فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 119 (بهار 1401)

Drought has imposed a huge damage to country’s economy especially in recent decades. Pasture yield losses, reduction of agricultural and drinking water, reduction of groundwater and surface water resources, outbreak of plant and animal pests and diseases and an increase in migration are among the negative effects of drought. The aim of this research is understanding atmospheric circulation patterns with modern methods and based on further data; therefore in case of synoptic patterns identification associated with drought and wet years, these patterns can be used to forecast country’s wet and dry periods with a very high accuracy.

To identify and extract widespread Iran’s droughts and wet years and in order to achieve the objectives of the research, two databases were required: Surface environment data and upper atmosphere data. Surface environment data was used from rainfall data of 43 synoptic stations in a 33-year period (1976-2009) which was received from the Iran Meteorological Organization. High atmospheric variables which were used in this research include: Geopotential height, sea level pressure, zonal wind, meridional wind, air temperature and specific humidity. Data and maps of all these variables were collected from www.esrl.noaa.gov/psd website as monthly data. Data related to Geopotential height variables; zonal wind, meridional wind and the air temperature at 17 levels and specific humidity in 8 level data are available. A suitable index was selected in order to analyze inclusive droughts and wet years of Iran, so droughts and wet years could be separated according to it. In this research Standardized Precipitation Index (SPI) was selected given its advantages compared to other drought indexes. Synoptic analysis method of patterns related to Iran’s monthly inclusive droughts and wet years, were divided into three categories based on a principle location: local droughts (or wet years): Droughts (or wet years) which declared about 25% and less of studied stations in Iran’s droughts (or wet years). Semi-inclusive droughts (or wet years): Droughts (or wet years) which declared about 25 to 75 percent of studied stations in Iran’s droughts (or wet years). Inclusive droughts (or wet years): Droughts (or wet years) which declared about 75% and more of studied stations in Iran’s droughts (or wet years).

The results showed that in the inclusive droughts, positive temperature anomalies and in inclusive wet years, negative temperature anomalies can be observed in Iran. Composite maps of humidity anomalies also nicely demonstrate that in the times of drought across Iran, negative anomalies have been found and in the wet years contrast status of droughts have happened and the positive anomaly got over Iran. In the months that inclusive drought happened in Iran, it can be seen that European and Asian high pressure combined together and put the whole of Europe and Asia under domination which under these conditions, Iran experienced drought months as during this time cyclonic circulation formed on the Arabian Sea which made humidity of the Arabian Sea not to transferred to Iran. The second affecting anomalies on this phenomenon, is the Mediterranean Sea; in these months a major anti-cyclonic circulation dominated on Europe. This major anti-cyclonic circulation affected Mediterranean Sea and even North Africa. In these circumstances Mediterranean cyclone formation was disrupted and so no humidity transferred to Iran. When European high pressure move to the North Atlantic Ocean and provide space for the establishment of a polar low pressure on Europe; in these circumstances regional low pressure is formed on Mediterranean Sea which causes more humidity entering Iran which results will be wet months; as well as Anti-cyclonic circulation ruling on of the Arabian Sea that crosses over the Red Sea is causing humidity injection in Iran.

In the months that inclusive drought happened in Iran, it can be seen that European and Asian high pressure combined together and put the whole of Europe and Asia under domination which under these conditions, Iran experienced drought months as during this time cyclonic circulation formed on the Arabian Sea which made humidity of the Arabian Sea not to transferred to Iran. The second affecting anomalies on this phenomenon, is the Mediterranean Sea; in these months a major anti-cyclonic circulation dominated on Europe. This major anti-cyclonic circulation affected Mediterranean Sea and even North Africa. In these circumstances Mediterranean cyclone formation was disrupted and so no humidity transferred to Iran. When European high pressure move to the North Atlantic Ocean and provide space for the establishment of a polar low pressure on Europe; in these circumstances regional low pressure is formed on Mediterranean Sea which causes more humidity entering Iran which results will be wet months; as well as Anti-cyclonic circulation ruling on of the Arabian Sea that crosses over the Red Sea is causing humidity injection in Iran.

Wind is one of the important causes of erosion in arid and semi-arid regions, which due to the limited vegetation in these areas, is able to move portable particles and results in wind erosion. Erosion defines as the transfer of discontinuous sediment from the earth's surface by water or wind. Wind erosion is a process that occurs because of increased speed and due to wind turbulence on a surface free of cover and as one of the main factors of land degradation and limiting soil fertility has created serious problems in many parts of the world, including Iran. Therefore, there is a serious problem against sustainable production and management of agricultural lands. The process of wind erosion destroys the soil and makes it inaccessible.In arid and desert areas, soil erosion and particle transport are more affected by wind than other factors. Wind erosion and deposition of materials by wind or wind erosion occurs in more than one third of the land surface. Soil displacement and its destruction in such areas is very important; Due to the climatic conditions of these areas, soil is formed late. Wind erosion first picks up loose soil without vegetation and during the course of the attack, the impact of moving particles by the wind intensifies the erosion and increases the damage, and finally leaves the transported material in the form of sand dunes.

