Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models
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Monthly precipitation assessments in association with atmospheric circulation indices by using tree‑based models Mohammad Taghi Sattari1,2 · Fatemeh Shaker Sureh1 · Ercan Kahya3 Received: 19 September 2019 / Accepted: 17 April 2020 © Springer Nature B.V. 2020
Abstract The Urmia Lake basin is one of the most important basins in Iran, facing many problems due to poor water management and rainfall reduction. Under current circumstances, it becomes critical to have an advanced understanding of rainfall patterns in the basin, setting the motivation of this study. In this research, the mean monthly meteorological data of six synoptic stations of Urmia Lake basin were used (including relative humidity, temperature, minimum–maximum temperature and pressure) and large-scale atmospheric circulation indices (Southern Oscillation Index, North Atlantic Oscillation, Western Mediterranean Oscillation, Mediterranean Oscillation-Gibraltar/Israel and Mediterranean OscillationAlgiers/Cairo) and sea surface temperatures of the Mediterranean, Black, Caspian, Red seas and Persian Gulf in the period 1988–2016. Various combinations of these variables used as input to the M5 tree and random forest models were selected by Relief algorithm for each month in three scenarios including atmospheric circulation indices, meteorological variables and combination of both. After the implementation of two models with three different scenarios, the evaluation criteria including correlation coefficient (R), mean absolute error and root-mean-square error were calculated and the Taylor diagram for each model was plotted. Our results showed that the M5 tree model performed superior in January, February, March, April, June, September, November and December, while the random forest model did in the remaining months. In addition, the indications of this study showed that the combination of atmospheric circulation indices and meteorological variables used as input to the models mostly constituted improved results. Keywords Monthly precipitation · Atmospheric circulations · Meteorological variables · Random forest · M5 tree model · Iran
* Mohammad Taghi Sattari [email protected] 1
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
2
Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, 06110 Ankara, Turkey
3
Hydraulics and Water Resources Division, Hydrology Civil Engineering Department, Istanbul Technical University, Istanbul, Turkey
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Natural Hazards
1 Introduction Iran is located in a dry belt, and its rainfall mean is only equivalent to one-fifth of the global mean (Sureh et al. 2019). Naturally, it is quite critical to know the factors affecting rainfall patterns in such areas. Atmospheric teleconnection patterns refer to the occurrences of large-scale patterns of circulation and air pressure that have spread over a wide geographical range. These patterns have oscillatory behaviors of low frequencies. These teleconnection mechanisms are one of the
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