Statistical and multi-criteria methods for preprocessing meteorological data in reference evapotranspiration

Parviz, Laleh; Ghanbari-Maleki, Fardin

doi:10.22098/mmws.2025.18259.1669

Statistical and multi-criteria methods for preprocessing meteorological data in reference evapotranspiration

نوع مقاله : Special issue on "Climate Change and Effects on Water and Soil"

نویسندگان

¹ Associate Professor, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran

² M.Sc Student, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran

10.22098/mmws.2025.18259.1669

چکیده

The capability of reference evapotranspiration (ET0) in water loss consideration benefits irrigation planning, agricultural studies, and water resources management. The requirement for effective meteorological data determination is noteworthy in the ET0 estimation, in which Pearson, Kendall’s tau-b correlation coefficients, the standardized Beta coefficient, stepwise regression (in statistical approach), simple additive weighting with fuzzy normalization (F-SAW), and Shannon’s entropy (multi-criteria) were considered as the preprocessing methods. Different combinations of meteorological data in 11 synoptic stations in Iran were applied to test the performance of the methods. The first three statistical methods yield similar results. Root mean square error decreasing from Pearson and stepwise regression to F-SAW is 15.4% and 14.7%, respectively; therefore, the F-SAW could enhance the ET0 simulations. The differences between the two preprocessing methods based on the MCDM approach in all 132 cases are low, except for 34 cases with better performance of F-SAW. F-SAW performance investigation among all stations on annual and monthly scales stated that Zanjan and Semnan stations, with Nash-Sutcliffe efficiency equal to 0.97 and 0.99, respectively, have better performance than the other stations. The temperature, humidity, wind speed, and solar radiation were obtained as the effective data. In the decision-making analysis with high efficiency, the procedure of assigning weights to the criteria has an important role, and the high performance of F-SAW can be linked to the normalization structure. In the different climates of stations, the performance of each dataset is distinctive; therefore, an efficient preprocessing method can upgrade the adequacy of ET0 estimation.

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