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مقایسه کارکرد مدل های مفهومی در شبیه سازی هیدرولوژیک رودخانه | ||
| نشریه محیط زیست طبیعی | ||
| مقاله 7، دوره 71، شماره 4، دی 1397، صفحه 509-521 اصل مقاله (1.7 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jne.2018.227408.1339 | ||
| نویسندگان | ||
| مهدی شیخ گودرزی1؛ بهمن جباریان امیری* 1؛ حسین آذرنیوند2 | ||
| 1دانشگاه تهران، دکترای محیط زیست | ||
| 2دکتری، دانشگاه تهران | ||
| چکیده | ||
| مدلهای هیدرولوژیک بارش-رواناب ابزارهای مهمی در پروژه های منابع آبی هستند. بطور کلی کارایی این گروه مدلها وابسته به انتخاب مناسب پارامترهای مدل است. براین اساس روشهای متعددی برای برآورد پارامترهای هیدرولوژیک ابداع شده اند. تحقیق حاضر با هدف مقایسه عملکرد مدلهای هیدرولوژیک ادراکی TANK،SIMHYD و AWBM که از قابلیت برآورد غیر مستقیم پارامترهای مدل بهره می برند، در شبیه سازی جریان دبی حوزه آبخیز بابلرود واقع در استان مازندران شکل گرفت. فرآیند کالیبراسیون خودکار این مدلها با کاربرد الگوریتم جستجوی تکاملی ژنتیک و استفاده از توابع هدف NSE و RMSE به عنوان عوامل تعیین کننده سطح خطا و آستانه های توقف شبیه سازی طراحی شد. در این روال داده های هواشناسی و آب شناسی دما، تبخیر و تعرق، بارش و دبی در مقیاس روزانه از سازمانهای مربوطه تهیه و پس از اعتبارسنجی مقدماتی و ترمیم گپ های موجود به بخشهای تعادل سنجی، آموزش و تست تقسیم بندی شدند. براساس نتایج حاصل، نمایه NSE برای گام آموزش و تست مدل TANK (0,59 تا 0,72)، و نمایه RMSE برای گام آموزش مدل SIMHYD (0,83) و گام تست مدل TANK (0,15) را برترین شبیه ساز معرفی می کند. مطابق با نتایج نمایه های تحلیلی مشخصه های جریان، بطور کلی شبیه سازی های انجام شده در مقادیر دبی کم آبی (به استثنای مدل TANK)، دبی متوسط و دبی پرآبی جریان با تطابق قابل قبولی انجام شده است. این درصورتی است که شبیه سازی شیب منحنی تداوم جریان که به نوعی بیانگر شدت تغییرات است (به استثنای مدل TANK در گام آموزش)، نتایج قابل قبولی ارایه نکرد. لیکن با توجه به ضعف و قوتهای مطرح شده، مدلهای مذکور پس از انجام تست های مقدماتی در شرایط متفاوت اقلیمی کشور، می توانند به عنوان شبیه سازهایی قابل قبول جهت مدیریت منابع آبی خصوصا در حوزه های فاقد اطلاعات آماری مورد استفاده قرار گیرند. | ||
| کلیدواژه‌ها | ||
| پدیده بارش-رواناب؛ شبیه سازی جریان دبی؛ مدلهای هیدرولوژیک مفهومی؛ بهینه سازی خودکار؛ حوزه آبخیز بابلرود | ||
| عنوان مقاله [English] | ||
| Investigating performance of the conceptual models in river hydrologic simulation | ||
| نویسندگان [English] | ||
| Bahman Jabbarian Amiri1؛ | ||
| چکیده [English] | ||
| Rainfall-runoff hydrological models are important tools in water resources projects. Generally, performance of this group of models is dependent on the proper selection of parameters. Accordingly, several methods have been developed to estimate hydrological parameters. The present study aimed to compare the performance of conceptual hydrologic models such as TANK, SIMHYD and AWBM which benefit from the indirect model parameters estimation approach in discharge simulation of Babolroud watershed, Mazandaran province, Iran. The automatic calibration process of these models was designed using genetic evolutionary search algorithm and objective functions (NSE and RMSE) as error thresholds determinants. Hence, meteorological and hydrological data consist of temperature, evapotranspiration, precipitation and discharge (in daily scale) were gathered from authorities. Input data was also divided into warm-up, train and test steps after preliminary validation and recovery. Based on the results, NSE metric introduced TANK model as the best simulator respectively for train and test step (0.59 to 0.72). Depends on RMSE metric, SIMHYD (0.83) and TANK (0.15) models were introduced as the best simulator respectively for train and test step either. According to the catchment flow signatures, general simulation of low-flow (excluding the Model TANK), mean-flow and high-flow were conducted with acceptable agreement. While simulation of the flow duration curve slope which represents an intensity of changes (excluding TANK model in train step), did not provide acceptable results. Given the weaknesses and strengths of the proposed models, they can be used as an acceptable simulator in water resources management especially in terms of ungauged basins, after preliminary verification in different climatic conditions. | ||
| کلیدواژه‌ها [English] | ||
