Evaluation of the Efficiency of AWBM, Sacramento and Tank Rainfall Runoff Model in Runoff Simulation in Arazkoose - Goorganrood Basin, Golestan porovince

Document Type : Research Article

Authors

1 PhD Student, Gorgan University of agricultural sciences and natural resources, Gorgan

2 Gorgan University of agricultural sciences and natural resources, Gorgan

3 PhD Student, Faculty of Natural Resources, University of Tehran, Karaj

Abstract

According to variety of accessible hydrological models, evaluateing the efficiency of models for different management objectives are essential. Therefore, models with simple structure and using minimal input that can provide an acceptable result can serve as an effective tool to watershed managers. Hence, the objective of this research is to evaluate the relative performances of the lumped conceptual-based hydrological AWBM, Sacramento and Tank rainfall runoff models for daily simulation in Arazkoose tributary of Goorganrood River, Golestan porovince. Automatic calibration of the models were done for a 5-year period (1984-1988), followed by a 4-year validation period (1992-1995). The results were evaluated by several criteria, i.e. Root Mean Square Error (RMSE) and Nash coefficient efficiency coefficient (ENS) and the coefficient of determination (R2). According to the evaluation criteria the results were satisfactiory. Statistic and graphic results show that Sacramento model is performing most accurately among the models with R2= 0.823, ENS= 0.677 and RMSE= 4.565 in calibration periods and R2= 0.719, ENS= 0.669 and RMSE= 7.905 in validation periods. Simulation of the Models was more accurate for calibration period rather than validation period. Also the models’ minimum and average values of runoff are accurate and despite good simulated peak flow values, the results show that the models cannot accurately simulate all of the peak values. Since these models do not require much input data and their use is not time-consuming and costly, they can be preferred for surface water resources management.

