جستجوی مقالات مرتبط با کلیدواژه « Remote sensing » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Evapotranspiration govern the water and energy balance of the soil, primarily used in general circulation models and hydrological modeling of weather conditions. Consequently, predicting river flow, forecasting crop performance, water management systems, and the quality of rivers or lakes all depend on evapotranspiration levels. Therefore, accurate estimation of water bilan is essential. Improved and precise estimations of evapotranspiration enable effective irrigation planning and optimal water usage for other agricultural purposes. The goal of this research is to evaluate and validate the surface energy balance model, SEBS, in determining evaporation-transpiration in the Sohrin-Qareh Chiran Plain, located 30 kilometers northwest of Zanjan city.

In this study, the actual evapotranspiration were determined using the SEBS energy balance model and Landsat imagery in the Sohrin-Qareh Chiran Plain. A total of 17 Landsat-8 images, along with weather data from the Zanjan airport station, were examined. Additionally, with the help of the SEBS model, the evapotranspiration of a five-hectare wheat field was calculated and validated for the time period from September 22, 2020, to September 22, 2021. The required inputs for the SEBS model, including albedo, land surface temperature, emissivity, NDVI, vegetation cover index, leaf area index, vegetation height, and canopy density, were prepared in the ENVI software environment. The data on precipitation and water consumption volume were also determined using the soil water balance model.

The study found that the evapotranspiration estimated by the soil water balance model was 24115 m3, while the evapotranspiration estimated by the SEBS model was 28750 m3, with a 16.12% error. Additionally, the SEBS model was calibrated using four indices - R2, RMSE, MAE, and MBE - with values of 0.844, 1.06, 1.12, and 0.25, respectively. These results indicate that the SEBS algorithm is accurate enough to estimate ETa in the study area.

Conclusion and Suggestions:

Therefore, based on the results of this research, it can be said that the use of satellite images in estimating actual evapotranspiration (ET) is reliable compared to field measurement methods. Considering the limitations of traditional moisture balance methods (such as being point-specific, time-consuming, costly, requiring precise instruments, and almost impractical with the use of lysimeters) and the advantages of utilizing satellite images (covering a wide area of farms or orchards, being cost-effective and fast), acceptable results can be achieved in estimating evapotranspiration while accepting a low margin of error.

Determining the organic carbon content of rangeland vegetation is essential for monitoring rangeland condition and facilitating reclamation efforts. Satellite data provides a valuable tool for conducting extensive vegetation studies. This research aimed to estimate the organic carbon content of vegetation using field assessments and remote sensing indices in the rangelands of Lashgardar protected area, Malayer. Leveraging Landsat time series images, this study utilized Landsat 8 data from the Operational Land Imager (OLI) sensor.

Field sampling was conducted in the rangelands of Lashgardar protected area on May 28, 2016. The dominant growth form in these rangelands is herbaceous-shrub, with Asteraceae family species being the most abundant. Forty points were randomly selected as the centers of sampling plots for plant biomass. To account for GPS accuracy error, a factor of twice the pixel size was applied, resulting in the selection of 40 plots measuring 30×30 m2 for field sampling. Subplots measuring 1×1 m2 were utilized to collect composite aboveground biomass samples from the central point and the four corners of each main plot. Samples were processed in the Rangeland Science Laboratory at Malayer University following coding for laboratory procedures. The organic carbon content of vegetation was determined using the loss on ignition (LOI) method after air-drying. Vegetation indices were extracted from Landsat 8 satellite images captured by OLI sensors, including digital bands 1 to 7 with a spatial resolution of 30 meters. Various vegetation indices such as Greenness, RVI, NDVI, IPVI, DVI, WDVI, ARVI, SAVI, TSAVI, BI, OSAVI, GEMI, EVI, LAI, and GARI were derived from Landsat images.