In this study, the physical-laboratory method of wind tunnel was used to estimate the amount of erosion and sedimentation of geomorphological facies of Gonabad city compared to wind erosion. Firstly, the landform units and their types were distinguished and after identifying the facies, one square meter was taken from the surface soil and the samples were transferred to the wind tunnel aerodynamics laboratory of Hakim Sabzevari University for analysis. Then, in order to measure the amount of sedimentation of lands through wind erosion measuring device, first the erosion threshold for each sample was determined and the sediments were exposed to wind erosion for ten minutes and its erodibility was determined. To measure the wind erosion threshold, initially the tray was filled with sediment and weighed, and the sediment sample was placed inside the wind tunnel and the wind tunnel was lit at a base speed of 4 meters per second. Then the speed was increased to reach the wind erosion threshold. Wind erosion threshold is the speed at which the first sediments begin to move. At the wind tunnel threshold speed remained on for ten minutes and the sediments were exposed to wind. After this time, re-sedimentation of weight and after calculating the percentage of pebbles in each facies, the amount of winding was estimated. Three samples of each facies were placed in the wind tunnel and the average velocity of the winding threshold was considered as the wind erosion threshold of the facies.

The results of measuring the amount of eroded soil of geomorphological facies were estimated for the erosion threshold speed of each facies. Wind erosion threshold varies from 6 m / s in sand dunes to 15 m / s in low and high plains and relatively high water erosion and new deposits. Wind gauge was measured between 20 g / m2 in earthen hills with low and high erosion and moderate erosion up to 350 g / min in 10 minutes in sand dunes.Examination of wind erosion threshold map in different facies and its adaptation to sand transport flow direction show that in sand dunes facies with threshold velocity of 6 m / s dominant direction of northwest flow was consequently threatening in north part It is in the way of communication. However, part of this facies is located in the north of Gonabad, which can be hazardous due to the location of industrial settlements and the establishment of industries for moving sand.

Lowland plains with relatively high water erosion and new deposits with a threshold speed of 15 meters per second are resistant to wind erosion and sand dunes with a threshold speed of 6 meters per second (12 knots) have the most sensitive facies and the highest amount erosion (350 g / m2 in ten minutes). After that, plains with gentle slopes without elevations and facies of earthen hills with elevations and thresholds with a threshold speed of 10 meters per second were estimated from areas sensitive to wind erosion in the region. The adaptation of the sand dunes to the speed of different thresholds showed that the city of Gonabad is prone to wind erosion by being located in the sensitive facies of a plain with a gentle slope and without elevation and the direction of the northern flow of sand in this facade. The results of calculating the wind erosion threshold in the study area showed a threshold speed of 6-15 m / s in different facies. The most sensitive facies are sand dunes (6 m / s) followed by plains facies with gentle slopes without elevation and elevation of earth hills with elevation and wind erosion (threshold speed 10 m / s). Considering that the city of Gonabad and Beidokht, as the two most important population centers, are located in the facies of plains with gentle slopes and without slopes and heights, special attention should be paid to this facies for stabilization. Also, sand dunes cover a large part of the city and in terms of location, are located on the main road north to south of the country (Mashhad-Zahedan) and also has the lowest erosion threshold and the highest wind volume. Management of these lands and planning to stabilize this facies seems to be necessary. An overview of the wind erosion susceptibility map showed that the northern areas of the city have more potential than the southern areas. As moving toward south, wind erosion decreases, and the topographic factor plays an important role. Due to the fact that the study area is one of the arid regions in terms of climate, so the unfavorable climate has prevented the establishment of vegetation and has intensified wind erosion in the region. It is suggested that in areas where the soil surface has hardened (floodplain facies and clay soils) and gravelly lands (sandy plains) the most important action to prevent wind erosion is to maintain this cover and prevent from damage cover.

The interactions of circulation patterns are the interference or synergies of different circulation patterns from different latitudes and at different atmospheric levels. When interacting with circulation patterns, they will have different effects on the environmental phenomena of the Earth's surface than when acting individually. The mechanics of moving the telescopic link between the tropical zone and the subtropics is a major issue in geographical research. Considerable evidence suggests a dynamic relationship between tropical regions and mid latitudes. Tropical-extratropical interactions occur in a wide range of processes and at different scales.Tropical cloud plumes, reflects tropical- extratropical interaction in relation to the transfer of moisture from tropical to extratropical. TP was first defined by McGregor et al. (1984); a continuous strip of upper and middle clouds that are at least 2000 km long . clouds with minimum latitude of 20 degrees and maximum longitude of 5 degrees are in the tropical region. considers tropical cyclones as long bands of mid- and upper-level clouds moving from the tropical region to the polar-eastern direction to the subtropical region, especially continued by a tropical jet and a trough in its eastern part. TPs are relatively narrow at low latitudes (20–15 degrees) and widen at about 30 degrees north latitude. Studies of TP have expanded since the advent of satellite imagery in the 1960s. Prior to satellite imagery, McGurick et al. Described the development of TP in adaptation to a deep trough in subtropical jets and the turbulence of tropical winds, which ceases whenever this alignment is lost. The location and time of occurrence of plum clouds have been reported differently in different studies. In this study, we seek to investigate the Plum tropical clouds and the dynamic climbing factors that lead to their transfer to the midlatitude as a source of tropical moisture that leads to rainfall in Iran.

