Temporal and spatial trend detection of maximum air temperature in Iran during 1960–2005

Kousari, Mohammad Reza; Ahani, Hossein; Hendi-zadeh, Razieh

doi:10.1016/j.gloplacha.2013.08.011

Cited by 81 publications

(42 citation statements)

References 84 publications

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“…Other variability analyses are reported for extreme climate indices in Iran and Turkey (e.g. Rahimzadeh et al 2009, Erlat & Türkeş 2012, Kousari et al 2013. Kousari et al (2013) found positive trends of annual, seasonal and monthly maximum temperature in the warm season of the period 1960− 2005 for 32 stations in Iran with an increasing trend after 1970.…”

Section: Introductionmentioning

confidence: 81%

“…Rahimzadeh et al 2009, Erlat & Türkeş 2012, Kousari et al 2013. Kousari et al (2013) found positive trends of annual, seasonal and monthly maximum temperature in the warm season of the period 1960− 2005 for 32 stations in Iran with an increasing trend after 1970. Erlat & Türkeş (2013) show increasing trends in the annual number of summer and tropical days of 97 meteorological stations of Turkey.…”

Section: Introductionmentioning

confidence: 92%

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Heat wave characteristics in the eastern Mediterranean and Middle East using extreme value theory

Tanarhte

Hadjinicolaou²,

Lelieveld

2015

Clim. Res.

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Heat waves in the eastern Mediterranean and Middle East can have large socioeconomic impacts. We apply a newly developed statistical framework, based on the extreme value theory, to study the characteristics of heat waves in the region during the period 1973-2010 using data from 15 measurement stations across the region. The analysis shows increasing trends in the highest daytime temperatures in the Persian Gulf region in summer. Increasing trends in the number of heat waves are found at all stations, whereas the maximum temperature during heat waves is found unchanged, implying no change in their intensity. Furthermore, no significant trends in the heat wave duration are observed. Return levels are calculated for the individual hot days and found to be very high in the Persian Gulf region.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 92%

Heat wave characteristics in the eastern Mediterranean and Middle East using extreme value theory

Tanarhte

Hadjinicolaou²,

Lelieveld

2015

Clim. Res.

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“…These mountains have a significant effect on distributing perceptible atmospheric water vapor. Therefore, the central and eastern parts of Iran receive less precipitation than the north and west [16,17]. …”

Section: Methodsmentioning

confidence: 99%

A Pilot Investigation of the Relationship between Climate Variability and Milk Compounds under the Bootstrap Technique

Milani

Hense

Rahmani

et al. 2015

Foods

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This study analyzes the linear relationship between climate variables and milk components in Iran by applying bootstrapping to include and assess the uncertainty. The climate parameters, Temperature Humidity Index (THI) and Equivalent Temperature Index (ETI) are computed from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis (2002–2010). Milk data for fat, protein (measured on fresh matter bases), and milk yield are taken from 936,227 milk records for the same period, using cows fed by natural pasture from April to September. Confidence intervals for the regression model are calculated using the bootstrap technique. This method is applied to the original times series, generating statistically equivalent surrogate samples. As a result, despite the short time data and the related uncertainties, an interesting behavior of the relationships between milk compound and the climate parameters is visible. During spring only, a weak dependency of milk yield and climate variations is obvious, while fat and protein concentrations show reasonable correlations. In summer, milk yield shows a similar level of relationship with ETI, but not with temperature and THI. We suggest this methodology for studies in the field of the impacts of climate change and agriculture, also environment and food with short-term data.

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“…The average annual precipitation in Iran is about 251 mm, which is less than 1/3 of the average of the world (831 mm). The received precipitation extremely varies spatially and temporally and more than 70% of the annual precipitation is lost through evapotranspiration (Kousari et al, 2013). In addition, with recent multi-year drought and intensive atmospheric systems affecting West Asia, Iran has become a country vulnerable to climate change related phenomena, especially sand and dust storms.…”

Section: Study Areamentioning

confidence: 99%

Identification of sand and dust storm source areas in Iran

Jian²,

et al. 2015

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Sand and dust storms (SDS) are common phenomena in arid and semi-arid areas. In recent years, SDS frequencies and intensities have increased significantly in Iran. A research on SDS sources is important for understanding the mechanisms of dust generation and assessing its socio-economic and environmental impacts. In this paper, we developed a new approach to identify SDS source areas in Iran using a combination of nine related datasets, namely drought events, temperature, precipitation, location of sandy soils, SDS frequency, human-induced soil degradation (HISD), human influence index (HII), rain use efficiency (RUE) and net primary productivity (NPP) loss. To identify SDS source areas, we firstly normalized these datasets under uniform criteria including layer reprojection using Lambert conformal conic projection, data conversion from shapefile to raster, Min-Max Normalization with data range from 0 to 1, and data interpolation by Kriging and images resampling (resolution of 1 km). After that, a score map for the possibility of SDS sources was generated through overlaying multiple datasets under average weight allocation criterion, in which each item obtained weight equally. In the score map, the higher the score, the more possible a specific area could be regarded as SDS source area. Exceptions mostly came from large cities, like Tehran and Isfahan. As a result, final SDS source areas were mapped out, and Al-Howizeh/Al-Azim marshes and Sistan Basin were identified as main SDS source areas in Iran. The SDS source area in Al-Howizeh/Al-Azim marshes still keeps expanding. In addition, Al-Howizeh/Al-Azim marshes are now suffering rapid land degradation due to natural and human-induced factors and might totally vanish in the near future. Sistan Basin also demonstrates the impacts of soil degradation and wind erosion. With appropriate intensity, duration, wind speed and altitude of the dust storms, sand particles uplifting from this area might have developed into extreme dust storms, especially during the summer.Keywords: sand and dust storm; weight allocation criterion; Kriging interpolation; score map; Al-Howizeh/Al-Azim marshes; Sistan Basin Citation: CAO Hui, LIU Jian, WANG Guizhou, YANG Guang, LUO Lei. 2015. Identification of sand and dust storm source areas in

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Temporal and spatial trend detection of maximum air temperature in Iran during 1960–2005

Cited by 81 publications

References 84 publications

Heat wave characteristics in the eastern Mediterranean and Middle East using extreme value theory

Heat wave characteristics in the eastern Mediterranean and Middle East using extreme value theory

A Pilot Investigation of the Relationship between Climate Variability and Milk Compounds under the Bootstrap Technique

Identification of sand and dust storm source areas in Iran

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