تحلیل روند و سری زمانی تبخیر و تعرق مرجع (مطالعه موردی: دشت خرم‌آباد).

نویسندگان
یاسر سبزواری 1
جهانگیر عابدی کوپائی 2
1 دانشجوی دکتری آبیاری و زهکشی، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه صنعتی اصفهان.
2 دانشگاه صنعتی اصفهان
چکیده
این مطالعه به بررسی روند تغییرات تبخیروتعرق مرجع با استفاده از آزمون ناپارامتری من­کندال و پیش‌­بینی آن با استفاده از تحلیل سری زمانی می‌­پردازد. برای محاسبه­‌ی تبخیروتعرق­مرجع به روش فائوپنمن­مانتیث، از اطلاعات حداقل و حداکثر دما، حداقل و حداکثر رطوبت­‌نسبی، ساعات آفتابی و سرعت باد ایستگاه خرم­آباد در دوره 1395-1369 استفاده شد. نتایج نشان داد روند تغییرات سالانه تبخیروتعرق مرجع معنی­دار نبوده و روند تغییرات ماهانه نشان داد که در ماه­‌های مهر و مرداد با آماره­‌های به میزان 0/2- و 0/7- روندکاهشی و در بقیه­ی ماه­ها افزایشی است. در ماه­‌های فروردین، آذر، دی در سطح اطمینان 95 درصد و در ماه‌­های آبان و بهمن در سطح اطمینان 99 درصد معنی­دار می‌باشد. جهت مدل‌سازی با مدل SARIMA، از دوره زمانی 1393-1369 جهت آموزش مدل و دوره زمانی 1395-1394 (24 ماه) جهت صحت‌­سنجی مدل استفاده شد و شروط نرمال، تصادفی و استقلال باقیمانده‌­های مدل برازش داده شده بررسی گردید. نتایج نشان داد از بین مدل‌­های مختلف، الگوی تبخیروتعرق (0،1،1)(1،0،1) SARIMA برای ایستگاه مطالعاتی دارای بهترین دقت است. مقادیر RMSE و R2 در پیش‌­بینی با این مدل به‌ترتیب  0/674 و 0/97 میلی­‌متر در ماه بود که گویای دقت مناسب مدل می‌­باشد.
کلیدواژه‌ها
پیش‌بینی
خودهم‌بستگی
روند
نیازآبی
BIC
SARIMA

عنوان مقاله English

Trend and Time Series Analysis of Reference Evapotranspiration (Case Study: Khorram Abad Plain)

نویسندگان English

Y. Sabzevar 1
jahangir abedi koupaei 2
2 isfahan university of technology
چکیده English

This study examines the trend of ET0 changes using the Mann-Kendall nonparametric test and predicts it using time series analysis. To calculate FAO-Penman-Monteith ET0, Tmin and Tmax, RHmin and RHmax, sun-hours and wind-speedy of synoptic station Khorramabad during the 1991-2017 were used. The results showed that the trend of annual change in reference evapotranspiration with the Mann-Kendall statistics 0.2, was not statistically significant. The trend of monthly changes showed that the trend in September and July was decreasing by Mann-Kendall statistics -0.2 and -0.7, and in the rest of the months, it was increasing. In the stagnation time series check, the trend and ACF graph, showed an increasing trend in the series. for staging the series, the method of differentiation was used and the data were stagnant withfirstfirst-ordered differentiation. For modeling with SARIMA model, from period 1991-2015 for the modeling and from period ‎‎2016-2017 model verification was used, and condition of normality, accidentally and independent ‎of residual of the fitted model was examined. The results showed that the SARIMA‎(0,1,1)(1,0,1) for ET0 modeling, has the best accuracy. The RMSE and R2 ‎values for prediction with this model were 0.674 and 0.97 mm/month, which indicates that the ‎model is accurate.‎

