جستجوی مقالات مرتبط با کلیدواژه "climatic" در نشریات گروه "آب و خاک"

Considering the climate suitability in cropping pattern selection is vital in maintaining food security. In this study, the climate suitability of East Azerbaijan Province, north east of Iran, for the cultivation of strategic crops of wheat and barley during four decades 1980 to 2000s was evaluated. Land units were classified based on the covered area of each synoptic weather station using Thiessen method. Climate suitability for rainfed and irrigated wheat and barley was evaluated according to their climatic requirements using the square root-parametric method. Results revealed that during the recent four decades, the climate suitability class for irrigated studied crops was very suitable across the province, but in case of rainfed cultivation, various suitability classes including moderate to unsuitable were observed in different regions. The results confirmed that despite of some unfavorable effects of climate change on suitability of region for both crops cultivation, these impacts might be compensated, to some extent, by technology or management improvements. According to this study, determination of an optimum cropping pattern, adjusting agronomic practices and changing of rainfed wheat to barley in this province may be recommended.

The purpose of this study was to model the relationship between the frequency of dust storms and climatic variables in desert areas of Iran. For this purpose, climatic data of temperature (maximum and minimum), rainfall, wind speed (maximum and minimum), and their relationship with the number of days with dust recorded in 25 meteorological stations (statistical period since their inception until 2014) in summer using Pearson correlation coefficient and linear regression method multivariate was analyzed in SPSS software. Also, due to regional analysis, correlation coefficient between climatic variables and frequency of drought storms in desert areas of Iran, the mapping of these coefficients was prepared by method of Inverse distance weighting (IDW) in Arc GIS software. The results showed that the stations in the south and southwest of the study area have the highest dust incidence in the summer season. So that Zabul station with (3892 days) has the most frequent occurrence of dust storms. In most stations, there was a significant relationship between the frequency of dust storms and the variables of average wind speed and maximum wind speed. The highest correlation coefficient of the mean wind speed was related to the station of the Chabahar Konarak with correlation coefficient of 0.710 and Iranshahr station with a correlation coefficient of 0.65, showed the highest correlation with maximum wind speed. The maximum temperature variable at Qom station with a correlation coefficient of 0.398 shows a significant and positive relationship. Iranshahr station has a correlation coefficient of -0.620 with a mean temperature and Minab station has a correlation coefficient of -0.446 with maximum temperature. The results of temperature correlation with the frequency of dust storms indicate that ground low pressure is effective in creating the phenomena in the warm course of the year. Most stations have inverse correlation with precipitation. The highest correlation coefficients between precipitation and dust events were observed at -0.208 and -0.185 at east of Isfahan and Torbat Heidariyeh stations, respectively. Multivariate regression modelling between dust and climatic variables in summer also shows that the most important parameter in dust events are average wind speed, maximum wind speed and average temperature. Regression models show that, at the best condition, climate variables explain only 70% of the variation of dust frequency.

Natural characteristics, especially the climate of each area plays a major role in planning and preparation. Charmahale Bakhtiary is a province that located in the west mountainous area of Iran. Due to topography diversities, this region has a different distribution of climate elements (especially, rainfall and temperature). In order to detect sub-climatic regions, statistical methods such as factor analysis and cluster analysis were used. The climatic parameters were obtained from 6 meteorological stations in the province and to overcome the lack of climate control stations throughout the area Interpolation technique (IDW) was used. Then data matrix with dimensions of 26 × 46 (variables× stations) was produced and based in factor analysis. The results showed, four major factors which affect the climate of province in order of importance are: the precipitation, the clear sky, the temperature and the humidity. Finally, cluster analysis on the matrix of factor scores recognized the four climate zones.