In order to investigate the role of TP event as an important factor in tropical-extraterrestrial interactions and as a source of moisture in the days with inclement rainfall, the 25th, 26th days of 2019 that Iran has experienced extreme precipitation were selected. The data used in this study are satellite imagery and atmospheric data. Since TPs are clouds that can only be seen in satellite imagery, IR Meteosat satellite imagery was used. Required daily atmospheric data, geopotential height (in meters), wind speed (in meters per second) and wind direction were used on an hourly time scale. The origin, path and direction of the clouds were identified using satellite images. In order to identify synoptic patterns at the time of TP occurrence, synoptic maps of geopotential height and jet stream for atmospheric indices (200 and 300 hPa) were drawn in GRADS environment using ERA5 data. Also, combined images of clouds with geopotential height and tornadoes were used for better investigation.

On March 25, at 0:00 a.m., the TP oceanic strip reaches 17 degrees along the North Atlantic cut-off low divergence zone, and along the orbital currents of westerly winds entering the tropics below the equatorial equator the subtropical jet stream is flowing. The bifurcation of TP corresponds to the bifurcation of polar front and subtropical jet streams. TPs correspond exactly to the kernels of jet stream. Jet stream core has not reached the east, southeast and parts of the northwest, so TP is not observed in these areas. The widening of the clouds occurred in the divergence zone of western trough over Iran and in the cut-off divergence zone of the Atlantic Ocean over western North Africa. At 06.00, with most of the jet stream core entering the northeastern and northern regions of Iran, TP has entered these areas. At 0:12, the wind speed of the core of jet stream has several degrees of southward displacement, which has led to the entry of TP into the southern and southwestern regions of Iran. At 0:18 East and TP continents originate from the Central African equator and enter Iran in the direction of the divergence zone of west trough and jet stream. Jet stream entered Iran in a more southerly direction, which prevented TP from entering northwestern Iran. On March 26, the subtropical jet stream is not orbital, unlike the day before. This factor can prevent the transmission of the TP orbit of the oceanic band to the east. The TP oceanic band reaches the Mediterranean with a south-north direction in the direction of the cut-off divergence of the Atlantic Ocean. Due to the location of jet stream core on the southwest-northeast diameter of Iran, the highest volume and extent of TP is observed in these areas of Iran. The entry point of TP corresponds to the entry point of jet stream core. At 18:00, the presence of the highest TP on the northeast of Iran indicates the presence of a speed core on these areas. On March 26, we see the southern transfer of TP in the direction of southwest-northeast diameter, which is in line with the southward displacement of the jet stream core to more southern widths.

On March 25 and 26 ,2019 The tropical intrusion of the extratropical dynamic factors has occurred. These factors are the deep extratropical western troughs and polar front jet streams. Their tropical intrusion has led to the transfer of tropical moisture to the extratropical region. The place of penetration of the western troughs into the tropics has determined the origin of the formation of clouds in the tropics. Also, the path and direction of the clouds from the tropics to the subtropics have been determined by the western trough divergence zone and the polar front jet stream at the level of 300 hPa and the subtropical jet stream at the level of 200 hPa. TP has entered Iran with both oceanic and continental origins and through the Red Sea and the Arabian Peninsula along the western trough divergence zone and speed core of the subtropical jet stream and polar fronts in a southwest-northeast direction. Two cloud bands merge on the Red Sea, Which lead to an increase in the Cloud moisture capacity on Saudi Arabia and Iran. With Transfers of clouds over Iran due to insufficient instability, clouds have led to precipitation. Tropical Plume clouds can be a source of moisture for offshore precipitation Tropical plume clouds can be a source of moisture for precipitation in extratropical. The presence of such a moisture source along with lower level moisture sources can increase the capacity of moisture, which in the presence of instability and lifting factors, cause more precipitation.

