mahdi boroughani

The aim of the present study is to dynamically model the spatial-temporal characteristics of dust in the south and southeast of Iran with REG-CM4 model using monthly dust data and RegCM4 data. For this purpose, the dust distribution of the IDW method along with the dust diagrams were plotted. The RegCM4 model was implemented with the paired Lambert image imaging system for 40 km horizontal separation with the paired chemistry model. The location of monthly and annual dust distribution shows the highest amounts of dust for the cities of Zabol, Bandar Abbas, Zahedan and Jask compared to Sirjan, Kahnooj and Lar stations. The highest frequency of dust in Sistan and Baluchestan (48%), Hormozgan (27%) and Fars provinces with 16% and the lowest frequency for Kerman province (9%). In general, the summer seasons (at Sistan station) and the winter (Kerman station) have the highest and minimum dust events, respectively. The time survey also has the highest amount of dust for the warm months of the year and the lowest for the cold months of the year. July at Zabol station and November and December at Sirjan station have the highest and lowest dust levels, respectively. The RegCM4 climate model also shows maximum dust on the southeast, south and south coasts for different variables.

Dust phenomenon is one of the destructive and common phenomena in arid and desert areas of Iran and the world that has harmful effects on human life and the environment. The purpose of this study is to statistically investigate the number of dust occurrences for the period 1990 to 2016 for the whole of Iran. In this study, the number changes of dust occurrences in months of the studied years (1990-2016) were analyzed at 30 stations in the country, and the correlation between stations at four regions and the whole country was investigated. The results of the occurrence number of dust in different seasons in the country indicated that the highest and lowest occurrence of dust in the country were in summer and winter, respectively. The highest and lowest dust levels were on July and December, respectively, in most stations in the country compared to other months. Also, the results showed that Zabol and Zanjan had the highest and lowest dust levels with 3666 and 130 events during the year, respectively. Most of the stations in the country had a significant correlation in terms of dust occurrence at the level of 95 and 99%, which indicates that almost most of the country have similar conditions in terms of dust storm. The results also showed that the stations that were geographically close to each other had a higher correlation coefficient.

Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust centers during the years 2005 to 2018 in the Central Desert of Iran. For this purpose, the dust source areas were extracted in the study area using MODIS satellite images using four indicators including BTD3132, BTD2931, NDDI and variable D, and finally 135 hotspots were identified and used in modeling. In this study, conditional factors affecting dust were considered for modeling including land use, soil science, geology, distance from waterway, normalized vegetation difference index (NDVI), land slope and climate. The results showed that among the applied algorithms, random forest with 63.5% accuracy was the most accurate model and followed by artificial neural network with 43.4% accuracy and generalized linear model with 43.2% accuracy. In addition, among factors, land use and soil were identified as the most effective factors on dust source area. The results of this study can provide valuable information for regional managers and policy makers and help them to make useful decisions in management.

The phenomenon of climate change can have different effects on different systems. Due to the vital role of water in human life, the study of its negative effects on the occurrence, severity and duration of drought is of particular importance. In this study, the effects of climate change on droughts on the southern shores of the Caspian Sea in future periods will be evaluated

The region under study covers three Northern provinces of Gilan, Mazandaran, and Golestan and in general, it covers 4 percent in terms of area and 12 percent in terms of population of the whole Iran. In this study, the effects of climate change on droughts on the southern shores of the Caspian Sea in future periods will be evaluated. For this purpose, precipitation data from the HadCM3-A2 general circulation model for future periods (2099-2070, 2069-2040, 2039-2010) have been scaled statistically and taking into account the uncertainty of climate fluctuations in the 20 series. At the first, daily precipitation was produced. Then the standardized precipitation index was calculated for the monitoring period and future periods. Finally, the downscaled variables of the region were compared with the observed variables and after ensuring the ability of the HadCM3 model in downscaling temperature and precipitation variables of the region, observed daily time series of precipitation and temperature and large-scale variables from HadCM3 model in the periods of 2010-2039, 2040-2069, and 2070-2099) are fed to the SDSM model.