Comparative analysis of estimated organic carbon data with measured organic carbon content revealed that only the Green Atmospherically Resistant Vegetation Index (GARI) could effectively estimate the organic carbon content of vegetation. The best model was achieved using the GARI index for organic carbon estimation, represented as OC = 5.4 + 1.38 GARI, with an explanatory coefficient (R2) of 0.13 and Root Mean Square Error (RMSE) of 0.7. These findings suggest that remote sensing indices can serve as complementary methods in vegetation studies.

The GARI index demonstrated promising results for estimating organic carbon content in vegetation within the study area and is recommended as a suitable indicator for similar areas. However, the efficacy of each index may vary depending on specific area characteristics and vegetation types. It is advisable to conduct time-series studies with larger sample sizes tailored to the unique conditions of the study area to identify the most appropriate indices.

Population growth and the excessive use of natural resources have caused significant changes in natural ecosystems, including a decrease in rainfall and an increase in temperature. The potential exists for them to decrease vegetation and increase barren areas. Serious economic, social, and environmental damage can occur in natural ecosystems due to the destruction of land cover and other damages, such as dust storms. Therefore, ecosystem changes are taking place worldwide, both at the temporal and spatial scale, due to human activities and natural factors. So, investigating the amount of land use/cover changes, their effect on dust storms, and predicting these changes for the coming years can be an important step in reducing and controlling unprincipled changes, planning, and optimizing resource. Climate change and human activities, such as drought, human activities, and non-compliance with water rights, have a significant impact on the Hamon wetland area, so that the dry bed of the wetland has become the main sources of dust. This research is focused on investigating the impact of land use changes on dust storms and forecasting land use changes in the Sistan region for the next 20 years.

The impact of land use changes on dust storms in the Sistan region was examined using Markov chain forecasting methods. For this purpose, first of all, the land use maps of 2002, 2011 and 2022 were prepared using satellite images. An anomalous method was used to investigate climatic parameters, including temperature, rainfall, and the number of days with dust, in the next step. To evaluate climatic changes, it is necessary to use a method that shows long-term changes. The anomaly method was employed for this purpose. The values of this index can be either positiveor negative. In order to predict land use changes for the next 20 years, the combination of the maps of 2002 and 2022 for severe drought conditions were used by using Markov chain and Cell models. The Markov model was predicted to generate multiple images. The transfer probability matrix allows for the expression of the probability that any type of land cover will be found in any location in the future. Despite the accuracy of transmission probabilities for each user is unknown, due to the lack of information on the spatial distribution of users, the Markov model does not have any spatial dependence information.  In contrast, to the automatic network, it is an agent that has the ability to change its state based on the application of the law that shows the new state in accordance with the previous state and the state of its neighbors.

This study examined the impact of land use change on dust in the Sistan region. At first, climatic changes of temperature, rainfall and number of dusty days were investigated and the results showed that the temperature has increased and rainfall has decreased in the Sistan region during the last two decades. The land use maps also showed that in the years when the Hamon wetland has been drained, pastures and dense vegetation have increased and barren lands and salt marshes have decreased. But due to the recent droughts like the year 2022, when a drought has occurred in the region, the use of vegetation and pasture has decreased and barren and salt marshes have increased. These conditions cause an increase in the level of dust in the region. The land use map for severe drought conditions in the next 20 years was predicted using the Markov model.  It showed that in the future, pastures and dense vegetation will decrease, but barren lands and salt marsh areas will increase dramatically. As desertification and wind erosion increase, dust storms will also increase as a result of these conditions. The economic, social, environmental, and health conditions of residents in the region are adversely affected by dust storms. Therefore, proper planning and management can reduce the damages caused by dust storms in the Sistan region.

