Spatial Analysis of Environmental Hazards

Spatial Analysis of Environmental Hazards

Spatial analysis of climate change in Iran

Authors
Alireza Sadeghinia 1
Somayeh Rafati 2
Mehdi Sedaghat 3
1 Farhanghian University
2 Seyed Jamaldin Asadabadi University
3 PNU University
10.52547/jsaeh.8.4.55
Abstract
Introduction

Climate change is the greatest price society is paying for decades of environmental neglect. The impact of global warming is most visible in the rising threat of climate-related natural disasters. Globally, meteorological disasters more than doubled, from an average of forty-five events a year to almost 120 events a year (Vinod, 2017). Climate change refers to changes in the distributional properties of climate characteristics like temperature and precipitation that persist across decades (Field et al., 2014). Because precipitation is related to temperature, scientists often focus on changes in global temperature as an indicator of climate change. Valipour et al. (2021) reported the mean of monthly the global mean surface temperature (GMST) anomalies in 2000–2019 is 0.54 C higher than that in 1961–1990. Many studies have been done on climate change in Iran. These studies have mostly studied the mean and extreme temperature trends (Alijani et al., 2011; Masoudian and Darand, 2012). In general, the results of previous studies showed that the statistics of mean, maximum and minimum air temperature in most parts of the Iranian plateau have increased in recent decades. Also, the increase of minimum temperature is greater than maximum temperature.

A review of the research background shows that we need to understand more about regional climate change in Iran. Therefore, present study performs the climate change of 14 extreme temperature indices using multivariate statistical methods at the regional scale.



Data and methodology

Historical climate observations including daily maximum and minimum temperature were obtained from the Iranian Meteorology Organization for the period 1968 to 2017 at 39 stations. In this paper, 14 extreme temperature indices defined by ETCCDI were analyzed. The indices are as follows: (1) Annual maxima of daily maximum temperature (TXx); (2) Annual maxima of daily minimum temperature (TNx); (3) Annual minima of daily maximum temperature (TXn); (4) Annual minima of daily minimum temperature (TNn); (5) Cold nights (TN10p); (6) Cold days (TX10p); (7) Warm night (TN90p); (8) Warm day (TX90p); (9) Frost days (FD); (10) Icing days (ID); (11) Summer day (SU); (12) Tropical nights (TR); (13) The warm spell duration index (WSDI) and (14) the cold spell duration index (CSDI). The extreme temperature indices were extracted using R software environment, RclimDex extension. The Mann–Kendall Test and Sen’s Slope Method was employed to assess the trends in 14 extreme temperature indices. To identify homogeneous groups of stations with similar annual thermal regimes, Principal Component analysis (PCA) and Clustering (CL) was applied. Pearson correlation coefficient was used to investigate the relationship between height and trend slope.



Result

All the extreme temperature intensity indices (TXx, TNx, TXn, and TNn) showed increasing trends during 1968 to 2017. The increasing trends of TXx, TNx, TXn, and TNn were 0.2, 0.3, 0.44, and 0.5 ° C per decade, respectively. These results indicated that the extreme warm events increased and the extreme cold events decreased. The average of the extreme temperature frequency indices over Iran showed that the frequency of warm night (TN90p) and warm day (TX90p) significantly increased with a rate of 6.9 and 4.2 day per decade, respectively. Also, the frequency of cold night (TN10p) and cold day (TX10p) significantly fell with a decrease rate of 3.8 and 3.8 day per decade, respectively. The frequency of warm nights (TN90p) was higher than that of warm days (TX90p). The result indicated that the trend of nighttime extremes were stronger than those for daytime extremes. The average of frost days (FD) and icing days (ID) indices over Iran showed decreasing trends during 1968 to 2017 with rates of 3 and 1.1 d per decade, respectively. While, the averaged of summer days (SU) and tropical days (TR) indices over Iran showed increasing trends with rates of 4.4 and 6.4 day per decade, respectively. The warm spell duration index (WSDI) indices showed a clear increase, with a rate of 2.1 per decade. In contrast the cold spell duration index (CSDI) showed a significant decrease, with a rate of 1.7 per decade. In general, the cold indices displayed decreasing trends, whereas the warm indices displayed increasing trends over most of Iran. Pearson correlation coefficient between height and Sen’s Slope was estimated to be equal to -0.62 (p < 0.01). In general, the results of this study showed that there is a negative correlation between the elevation factor and the Sen’s Slope of warm extreme indices. That is, as the altitude decreases, the Sen’s Slope increases. Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of ​​Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Approximately 38% of the studied stations were located in cluster 1. Cluster 2 showed a moderate heating trends. 33% of the stations were located in cluster 2. Most of the stations of cluster 2 are located in the northwest and west of Iran. Cluster 3 showed a weak increasing trends compared to clusters 1 and 2. The stations of cluster 3 did not show a special geographical concentration and were scattered in all parts of Iran. 18% of the studied stations are located in cluster 3. The stations of Cluster 4, have experienced weak decreasing trends, which was different from the other three clusters



Conclusion

In this study we analyzed the climate change of extreme temperature indices in Iran. The result showed that the frequency of warm nights, warm days, summer days and tropical days increased. Also, the frequency of cold nights, cold days, Frost days and icing days decreased. The warm spell duration index showed a clear increase. In contrast the cold spell duration index showed a significant decrease. In general, the extreme warm events increased and the extreme cold events decreased over most of Iran. There is a negative correlation between the elevation factor and the Sen’s Slope of extreme warm indices (R = -0.62). Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of ​​Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Therefore, the most heating have occurred in Low-lying areas of Iran. Cluster 2 and Cluster 3 showed a moderate and weak heating trends, respectively. The stations of Cluster 4, have not experienced clear trends.



Key words: climate change; Extreme temperature; clustering; Iran






Keywords
Climate Change
Extreme Temperature
Clustering
Iran
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Spatial Analysis of Environmental Hazards
Volume 8, Issue 4
Winter 2021
Pages 55-70