Correlation Analysis of large-scale Teleconnection Indices with Monthly Reference Evapotranspiration of Iran Synoptic Stations

Document Type : Research Paper


1 Department of Irrigation and Reclamation Engineering Department, Faculty of College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.

2 Assistant professor, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran


Reference evapotranspiration (ETo) is considered as an important component in the hydrological cycle and determination of water requirement. In this study, an attempt was made to investigate the effect of large-scale teleconnection indices (LSTIs) on estimation of monthly reference evapotranspiration (ETo) in Iran. For this purpose, daily and monthly ETo using Penman–Monteith FAO (PMF-56) equation was calculated in 123 synoptic stations of Iran for the period of 1990-2019 and its correlation with 37 LSTIs with lag time of 0 to 12 months was obtained using the Pearson correlation method and the Significant Correlation Frequencies (SCF) was also calculated. Finally, the correlation coefficient was performed in Iran using the Kriging method in the ArcGIS 10.4 software package. The results show that the highest positive correlation belongs to AMO, CO2, NTA, TNA, and TSA indices and the highest negative correlation belongs to MEI and SST3.4 indices in different lag times. The highest SCF with ETo belongs to AMO, CO2, NTA, TNA, and WHWP indices, which include 35, 58, 23, 23, and 21% of the studied stations, respectively. The widest spatial distribution of SCF belongs to the CO2 obtained in all lag times and all months studied until November and December. The results of this study showed that the LSTIs and CO2 could have a good correlation in lag times of 0 to 12 months and could be used for prediction of monthly ETo, if an appropriate machine learning model is used.


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