Factors affecting severe weather threat index in urban areas of Turkey and Iran

Abstract

BackgroundDistinguishing dynamic variations of the climate from the physical urban indicators is a challenge to assess the factors affecting weather severity. Hence, the time-series of the severe weather threat index (SWEAT) were considered in the four urban areas of Turkey and Iran to identify its affecting factors among the climatic variables and urban indicators in 2018. The SWEAT data were obtained from the upper-air sounding database of the University of Wyoming. The climatic variables were extracted from the Asia Pacific data research center (APDRC). The spatial statistics for urban expansion were collected from global human built-up and settlement extent (HBASE) data sets. A quantitative measuring of the Pearson correlation test was used to expose the relationships between dependent index (SWEAT) and independent variables (climatic and anthropogenic).ResultsResults revealed that the high and extreme severity classes of the weather condition in the Ankara, Istanbul, Mashhad, and Tehran are estimated as 7.7% (28 days), 15.3% (56 days), 1.1% (4 days), and 4.4% (16 days), respectively. The strongest values of the annual SWEAT index, exposing the unstable and severe weather conditions, were observed for Istanbul and Ankara urban regions. This result may be corresponding to the highest values of mean annual precipitation and relative humidity in addition to the largest values of urban expansion and sprawl index. The statistical correlation tests in annual scale confirmed the effective role of climatic elements of precipitation, relative humidity, and cloudiness (R from 0.94 to 0.99) and the urban expansion indicators (R from 0.86 to 0.91) in increasing annual severe weather index of SWEAT at above 85–95% of confidence level.ConclusionsThe correlations between the urban expansion indicators and outcome SWEAT index can be strengthened by some climatic elements (e.g., precipitation, humidity, and cloudiness), revealing the mediator and magnifier task. However, the mentioned correlations can be weakened by another climatic variable (i.e., air temperature), revealing a moderator and modifier task. Ultimately, investigation of the weather severity indices (e.g., SWEAT index) could be applied to identify the local and regional evidence of climate change in the urban areas.