The results obtained from the output of this index in the monitoring period and future periods with different time scales show that the intensity of drought in the coming decades will increase compared to the monitoring period. This increase is more severe for the east of the Caspian Sea, so that the intensity of the drought of the 2080s will be three times the observation period. The results indicated that climate changes result in increasing the frequency and drought severity in the studied region. Findings of the present research indicate that in spite of the existence of variability in the SPI lower than 9 months running, in longer periods of 1 to 2 year periods has increased as 25 and 23.5% in comparison with the historical period. The incompatibility of drought simulated in the future with observation period in Gorgan station with four remaining stations of the southern regions of the Caspian Sea should be noted. This situation may be due to differences in the resources of precipitation in the eastern and western coasts of the Caspian Sea and different response of two regions against the global warming. Different studies such as Alijani (1991) indicated that the Caspian Sea west coasts are more affected by the northerly flows of Siberian high pressure system than its east coasts, and in case of the establishment of the high pressure over Caspian Sea, precipitations in the west of the Caspian Sea will be higher than its eastern region. Regarding the fact that the phenomenon of global warming has resulted in weakening the Siberian high pressure system in general therefore, the degree of the reduction in precipitations in central and western regions of the Caspian Sea will be relatively higher than in its eastern regions such as Gorgan station.

Occurrence of successive droughts and increasing drought intensity in the coming decades of the southern Caspian coastal areas will reduce water resources and the region-based economy will face a more severe crisis, in which case water resources management and development of strategic irrigation document and cultivation pattern a region compatible with socio-economic and climatic trends seems absolutely necessary.

Sistan watershed has caused severe dust storms in the region in recent years due to the drying up of Lake Hamoon and consecutive droughts. This study aimed to identify and determine the characteristics of dust source areas (land use, lithology, slope, and geomorphology) in the Sistan watershed. To identify the dust source area, MODIS satellite images of dust days for the period 2015 to 2019 were used using dust detection indicators including BTD3132, BTD2931, NDDI, and D. The results of identifying dust source area indicate that a total of 211 dust source areas were identified in the whole region. Of these identified dust source area, 61 are in Iran and 150 in Afghanistan. The results of the distribution of dust collection centers in different uses indicate that dryland agricultural lands with 172 centers, dried lake surfaces, and irrigated agricultural lands with 16 and 12 sources have the highest dust collection sources. From the overlap of the lithology map and dust source, it was obtained that the most dust source area with 111 and 98 sources, respectively, are located in continuous and discontinuous sedimentary formations. The results of the distribution of dust source area in geomorphological units indicate that the erosion plain unit with an area of 35.69 percent, 48 dust source area has the highest collection source. Also, the results of the distribution of dust source area in different slopes show that the highest dust source area is in the slopes of 0 to 2% with 107 sources and the lowest located in more slopes of 32%% with 6 sources.

Base-flow is the percentage of river flow which is associated with groundwater discharge and demonstrator of the extent of groundwater participation in total runoff. Therefore, base-flow has an important role in hydrological and water resources researches such as quantitative and qualitative water resources assessment, hydrologic modelling, calibration and validation of these models and also evaluating the environmental water requirements in different regions. In present research the daily data of river flow for the period of 30 years is selected to evaluate base-flow in Shamkan River which is located in Sangerd catchment. The daily base-flow is calculated during the years of 1987 to 2016 using BFI, Lyn and Hollick, Eckhart and Local Minimum methods. Due to the lake of observation base-flow data, the BFI method is considered as the base method and the results of other methods were compared with the results of the mentioned method. The results indicated that the estimated base-flow by different methods is averaged between 71 to 81 percent of the river flow. The results of error criteria showed that Lyn and Hollick method with the filter parameter of 0.95 is the most suitable method to separate base-flow in the case study. While the Eckhart method with the filter parameters of 0.99 and 0.995 have had highest RMSE and MAE values and also had the lowest Nash-Sutcliff values.