Keywords

Main Subjects

References

  1. برهانی داریان، علیرضا؛ فرهمندفر، زینب، 1390، کالیبراسیون مدل‌های بارش- رواناب به‌کمک الگوریتم‌های فراکاوشی، فصلنامۀ آبیاری و آب، سال اول، شمارۀ 4: 71-60.
  2. بهمنش، جواد؛ جباری، آناهیتا؛ منتصری، مجید؛ رضایی، حسین، 1392، مقایسۀ مدل‌های AWBM و SimHyd در مدل‌سازی بارش- رواناب (مطالعۀ موردی: حوضۀ آبریز نازلوچای استان آذربایجان غربی). مجلۀ جغرافیا و برنامه‌ریزی محیطی. 52(4) : 168-155.
  3. روحانی، حامد؛ فراهی مقدم، محسن، 1392، واسنجی خودکار دو مدل بارش- رواناب تانک و SIMHYD با استفاده از الگوریتم ژنتیک. نشریۀ مرتع و آبخیزداری. 66(4) : 533-521.
  4. زرین، هدایت‌الله؛ مقدم‌نیا، علیرضا؛ نام‌درست، جواد؛ مساعدی، ابوالفضل، 1392، شبیه‌سازی رواناب خروجی در حوزه‌های آبخیز بدون آمار با استفاده از مدل بارش-رواناب  AWBM(مطالعۀ موردی: استان سیستان و بلوچستان). مجلۀ پژوهش‌های حفاظت آب و خاک، (20)2: 208-195.
  5. سلمانی، حسین، 1390، بهینه‌سازی پارامترهای مؤثر در بارش – رواناب در مدل نیمه‌توزیعی SWAT(مطالعۀ موردی زیرحوزۀ قزاقلی حوزۀ گرگان‌رود استان گلستان)، پایان‌نامۀ کارشناسی ارشد. دانشکدۀ منابع طبیعی. دانشگاه تهران. 158 صفحه.
  6. شریفی، فرود؛ صفار‌پور، شبنم؛ ایوب‌زاده، علی؛ وکیل‌پور، جعفر. 1383.بررسی عوامل مؤثر در تعیین آستانۀ شروع رواناب در مناطق خشک و نیمه‌خشک کشور به‌کمک استفاده از شبیه‌سازی و داده‌های بارش- رواناب. مجلۀ منابع طبیعی ایران، (1) 57: 45-33.
  7. گودرزی، محمد‌رضا؛ ذهبیون، باقر؛ مساح بوانی، علیرضا؛ کمال، علیرضا، 1391، مقایسۀ عملکرد سه مدل هیدرولوژی SWAT، IHACRES و SimHyd در شبیه‌سازی رواناب حوزۀ قره‌سو. مدیریت آب و آبیاری، (1) 2: 40-25.
  8. محمدی قلعه­نی، مهدی و ابراهیمی، کیومرث. 1391، ارزیابی الگوریتم‌های جستجوی مستقیم و ژنتیک در بهینه‌سازی پارامترهای مدل غیرخطی ماسکینگام- یک سیلاب از کارون، مدیریت آب و آبیاری، 2(2) : 12-1.
  9. هاشمی، مهدی؛ مهرابی، حمید، 1386، توسعۀ مدل بارش - رواناب با استفاده از GIS. همایش ژئوماتیک. 9 ص.
    1. Audet, C., and Dennis Jr, J.E., 2002. Analysis of generalized pattern searches. SIAM Journal on Optimization, 13(3): 889-903.
    2. Beven, K.J., 2001. Rainfall- Runoff modeling, the primer, Wiley, Chichester, UK: 361pp.
    3. Boughton, W. 2002. AWBM Catchment Water Balance Model. Calibration and Operation Manual: 30p.
    4. Chen, J. and Adams, B.J., 2006. Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. Journal of hydrology. 318: 232-249.
    5. Divya, B., and Ashu, J., 2010, Comparison of Various Optimization Methods for Calibration of Conceptual Rainfall-Runoff Models, EGU General Assembly: p.9463.
    6. Gassman, P.W., Reyes, M.R., Green, C.H. and Arnold, J.G., 2007. The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions. Trans. ASABE: 50(4): 1211-1250.
    7. Lowry, B., 2005. Evapotranspiration estimation methods for Sacramento Soil Moisture Accounting model streamflow prediction. Msc thesis university of New Hampshire.
    8. Nash, J.E. and Sutcliffe, J. 1970. River flow forecasting through conceptual models, Part 1, A discussion of principles. Journal of Hydrology. 10: 282–290.
    9. Rostamian, R., Jaleha, A., Afyunia, M., Mousavian, S.F., Heidarpour, M., Jalalian, A. and Abbaspour, K.C., 2010. Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrological Sciences Journal: 53(5): 977 – 988.
    10. Sanaeeiniya, Gh. 2000. Evaluation of AWBM Rainfall-Runoff Simulation model, M.Sc. Thesis of Irrigation and Drainage, Islamic Azad University, 145p.
    11. Santhi C., Arnold JG., Williams JR., Dugas WA., Srinivasan R. and Hauck LM. 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources. J. American Water Resour. 37:5: 1169-1188.
    12. Sharifi, F., and Boyd, M.J. 1994. A Comparison of the SFB and AWBM Rainfall-Runoff Models, 25th Congress of the International Association of Hydrologeologists/ International Hydrology & Water Resources Symposium of the Institution of Engineers, Australia. ADELAIDE. 21-25 November: 491-495.
    13. Spruill, C.A., Workman, S.R. and Taraba, J.L., 2000.Simulation of daily and monthly stream discharge from small watershed using the SWAT model. Soil and Water Division of ASAE: No.98-05-109: 1431-1440.
    14. Sugawara, M., 1995. Tank model. In: Singh VP (Ed). Computer models of watershed hydrology. Water Resources Publication, Littleton, Colorado. 177-189.
    15. Vaze, J., Chiew, F.H.S., Perraud, J.M., Viney, N., Post, D., Teng, J., Wang, B. and Goswami, M., 2010. Rainfall-runoff modeling across southeast Australia: Datasets, models and results, Australian Journal of Water Resources. 14(2): 101-116.
    16. Vrugt, J.A., Gupta, H.V., Dekker S.C., Sorooshian S. and Wagener T.B.W., 2006. Application of stochastic parameter optimization to the Sacramento soil moisture accounting model. Journal of Hydrolog. 325(1): 288-307.
    17. Wang, G., Zhang, J., and He, R., 2007, Comparison of hydrological models in the middle reach of the Yellow River. Methodology in hydrology: 158-163.
    18. Willems, P., 2009. A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models. Environmental Modeling Software 24(3), 311-321.
    19. Yue, S., and Hashino, M., 2000. Unit hydrographs to model quick and slow runoff components streamflow. Journal of Hydrology. 227: 195-206.
Iranian journal of Ecohydrology
Volume 1, Issue 3 - Serial Number 3
January 2015
Pages 207-221

Files

History

  • Receive Date: 06 January 2015
  • Revise Date: 24 January 2015
  • Accept Date: 02 March 2015
  • First Publish Date: 02 March 2015
  • Publish Date: 22 December 2014

Share

How to cite

Statistics