Extended AbstractIntroductionMapping and assessment of surface water dynamics is essential for continuous monitoring of water resources as it has significant implications in engineering and scientific research for floodplain delineation, wetlands, disaster management, biodiversity, climate change, and water resource management (Huang et al., 2018; Jawak et al., 2015). Traditional surface water monitoring methods mainly rely on field surveys or on established measuring stations. Although the accuracy of the data obtained by this method is high, it is time-consuming and has low efficiency. In addition, many aquifers are very difficult to access because they are located in remote and rugged places, and only data from limited points in incomplete time series are obtained due to the limitations of economic and land factors (Ogilvie et al., 2018). With the expansion and development of remote sensing science and geographic information system, better ways have been provided to monitor water bodies in a long period such that the use of multispectral satellite images and different spectral indices for monitoring water bodies and floods is a fast and economical method in terms of time and cost. The high number of existing sensors and their differences in estimating the spectral and spatial characteristics of spectral indices have led to a good representation of the potential and limitations of satellite data. Therefore, in this research, to investigate the flood of 2019 in the southwest of Iran, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Diference Vegetation Index (NDVI) obtained from the Landsat 8 satellite images were used.Materials and MethodsThe study area is a part of the southwest of Iran, which includes the southern part of Ilam province and the northern, northwestern, western and southwestern parts of Khuzestan province. In this study, to investigate the flood of 2018-2019, Landsat 8 sensor images were used for three months of January, February and March in 2019. This section used QGis3.28, GIS10.8, Excel software and remote sensing data including satellite images related to Landsat 8 sensor. These multispectral data were obtained from the United States Geology website (earthexplorer.usgs.gov) and were prepared for preprocessing and necessary processing. To prepare a map of the flood area and vegetation, radiometric and atmospheric corrections were performed on the received images (Eskandari Damaneh et al., 2016). After applying the necessary pre-processing, the MNDWI and NDVI were used to prepare the map of changes in water bodies and vegetation for the years 2018 and 2019, respectively (Rugel et al., 2017; Abutaleb et al., 2015; Arekhi et al., 2019).ResultsAccording to the results, the highest values of MNDWI in 2018 corresponded to those on March 12, which includes the northern and central parts. In 2019, the highest value of this index was on the date of March 19, which included the southern to southwestern parts of the study area. Examining the changes in MNDWI classes showed that in 2019, on February 2nd, 19th and March 7th, classes ranging from 0.11 to 0.15 occupied the highest level of the studied range, which is more than 68.75 percent of the area. This range and its trend were increasing. On the other hand, on the mentioned dates, classes ranging from 0.15 to 0.2 covered an area of more than 24.32% of the region, and these classes were decreasing. Accordingly, in 2019, on March 8th and May 14th, the largest percentage of the area under study was still in classes ranging from 0.11 to 0.15, and the total of these areas was more than 76.13% of the region, which was decreasing. On these dates, classes ranging from 0.15 to 0.2 included more than 20.03 percent of the study area, and these classes had gone through an increasing trend.Examining the changes of NDVI classes showed that in 2019, on February 2nd, 19th and March 7th, the largest percentage of the study area was taken by classes ranging from 0.2 to 0.3, which totaled 71.81%, and was increasing. Also, on these dates, classes ranging from 0.3 to 0.5 included more than 11.46% of the study area and these classes were decreasing. Also, in 2019, on March 8th and May 14th, the largest percentage of the study area was still taken by classes ranging from 0.2 to 0.3, and the total of these areas was more than 82.54%, which was decreasing. Meanwhile, on these dates classes ranging from 0.3 to 0.5 included more than 11.64% of the study area, and these classes had been increasing.Discussion and conclusionThe trend of spatial and temporal changes of the MNDWI index in this period shows that in February and March of 2019, the highest value of this index was in the northern and central parts of the studied region. While the highest value of this index was in March and May of 2019, it has been seen in the southern and southwestern parts of Khuzestan province. While this precipitation is in the season when the vegetation in this area is in good condition, the classes above 0.3 NDVI vegetation index in March 2018 and March 2019 are more than 42 and 38% of the area, respectively. Because the southern parts of Khuzestan province have lower altitudes than the northern and northwestern regions, it is plain and flat. This has caused it to serve as the foothills of the upper elevations of Khuzestan and Lorestan provinces, which in turn causes the influx of upstream waters into this region. Even if the vegetation cover is suitable in the season, it has caused a large and unexpected influx of water in these areas, which itself causes flooding of the residential regions, facilities and agricultural lands. In general, it can be concluded that by using the MNDWI and NDVI indices obtained from the Landsat satellite images, it is possible to monitor the water bodies and waterlogged areas resulting from natural hazards such as floods, as well as the vegetation of different regions with high accuracy. The findings of this study can be used in studies and decision making with sufficient confidence.

