Projection of drought indices in Iran based on CMIP5 multi-model ensemble

Document Type : Original Article

Authors

1 Assistant Professor of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran

2 2. Postdoctoral Researcher of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran.

10.30488/ccr.2021.317280.1058

Abstract

Increasing the intensity and frequency of drought indices due to global warming can severely affect the natural environment. Therefore, it is necessary to project drought indices using General Circulation Models (GCMs) in order to provide projection of drought conditions as well as climate risk management. For this purpose, nine models were selected from a set of CMIP5 models with horizontal resolution of 0.5 ° and bias corrected by Quantile Delta-Mapping (QDM) method. Then, using a Bayesian mean model (BMA), an ensemble model was generated and its performance was evaluated using Taylor diagram. The results showed that the CMIP5-MME model generated by BMA method performed better than the existing nine individual models. The generated ensemble model simulates the dry spells and dry days more accurately than the intensity of drought in Iran. It shows that CMIP5 models simulate the precipitation event better than the amount of precipitation. The results showed that the frequency of dry days, drought period and also the severity of drought in Iran will increase in the future. The aridity index (AI), which shows the balance between water supply and demand in the atmospheric-Earth interaction, will increase by a maximum of 3.15% in the average area of Iran. Also, dry days and dry spells will increase by 7.50% and 28.84%, respectively, in the upcoming decades. The results show that under climate change conditions, the length of the drought period will increase more than the aridity index (intensity of the drought). This result is considered a serious threat to water resources and ecosystems and requires special attention to drought management programs (DMP) in the country.

Keywords


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