Dust storms are natural hazards that effects on weather conditions, human health and ecosystem. Atmospheric processes are directly affected by the absorption and diffusion of radiation by dust, and dust in the cloud acts as a nucleus of congestion. The main dust regions in the world are arid topographies that have soil that is vulnerable to erosion and poor vegetation, easily eroded by wind. Due to its presence in the dry and semiarid belt of the world, Iran is exposed to multiple local and transboundary dust and dust systems. Iran and especially eastern Iran are exposed to severe environmental challenges such as storm dust. Land use change and soil erosion-sensitive soils are one of the most important factors affecting the creation of dust source areas.

The purpose of this study is to identify and determine the characteristics of dust source areas in eastern Iran. The study area is in the eastern part of Iran with an area of about 3711854.1704 km2. The altitude in the study area is 107 to 3527 meters above sea level. This area includes 7 provinces including North and South Khorasan, Khorasan Razavi, Semnan, Kerman, Yazd, Sistan and Baluchestan. In this research, MODIS data was used to identify the dust source area. To identify dust source area from MODIS satellite imagery for the period of 2004 to 2017 and using dust detection indicators including BTD3132, BTD2931, NDDI and D, were used. After determining the areas for dust source and preparing the distribution map of these points in the study area, the surface properties of dust source areas were investigated. In this study, landslide, geology, slope and normalized vegetation index (NDVI), which are effective in creating dust source area, were studied. Finally, to determine the characteristics of dust source areas, the mapping of the distribution map of dust source areas with land use maps, slope, NDVI and lithology in ArcGIS software environment was performed. After covering the layers, land use characteristics, slope, NDVI and lithology of dust source areas were extracted.

Using the four parameters and the method of false color combination and applying it to the MODIS image, the dust mass was detected on the images and then, by their visual interpretation, the starting point of the dust was determined. 147 dust points were identified in the study area. The results show that the highest dust points in land use, rangeland and agricultural land with 54, 45 and 18 points, which is about 36.73%, 30.61% and 24.24% The total points of the study area are located. The distribution results show the dust points on different slopes. As you can see, most of the dust points are two slopes of 0-2 and 2-5 with 104 and 27 points, with 90% of the source points in these two slopes. The results indicate that clay-loamy soils with the 61 point of dust, accounting for about 41.5% of the total points, have the highest point. This soil contains 38.084 percent of the total area of the region. The Vegetation Indicator Map (NDVI) in the region indicates that most of the area has no vegetation or vegetation, and only a very small part of the area (less than 1%) has a vegetation that indicates the land Far East of Iran. The results also indicate that all dust points are located on the 0 to -0.394673 class, and the rest of the classes have no point of view. The results showed that the highest and lowest dust points were in the geological units of chemical-sedimentary formations and intrusive rocks with a number of 43 and 0 source of dust.

The results show that most of the dust source areas in land use and rangeland uses 54 and 45 points, which is about 37% and 31% of the total source points of the study area. The results also showed that among different soils, the highest point of dust with clay-loamy soil texture with 61 points was observed (about 41.5% of total source points). Also, most dust source area are visible in areas with low slope and no vegetation cover. From the perspective of geology, the highest and lowest dust points in the region are located in the geological units of the sedimentary chemical formations and intrusive rocks of the player.

Dust storms are natural hazards that effect on weather conditions, human health and ecosystem. Atmospheric processes are directly affected by the absorption and diffusion of radiation by dust, and dust in the cloud acts as a nucleus of congestion. The main dust areas in the world are flat topographically dry areas with erosion-sensitive soil and poor vegetation that is easily eroded by wind. Atmospheric processes are directly affected by the absorption and scattering of radiation by dust, and the dust in the clouds acts as a condensation nucleus. Iran is exposed to numerous local and trans-regional dust systems due to being in the arid and semi-arid belt of the world. Recent observations indicate that this phenomenon is increasing in the country and region under study. Khorasan Razavi province has arid and semi-arid climates with numerous dust storms occurring annually. In some years, more than 50 dust events have been recorded in the province. The large amount of dust in this area indicates large harvesting areas across the province, indicating the importance of research in this area. The purpose of this study was to evaluate the factors affecting the occurrence of dust and to prepare its susceptibility map in Khorasan Razavi province using logistic regression and random forest regression models.