 The conditions and performance of the ecosystem are affected by the changes made in some phenomena and conditions of the earth's surface (such as vegetation condition) over time due to various factors, including natural or human ones, making it necessary to identify, predict, and pay attention to such changes.
Considering the extreme vulnerability of arid and semi-arid regions to climate change worldwide, it is crucially important to investigate and evaluate climate change-induced alterations in vegetation in such regions. Therefore, this study sought to investigate the trend of vegetation changes in the three pastures of Farash Sardo, Kal-Bido, and Shoroiye located in Jiroft city with a dry and semi-arid climate, trying to examine the relationship between such changes and climatic factors. 

 The study area comprises three pastures, namely the Farash Sardo, Shoroiye, and Kal Bido, that were selected out of 375 pastures existing in Jiroft city, taking into account their significance for vegetation. To conduct the study, the monthly data of the Normalized Difference Vegetation Index (NDVI) and climatic data, including evaporation, average air pressure, average precipitation, relative humidity, sunny hours, dew point temperature, average maximum and minimum temperature rates, average temperature rate, and wind speed were used.
To this end, first, the long-term temporal changes in vegetation and climatic variables were evaluated using Mann-Kendall statistical test. Then, the most important climatic parameters affecting such changes were identified through multivariate regression, followed by the application of the root mean square error (RMSE) and the coefficient of determination (R2) to compare and assess the performance of the obtained models. Finally, the most important climatic factors involved in the changes made in the vegetation conditions of the study area were identified based on the selected model.

 The study’s findings revealed that vegetation changes had an increasing trend in Farash Sardo pasture at annual, winter, and spring time scales throughout the study period. Moreover, significant increasing trends were observed in the pasture of the Cal-Bido at annual, autumn, winter, and spring scales. However, no significant trend was found in the Shoroiye pasture.
On the other hand, the investigation of annual climatic factors indicated a significant increase and decrease in the wind speed and the dew point temperature in the Farash Sardo pasture, respectively. Also, while a significant increasing trend was found in evapotranspiration and average minimum temperature in the Cal-Bido pasture, the trend of changes was decreasing trend in the pasture in terms of air pressure, relative humidity, dew point temperature, average maximum temperature, and average temperature. As for the Shuroiye pasture, the results suggested a significant increasing trend in evapotranspiration and average minimum temperature, and a significant decreasing trend in the values of dew point temperature, relative humidity, air pressure, and average temperature.
Moreover, the modeling results showed that the most important climatic factors involved in NDVI changes in the Farash Sardo pasture were temperature, air pressure, spring evapotranspiration, and changes in winter air pressure and annual precipitation. On the other hand, the most important climatic factors affecting NDVI changes in the Cal-Bido pasture were identified as spring dew point temperature, air pressure, autumn air pressure, winter air pressure, maximum temperature, and annual air pressure. As for the Shoroiye pasture, the most important climatic factors involved in NDVI changes were found to be wind speed, precipitation, minimum and maximum temperature rate, average spring temperature, summer air pressure, autumn sunny hours, and winter sunny hours.
In general, the investigation of the relationship between drought and vegetation status of the pastures via land sampling and remote sensing data is suggested to be used for improving the results and increasing awareness concerning climate hazards for vegetation. Such a study together with the examination of the contribution of human interventions on vegetation changes can also be used for managing the environment during critical periods and setting appropriate plans.

