واسنجی و ارزیابی پنج روش تخمین تبخیر-تعرق مرجع مبتنی بر تشعشع خورشیدی در استان یزد

نویسندگان

1 دانش آموخته مهندسی عمران- محیط زیست، دانشکره فنی عمران، دانشگاه تبریز

2 دانشجو دکتری آبیاری و زهکشی، دانشکده کشاورزی، دانشگاه تبریز

چکیده

هدف این مطالعه مقایسه و واسنحی پنج روش مختلف تخمین تبخیر-تعرق گیاه مرجع در مقیاس داده‌های نه‌ساله روزانه در استان یزد می‌باشد. روش‌های انتخاب شده شامل هارگریوز - سامانی HS، پریستلی - تیلور PT، تورک Turc، مک کینگ MK و دالتون D بودند. برای این منظور از اطلاعات ده ایستگاه هواشناسی سینوپتیک در دوره آماره 2010 تا 2018 استفاده شد. نتایج روش‌ها مذکور با روش FPM-56 مورد ارزیابی قرار گرفت. همچنین با استفاده از روش FPM-56 روش‌های مذکور برای ایستگاه‌های موردمطالعه واسنجی شدند. برای ارزیابی نتایج از معیارهای آماری RMSE, NS, SI, MAE, R^2 استفاد شد. نتایج نشان داد که قبل از واسنجی نتایج روش‌های مختلف اختلاف زیادی با FPM-56 دارند. تنها مدل قابل قبول قبل از واسنجی مدلHS بود. بعد از واسنجی نتایج مدل ها بهبود یافت و مدل D که قبل از واسنجی بدترین مدل بود، بعد از واسنجی بهترین مدل تخمین تبخیر-تعرق در استان یزد در بین پنج روش منتخب شناخته شد. مقادیر میانه مدل D قبل از واسنجی 83/3=MAE،, R^2=0/8499/1- =NS ,83/0=SI ,21/4=RMSE و بعد از واسنجی 83/0=MAE، 86/0, R^2=72/0= NS ,22/0=SI ,02/1=RMSE بدست آمد. بعد از واسنجی مدل های Turc، PT و MK باتوجه‌به شاخص های آماری خطا و NS و همجنین مقادیر R^2 در رده های بعدی بهترین مدل قرار گرفتند.

کلیدواژه‌ها


عنوان مقاله [English]

Calibration and evaluation of five radiation-based reference evapotranspiration estimation methods in Yazd province

نویسندگان [English]

  • Naser Shiri 1
  • Mohammad Hossein Kazemi 2
1 Graduated Civil and Environmental Eng., Faculty of Civil Eng., Univ. of Tabriz, Iran
2 PhD Student, Dept. of Water Engineering, Univ. of Tabriz, Iran
چکیده [English]

The purpose of this study is to compare and evaluate five different methods of estimating the evapotranspiration of the reference plant on a nine-year daily data scale in Yazd province. Selected methods included Hargreaves-Samani (HS) , Priestley-Taylor (PT) , Turk (Turc) , Makkink (MK) and Dalton (D). For this purpose, data from ten synoptic meteorological stations were used covering a period of 9 years. The results of the mentioned methods were evaluated by FPM-56 method. Also, using FPM-56 method, the mentioned methods were calibrated for the studied stations. Also, using FPM-56 method, the mentioned methods were calibrated for the studied stations. RMSE, NS, SI, MAE, R^2 statistical criteria were used to evaluate the results. The results showed that before calibration the results of different methods are very different from FPM-56. The only acceptable model before calibration was the HS model. After calibration, the results of the models improved and model D, which was the worst model before calibration, was recognized as the best model for estimating evapotranspiration in Yazd province among the five selected methods. Mean values of Model D before calibration MAE = 3.83, R^2= 0.84, NS = -1.99, SI = 0.83, RMSE = 4.21 and after calibration MAE=0.83, R^2=0.86, NS=0.72, SI = 0.22, RMSE=1.02 was obtained. After calibration, Turc, PT and MK models were in the next categories of the best models.

کلیدواژه‌ها [English]