Khorasan Razavi Province with an area of 143864.118 square kilometers and with a population of 5.999529 is the fourth largest province in the country. The highest point of the province is in the highlands of Binalood, north of Neyshabur, at an altitude of 3211 meters above sea level, and the lowest point of the province is 300 meters above sea level, on the border of Iran and Turkmenistan. Dust indices include BTD2931, D, NDDI and BTD3132, which were calculated for all satellite images of these indices. Then there are four false color combination methods including 1- R: NDDI, G: B4, B: B3, 2- R: D, G: BTD2931, B: NDDI, 3- R: D, G: BTD3132, B: NDDI and 4- R: BTD2931, G: B4, B: B3, were used to determine the best method for identifying dust source. Initially, 65 dust sources were identified in the study area and for the period 2005-2016. Of the identified focal points, 70% (46 dust source) were used for modeling and 30% (19 dust source) were used for evaluation. Then, 7 factors including soil, lithology, slope, vegetation index (NDVI), distance from the river, geomorphic units and land use as an independent and effective variable on dust were prepared. Then using logistic regression and random forest regression models, the weight of each of the factors in R software was calculated, finally, the dust hazard sensitivity maps were prepared.

Four different false color combinations were used to detect dust areas in all images. Each of these methods revealed a dusty mass. The second method (R: D, G: BTD2931, B: NDDI) is the best method for detecting dust and determining dust collection zones. Finally, by employing these techniques and creating false color combinations and applying them to existing images and their visual interpretation, 65 dust collection points were identified throughout the area. Based on the results, slope had the greatest impact on creating dust source zones. Geomorphology, lithology, vegetation index, soil and land use are the next influential categories of dust source area. Negative coefficients indicate the inverse relationship of the dependent variable (dust source) with the independent variables; in fact, negative coefficients do not indicate a lack of correlation, but mean a weaker correlation than other factors. The distance factor from the river has the least impact on the formation of these dust source. The OOB results show a predicted error rate of about 35.16%. In other words, the accuracy of the model can be considered 64.84%. The results of logistic regression algorithm showed that 24.5% of Khorasan Razavi province was in low sensitivity area, 28.2% in medium sensitivity area, 29.4% in high sensitivity area and 17.9% in high sensitivity area. It is very sensitive. The results show that the highest dust source points (57.9%) are located in high sensitivity area. The results indicate that about 84.2% of the dust collection points are located in the area of high and high sensitivity. Surveying the area of dust susceptibility map using random forest algorithm showed that the area with the highest sensitivity had the lowest area (16.7) and the areas of high, medium and low were 23.5, 31 and 8.8, respectively. 28 estimated. About 63.2% of source are located in the area of high sensitivity and there is no source for the area of low sensitivity.

The results of both models show that slope and land use with the significant coefficients of 0.852 and 0.673 had the most effect on the creation of dust source area. The results also indicate that most dust removal sites (57.9%) are located in high sensitivity area. The results indicate that about 84.2% of the dust collection points are located in the area of high and high sensitivity. The evaluation of models using the ROC curve showed that in terms of success rate, logistic regression models and random forest had a precision of 781 and 907 respectively. Each of the two high correlation models between the sensitivity maps and the distribution of dust source areas can be observed. But in general, the random forest model has a higher accuracy than the other logistic regression models.

Due to the drought and land use changes in recent years, the phenomenon of storm dust in Iran is increasing as a dangerous environment. Dust influences climate change and human health, causing serious damage. The subject of this research is to identify and prepare a map of sensitivity of dust source area for controlling and determining the role of each of the factors affecting its occurrence using the regression tree data mining model (BRT) in eastern Iran. For this purpose, at first 147 dust source area were identified in the region and divided into two groups for modeling and evaluation. According to the studies, eight effective factors including land use, geology, slope degree, elevation, normalized vegetation index (NDVI), distance from the river, wind speed and rainfall were identified and the layers of these factors were prepared in GIS environment. To evaluate the results, the ROC curve was used. The results showed that the BRT model with the area under the curve (79.6) had a good performance in producing a dust sensitivity map in the study area. Based on the results of the model, vegetation index, elevation and slope had the most impact on the occurrence of dust in the region.