Mahdi Sedaghat; Hamid Nazaripour
Abstract
Extended Abstract Introduction Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction ...
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Extended Abstract Introduction Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction results in the creation of centers susceptible to dust storms. With socio-economic impacts ranging from urban to intercontinental and from a few minutes to several decades, this can challenge regional development. The first estimate of potential dust sources is derived from the soil properties. With the reduction of surface soil moisture and the wind speedcrossing a certain threshold level, wind erosion process can cause the formation of dust storms. Field studies have proved that increasing the moisture content in soil from zero to about 3%, reduces the dust concentrationsignificantly. To understand the climatology of dust and develop related numerical predictive methods, continuous recording of dust storms is essential, which requires effective and continuous monitoring of the variations in surface soil moisture. Remote sensing technology is an effective method for calculating soil moisture. This technology was first used for the estimation of energy flux and surface soil moisture in the 1970s. To extract the surface soil moisture content, some remote sensing methods use surface radiation temperature and some others apply water transfer (soil/vegetation/air) (SVAT) model. Various indices have been developed for soil moisture monitoring, such as soil moisture (SM), soil water index (SWI), Temperature-Vegetation-Dryness Index (TVDI), Soil Moisture Index (SMI) and Perpendicular Soil Moisture Index (PSMI), all of which combine vegetation and surface temperature variables. Materials and Methods Soil moisture is considered to be a significant parameter in the exchange of mass and energy between the Earth surface and the atmosphere. Lack of soil moisture or decreased moisture in soil is considered to be a factoraccelerating the process of dust storm formation. During the previous decades, water stresses on the ecosystem of Hour-al-Azim have transformed this wetland into one of the main dust centers in the southwest Iran. Hour-al-Azim is one of the largest wetlands in southwestern Iran. This wetland is shared between in Iran and Iraq. It is located between N 30° 58´- N31° 50´ and E 47° 20´- 47° 55´. The Iranian part of this wetland encompassed an area of 64,100 ha in the 1970s, while in the 2000s, the area has decreased to only 29,000 ha. The present study aims to monitor the spatial-temporal variability of soil moisture in Hour-al-Azim wetland and to investigate the relation between these changes and dust storms in the southwest Iran. To reach this end, we used 8-day images obtained from the Aqua satellite in the period of 2003 to 2017 and also annual frequency of dust storms with a visibility of less than 1000 m in the period of1987–2017. A database consisting of 189 images of the red band, near-infrared band, and ground surface temperature (LST) was created, which contained 4 images per year (one image per season). The resolution of the red / near-infrared band data and daily LST values were 231.65 and 926.62 meters, respectively. Then, soil adjusted vegetation indices (SAVI) and perpendicular soil moisture index (PSMI) were extracted. SAVI index is used to reduce the effect of background soil on vegetation cover in semi-arid and arid environments with less than 30% vegetation cover.Compared to NDVI, SAVIwith L = 0.5reduces the effect of soil changes on green plants. In the next step, a trapezoidal method was used to calculate the PSMI index. In order to investigate changes in the soil moisture content of the Hour-al-Azim wetland, three time series obtained from regional mean of SAVI, LST and PSMI remote sensing indices and a time series consisting of the number of days with dust storms observed in the 9 stations were evaluated using simple linear regression test. Results and discussion Extracting Soil Adjusted Vegetation Index indicated that in the study period, the highest values of this index was observed with a regional mean of 0.15 on 4/7/2014 and the lowest values was observed with a regional mean of 0.08 on 1/1/2005. Land Surface Temperature survey showed that during the study period, the highest values of this index was observed with a regional mean of 54.42 ° C on 7/4/2010 and the lowest values was observed with a regional mean of 17.28 ° C on 1/1/2007. The regional mean of Perpendicular Soil Moisture Index indicates that despite winter is considered to bethe wettest season of the region, PSMI index with a regional mean of 0.2 has experienced the driest soil moisture conditionsat the beginning of winter (1/1/2016),while it had experienced the wettest soil moisture conditionsin the same season on 1/1/2009 with a regionalaverage of 0.13. Conclusion Finding of the present study indicate an increasing trend in the range of remote sensing indicators. The range of SAVI index is increasing, which means that the density of vegetation in the Wetland is decreasing. Perpendicular Soil Moisture Index values also show an increasing trend, indicating a decrease in soil moisture content. As a result of the decrease in soil moisture, the vegetation density also has decreased and the land surface temperature has increased. Results of statistical tests indicate the role of changes in environmental conditions of Hour-al-Azim wetland in the frequency of dust storms. Using findings of the present study, or studies such as Kim et al. (2017), it is possible to take advantage of soil moisture variations for the prediction of dust generation, its emission, and spread level.
Seyyed Keramat Hashemi-Ana; Mahmud Khosravi; Taghi Tavousi; Hamid Nazaripour
Abstract
Extended Abstract Introduction Precipitation is one of the vital climatic parameters that plays a major role in human life. Therefore, the impact of Precipitation in occurrence or non-occurrence of droughts and dry spells have been very effective. Identification and extraction length of dry spells ...