کلیدواژه‌ها English

BIC
Forecast
SARIMA
Self-affiliation
Trend
Water requirements
  1. Abdullahnejad, K. 2015. Time series models for prediction of monthly rainfall (Case study: Hashem Abad Station of Gorgan). Journal of Geography Space. 5(17): 15-25. ‎(In Persian)‎
  2. Ahmadi, F. 2004. Simulation of annual rainfall in Khorasan province using time series. Master's degree in Irrigation and Drainage Engineering. Mashhad Ferdowsi University. 284 pages. (In Persian)
  3. Ahmadi, H., Fallah Ghalheri, Gh. A., and Shamei, A. 2016. Estimation and evaluation of annual evapotranspiration changes based on effective climatic parameters in northeastern Iran. Journal of Water and Soil Science. 25(2): 257-269. (In Persian)
  4. Alizadeh, A., Kamali, Gh. A., Khanjani, M.J., and Rahnavard, M.R. 2004. Estimation of methods for estimating evapotranspiration in arid regions of Iran. Quarterly journal of geographic research. 19(2 (73)): 97-105.
  5. Allen, G.R., Pereira, S.L., Raes, D., and Smith, M. 1998. Crop evapotranspiration. Guidelines for computing crop water requirement. FAO Irrigation and Drainage Paper 56. Rome. Italy.
  6. Azadtalatapeh, N., Behmanesh, G., Montaseri, M. 2013. Estimation of Potential Evapotranspiration Using Time Series Models (Case Study: Urmia). Journal of Water and Soil (Agricultural Sciences and Technology). 27(1): 223-213. ‎(In Persian)‎
  7. Azizi, Gh. 2000. Estimation of effective rainfall in relation to rainfed cultivation (Case: Khorramabad plain), Journal of Geographical research. 39: 123-115. ‎(In Persian)‎

 

