Climate Regionalizing for the Assessment of ENSO, NAO and SST Effect on Regional Meteorological Drought: Application of Fuzzy Clustering

The effect of teleconnection climate signals on climate variability of different regions in the world has been the focus of interests of many investigations since early 90s. The feasibility of long-term climate prediction through understating of how climate signals may affect the regional rainfall, have made these studies important for strategic watershed management. This study is consisted of a regional assessment of rainfall and the long-term variation of climate in the southwestern part of Iran. In this region, there are considerable water resources in major rivers of Karoon, Dez and Karkheh. The climate regionalizing of the study area is done based on the long-term variations of observed rainfall during dry spells at each station over the region. Drought characteristics such as duration and severity are considered as the main criteria in the regionalizing process. Fuzzy clustering by equivalence relation method is used to cluster 23 stations over the region. The clusters show the regions with similar pattern of drought events. The relation between extreme events of North Atlantic Oscillation (NAO) and El-Nino Southern Oscillation (ENSO) and the seasonal rainfall at each station is assessed. This analysis is also done for the Sea Surface Temperature of Persian Gulf (PG-SST) as an indicator of rainfall in the southwestern part of Iran. The method of Fuzzy clustering is also used for this purpose. This method provides three regional maps, each showing the regions with similar behavior during the extreme events of ENSO, NAO, and PG-SST. Then clusters of similar signal-affected stations are compared with the clusters of similar-drought pattern. The similarity between these two kinds of clusters shows that drought pattern in which stations is affected by climate signals. The results show that the global circulation patterns such as ENSO, NAO, and PG-SST could be indicators of meteorological drought in the study area.