Journal of Spatial Analysis

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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.

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Analysis of the Characteristics of Sultry Days in Iran
 
Extended Abstract
Sultry phenomenon occurs due to the combined effect of high temperature and humidity. Sultry intensity increases with increasing relative humidity and decreases with decreasing temperature. This phenomenon has a tremendous impact on comfort and other human activities. Various indices have been used to study this phenomenon in Iran and in the world. According to previous studies, and as far as information is concerned, there has not been a comprehensive study across Iran on the characteristics of sultry days based on degree of severity. Therefore, the purpose of this study is to investigate the frequency, duration and severity of sultry days and its temporal and spatial analysis throughout Iran.
To do this research, daily temperature, relative humidity and partial water vapor pressure of 101 synoptic stations were used for a 28-year period (1987-2014). In choosing the indices of sultriness, the goal was to select indices that show the sultry state on a daily scale. For this purpose, in the first stage, 16 empirical sultry or sultry-related indices were used, all of which used climatic parameters such as temperature, relative humidity, water vapor pressure and cloudiness to calculate the sultry state or comfort. Among them, 13 indices were eliminated because they surveyed the phenomenon on a monthly or annual basis or were not consistent with the objectives of this study. Finally, according to the objectives of the study, three indices were chosen: 1- Sultry Intensity Index (Lancaster-Carstone empirical equation), 2- Partial Water Vapor Pressure Index (partial water vapor pressure equal to or greater than 18.8 hPa), and 3- Heat Index (HI).
The results of this study showed that two indices of Sultry Intensity and Partial Water Vapor Pressure are suitable for explaining the conditions in Iran and their outputs are not significantly different. But Heat Index did not lead to desirable results. According to the results of the Sultry Intensity Index, the sultry phenomenon is not comprehensive in the country and is limited to 21 stations adjacent to the Caspian Sea coasts in the north (besides Parsabad Moghan Station) and the Persian Gulf coasts (besides Ahwaz station) and the Oman Sea coasts in the south. In other parts of the country, due to their internal and leeward position, being away from moisture sources, poverty or lack of vegetation and insufficient penetration of wet and rainy systems, there is no sultry condition and, on average, even one day is not seen with sultry circumstances. On the southern coasts, on average, sultry conditions begin on April 3 and end on November 16. Therefore, in this area, 7 months and 11 days of the year have sultry conditions. This is natural due to the lower latitude and the Azores high pressure sovereignty in the south. But on the northern coasts, the sultry period is shorter and with a 48-day delay compared with the southern coasts, the average sultry day begins on May 22 and ends on October 12. Therefore, the duration of the sultry period is on average 4 months and 19 days. In terms of the number of sultry days, the most frequencies belong to the southern coasts stations. The largest number of sultry days related to the Chabahar port on the coasts of the Oman Sea with 291 days, followed by Jask port with 264 days. The lowest number of sultry days is also from Ahwaz station with 1 day and then Mahshahr port with 42 days. Among the stations on the southern coasts, the Oman Sea stations compared with the Persian Gulf stations have more sultry days due to lower latitudes, Azores high pressure sovereignty and Southeast Asian monsoon moisture influence. In contrast, the number of sultry days on the northern coasts is much lower and averages 140 to 150 days a year. Sultry severity is also less, so that there are no extreme severe sultry days in any of the stations on the northern coasts. But the number of extreme sultry days is remarkable on the coasts of the South, to 160 days in the port of Chabahar and 111 days in the port of Jask. At Parsabad Moghan in the north and port of Mahshahr in the south, due to distance from the coast and lack of sufficient moisture, the duration and severity of sultry is much lower and there are basically no days of severe and extreme sultry states. The annual trend of the number of sultry days at any station is not significant.
 
Keywords: Sultry, Temperature, Relative Humidity, Sultry Indices, Iran
 
 

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Introduction: Nowadays, climate change and global warming caused by increasing concentrations of greenhouse gases, especially carbon dioxide, is one of the major challenges facing sustainable development. Carbon accumulation in plant biomass and soils is the simplest and economically way to reduce levels of this atmospheric gas. No research has been done on the assessment of the potential of soil carbon deposition and accumulation in the Capparis decidua and Prosopis cineraria species.
Data and Methodology: The objective of this study was to evaluate the soil carbon accumulation in Capparis decidua and Prosopis cineraria plant species in Keshtegan of Saravan, Iran. Therefore, in order to determine the amount of carbon stored in the soil, soil sampling was done by random-systematic method. One hundered-meter transects were randomly selected in the study areas and sampling points were dug at similar intervals along the transect for sampling.Soil samples were taken from depth of 0 to 30 centimeters under the canopy of Capparis decidua and Prosopis cineraria and bare soil as control (20 samples from each area). Soil organic carbon, soil bulk density, pH, salinity and content of clay, silt and sand were recorded.
Description and Interpretation of Results: The analysis of the data showed that there is a significant difference between the investigated treatments in terms of the amount of clay, organic carbon and carbon accumulation. The average comparison results showed that there is a significant difference between the soil covered by plant species and the soil of the control area. The amount of soil carbon accumulated in the area covered by Capparis decidua (1.32 tons per hectare) was significantly higher than that in area covered by Prosopis cineraria (0.75 tons per hectare) and the control area (0.25 tons per hectare). It shows the positive effect of two plant species on the amount of soil carbon accumulation. The average amount of organic carbon in the area with the Capparis decidua, Prosopis cineraria and the control area was  0.75, 0.31 and 0.1 tons per hectare, respectively.Soil organic matter and sand percentage under the canopy of both plant species were higher than the control. In terms of other characteristics, no significant difference was observed in the three regions. According to the results, it can be stated that the presence of plant canopy can increases the amount of carbon accumulation in the soil and led to global warming mitigation.
 

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 Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R = -0.85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0.58, indicating good accuracy of the model.