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  • بررسی مقایسه ای کارایی مدل‌های بارش رواناب AWBM، Sacramento، SimHyd، SMAR و Tank

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کد مقاله : 527634 بازدید : 210 صفحه: 47 - 63

نوع مقاله: پژوهشی

بررسی مقایسه ای کارایی مدل‌های بارش رواناب AWBM، Sacramento، SimHyd، SMAR و Tank

محورهای موضوعی : جنگلداری

محمد رستمی خلج 1 , علیرضا مقدم نیا 2 , حسین سلمانی 3 , علیرضا سپهوند 4

1 - دانشجوی دکتری، دانشگاه تهران، پردیس کشاورزی و منابع طبیعی، کرج، ایران
2 - دانشیار، دانشگاه تهران، پردیس کشاورزی و منابع طبیعی، کرج، ایران
3 - دانشجوی دکتری، دانشگاه گرگان، دانشکده منابع طبیعی، گرگان، ایران
4 - دانشجوی دکتری، دانشگاه تهران، پردیس کشاورزی و منابع طبیعی، کرج، ایران،

تاریخ دریافت : 1394/09/26 تاریخ پذیرش : 1395/04/13 تاریخ انتشار : 1395/06/01

کلید واژه: مدل مفهومی, حوضه نوده, بسته نرم افزاری RRL, مدل AWBM, مدل SMA,

چکیده مقاله :

مدل های بارش- رواناب یکی از ابزارهای مهم در مدیریت حوزه آبخیز می‌باشند. فرآیندهای هیدرولوژیکی مختلفی از جمله: نفوذ، مقادیر ذخیره خاک، جریان زیر قشری و ذخیره آب زیرزمینی در مدل های بارش- رواناب برای شبیه سازی رواناب در نظر گرفته می شوند. به دلیل اینکه امکان اندازه گیری تمام کمیت‌های مورد نیاز جهت بررسی عکس‌العمل حوضه میسر نمی باشد، بررسی کارایی مدل هایی که بتوانند در عین سادگی ساختار و با استفاده از حداقل ورودی، پیش بینی قابل قبولی را از فرایندهای هیدرولوژیکی ارائه کنند امری ضروری به نظر می رسد. لذا در این مطالعه به بررسی مقایسه ای کارایی مدل های بارش رواناب AWBM، Sacramento، SimHyd، SMAR و Tank برای شبیه سازی رواناب خروجی از حوزه آبخیز نوده در استان گلستان پرداخته شده است. این مدل ها از نوع مدل های مفهومی یکپارچه می باشند که در بسته نرم افزاری RRL به همراه هشت بهینه ساز کالیبراسیون موجود است که ورودی های این مدل ها شامل مقادیر روزانه بارش، مقادیر تبخیر و تعرق پتانسیل روزانه و دبی خروجی از حوضه می باشد. نتایج نشان داد بهینه ساز جستجوی مستقیم در بین سایر بهینه سازها نتایج بهتری ارائه می‌کند. مدل AWBM با ضریب ناش 71/0 برای واسنجی و 63/0 برای دوره ارزیابی بهترین کارایی را در بین مدل‌های دارد و مدل SMAR با ضریب ناش 417/0 و 338/0 به ترتیب برای دوره های واسنجی و ارزیابی پایین‌ترین کارایی را داشت است. همچنین مدل‌های بررسی شده توانایی شبیه سازی مقادیر کمینه و بیشینه را نداشته‌اند اما مقادیر متوسط را بطور قابل قبولی شبیه سازی می کنند از آنجایی که این مدل ها نیاز به داده های ورودی زیادی ندارند و استفاده از آن‌ها نیاز به صرف وقت و هزینه زیادی ندارد می توان از این مدل ها با توجه به نیاز در مدیریت منابع آب استفاده کرد.

چکیده انگلیسی:

Abstract Rainfall Runoff models is an important tool in the study of water management of the watersheds. Different hydrologic processes such as: infiltration, soil storage, percolation, and groundwater storage are considered to simulate runoff from the catchment in Rainfall Runoff model. Because the possibility of measurement all parameters needed to evaluate watershed response is impossible, evaluate performance of a model with simple structure and using minimal input that can provide an acceptable prediction of hydrological processes, is too necessary. Purpose of this study is compare the performance of AWBM, Sacramento, SimHyd, SMAR and Tank rainfall - runoff models to simulate runoff from the Nodeh watershed in Golestan Province. The types of these models are conceptual lumped models that are in RRL with 8 calibration optimisers, the model inputs include daily precipitation, daily evapotranspiration and daily runoff values of the basin. Results showed that pattern search among other calibration optimisers provide better results. AWBM model with Nash coefficient of  0.71 for calibration and 0.63 for the evaluation have best performance among the models and the SMAR model with Nash coefficient of 0.417 and 0.338 respectively for calibration and evaluation periods among models that considered in this study have lowest performance. Also investigated models are not ability to simulate minimum and maximum values but the average values are acceptable simulation, since this model does not require much input data and their use does not require time-consuming and costly these models can be used according to the needs in water resources management.