  1. Babamiri, A., Nozari, H., Maroufi, S. 2017. Estimation of potential evapotranspiration based on random time series models (case study of Tabriz station). Journal of Watershed Management Research. 15. ‎(In Persian)‎
  2. Bandyopadhyay, A., Bhadra, A., Raghuwanshi, N.S., and Singh, R. 2009. Temporal trends in estimates of reference evapotranspiration over India. Journal of Hydrologic Engineering. 14.5:508-515.
  3. Barnston, A.G. 1991. An empirical method of estimating rain gauge and radar rainfall measurement bias and resolution. Journal of Applied Meteorology and Climatology. 30: 282–296.
  4. Behmanesh, G., and Azadtalatapeh, N. 2015. Investigation of Changes in Meteorological Parameters Affecting the Urmia Climate. Journal of Geography and Planning. 19(51): 58-41. ‎(In Persian)‎
  5. Box, G.E.P., and Jenkins, G.M. 1976. Time Series Analysis Forecasting and Control. Revised ed., Holden-Day, San Francisco.
  6. Chiew, F. H. S., Kamaladasa, N. N., Malano, H. M., and McMahon, T. A., 1995. Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agriculture Water 28: 9–21.
  7. Dinpajouh, Y. 2010. Study of the evapotranspiration trend of the reference plant potential in Zabol. Scientific and Technical Journal of Nivar. Number 71, Autumn and Winter 2010. ‎(In Persian)‎
  8. Feng, Y., Peng, Y., Cui, N., Gong, D., and Zhang, K. 2017. Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture. 136: 71-78.
  9. Garbrecht, J., Van Liew, M., and Brown, G. O. Trends in precipitation, streamflow, and evapotranspiration in the Great Plains of the United States. Journal of Hydrological Engineering. 9. 5: 360-367.
  10. Gautam, R., and Sinha, A. K. 2016. Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India. Journal of Water and Land Development. 30. 1: 51-56.
  11. Grismer, M. E., Orang, M., Snyder, R., and Matyac, R. 2002. Pan evaporation to reference evapotranspiration conversion methods. Journal of irrigation and drainage engineering. 128.3, 180-184.
  12. Gundekar, H. G., Khodke, U. M., and Sarkar, S. 2008. Evaluation of pan coefficient for reference crop evapotranspiration for semi-arid region. Irrigation Science 26:169–175.
  13. Hargreaves, G. H. 1994. Defining and using reference evapotranspiration. Journal of Irrigation and Drainage Engineering. 120.6: 1132-1139.
  14. Hess, T.M. 1998. Trends in Reference Evapotranspiration in the North East Arid Zone of Nigeria. 1961-91, Journal of Arid Environments. 38: 99-115.
  15. Hipel, K.W., McLeod A.I., and Lennox W.C. 1977. Advances in Box-Jenkins modeling 1. model construction. Water Resources Research. 13. 1: 567-575.
  16. Kendall, M. G. 1975. Rank Correlation Methods. Charles Griffin. London.
  17. Kool, D., Agam, N., Lazarovitch, N., Heitman, J.L., Sauer, T.J., and Ben-Gal, A. 2014. A review of approaches for evapotranspiration partitioning. Agriculture For Meteorology. 184: 56–70.
  18. Landeras, G., Ortiz-Barredo, A., and López, J. J. 2009. Forecasting weekly evapotranspiration with ARIMA and artificial neural network models. Journal of Irrigation and Drainage Engineering. 135. 3: 323-334.
  19. Lopez-Urrea, R., Martín de Santa Olalla, F., Fabeiro, C., and Moratalla A. 2006. Testing evapotranspiration equations using lysimeter observations in a semi-arid climate. Agriculture Water 85:15–26.
  20. Mann, H. B. 1945. Nonparametric tests against trend. Econometrical. Journal of the Econometric Society. 245-259.
  21. Martí, P., González-Altozano, P., López-Urrea, R., Mancha, L., and Shiri, J. 2015. Modeling reference evapotranspiration with calculated targets: assessment and Agriculture Water Management. 149. 2: 81–90.
  22. Masoumpoursamakuosh, J., Rajaei, S., Yeganehfar, M. 2014. Time-space variability and evapotranspiration of reference plant in Iran. Journal of Applied Geosciences Research. Fourteenth. 34: 93. ‎(In Persian)‎
  23. Niroumand, H. 2012. Analysis of Time Series: Multiple and Multivariate Methods. Ferdowsi University Press, Mashhad. Second edition 602 pages. ‎(In Persian)‎
  24. Psilovikos, A., and Elhag, M. 2013. Forecasting of remotely sensed daily evapotranspiration data over Nile Delta Region Egypt. Water Resources Management. 27: 4115–4130.
  25. Gheysouri, M., Soltanigordfaramarzi, S., and Ghasemi, M. 2018. Investigation and Forecasting of Changes in Climatic Parameters on Dubai (Case Study: Badkhosh Watershed). Journal of Natural Hazards. 7: 17. ‎(In Persian)‎
  26. Sharifan, H., Ghahreman, B., Alizadeh, A., and Mirlotfi, M. 2005. Evaluation of different radiation and humidity methods for estimating reference evapotranspiration and air-drying effects in Golestan province. Journal of Soil and Water Sciences. 19(2): 280-290. ‎(In Persian)‎
  27. Shirvani, O., and Honar, T. 2013. Application of time series models for prediction of evapotranspiration of Bazeh station. Journal of Iranian Journal of Water Research. 8: 135-142. ‎(In Persian)‎
  28. Snyder, R. L., Orang, M., Matyac S., and Grismer ME 2005. Simplified estimation of reference evapotranspiration from pan evaporation data in California. J Irrigation Drain Engineering. 131. 3:249–253.
  29. Soltanigordfaramarzi, S., Saberi, A., and Qeysouri, M. 2017. Selection of the best time series model for forecasting precipitation of selected stations in West Azarbaijan province. Journal of Applied Geosciences Research. 44: 87-105. ‎(In Persian)‎
  30. Tabari, H., Marofi, S., Aeini, A., Hosseinzadeh Talaeea, P., and Mohammadi, K. 2011. Trend analysis of reference evapotranspiration in the western half of Iran. Agriculture Forest Meteorology. 151. 2: 128-136.
  31. Zaninovi, K., and Gaji-Capka, M. 2000. Changes in Components of the Water Balance in the Croatian Lowlands. Theoretical and Applied Climatol. 65: 111–117.
  32. Zareabyaneh, H., Afroozi, A., Mirza'i, M., and Bagheri, H. 2016. Estimation of monthly evapotranspiration using time series models. Journal of Water and Soil (Agricultural Sciences and Technology). 30(1): 111-99. ‎(In Persian)‎
  33. Zareabyaneh, H., Saghaei, S., Ershad Fath, F., and Nozari, H. 2014. Modeling and prediction of evapotranspiration of reference plant with time series (Case study: Kermanshah province). Journal of Agricultural Meteorology Journal. 2(1): 56-45. ‎(In Persian)‎
  34. Zeinizadeh, K., and Khanmohammadi, N. 2018. Comparison of the efficiency of linear and nonlinear time series models in simulation and prediction of reference evapotranspiration. Journal of Geography and Planning. 22(63): 160-139. ‎(In Persian)‎

Zhang, B.Z., Liu, Y., Xu, D., Zhao, N.N., Lei, B., Rosa, R.D., Paredes, P., Paco, T.A., and Pereira, L.S., 2013. The dual crop coefficient approach to estimate and partitioning evapotranspiration of the winter wheat-summer maize crop sequence in North China Plain. Irrigation Science. 31. 6: 1303–1316.