| Rainfall-Runoff Process, Discharge Simulation, Conceptual Hydrological Models, Automatic Optimization, Babolroud Watershed | ||
| مراجع | ||
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Allen, R.G, Pereira, L.S, Raes, D., Smith, M., 1998. Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. Food and Agriculture Organization, Rome, Italy. Behmanesh, J., Jabari, A., Montaseri M., Rezaei, H., 2014. Comparing AWBM and SimHyd models in rainfall-runoff modeling (Case study: Nazlou Chay catchment in west Azarbijan). 24th Year, 52(4). (In Persian). Beven, K., Binley, A., 2014. GLUE: 20 years on. HYDROLOGICAL PROCESSES Hydrol.Process. 28, 5897–5918. Bormann, B., Breuer, L., Giertz, S., Huisman, J.A., Viney, N.R., 2009. Uncertainties in Environmental Modelling and Consequences for Policy Making Part of the series NATO Science for Peace and Security Series C: Environmental Security. Chapter: Spatially explicit versus lumped models in catchment hydrology – experiences from two case studies, 3-26.Boughton, W., 2002. AWBM Catchment Water Balance Model, Calibration and Operation Manual, 30p. Burnash, R.J.C., 1995. The NWS river forecasting-catchment modeling. In: Singh, V.J. (Ed.), Computer Models of Watershed Hydrology. Water Resources Publication, Highlands Ranch, Colorado, 311–366.Burnash, R.J.C., Ferral, R.L., McGuire, R.A., 1973. Joint Federal-State River Forecast Center,. A Generalized Streamflow Simulation System; Conceptual Modeling for Digital Computers. US Dept. of Commerce National Weather Service and State of California Dept. of Water Resources, Sacramento. p. 204.Chai, T., Draxler, R. R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Journal of Geoscience, Model Development 7, 1247–1250. Chen, J., Adams. B.J., 2006. Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. J Hydrol 318, 232-249. Chiew, F.H.S., Peel, M.C., Western, A.W., 2002. Application and testing of the simple rainfall-runoff model SIMHYD. In: Mathematical Models of Watershed Hydrology, Water Resources Publication, and Littleton. Colorado. Collopy, F., Armstrong, J. S., 1992. Rule-based forecasting: development and validation of an expert Systems approach to combining time series extrapolations. Management Science 38, 1394–1414. Downer, C., Ogden, F., Martin, W., Harmon, R., 2002. Theory, development, and applicability of the surface water hydrologic model CASC2D. Hydrol. Process 16(2), 255-275. Duan, Q., 2003. Global optimization for watershed model calibration. In: Calibration of Watershed Models (ed. by Q. Duan, H. V. Gupta, S. Sorooshian, A. N. Rousseau & R. Turcotte), 89–104. Water Science and Application 6, Am. Geophys. Union, Washington DC, USA. Ekenberg, M., 2016. Using a lumped conceptual hydrological model for five different catchments in Sweden. Master’s thesis Physical Geography and Quaternary Geology, University of Stockholm.FAO. 1996. Irrigation and Drainage paper, Guideline for predicting Crop water Requirements. Food and Agriculture Arganization of the United Nations, Rome. 154p. Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Boston, USA. Goodarzi M. R., Zahabiyoun, B., Massah Bavani, A. R., Kamal .A. R., 2012. Performance comparison of three hydrological models SWAT, IHACRES and SIMHYD for the runoff simulation of Gharesou basin. Water and Irrigation Management, 2(1). Spring 2012.( In Persian). Gooijer, J. G D., Hyndman, R. J., 2006. 25 Years of Time Series Forecasting. International journal of forecasting 22, 443-473. Goswami, M., o’Connor, K, M., 2007. Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall—runoff model, Hydrological Sciences Journal 52:3, 432-449. Hashemi, M., Mehrabi, H., 2007. Developing a rainfall-runoff model using GISConference of Geomatics. Haydon, S., Deletic, A., 2007. Sensitivity testing of a coupled Escherichia coli – Hydrologic catchment model. Hydrology 338, 161-173. Heryansyah, A., 2001. Application of tank model on runoff and water quality for land uses management in Cidanau watershed. Master’s Thesis. Bogor Agricultural University. Bogor. Indonesia. Holland, J. H., 1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, USA. Khakbaz, B., Imam, B., Hsu, K., Sorooshian, S., 2012. From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models. Journal