منابع و مأخذ:

 

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  2. Bashar, K., 2012. Comparative Performance of Soil Moisture Accounting Approach in Continuous Hydrologic Simulation of the Blue Nile. Nile Basin Water Science & Engineering Journal,.5:2. 10 p.
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  5. Chen, J & B.J, Adams. 2006. Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. J Hydrol 318: 232-249.
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  6. Chiew F.H.S., M.C. Peel. & A.W. Western. 2002. Application and testing of the simple rainfall-runoff model SIMHYD. In: Mathematical Models of Watershed Hydrology, Water Resources Publication, and Littleton. Colorado.
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  9. 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)
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  10.  Hashemi .M & H. Mehrabi. 2007. Developing a rainfall-runoff model using GISConference of Geomatics.
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  11. Haydon S.& A Deletic. 2007. Sensitivity testing of a coupled Escherichia coli – Hydrologic catchment model. Hydrology. 338: 161-173.
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  12. Jones Roger N., Hs. Chiew Francis., C. Boughton Walter & L.u. Zhang. 2006. Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models. Advances in Water Resources، 29(10) : 1419-1429.
    13.
  13. Kamal .A.R.  & A.R .Massah Bavani. 2010. Climate Change and Variability Impact in Basin’s Runoff with Interference of Tow Hydrology Models Uncertainty. Journal of Water and Soil Vol. 24, No. 5, Nov-Des 2010, p. 920-931. In Persian
    14.
  14. Li Cz., Hao. Wang., Jia. Liu ., Dh. Yan ., Fl. Yu & Lu .Zhang. 2010. Effect of calibration data series length on performance and optimal parameters of hydrological model. Water Science and Engineering، 3(4): 378-393.
    15.
  15. Lowry, B. 2005. Evapotranspiration estimation methods for Sacramento Soil Moisture Accounting model streamflow prediction. Msc thesis university of New Hampshire.
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  16. Mahdavi,M. 2006. Applied Hydrology. First volume. University of Tehran. In Persian.
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  17. Mohammadi Ghaleni . M. & K .Ebrahimi.. 2012 .Evaluation of direct search and genetic algorithms in optimization of muskingum nonlinear model parameters - a flooding of Karoun river, Iran.  Water and Irrigation Management, Vol. 2, No. 2, autumn 2012. (In Persian).
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  18. Moreda, F., 1999. Conceptual rainfall-runoff models for different time steps with special consideration for semi-arid and arid catchments. Laboratory of Hydrology and Inter-University Program in Water Resources Engineering, Vrije Universiteit Brussels (VUB).
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  20. NOAA Technical Report NWS 53. 2007. Physically-Based Modifications to the Sacramento Soil Moisture Accounting Model: Modeling the Effects of Frozen Ground on the Rainfall-Runoff Process. U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration National Weather Service.
    21.
  21. NOAA Technical Report NWS 53. 2010. Modification of Sacramento Soil Moisture Accounting Heat Transfer Component (SAC-HT) for Enhanced Evapotranspiration. U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration National Weather Service.
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  22. O'Connell, P.E., J.E .Nash & J.P. Farrell. 1970. River flow forecasting through conceptual models, Part II . J. hydrol. 10: 317-329.
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  23. Peel MC, FHS .Chiew., AW. Western & TA. McMahon .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.
    24.
  24. Sanaei nia,QH, 2000.  Evaluation of the simulation model AWBM (rainfall runoff). A thesis submitted for Master of Science at Irrigation and drainage, Islamic Azad University, science and research branch. (In Persian).
    25.
  25. Sharifi, F& M.J. Boyd. 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, Pp: 491-495.
    26.
  26. sharifi,F. safarpoor .S & S.A. Ayubzadeh,. 2004. Evaluation of AWBM 2002 Simulation Model in 6 Iranian Representative Catchments. Pajouhesh & Sazandegi No: 63:35-42. (In Persian).
    27.
  27. Sugawara M. 1995. TANK model. In: Singh VP (ed). Cumputer models of watershed hydrology. Water Resources Publication, Littleton, Colorado. pp 177-189.
    28.
  28. Tahmasebi .R., F. Sharifi ., F .Kaveh & A.  Tavassoli. 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).
    29.
  29. Tingsanchali, T & , M.R. Gautam., 2000. Application of tank, NAM, ARMA and neural network models to flood forecasting. Hydrological Processes, 14(14): 2473-2487.
    30.
  30. Vrugt J. A., V .Gupta Hoshin ., C .Dekker Stefan ., S .Sorooshian & T.B.W. Wagener. 2006. Application of stochastic parameter optimization to the Sacramento soil moisture accounting model. Journal of Hydrology، 325(1): 288-307.
    31.