Vegetation indices (VI) time-series have been used for land surface phenology retrieval but these time series are affected by clouds and aerosols, which add noise to the signal sensor. In this sense, several smoothing functions are used to remove noise introduced by undetected clouds and poor atmospheric conditions, but a comparison between methods is still necessary due to disagreements about its performance in the literature. The application of a smoothing function is a necessary previous step to describe land surface phenology in different ecosystems. Satellite-derived phenological parameters do not specifically provide information on the phenology of a single plant, their species or pheno-phases (e.g., bud opening, leaf emergence, leaf opening and flowering). Remote sensing Vegetation Indices are usually able to estimate a few phenological parameters such as start of season (SOS), end of season (EOS). The aims of this research were to evaluate the consistency of different smoothing functions from TIMESAT software and agricultural regions using the Greenness-Landsat time-series. To overcome the problems associated with remaining noise, various methods have been developed to estimate phenology and production metrics based on the VI time series. Some of them are wavelet decomposition, double logistic (DL) function, the asymmetric Gaussian (AG) function fitting, Savitzky–Golay (SG) filters, the Weighted Least Square (WLS). Some studies have compared these smoothing approaches, but most of them focus on coarse spatial resolution satellite image time series, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). Due to the variety of results and the lack of consensus on smoothing methods, quality evaluation of smoothing operations should be done for each plant index and crop. Thus, the objectives of this paper are to evaluate and analyze the performance of various smoothing functions in TIMESAT software and their effects on estimating the phenological parameters of start of season (SOS) and end of season (EOS) of rapeseed.

In this study, we used two kinds of data: 1- phenological data of rapeseed that was obtained from Field observation, and 2- GREENNESS index data extracted from Landsat 8 satellite images in the Agricultural Years (2016-2017, 2017-2018, 2018-2019). Geometric and radiometric corrections were applied to satellite images. The DN value was also converted to TOA to calculate Vegetation Indices. An adaptive Savitzky–Golay (SG) filter, Asymmetric Gaussian (AG), and Double Logistic (DL) functions to fitting Greenness data were used and their performances were assessed using the measures root mean square error (RMSE), Pearson correlation coefficient(r). Besides, differences in the estimation of the SOS and EOS were obtained. In all methods, the adaptation to upper envelope with the raw GREENNESS time series was used to reduce bias. In the Savitsky-Goli method, in addition to adapting upper envelope, the window size parameter (r) was also used.

Statistical evaluation of smoothed time series Statistical analysis of the output of smoothing functions showed that the time series produced by the AG model compared to the raw time series of the GREENNESS index had the lowest root mean square error (RMSE = 0.415) and the highest correlation (r = 0.935) belong to S-G model. The advantage of DL and AG models is that the difference between the mean correlation coefficient for all performances and the correlation coefficient for the best execution is small and it can be inferred that the software parameter settings have little effect on the outputs of these models. After plotting the smoothed time series curves, the results showed that the use of smoothing models effectively eliminated noise and disturbed the raw time series of the GREENNESS index, and reconstructed smoother and softer time series. The results also showed that time series that have a higher correlation coefficient show more details and changes within the inter-season, such as the recession stage(dormancy). Overall, it can be concluded that for reconstructing GREENNESS time series data, Pearson correlation coefficient (r) is more accurate than root mean square error (RMSE) and S-G model is more accurate than the other two models.

In this study, we showed to what extent the time series of the three smoothing methods SG, AG and DL in the reconstruction of the raw time series of the GREENNESS from the Landsat 8 and estimating the phenological parameters of the start and end of the season are accurate. The results of this study showed that the adaptive S-G model is more robust for reconstructing raw time series than AG and DL functions, and this is due to the sensitivity of this model to small changes in the GREENNESS time series. The AG and DL functions tend to eliminate noise at the peaks and bottoms of the time series. The results also showed that the time series with the highest correlation coefficient (r) are more suitable for reconstructing the raw time series of the GREENNESS index compared to the time series that produced the smallest RMSE. In SOS estimation, the S-G model performs worse than the AG and DL functions. Compared to the observational data, all smoothing methods used in this study estimate EOS late and SOS early. The results also showed that both AG and DL functions have time lag in SOS estimation compared to S-G model and time precedence in EOS estimation compared to S-G model. The efficiency of any smoothing method depends on the choice of parameters. For example, the use of adaptation upper envelope generally improves the results. AG and DL fitting function methods are the preferred option for smoothing low-quality data (eg high noise and high data loss) due to less sensitivity to regulatory parameters. The AG and DL fitting functions are limited when giving inter-seasonal details of the time series curve. Numerous factors such as vegetation index selection, satellite sensor data and vegetation type are affected in evaluating time series and estimating phenological parameters. However, the results of this study are valid for the data and the location under study, and the results may vary with other data or under other circumstances.This study showed that the statistical criterion of Pearson correlation coefficient (r) is superior to the root mean square error (RMSE) and the S-G model is superior to the AG and DL models for reconstruction of time series. The DL function and AG function show the best performance for estimating SOS and EOS phenological parameters, respectively.

