نوع مقاله : مقاله علمی پژوهشی
1 استادیار گروه مرتع و آبخیز داری-دانشگاه محقق اردبیلی
2 کارشناس ارشد مدیریت مناطق بیابانی- دانشگاه علوم کشاورزی منابع طبیعی گرگان
3 دانشجوی دکتری علوم و مهندسی آبخیزداری - دانشگاه تربیت مدرس
عنوان مقاله [English]
Extended Abstract: Introduction: Drought means scarcity of water, which various sectors of human society, agriculture, water supply, industry and environment can adversely affect due to its occurrence. Drought is best characterized by multiple climatological parameters as a natural hazard. An understanding of the spatio-temporal dependence relationships between effective variables on drought is necessary to its description and analysis. Methodology: The main purpose of this study is investigation of climatological drought in synoptic station of the Kurdistan Province, Iran, by using Triple Diagram Model (TDM). The data consists of monthly average temperature, precipitation, and also relative humidity from seven synoptic stations. The standardized precipitation index (SPI) was calculated for each station. The semivariogram of different used models have been analysed and were been chosen using the cross validation method. According to coefficient of determination and residual sum of squares indices appropriate method was selected for drawing triple diagram models. The triple diagram models have been prepared based on geostatistic principles by SPI, Temperature, and relative humidity variables. Discussion: The results indicate that SPI classification is characterized by the scattering in two input variables can provide the analysis framework of the characteristics and variations of climatological factors. Conclusion: The spatial relationship between stations/regions can be carried out by comparison of the humidity conditions. Application of the triple diagram models can be recommended for drought analysis in other climatological stations. The triple diagram plots help to make interpretations in spite of extreme scatter points facilitate the explanations of the influencing trend among variables.
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