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Extended Abstract Introduction Precipitation is one of the vital climatic parameters that plays a major role in human life. Therefore, the impact of Precipitation in occurrence or non-occurrence of droughts and dry spells have been very effective. Identification and extraction length of dry spells in arid and semi-arid regions are very important. According to the most recent climate classification that has been done, about 90 percent of the areas of Iran are located in arid and semi-arid climate, and more than 40 percent are facing a severe water crisis. Therefore, understanding the behavioral mechanisms of dry spells have a great significance in arid and semi-arid areas like Iran, especially with the pose of the phenomenon of climate change that caused the worsening dryness and desertification in some of the regions. Many researches simulated dry spells with climate change approach and use of the output of AOGCM models. Researches in this category are in less numbers, but the most recent research has been done by the authors (hashmy titles et al., 2015), investigating and modeling the length of dry spells in the Southwestern area of Iran. The aim of this research is to examine the Validation of AOGCMs Capabilities for Simulation Length of Dry Spells under the Climate Change and Uncertainty in Iran Materials & Methods According to the aim of this research, we used two databases in this study. The first database involves collecting and analyzing all data base information (minimum temperature, maximum temperature, rainfall and sunshine) on a daily scale in 234 synoptic stations (with different statistical period). But the format for the data station and point during the period of statistical modeling was needed for more than 30 years, which has a large statistical defects were excluded, and finally 45 synoptic stations that have favorable conditions (the maximum area coverage and continuous and reliabledata) were selected for the final processing of the first data base. The period of 1981-2010 was used as the base period.The second database contains data provided by version 5 models (LARS-WG) and on emission scenarios (B1, A1B, A2) from AOGCM models for the 2050s to be downscaled. In fact, this data is the first data base (minimum temperature, maximum, precipitation and sunshine) prepared based on the format models for analysis and predicting climate change, after downscaling it. Because this research was based on study and extraction length of dry spells in the range of long-term with the approach to climate change, so the methodology is based on several stages. At first, verification (validation) of LARS-WG, to ensure efficiency in the process model simulation will be discussed. Then the performance and capabilities of 15 AOGCM models in the new version of Lars-wg will be assessed. At the end, the precipitation threshold is defined and extraction of the longest length of dry spells and comparing it with the maximum length of the dry spells will be simulated. Results & Discussion After calibrating the model of statistical properties (comparison tests T, F and P values (decision criteria), all stations were used to confirm the validity of the model. The results of this calibration indicate that in more than 96% of the stations, for the minimum and maximum temperature and sunshine model, show high accuracy (results of error in Dezful and Gorgan stations were greater). In all of these stations like Abadan station, variables significant (P-value) were at./05. It is acceptable that the data generated is random.Considering the bias error, at more than 95 percent of stations there were very good agreements between the observed and modeling data (for every 4 variables). Based on the principles of (1 to 3), and using statistical methods and indicators, the AOGCM models to simulate and extract during dry spells were examined and it was found that two models (Hadcm3 and GFDL-CM2.1) had maximum performance (correlation) and the lowest error in estimating for simulation data precipitation. The model (INM-CM3 and NCPCM) have least amount of correlation and efficiency. To estimate the maximum length of dry spells Hadcm3 results were used under scenario (A2 and B1) for the decade 2050 and the use of the results of other models was skipped in this research. Maximum dry spells in Iran comply with dryness condition in central and eastern areas. So that the country could be on the threshold of ./1 mm divided into 6 orbital regions of the northern circuit during the period of 37 days (in Rasht station) minimum and 351-day observation period in Southeastern Chabahar stations. The values show that the threshold of ./1 mm at more than 65 percent of the area’s dry spells over 7 months there was no rain on them yet. With a threshold of 5 mm needs maximum length of dry spells that lasted about a year with 364 days in Yazd station. That is roughly the size of 5 mm precipitation a year not registered at this station. Conclusion Modeling dry spells by computing scenarios of climate change and taking into consideration uncertain resources at the AOGCM models output, showed that based on the worst-case scenario (A2), and the most critical situation (2080), the average temperature of the country has increased 2.7 degrees (ºC) and Despite increased precipitation in some Stations, the average rainfall is facing a 33% reduction in the whole country. According to the most optimistic scenario (B1), the average temperature of the country is increasing by 1.4 (ºC) and the precipitation is decreasing by 14% in relation to the observation period. The results of the uncertainty examination for dry spells in Iran showed that in both 2050s and 2080s and based on all three scenarios (B1, A1B, A2), length of dry spells increases in all areas of Iran. Most of the changes in length of dry spells belong to the northwestern areas of Iran (Urmia, Khoy, Kermanshah, Hamedan and Lorestan).
Javad Khoshhal; Hamid Nazaripour
Volume 18, Issue 72 , February 2010, , Pages 21-24
Abstract
In this study, data of eleven climate variables (wind direction, wind speed, dry temperature, wet temperature, relative humidity and station atmospheric pressure) for 9:00 GMT, which is equivalent to 12:30 in Iran, and the minimum temperature, maximum Temperature, total precipitation, evaporation and ...
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In this study, data of eleven climate variables (wind direction, wind speed, dry temperature, wet temperature, relative humidity and station atmospheric pressure) for 9:00 GMT, which is equivalent to 12:30 in Iran, and the minimum temperature, maximum Temperature, total precipitation, evaporation and sunny hours of Tabas station during the years 1985-2004 were examined. Due to its proximity to the climatic middle of the day, it is a better representative of the weather conditions of a day. Therefore, we only used the data of this hour. In addition to examining the studied time, 3825 days had complete data for the variables mentioned. The matrix (3825 × 1150) was standardized, and then, a cluster analysis was carried out on this matrix, the length of which was the number of days and the width of the variables, and finally three synchronous weather types were obtained.