 

  1. Vrugt, J.A., H.V. Gupta., W. Bouten., S. Sorooshian. 2003. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res.39 (8), 1201. doi: 10.1029/2002 WR001642
    2.

 

 

  1.  Vrugt J. A., V .Gupta Hoshin ., C .Dekker Stefan ., S .Sorooshian & T.B.W. Wagener. 2006. Application of stochastic parameter optimization to the Sacramento soil moisture accounting model. Journal of Hydrology، 325(1): 288-307.
    2.

 

  1. Vrugt, J.A., H.V. Gupta., W. Bouten., S. Sorooshian. 2003. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res.39 (8), 1201. doi: 10.1029/2002 WR001642
    2.
  2.  Yapo, P.O., H.V. Gupta., S .Sorooshian. 1996. Calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J. Hydrol. 181, 23–48.
    3.
  3.  Yokoo Y, S. Kazama., M. Sawamoto & H. Nishimura. 2001. Regionalization of lumped water balance model parameters based on multiple regression. J Hydrol 246: 209-222.
    4.
  4.  Yue S, & M. Hashino, 2000. Unit hydrographs to model quick and slow runoff components streamflow. J Hydrol 227: 195-206.
    5.
  5. Zarin,H., A.R Moghaddamnia., J. Nam Dorost & A .Mosaedi . 2013. Simulation of outlet runoff in ungauged catchments by using AWBM Rainfall-Runoff Model. J. of Water and Soil Conservation,. 20(2).( In Persian).
    6.

_||_

 