Landslides are one of the most important geomorphological processes that affect the evolution of landscapes in mountainous areas, which in some cases also lead to catastrophic events (Hattanji and Moriwaki, 2009). In recent decades, due to the increasing trend of damages caused by natural disasters, especially landslides, the category of forecasting and preventing damages has been seriously discussed (Izadi and Entezari, 2013). Landslide risk zoning maps can provide an effective and efficient tool for planners and decision-makers to identify suitable zones for future development (Ghobadi Alamdari, 2019). The use of GIS and multi-criteria decision-making methods, with an integrated approach, can accelerate the planning process in the diagnosis of critical and emergency cases and lead to the issuance of appropriate results.Givi Chay watershed with an area of 1814 square kilometers is one of the sub-basins of Sefidroud, which is in the coordinates of 48 degrees and 4 minutes and 20 seconds to 48 degrees and 40 minutes and 12 seconds east longitude and 37 degrees and 26 minutes and 3 seconds. Up to 37 degrees and 55 minutes and 55 seconds north latitude, it is located in the south of Ardabil province and within the city of Khalkhal and Givi. This watershed is from the north to Qarasu watershed and the heights of Turka, Pileh, Chaleh Marz and, Gondab, from the west to Qarnaqo river catchment and from the east to Agh-e-Uler, Navroud and, Talesh mountain ranges and from the south to the basin The catchment area of the Ghezel Ozan River is limited. The minimum and maximum height of the Givi Chay watershed, are respectively; 873 and 3025 meters. Geologically, the study area is located in the West Alborz-Azerbaijan tectonic zone.

The present research is of applied type and its research method is analytical based on the combination of data analysis, GIS, and the use of multi-criteria analysis techniques. ENVI, Arc GIS, Idrisi, and Excel software have been used for image processing and data analysis. In this study, first the effective factors in Landslide (including slope, aspect, Elevation classes, lithology, land use, precipitation, distance from the communication road, distance from the waterway and distance from the fault), according to the natural and human conditions of the region were identified. In the next step, information layers related to each factor were prepared in the GIS environment. The information layers of curved lines, communication roads, and waterway networks were obtained by digitization from the topographic map of Givi and Khalkhal cities with a scale of 1: 50,000, and the slope and aspect layers were prepared using a digital elevation model. Information layers related to lithology (rock resistance) and faults, by digitization from the geological map of Givi, Khalkhal-Rezvanshahr, and Masouleh; Prepared at a scale of 1: 100,000. To extract the land use of the study area, first geometric and atmospheric corrections were made on the images using the Flaash method in Envi software. Then the classification was done by object-oriented method and nearest neighbor algorithm in Ecognition software, and the results obtained from the classification of uses in the present study, both in terms of individual uses and in terms of total accuracy and kappa statistics, are acceptable (greater than 85 Percent), in relation to the information produce. The precipitation map of the basin was drawn using meteorological and rainfall station data and the method of precipitation gradient equation (P: 0.11104H + 193.24). To prepare a landslide risk map, WLC, OWA, VIKOR, and MABAC multi-criteria decision algorithms, fuzzy standardization and cortical weighting method have been used. Landslide zoning maps have been evaluated using the relative performance detection curve (ROC).

According to the output obtained by using the WLC method, 427.352 square kilometers of basin area is in the high-risk class and 599.237 square kilometers is in the high-risk class. According to the landslide hazard zoning map obtained from the OWA method, respectively; 284.262 and 670.628 square kilometers of the basin area are very high-risk and high-risk classes. According to the hazard map obtained from the VIKOR method, high-risk and high-risk classes, respectively, occupied 745.457 and 394.471 square kilometers of the basin area. Also, the results obtained using the MABAC method show coverage of 572.900 square kilometers of high-risk floor and 551.030 square kilometers of the high-risk floor of the basin area.Results of output overlap of the studied models, with a scattering of landslide points; Showed that according to WLC, OWA, VIKOR, and MABAC multi-criteria decision algorithms, respectively, 37.84, 46.73, 59.46, and 48.65% of the slip points in the high-risk category and 37.84, 51.35, 24.33 and 35.14% of slip points are in a high-risk category. The matching of slippery surfaces and hazardous zones shows that at the output of all the studied algorithms, the areas in the high-risk, high-risk category have the largest area of ​​ landslide surfaces. In addition, in the low-risk classes introduced by the multi-criteria algorithms, a limited number of landslides are observed, and in the low-risk classes, no distribution of landslides occurs in the basin. Therefore, it can be concluded that due to the distribution of landslides in each of the hazard classes, all the studied algorithms and especially the Vikor method by covering 22 landslides in a very high-risk class, of high relative accuracy in They have a landslide risk assessment.

According to the results, respectively; Slope factors with a weight of 0.16, lithology with a weight of 0.15, and land use with a weight of 0.13 had the most role in the occurrence of landslides in the basin, according to the output of the studied algorithms, area Low and very low-risk areas have the lowest area in the basin. On the other hand, medium, high, and very high-risk zones have the largest area of ​​the basin. It can be said that the results of this study indicate the high power of the Givi-chay basin in terms of the occurrence of landslides. Due to the distribution of landslides in each of the hazard classes, all studied algorithms have a high relative accuracy in landslide risk assessment. According to the validation results, the area under the ROC curve for OWA, VIKOR, and MABAC methods was calculated to be 0.72, 0.73, 0.85 and 0.76, respectively. Therefore, the accuracy of OWA, WLC and MABAC methods is very good and the accuracy of the VIKOR method is excellent.