In the mountainous regions of Iran, a significant part of the precipitation is in the form of snow, which is considered an important source of river flow. Accurate knowledge of the quantity of these resource is necessary in terms of the ever-increasing value of fresh water and also in terms of the optimal use of water resources. From a global point of view, snow monitoring and accurate information on the spatial distribution of snow cover, are necessary for weather forecasting and hydrological and meteorological modeling. An important feature of mountainous regions is the snow cover, which has a high reflectivity, has a great influence on the local weather, reduces the net radiation at the surface and as a result, transfers energy. In addition to being an important factor for ecosystem development, snow cover is very important for human activities. Accurate estimation of the coverage level is considered as one of the central and fundamental operations in the field of water resources management, especially in areas where snowfall is a major part of precipitation. Revealing and determining different characteristics of snow and ice using remote sensing data, which is widely used in hydrology, has created a new method to obtain the required parameters of hydrology.

The Alborz Mountain range which is under study of the current research, separates the coastal plains of Mazandaran Province from the interior of Iran. The eastern half of Western Alborz and all of Central Alborz and a part of Eastern Alborz are within Mazandaran Province. In this way, along with other natural factors, certain geographical conditions have emerged. In this region, snow plays a key role in the hydrological cycle and hydroclimate, and a significant part of the total annual runoff in this region is the result of snowmelt. So that global warming affects the management of watersheds and the downstream water requirements of its sub-basins. First, MODIS sensor data was obtained daily with a spatial resolution of 500×500 meters from NASA's National Snow and Ice Database (NSIDC). The received images are related to the period of 2000-2018. To process the images, first pre-processing wacovered ars applied in the ENVI 5.3 software environment. The NDSI index was used to monitor the snowed area. Mann-Kendall test, Sen’s slope estimator, and Pettitt's homogeneity test were used to investigate the snow cover variation trend. Also, the seasonal and annual anomalies of snow cover, temperature and precipitation in the study area were investigated based on standard Z score.

The results of the Mann-Kendall test and the Sen’s slope estimator method in the northern slope of Central Alborz, show that the largest reduction of the snow covered area occurred in January and winter season, respectively, equal to 220.39 and 50.41 km2 each year. The results of Petit's homogeneity test, using the Change Point Analysis (CPA) method, in January 2010 for the snow-covered area and May 2014 and June 2010 for the monthly mean temperature, showed a climatic jump at 0.05 significant level. Also, the change point in the snow-covered area time series of January has been descending, but the change point in the mean temperature time series of May and June has been ascending. Comparing the snow cover conditions with the mean temperature and total precipitation conditions, shows that in most cases the negative anomalies of snow cover are consistent with the positive anomaly of temperature and the negative of precipitation. The obtained results are a warning about the climate change in this region, which is known as the phenomenon of global warming and meteorological drought. Surely, these changes have a direct effect on the reduction of water resources for the agricultural and drinking sectors.

In general, the analysis of the snow-covered area variations in January during the studied 19 years, shows that for an increase in the average temperature of 0.13°c, the snow-covered area in this month decreased by 220.39 km2  every year. Also, according to the results of Pettitt's homogeneity test in 2010 and 2014, it can be concluded that global warming and meteorological drought caused a sudden change in the snow-covered area and temperature in these years and months. The comparison of precipitation and temperature conditions with the snow cover condition showed that in most years, the negative anomaly of snow cover was simultaneous with the positive anomaly of temperature and the negative anomaly of precipitation. The greatest effect of temperature increase has been observed in spring. Therefore, with the increase in temperature and the change in climatic conditions, the winter precipitation that will turn into snow accumulation has decreased and can affect the runoff caused by these precipitations in the spring season. Since this region has the ability to receive snow from mid-autumn to early spring, information about the snow covered area in this region is essential for many hydrological, meteorological, and climatological applications, as well as hydroelectric power generation and flood forecasting.

