applied Meteorology, 35(4), 601-609;
6. 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, Green, T. R. and Kralisch, S. (2015), The model optimization, uncertainty, and sensitivity analysis (MOUSE) Toolbox: overview and application, In: Ramirez, JA (Ed.), Proc. Hydrology Days 2015, March 23-25, Fort Collins, Colorado. Colorado State University, Fort Collins, Colorado;
7. Bergstrom, S. (1995), The HBV model, Computer models of watershed hydrology;
8. Beven, K. and Binley, A. (1992), The future of distributed models: Model calibration and uncertainty prediction, Hydrol Process, 6, 279–298;
9. Driessen, T., Hurkmans, R., Terink, W., Hazenberg, P., Torfs, P. 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;
10. Engeland, K., Xu, C. Y. and Gottschalk, L. (2005), Assessing uncertainties in a conceptual water balance model using Bayesian methodology, Hydrol Sci J, 50(1),45–63;
11. 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;
12. 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;
13. Hansen, M., DeFries, R., Townshend, J.R. and Sohlberg, R. (1981), UMD global land cover classification, 8 kilometers, 1.0. Department of Geography, University of Maryland, College Park, Maryland, 1994: 1998;
14. Hughes, D. A., Kapangaziwiri, E. and Sawunyama, T. (2010), Hydrological model uncertainty assessment in southern Africa, J Hydrol, 378, 221– 232;
15. Irannejad, P. and Shao, Y. (1996), The atmosphere-land-surface interaction scheme: Description and validation, Centre for Advanced Numerical Computation in Engineering and Science, The University of New South wales, Tech. Note 2, 31 pp;
16. Irannejad, P. and Shao, Y. (1998), Description and validation of the atmosphere- land-surface interaction scheme (ALSIS) with HAPEX and Cabauw data, Global. Planet. Change, 19, 87-114;
17. Jin, X., Xu, C. Y., Zhang, Q. and Singh, V.P. (2010), Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model, Journal of Hydrology, 383, 147–155;
18. Mirzaei, M., Huang, YF., El-Shafie, A. and Shatirah, A. (2015), Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: a review, Stoch Environ Res Risk Assess, 29,1265–1273, DOI 10.1007/s00477-014-1000-6;
19. Moriasi, D. N., Arnold, J. G., 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;
20. Quan, Z., Teng, J., Sun, W., Cheng, T. and Zhang, J. (2015), Evaluation of the HYMOD Model for Rainfall-Runoff Simulation Using the GLUE Method, Proceedings of the International Association of Hydrological Sciences, 368, 180-185;
21. Razavi, S., Tolson, B. A. and Burn, D. H. (2012), Review of surrogate modeling in water resources, Water Resources Research, 48(7);
22. Refsgaard, J. C. and Storm, B. (1995), MIKE SHE. In: Miller PC (ed) Computer Models of Catchment Hydrology, Water Resources Publications, Colorado, pp 809–846;
23. Shen, Z.Y., Chen, L. and Chen, T. (2012), Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to Three Gorges Reservoir Region, China, Hydrol. Earth Syst. Sci, 16, 121–132;
24. Shrestha, D., Bastola, S. and Babel, M. S. (2007), The assessment of spatial and temporal transferability of a physically based distributed hydrological model parameters in different physiographic regions of Nepal, J Hydrol, 347, 153–172;
25. Shrestha, D. (2009), Uncertainty Analysis in Rainfall-Runoff Modelling: Application of Machine Learning Techniques (Doctoral Dissertation), Delft University of Technology and of the Academic Board of UNESCO-IHE Institute for Water, Netherlands;
26. Singh, S. K. and Bardossy, A. (2012), Calibration of hydrological models on hydrologically unusual events, Adv Water Resour, 38, 81–91;
27. 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;
28. 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;
29. Wieder, W., Boehnert, J., Bonan, G. and Langseth, M. (2014), Regridded Harmonized World Soil Database v1. 2, Data set. Available on-line [http://daac. ornl. gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA;
30. Xue, C., Chen, B., Wu, H. (2014), Parameter uncertainty analysis of surface flow and sediment yield in the Huolin Basin, China. J. Hydrol. Eng. 19 (6), 1224–1236;
31. Yang, J., Reichert, P., Abbaspour, K. C. and Yang, H. (2007), Hydrological modelling of the Chaohe Basin in China: statistical model formulation and Bayesian inference, J Hydrol, 340, 167–182;
32. Yang, J., Reichert, P., Abbaspour, K.C., Xia, J. and Yang, H. (2008), Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China, J. Hydrol, 358, 1–23;
33. Ye, M., Meyer, P. D. and Neuman, S. P. (2008), On model selection criteria in multimodel analysis, Water Resources Research, 44(3);
34. Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S., Nemani, R.R. and Myneni, R.B. (2013), Global data sets of vegetation leaf area index (LAI) 3g and Fraction of Photosynthetically Active Radiation (FPAR) 3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011, Remote Sensing, 5(2), 927-948.