  • Evapotranspiration
  • Radiation
  • FAO-Penman-Monteith
  • Calibration
  • Yazd
Allen RG, Pereira LS, Raes D and Smith M, 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, FAO, Rome, Italy.
Arshad S, Morid S, Mobasheri MR and Agha Alikhani M, 2012. Monitoring and forecasting drought impact on dryland farming areas. International Journal of Climatology 33: 2068–2081.
Bannayan M, Sanjani S, Alizadeh A, Sadeghi Lotfabadi S and Mohamadian A, 2010. Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research 118: 105–114.
Basak D, Pal S and Patranabis DC, 2007. Support vector regression. Neural Information Processing 11: 203-225.
Battisti R, Sentelhas P and Boote k, 2017. Inter-comparison of performance of soybean crop simulation modelsand their ensemble in southern Brazil. Field Crops Research 200: 28-37.
Borelli A, DeFalco I, Della CA, Nicodemi M and Trautteur G, 2006. Performance of genetic programming to extract the trend in noisy data series. Physica A: Statistical Mechanics and its Applications 370: 104-108.
Boser BE, Guyon IM and Vapnik VN, 1992. A training algorithm for optimal margin classiers. Pp. 144-152. In: D.Haussler (ed.), 5th Annual ACM Workshop on COLT, Pittsburgh.
Edwards DC and McKee TB, 1997. Characteristics of 20th Century Drought in the United States at Multiple Time Scales. Climatology Report Number 97-2, Department of Atmospheric Science, Colorado State University, Fort Collins.
Elagib NA and Elhag M, 2011. Major climate indicators of ongoing drought in Sudan. Journal of Hydrology 409: 612-625.
Hui JU, Er-da1 L, Wheeler T, Challinor A and Shuai J, 2013.Climate change modelling and its roles to Chinese crops yield. Journal of Integrative Agriculture 12: 892-902.
Kang Y, Shahbaz Khan and Xiaoyi Ma, 2009. Climate change impacts on crop yield, crop water productivity and food security – A review. Journal of Progress in Natural Science 19: 1665–1674.
McKee TB, Doesken NJ and Kleist J, 1993. The relation of drought frequency and duration to time scales. Pp. 379-384, 8th Conference on Applied Climatology, 17-22 January, Anaheim, California
Mishra AK and  Desai VR, 2005. Spatial and temporal drought analysis in the Kansabati river basin, India. International Journal of River Basin Management 3: 31-41.
Mosaedi A and Ghabaei Sough M, 2011. Modification of standardized precipitation index (SPI) based on relevant probability distribution function. Journal of Water and Soil 25(5): 1206-1216 (In Persian with English abstract).
Mosaedi A, Mohammadi Moghaddam S and Ghabaei Sough M, 2015. Modeling rain-fed wheat and barley based on meteorological features and drought Indices. Journal of Water and Soil 29(3): 730-749 (In Persian with English abstract).
Padakandla SJ, 2016. Climate sensitivity of crop yields in the former state of Andhra Pradesh, India. Journal of Ecological Indicators 70: 431–438.
Rahmani E, Liaghat A and Khalili A, 2008. Estimating barley yield in Eastern Azerbaijan using drought indices and climatic parameters by artificial neural network (ANN). Iranian Journal of Soil and Water Research 39(1): 47-56 (In Persian with English abstract).
Samadianfard S and Asadi E, 2018. Prediction of SPI drought index using support vector and multiple linear regressions. Journal of Water and Soil Resource Conservation 6(4): 1-16 (In Persian with English abstract).
Sette L and Boullart L, 2001. Genetic programming: principles and applications. Engineering Applications of Artificial Intelligence, 14(6): 727-736.
Steinmann A, 2003. Drought indicators and triggers: a stochastic approach to evaluation. Journal of the American Water Resources Association 39: 1217-1233.
Tietjen B and Jeltsch F, 2007. Semi-arid grazing systems and climate change: a survey of present modelling potential and future needs. Journal of Applied Ecology 44: 425-434.
Tsakiris G and Vangelis H, 2004. Towards a drought watch system based on spatial SPI. Water Resources Management 18: 1-12.
Tsakiris G and Vangelis H, 2005. Establishing a drought index incorporating evapotranspiration. European Water 9: 3–11.
Tsakiris G, Pangalou D and Vangelis H, 2007. Regional drought assessment based on the reconnaissance drought index (RDI). Water Resource Management 21: 821–833.
Valizadeh J, Ziaei M and Mazloumzadeh SM, 2014. Assessing climate change impacts on wheat production (a case study). Journal of the Saudi Society of Agricultural Sciences 13: 107–115.
Vapnik VN, 1995. The Nature of Statistical Learning Theory. Springer, New York.
Vapnik VN, 1998. Statistical Learning Theory. Wiley, New York.
Xiao G, Zhang Q, Li Y, Wang R, Yao Y, Zhao H and Bai H, 2010. Impact of temperature increase on the yield of winter wheat low and high altitudes in semiarid northwestern China. Agricultural Water Management 97: 1360–1364.
Zare Abyaneh H, 2013. Evaluating roles of drought and climatic factors on variability of four dry farming yields in Mashhad and Birjand. Water and Soil Science-University of Tabriz 23(1): 39-56 (In Persian with English abstract).
Zimmermann A, Webber H, Zhao G, Ewert F, Kros J, Wolf J, Britz W and Vries W, 2017. Climate change impacts on crop yields, land use and environment in response to crop sowing dates and thermal time requirements. Agricultural Systems 157: 81–92.