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Volume 3, Issue 3 (10-2016)
Drought is one of the most important hazards that occur in all the earth especially in arid and semi-arid climates. Every year, about half of the earth’s surface experienced droughts and while drought is not a constant feature of any climate but occur more frequently in arid and semi-arid regions of the world. Although the occurrence of droughts cannot be prevented but by studying the nature and characteristics of droughts and also identify factors that affecting their occurrence useful information can be gained about drought and their destructive effects. The researches in recent years designed and proposed a lot of indices to study and analyze the droughts and today various characteristics such as intensity, duration, area and so on with these indices are studied. Many indices used by researches to analysis and identify properties of climatic droughts and dry periods. In these indices often the variables of precipitations, combination of precipitations and temperature, humidity or evaporation, crops yields and teleconnection climatic indices are used.
In this study using the CPEI index and 30 years (1980-2009) daily rainfall data in 40 synoptic stations overall Iran, to analysis and assess of Iran droughts suitable variables detected. Four seasons and annual period is considered in this study. To determine the appropriate variables in the design of suitable models and modeling of drought to assess and predict droughts Otun in 2005 proposed CPEI index as Conjunctive Precipitation Effectiveness Index. He selected 10 conjunctive precipitation variables as ORS(Onset of Rainy Season), CRS(Cessation of Rainy Season), LRS(Length of Rainy Season), TWD(The Total no of Wet Days), TDS(Total no of Dry Spell), TDW(Total no of Dry Days within a Wet Season), TDY(Total no of Dry Days within a Year), LDS(Length of the Dry Season), MDL(Maximum Dry Spell Length within a Wet Season), MAR(Mean Annual / Seasonal Rainfall Depth) and determined the relationships between variables in each synoptic stations and climatic regions. Since the units of measurement the rainfall variables are diverse, it is essential that the units be converted to a standard unit, in other words variables be standardized. The relationship between variables was determined by Pearson correlation coefficient. Finally, the right combination of precipitation variables for each station through the proposed formula Otun(2005) were determined. In the end, for each of the seasons and the annually period regionalization maps were prepared.
All 40 synoptic stations were evaluated by Otun’s method (Aton, 2005). The results showed that 95 percent of stations in spring, 75 percent in fall, 57 percent in winter and 75 percent in annual period are compatible with used method. Thus, spring, fall and winter seasons and also annual period are compatible with above mentioned index. Among the used variables MAR, MDL, TDY and TDS which with respectively are as follows: total amount of precipitation in any period, the maximum duration of dry periods in a wet period, the total number of dry days in a wet period and the total number of dry period during wet period among the stations are more abundant. In annually period, in addition to the above mentioned variables, precipitation variable of LPS (length of dry period) also seen among some stations. Also, results showed that CPEI index can be used on most stations and climatic regions of Iran. It was also found that the spring compared the other seasons and annual period is more comparable on the base of CPEI index.
Otun in 2010 used the CPEI index in semi-arid region of Nigeria and has achieved good results. The results of our study show good agreement with Otun’s work. The use of this index in the study of meteorology, climatology, agriculture and many environmental projects can be beneficial because in many of these fields of study, precipitation and its characteristics have an important role. In general we can say that in regions where CPEI index does not show a high proportion or set of variables are not enough it is better to use other indices such as SPI and RAI. The results obtained in similar climate zones such as Nigeria has shown that CPEI index has very good ability to identify and explain the precipitation effectiveness variables which can be used in modeling of droughts and dry periods. There are many similarities between combination of precipitation variables that identified by CPEI index for Iran and other regions of the world. Similarities, especially with respect to MAR, MDL, TDY and TDS are abundant.
Volume 8, Issue 4 (3-2022)
Application of multivariate techniques in-line with spatial regionalization of AOD over Iran
Introduction
Models, satellites and terrestrial datasets have been used to detect and characterize aerosol. Nontheless, micoscale classification using remote sensing parameters considers as a deficiency. Thus, regionalizion and modeling aerosol without regard to political boundaries or a specific stations over Iran demonstrates the spatial distribution of simple AOD structures.
Materials and methods
Present study attempted to simulate and detect homogeneous areaes of aerosol in Iran using AOD (areosol optical depth) datast at 550 nm across Iran. Among the eigen techniques, principal component analysis (PCA) is the most applicable and controversial classification applied as multivariate analysis approach. In the line of the target, PCA, S-Mode separate the AOD subgroups with similar correlations. In the mode, m time series apply to each n station or grid points as a variable in the analysis, which is the territory of the region or geographical area. Mathematically, if the input data column in the Z matrix is applied as mathematical variables and the Z matrix has n points in the time series and m is the time step, then in the Zs decomposition has 3654×9985. In addition, the scree test and North's rule were used to cut-off the principal components and to select the number of appropriate special vectors to be kept.
Results and Discussion
For the study purpose, 85 percentaile of loadings were used to determine AOD areas over Iran. Using the method, the spatial patterns of Iran's aerosolshave been divided into six subregions, which are the major centers affected by the AOD. These major AOD hotspots affect by AOD extermes that are originated from aerosol surrounding sources. So that, the geographical location of sources areas have caused the northeastern atmosphere of Iran to be influenced by severe storms originating from the Karakum Desert. The same is correct concerning the East and Southeast regions. While, the intensification and transfer of aerosol from the Sistan plain to the south is increased AOD load over southeast Iran. Moreover, this study revealed a set points associated with distinguishing spatial differences between the west-northwest and southwest regions as well as central region that have not addressed in previous studies because of focus on ground-based observations. Also, the method illustrated that formation of the identified regions are a function of the volume, growth, and spread of aerosol particles resulting from the source regions in the Middle East. Finally, the classification techniques converting dynamic phenomenon such as aerosol into simpler structures presented a interpretable understanding of the geographical distribution of the phenomenon.
Conclusion
The present study identified the spatial patterns of AOD hotspots into six distinct regions including northeast, west-northwest, southeast, southwest, central and eastern Iran affected by the aerosol as well as major centers or high gradient areas. In addition, the present study not only supported by previous studies, but also it make sense a regionalization that was neglected by former studies, whileseperated the boundaries of the AOD areas without considering provincial boundaries. Overall, the classification techniques, PCA, simplified a dynamic phenomenon such as aerosol into a simpler and illustrated geographical and interpretable understanding of the spatial distribution of the phenomenon.
Keywords: Aerosol Optical Depth (AOD), Multivariate Techniques, Regionalization, Iran
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