Identifying pressure patterns during thunderstorms and Widespread precipitation is important. One of the methods of studying the climate of a region is the study of atmospheric phenomena in relation to the pressure pattern prevailing in that region. Classification of atmospheric patterns is a useful tool for managing a huge and unlimited continuity of atmospheric patterns. Classifications by identifying a number of representative patterns called moment patterns, simplify the physical reality of the atmosphere. One of the purposes of synoptic classifications is to help describe the effects of atmospheric circulation on the surface climate, which is the main task of synoptic climatology. In this paper, atmospheric pressure patterns are studied using factor analysis method. Then, by drawing synoptic maps of each pattern, the characteristics of the desired pattern were examined.

The present study investigates the recognition of prevailing atmospheric patterns during precipitation and Widespread thunderstorms in northwestern Iran. To conduct this research, first rainfall and thunderstorm data were received daily from 18 synoptic stations with a common statistical period of 20 years (1993-1992) from the Meteorological Organization. Then, to determine the selected days, the days that were reported in 5 stations and more rain and thunderstorms were selected. Then 108 days were selected by principal component analysis (PCA). Six components out of the total components that explain more than 73% of the total variance were selected for the next analysis. Then, using Euclidean distance and ward’s method, a cluster analysis on the matrix, Factor scores were performed. Then the clustering tree was drawn and the observations were divided into 5 clusters. To analyze the atmosphere in the obtained patterns, re-analyzed data were prepared with a resolution of 2.5 * 2.5 degrees from the National Center for Environmental Prediction and Atmospheric Research, USA (NCEP / NCAR). Using these data, synoptic maps in each pattern were drawn and analyzed.

Investigations showed no compression patterns with Widespread thunderstorms in March. Because this month has winter features, extensive local climbs are less common this month. Therefore, Widespread thunderstorms in this month and other cold months of the year are considered a random phenomenon. It can also be said that hot and cold fronts can not create Widespread thunderstorms without local thermal rise. In other words, in summer due to lack of sufficient humidity and in cold seasons due to lack of surface heating we do not see the occurrence of Widespread thunderstorms.At the time of the occurrence of rains and Widespread thunderstorms in northwestern Iran, It is often located at the 500 hpa level of Trough in central Iraq to the eastern Mediterranean. Differences in the location, depth and extent of this trough have caused the patterns to differ from each other, resulting in differences in the occurrence of atmospheric events in the study area. In this study, pattern three had the highest number of events compared to the 5 extracted patterns. Patterns that are limited to April and the cold days of May are more widespread. Also, patterns that occur limited to June and hot days in May are less widespread. In the Deep trough and minor Trough pattern, the precipitation is mostly influenced by local moisture sources and moisture sources close to the study area (such as the Caspian Sea). Also, in these patterns, the sources of moisture in the lower levels of the atmosphere play a greater role in the occurrence of precipitation. At the time of the Deep Trough and Minor Trough patterns, it is the combination of western systems with local ascent that creates Widespread precipitation. These patterns are limited to May and June. During these months, the ground receives the energy it needs to climb locally. The integration of the local ascent with the western system strengthens it and creates Widespread thunderstorms and rainstorms.In patterns closed low, blocking , the Eastern Mediterranean Trough, the Red Sea and the Mediterranean Sea are the most important sources of moisture, respectively. In the mentioned patterns, moisture sources at the levels of 700 and 500 hPa play the most important role in creating Widespread precipitation. During the occurrence of closed low, blocking and trough patterns in the eastern Mediterranean, the surface temperature decreases and the role of western systems becomes more prominent. From pattern one to pattern five, the surface temperature decreases, respectively. In the Deep Trough and Minor Trough patterns, the study area experiences a temperature of 295 degrees Kelvin. In the closed low pattern, a temperature of 287 degrees Kelvin is observed in the northern half of the study area; But in the blocking pattern, the temperature of 287 is transferred to the more southern parts. In the Trough pattern of the eastern Mediterranean, the temperature of 283 degrees Kelvin is seen in the northern parts of the study area. In other words, from model one to five in the study area, the surface temperature decreases and from one model to another, the surface temperature decreases. As a result, with decreasing temperature, the effect of local ascent compared to the first and second patterns has decreased and it can be said that a significant part of thunderstorms in addition to local ascent, is affected by the front systems passing through northwestern Iran; But fronts alone cannot create widespread thunderstorms without integrating with local thermal rise.

The result was that the closed low pattern had the highest and the Deep trough pattern had the lowest repetition. Deep trough and minor trough patterns have less geographical extent than other patterns. When occur patterns closed low, blocking and trough eastern Mediterranean, the study area experiences a lower surface temperature. Also, precipitation is more extensive in these patterns and the maximum vertical air flow is observed at the level of 700 and 500 hPa. Widespread thunderstorms are more likely to occur in the spring. In winter, due to the lack of surface heating, we do not see Widespread storms. In hot seasons, too, Widespread thunderstorms cannot occur due to lack of moisture. In other words, Widespread thunderstorms in northwestern Iran occur when a local ascent is combined with a dynamic ascent resulting from the passage of a low-pressure system through the study area.