of Hydrology 418–419, 61–77. Li, Cz., Hao, W., Jia, L., Yan, Dh.,Yu, Fl., Zhang, Lu., 2010. Effect of calibration data series length on performance and optimal parameters of hydrological model. Water Science and Engineering 3(4), 378-393. Markstrom, S.L., Regan, R.S., Hay, L.E., Viger, R.J., Webb, R.M.T., Payn, R.A., LaFontaine, J.H., 2015. PRMS-IV, the precipitation-runoff modeling system, version 4: U.S. Geological Survey Techniques and Methods, book 6, chap. B7, 158 p. Nash, J. E., Sutcliffe, J. V., 1970. River flow forecasting through conceptual models, Part 1, A discussion of principles. J. Hydrol 10, 282–290. Peel, M.C., .Chiew, F.H.S., Western, A.W., McMahon, TA., 2000. Extension of Unimpaired Monthly Streamflow Data and Regionalisation of Parameter Values to Estimate Streamflow in Ungauged Catchments, Report prepared for the National Land and Water Resources Audit, In Australian Natural Resources Atlas. 37 p. Podger, G., 2005. Rainfall Runoff Library (RRL). Catchment Modeling Toolkit prepared by the © CRC for Catchment Hydrology, Australia. Pp: 110. Reed, S., Koren, V., Smith, M., Zhang, Z., Moreda, F., Seo, D.J., 2004. Overall distributed model intercomparison project results. Journal of Hydrology 298 (1–4), 27–60.Rostami Khalaj, M., Moghadam Nia, A., Salmani, H., Sepahvand, A., 2016. Comparison investigation of Tank, Smar, SIMHYD, Sacramento and AWBM rainfall-runoff model performance. Journal of Natural Ecosystem of IRAN 7(2), 47-63. Rouhani, H., Farahi Moghadam, M., 2013. Automatic calibration of Tank and SIMHYD rainfall-runoff models using genetic alghoritm. Journal of Range and Watershed 66(4), 521-533. Salmani, H., Bahremand, A., Saberchenari, K., Rostmi Khalaj, M., 2014. Investigation of Tank, Sacramento and AWBM rainfall-runoff model performance in simulation of Araz-Kouseh runoff. Journal of ecohydrology 1(3), 207-221 (in Persian). Setiawan, B. I., yanto, R., Ilstedt, U., Malmer, A., 2007. Optimization of Hydrologic Tank Model’s Parameters. Swedish University of Agricultural Sciences, Department of Forest Ecology, Umeå, Sweden. Sharifi, F., Boyd, M.J., 1994. A Comparision of the SFB and AWBM Rainfall-Runoff Models, 25th Congress of the International Assosiation of Hydrologeologists/ International Hydrology & Water Resources Symposium of the Insitution of Engineers, Australia. ADELAIDE. 21-25 November, 491-495. Sheikh Goodarzi, M., 2013. General review of ecosystem based hydrological modeling technics. PhD Seminar, Department of Environment, University of Tehran, Iran. 59p. Sugawara, M., Watanabe, I., Ozaki, E., Katsuyama, Y., 1984. Tank Model with Snow Component. Research note no, 65. National Research Center for Disaster Preventation, Japan. 293p. Tahmasebi, R., Sharifi, F., Kaveh, F., Tavassoli, A., 2010. Designing of Rainwater Collecting Systems in Micro Catchment by Using AWBM Model for Cultivating of Forage Maize SC704. Journal of Range and Watershed Management, Iranian Journal of Natural Resources 63(3), 359—373 (In Persian). USGS. 2008. Calculating Flow-Duration and Low-Flow Frequency Statistics at Streamflow-Gaging Stations. U.S. Department of the Interior, Scientific investigation report 2008-5126.Verstraeten, W.W., Muys, B., Feyen, J., Veroustraete, F., Minnaert, M., Meiresonne, L., De Schrijver, A., 2005. Comparative analysis of the actual evapotranspiration of Flemish forest and cropland, using the soil water balance model WAVE. Hydrol Earth Syst Sci 9, 225–241. Viglione, A., Parajka, J., Rogger, M., Salinas, J. L., Laaha, G., Sivapalan, M., Bl¨oschl, G., 2013. Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria. Journal of Hydrol. Earth Syst. Sci 17, 2263–2279. Willmott , C., Matsuura, K., 2005. Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) inassessingaveragemodelperformance,Clim.Res 30,79–82. Yokoo, Y., Kazama, S., Sawamoto, M., Nishimura, H., 2001. Regionalization of lumped water balance model parameters based on multiple regression. J Hydrol 246, 209-222. Zarin, H., Moghaddamnia, A.R., Nam Dorost, J., Mosaedi, A., 2013. Simulation of outlet runoff in ungauged catchments by using AWBM Rainfall-Runoff Model. J. of Water and Soil Conservation 20(2), (In Persian). | ||
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