  1. Audet, C & Dennis Jr, J.E., 2002. Analysis of generalized pattern searches. SIAM Journal on Optimization, 13(3): 889-903.
    2.
  2. Bashar, K., 2012. Comparative Performance of Soil Moisture Accounting Approach in Continuous Hydrologic Simulation of the Blue Nile. Nile Basin Water Science & Engineering Journal,.5:2. 10 p.
    3.
  3. Behmanesh, J., A. Jabari, M. Montaseri & H. Rezaei 2014. Comparing AWBM and SimHyd models in rainfall-runoff modeling (Case study: Nazlou Chay catchment in west Azarbijan). 24th Year, 52(4). (In Persian).
    4.
  4. Boughton, W. 2002. AWBM Catchment Water Balance Model, Calibration and Operation Manual, 30p.
    5.
  5. Chen, J & B.J, Adams. 2006. Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. J Hydrol 318: 232-249.
    6.
  6. Chiew F.H.S., M.C. Peel. & A.W. Western. 2002. Application and testing of the simple rainfall-runoff model SIMHYD. In: Mathematical Models of Watershed Hydrology, Water Resources Publication, and Littleton. Colorado.
    7.
  7. Clarke, R.T. 1994,” Statistical Modelling in Hydrology”, John Wiley and Sons.
    8.
  8. Dovonec. E. 2000. A physically base distributed hydrologic model, Msc Thesis, the Pennsylvania State University.
    9.
  9. 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)
    10.
  10.  Hashemi .M & H. Mehrabi. 2007. Developing a rainfall-runoff model using GISConference of Geomatics.
    11.
  11. Haydon S.& A Deletic. 2007. Sensitivity testing of a coupled Escherichia coli – Hydrologic catchment model. Hydrology. 338: 161-173.
    12.
  12. Jones Roger N., Hs. Chiew Francis., C. Boughton Walter & L.u. Zhang. 2006. Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models. Advances in Water Resources، 29(10) : 1419-1429.
    13.
  13. Kamal .A.R.  & A.R .Massah Bavani. 2010. Climate Change and Variability Impact in Basin’s Runoff with Interference of Tow Hydrology Models Uncertainty. Journal of Water and Soil Vol. 24, No. 5, Nov-Des 2010, p. 920-931. In Persian
    14.
  14. Li Cz., Hao. Wang., Jia. Liu ., Dh. Yan ., Fl. Yu & Lu .Zhang. 2010. Effect of calibration data series length on performance and optimal parameters of hydrological model. Water Science and Engineering، 3(4): 378-393.
    15.
  15. Lowry, B. 2005. Evapotranspiration estimation methods for Sacramento Soil Moisture Accounting model streamflow prediction. Msc thesis university of New Hampshire.
    16.
  16. Mahdavi,M. 2006. Applied Hydrology. First volume. University of Tehran. In Persian.
    17.
  17. Mohammadi Ghaleni . M. & K .Ebrahimi.. 2012 .Evaluation of direct search and genetic algorithms in optimization of muskingum nonlinear model parameters - a flooding of Karoun river, Iran.  Water and Irrigation Management, Vol. 2, No. 2, autumn 2012. (In Persian).
    18.
  18. Moreda, F., 1999. Conceptual rainfall-runoff models for different time steps with special consideration for semi-arid and arid catchments. Laboratory of Hydrology and Inter-University Program in Water Resources Engineering, Vrije Universiteit Brussels (VUB).
    19.
  19. National Weather Service .2002. “II.3-SAC-SMA, Conceptualization of the Sacramento Soil Moisture Accounting Model”, rfs:23sacsma.wpd, [online], April 12, 2002.
    20.
  20. NOAA Technical Report NWS 53. 2007. Physically-Based Modifications to the Sacramento Soil Moisture Accounting Model: Modeling the Effects of Frozen Ground on the Rainfall-Runoff Process. U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration National Weather Service.
    21.
  21. NOAA Technical Report NWS 53. 2010. Modification of Sacramento Soil Moisture Accounting Heat Transfer Component (SAC-HT) for Enhanced Evapotranspiration. U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration National Weather Service.
    22.
  22. O'Connell, P.E., J.E .Nash & J.P. Farrell. 1970. River flow forecasting through conceptual models, Part II . J. hydrol. 10: 317-329.
    23.
  23. Peel MC, FHS .Chiew., AW. Western & TA. McMahon .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.
    24.
  24. Sanaei nia,QH, 2000.  Evaluation of the simulation model AWBM (rainfall runoff). A thesis submitted for Master of Science at Irrigation and drainage, Islamic Azad University, science and research branch. (In Persian).
    25.
  25. Sharifi, F& M.J. Boyd. 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, Pp: 491-495.
    26.
  26. sharifi,F. safarpoor .S & S.A. Ayubzadeh,. 2004. Evaluation of AWBM 2002 Simulation Model in 6 Iranian Representative Catchments. Pajouhesh & Sazandegi No: 63:35-42. (In Persian).
    27.
  27. Sugawara M. 1995. TANK model. In: Singh VP (ed). Cumputer models of watershed hydrology. Water Resources Publication, Littleton, Colorado. pp 177-189.
    28.
  28. Tahmasebi .R., F. Sharifi ., F .Kaveh & A.  Tavassoli. 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).
    29.
  29. Tingsanchali, T & , M.R. Gautam., 2000. Application of tank, NAM, ARMA and neural network models to flood forecasting. Hydrological Processes, 14(14): 2473-2487.
    30.
  30. Vrugt J. A., V .Gupta Hoshin ., C .Dekker Stefan ., S .Sorooshian & T.B.W. Wagener. 2006. Application of stochastic parameter optimization to the Sacramento soil moisture accounting model. Journal of Hydrology، 325(1): 288-307.
    31.

 

  1. Vrugt, J.A., H.V. Gupta., W. Bouten., S. Sorooshian. 2003. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res.39 (8), 1201. doi: 10.1029/2002 WR001642
    2.

 

 

  1.  Vrugt J. A., V .Gupta Hoshin ., C .Dekker Stefan ., S .Sorooshian & T.B.W. Wagener. 2006. Application of stochastic parameter optimization to the Sacramento soil moisture accounting model. Journal of Hydrology، 325(1): 288-307.
    2.

 

  1. Vrugt, J.A., H.V. Gupta., W. Bouten., S. Sorooshian. 2003. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res.39 (8), 1201. doi: 10.1029/2002 WR001642
    2.
  2.  Yapo, P.O., H.V. Gupta., S .Sorooshian. 1996. Calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J. Hydrol. 181, 23–48.
    3.
  3.  Yokoo Y, S. Kazama., M. Sawamoto & H. Nishimura. 2001. Regionalization of lumped water balance model parameters based on multiple regression. J Hydrol 246: 209-222.
    4.
  4.  Yue S, & M. Hashino, 2000. Unit hydrographs to model quick and slow runoff components streamflow. J Hydrol 227: 195-206.
    5.
  5. Zarin,H., A.R Moghaddamnia., J. Nam Dorost & A .Mosaedi . 2013. Simulation of outlet runoff in ungauged catchments by using AWBM Rainfall-Runoff Model. J. of Water and Soil Conservation,. 20(2).( In Persian).
    6.


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