The Earth's ecosystems play an important role in regional and global climate. Most natural vegetation covers change through the year- as they are influenced by the seasons. In vegetation studies, different types of remote sensing images such as optics and synthetic aperture radar (SAR) have been used in different scales from leaf area to global scale. These images provide data that is difficult to access through other methods such as field surveys. Remote sensing sensors capture images from the Earth surfaces with an adequate spatial and temporal resolution for the environmental studies. In remote sensing approaches, the study of the phenological cycle (the study of plant life cycles and the way is affected by weather) is mainly based on changes in reflectance values in different spectral bands of optic sensors or vegetation indices (VI), such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and soil-adjusted vegetation index (SAVI). Spectral indices have been widely used to monitor the seasonal cycle of vegetation photosynthesis over the past decades. Many of these studies have reported promising results. SAR systems can capture images in all weather conditions and overcome the limitation of optic sensors in cloudy weather. Increased access to SAR images broadens the image application in vegetation studies. SAR sensors operate at a microwave range of electromagnetic spectrum and are able to penetrate more in vegetation canopy. In this study, the efficiency of Landsat 8, Sentinel-2, and Sentinel-1 images in monitoring vegetation phonological cycle have been verified. For that, three regions with different vegetation types including mangrove forests, woodland, and Shadegan date palms in Iran have been studied.

All available Landsat 8, Sentinel-2, and Sentinel-1images in 2017 have been acquired. The Landsat 8 and Sentinel-2 images have been pre-processed. NDVI, EVI, and SAVI have been calculated from corresponding optical bands. Field survey was not possible at the study areas, therefore, sample points have been chosen by the help of high-resolution Google Erath images. For that, archive Google Earth images with the acquisition date close to the acquisition date of images have been used to confirm the presence the vegetation covers at the specific location. The NDVI, EVI, and SAVI values have been extracted in the location of sample points. The Sentinel-1 images have been processed; speckle effect has been minimized and radiometric terrain corrections have been implemented by means of digital elevation model (DEM). Median filter with 5*5 window size has been applied. Median Filter has been chosen, because it is not affected by very high or low DN values and it is one of the efficient filter in minimizing speckle effect in SAR images. Then, digital number (DN) values have been converted into backscattering coefficient in dB. Backscattering responses in VV (vertical-vertical) and VH (vertical-horizontal) polarimetric bands have been extracted at the same sample location. The extracted NDVI, EVI, SAVI, VH, and VV values on different days of the year (DOY) have been separately analyzed for each study site.

In woodlands, EVI and SAVI indices in comparison to NDVI are more compatible with natural phenological cycles. However, optical images were not available for the whole year, therefore the changes of optical indices cannot be surveyed completely over the year. The changes of backscattering values follow the natural trend of vegetation cover, however, optical indices match better with the natural cycle. The growth cycle of woodlands is affected by temperature and rainfall variation; therefore, it will change in different years.The changes in spectral curves in date palms show that spectral indices present the initial steps of the growth cycles better than the final steps. Few optical images were available because there was cloud cover in this area. Spectral indices do not follow the last stages of natural phenological cycles. In this stage, backscattering values increase due to the increased volume scattering of the trees, therefore radar images are more efficient in presenting the last part of phenological cycles of date palms in comparison to optical images. Spectral indices are sensitive to the greenness of the leaves; in this stage, no substantial changes in vegetation greenness occur, therefor the spectral indices do not change accordingly.Mangrove forests have specific phenological cycles and are affected more by environmental conditions. Both spectral indices and backscattering values follow the natural trend of this kind of vegetation.VH backscattering values are more compatible with spectral indices in comparison to VV backscattering values. Spectral indices and VH backscattering values follow the natural seasonal changes of vegetation especially in deciduous vegetation such as woodlands. This matches with previous studies. The highest values of backscattering are observed in the time that vegetation cover reaches the highest amount of biomass. EVI and SAVI trends are more similar to the backscattering values trend in comparison to NDVI values. This study only considers images captured during one year (2017). Vegetation cover is influenced by seasonal, gradual, and sudden changes, therefore monitoring of vegetation in a longer period and shorter revisit time will provide complete monitoring of vegetation growth cycles.

Backscattering values in the cross-polarized VH (in comparison to the VV band) band show more sensitivity to vegetation changes over the year and are therefore more suitable for monitoring the annual growth cycle of plants. Among the optics indices, EVI and SAVI have shown more acceptable results since their variations are more consistent with the natural phenological cycle. In an aquatic ecosystem where mangrove forest grows, SAR responses show promising results as they can better represent the phenological cycle in comparison to spectral reflectance or vegetation indices. The results of this study show that backscattering responses at C-band follow the natural vegetation’s phenological cycle and can be used in vegetation monitoring in these three ecosystems. The results of this study can be further used to identify vegetation phenological stages in similar ecosystems. Additional studies are necessary to generalize these results to other areas.

