Mohammad Baaghideh; Gholamabbas Fallah Ghalhari; Hasan Hajimohammadi; hasan rezaei
Abstract
Extended Abstract Introduction The climatic conditions of each site play an important role in the dispersion of humans, animals and plants. Therefore, any activity or planning in different economic, agricultural and industrial fields at the ground level is not feasible without the knowledge of ...
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Extended Abstract Introduction The climatic conditions of each site play an important role in the dispersion of humans, animals and plants. Therefore, any activity or planning in different economic, agricultural and industrial fields at the ground level is not feasible without the knowledge of the climate. For this reason, climatic zoning and recognition of the most important factors and factors affecting each area is one of the ways of recognizing the climatic identity of the area. Lack of knowledge of the sub-regions of the country fails to meet the economic and agricultural plans of mankind. In general, the climate of a region is the average of the weather conditions in the region. Access to the average weather conditions in a specific location requires long-term weather information. Data and Methods In order to obtain the correct and comprehensive knowledge of the climate of Hamedan province, climatic zoning was performed with new statistical methods such as factor analysis and cluster analysis during the 20 years period (1993-2013). For this purpose, 23 variables were selected from 8 meteorological stations. Then, using a digital elevation model, a multivariable regression was applied between the meteorological parameters and the digital elevation model. Finally, a zonal matrix with a dimension of 23 × 88 was obtained. Since the aim of this research was the climate zone of Hamadan province based on altitude, a digital elevation layer (DEM) was used with a resolution of 90 meters. In the following, for climatic zoning, a regression relationship was made between climate parameters and length, width and height of the area. To identify the climatic sub-regions of Hamedan province, the raster data obtained from the zoning were converted to point data. Then, based on the analysis of the main components, the points were analyzed by clustering method and the dominant factors were identified. In this research, the resolution of each of the pixel was 15 × 15 km and a matrix with dimensions of 23 × 88 was developed. Finally, this matrix was clustered into the MATLAB software using the Wardclustering method. Results and discussion By studying 23 climatic elements, 5 climatic factors were identified and their maps were drawn. These factors include temperature, visibility, rainfall, thunder storm and radiation. Among these factors, the first factor with 37% of the variance of the total data has the most important role in determining the climate diversity of the province. This factor is most commonly observed in the South and Southwest of the province and with moving to the North and Northeast of the province, this factor is severely reduced. Conclusion According to the dendrogram, 6 climatic regions were identified and the characteristics of each separate area were investigated.
Hushmand Ata'ii; Mandana Basaatzadeh Karandi
Volume 17, Issue 66 , August 2008, , Pages 46-51
Abstract
In this paper, while introducing a brief overview of Chaharmahal and Bakhtiari province, the most important zoning method (Terjung) has been discussed using parameters of temperature, relative humidity, sunny and windy hours in 8 synoptic and climatological stations during a statistical period of 40 ...
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In this paper, while introducing a brief overview of Chaharmahal and Bakhtiari province, the most important zoning method (Terjung) has been discussed using parameters of temperature, relative humidity, sunny and windy hours in 8 synoptic and climatological stations during a statistical period of 40 years. Next, bioclimatic zoning maps were prepared on a seasonal scale.
In order to achieve these maps the Terjung method, statistical-graphical software, elevation gradient, and especially the buffering of AutoCAD map and ArcView were used. Data analysis during this period revealed the ecological groups of Chaharmahal and Bakhtiari province. In winter, due to the dominance of external climatic factors, only one bioclimatic region (K2) is discernible throughout the province; in the spring, the combined effect of external and local factors create three bioclimatic types (W2, M3, C2) and in the summer season the effect of local factors create two bioclimatic types (W4, H5). By change in the effective factors in the autumn, the bioclimatic types (C2, C3, K2) are dominant in the Chahar Mahal and Bakhtiari province during this season.