Hail is a natural disaster for all people, especially farmers. The Hail damage depends on the frequency and intensity of rainfall. Usually in the insurance industry to calculate the risk of hail damage in each area, the frequency of rainfall (in terms of days) and the average damage, which is statistically significant are used. Hail is one of the phenomena connected with thunderstorms that occur in unstable atmospheres with high humidity and in the presence of strong winds and with mechanisms that increase instability, and these conditions are affected by local topography and climatology of air masses. Therefore, according to the natural risk management strategy, which is a potential and very serious role in reducing the damage caused by natural disasters in the region, hail can be predicted and dealt with and led to control of the resulting damage. Therefore, in order to investigate the spatial and temporal distribution of hail damage on agricultural products of East Azerbaijan province, the zoning of vulnerable areas in terms of hail damage, the cause of possible differences in different areas and the conditions in which this rainfall is present, were examined.

East Azerbaijan is located in northwestern Iran between 36˚47' N and 39˚ 40' N latitudes and between 45˚ 3' E and 48˚ 50' E longitudes. East Azerbaijan with an area of ​​45261.4 square kilometers is located in the northwestern of the Iranian plateau. In this study, to investigate and analyze the losses of the agricultural sector due to hail, the data of the agricultural Insurance fund for the were useded from 2010-2019. In many cases the hail phenomenon occurs in small area where there are limited number of synoptic stations so the occurrence of this phenomenon cannot be seen and recorded. Therefore, in order to assess the damage caused by hail in the study area, the day's whit hail damages were extracted and examined from the data of the Agricultural Products Insurance Fund. Then, spatial statistics, hot spot index and ARC GIS software were used to identify areas vulnerable to hail.

The results showed that the damage caused by this phenomenon on Agricultural crops in East Azerbaijan province is an average of 123.5 hectares per year, Bonab with annual average 568.1 hectares and Ahar with 491.2 hectares and Tark with 476.2 hectares are in the next ranks. But in terms of damage to crops, it was determined that Qara Aghaj with an annual average of 1143.9 hectares has the highest level of damage and Hashtrood with 826.6 hectares and Ahar with 369.1 hectares are in the next ranks.In terms of the level of damage to the total crop and horticultural products during the study period, it was found that in the province, on average, about 262 hectares of the province's area under cultivation are damaged annually due to this phenomenon. The highest level of damage is related to Qara Aghaj region with an annual average of 1159.7 hectares, which includes 14.7% of the total hail damages in the study area, and Hashtrood and Ahar with 1057.9 (13.4%) and 860.3 hectares (10.9%, respectively). Are in the next ranks. The spatial statistics and spatial autocorrelation techniques were used identify areas vulnerable to hail, and the Gi* index was used to ensure areas with high and low value clusters. The results showed that in agriculture, the values ​​of positive spatial correlation are concentrated in parts of the south of the province, which is the most vulnerable area, the central parts of Charavimaq and Shadian, In the garden sector, the values ​​of positive spatial correlation are concentrated the northwest and southwest of the province, and among most vulnerable area in the study are in the central and Yamchi Marand districts.

The results of this study showed that the highest frequency of damaging hail occurred in May and the lowest frequency occurred in August. The results also showed that about 71% of the harmful hail in the study area occurred in the warm seasons, which coincides with the plant growing season in this area. In the period under review, the rainfall of harmful hail in East Azerbaijan province was on average between 09:00 and 15:00 (G.M.T) more than other hours, and in this 10-year period, the maximum rainfall occurred at 12:00.In the study of hot spots based on Gi* index, it was found that in agriculture, high values ​​(positive spatial autocorrelation) are concentrated in parts of the south and northwest of the province, respectively. Examination of the total damage of agriculture and horticulture showed that high values ​​(positive spatial correlation) are concentrated in parts of the south of the province, and the most vulnerable areas in the study are ​​the central parts of Charavimaq, Shadian and Nazar Kahrizi. On the other hand, a region with less vulnerability in parts of the west of the province, especially the central parts of Osku, Khosrowshahr, Mamqan, Gogan and the suburbs of Azarshahr, corresponds to areas with a spatial distribution pattern with the highest significant negative spatial self-correlation and 99 Percentages (strong-cold-cold cluster) are concentrated. By examining vulnerable areas, we can point to the high area under cultivation in these areas, as well as the impact of local factors such as topography, altitude and external factors, such as the entry of hail storms from the west and southwest of the province in its occurrence and intensification. The results of this study show the efficiency of spatial statistics techniques in identifying vulnerable areas and proper segregation based on the principles of spatial statistics and can be used as a model in other agricultural and economic sectors of the country. It is also recommended to study this index and combine the information obtained from spatial statistics with climatic information, studying the long-term impact of phenomena on changes in the pattern of hot spots and developing other spatial indicators in future studies.