Soil salinity is one of the most important environmental problems, especially in arid and semi-arid regions. Soil salinity is caused naturally and/or by humans. High soil salinity negatively affects crop growth and productivity and ultimately leads to land degradation. Monitoring and mapping of soil salinity Due to the serious problems of spreading this issue to regional ecology, food security and agricultural development in the early stages, it is necessary to implement an effective soil rehabilitation program to prevent and reduce soil salinity. Remote sensing science performs better than traditional methods for assessing soil salinity and offers fast and cost-effective techniques for monitoring and mapping soil salinity. Soil salinity can be identified using direct indices that are related to the properties of surface soil salts as well as indirect indices. The aim of this research is to review the challenges of selecting appropriate indices in soil salinity studies through the investigation of researches conducted in the field of soil salinity and spectral indices used in soil salinity cases, which are helpful in land management at the regional scale. It is worth noting that in this regard, the most common vegetation and salinity indices used to detect and mapping of soil salinity were discussed. Many researchers have used different remote sensing indicators to map soil salinity. Among these, BI Brightness index, SI salinity index, NDVI normalized differential vegetation index and NDSI normalized differential salinity index showed the highest correlation between data obtained from satellite images in salinity-affected soils. Choosing the most appropriate band or indices depends on the soil conditions, geographical area, climatic conditions, satellite data, physiography of the area and the type of land use.

Remote sensing, as a powerful tool in meteorology, is able to cover the gaps in ground measurements and provide a uniform platform for spatial analysis. However, due to the different results obtained from the accuracy and performance of sensors in different regions, it is necessary to evaluate and validate their products in each region independently. This study, therefore, aims to evaluate the performance of the satellite precipitation products (SPPs) of PERSIANN, GPM, ERA5, and TRMM in a daily rainfall scale over the Razavi Khorasan Province, against the ground observations collected from the 19 rain gauges. In this regard, 113,880 images were called to extract daily rainfall data from the above four databases. To evaluate the SPPs, statistical indices including correlation coefficient (CC), root mean square of the errors (RMSE), and percentage of bias (PBias), Kline Gupta Efficiency (KGE), and Agreement Index (d) were used. Moreover, POD, FAR, and CSI classification indices were used to evaluate the accuracy of the data presented in the indication of days with rainfall events. The climate effect was also investigated by incorporating the change in latitude. Our results revealed better performance for the ERA5 dataset with 0.2≤ CC ≤ 0.68, 2≤ RMSE ≤ 3.7, and its performance indicators have KGE ≥ 0.26 and d ≥ 0.68. Regarding classification indices, ERA5 has POD and CSI higher than 0.68 and 0.3, respectively, and TRMM has relative superiority only in the FAR index. At the level of the studied area, the index analysis shows the better performance of the ERA5 database compared to other studied data. Based on the results of this research, the increase in elevation has improved some statistical indicators in some products, whereas, in some other cases, changes in elevation have a negative effect on the accuracy. In general, the ERA5 database at the point and regional scale are evaluated to have a more appropriate performance in estimating daily precipitation data, and its data can be used in meteorological and hydrological analysis.

Iran is one of the countries that soil erodibility is becoming one of the acute environmental problems and every year millions of tons of fertile soil are left unusable due to lack of proper management.  In order to effectively protect and prevent the adverse effects of soil erosion, it is necessary to identify the factors affecting soil erosion and provide an appropriate estimate of their amount in the country. In this regard, the present study was conducted to estimate soil erodibility (K-factor) for a soil depth of 0-30 cm in Iran.

For this purpose, two database were used, including the Harmonized World Soil Database (HWSD) and global gridded soil information (SoilGrids), as well as RStudio and ArcGIS software. First, SoilGrids data was prepared at four depths of 0, 5, 15 and 30 cm and averaging was done. Also, the HWSD database was received in vector format for a depth of 0 to 30 cm. Finally, these data have been used to estimate the erodibility factor based on the soil content of organic carbon, clay, sand and silt using the EPIC equation. Finally, Relative Error (RE), Median Absolute Deviation (MAD) and Root Mean Square Error (RMSE) were calculated to compare two databases.

Assessments indicate that the average percentage of clay particles in the sub-basins of Iran varies between 15 and 32% and the average for the whole country is 23%. On the other hand, the average percentage of silt particles in the sub-basins of Iran varies between 19 and 45%, and the average for the whole country is 32%, among which the maximum and minimum percentage of silt particles are in the sub-basins of Qarasu-GorganRoud and Kavir Lut, respectively. Also, the average percentage of sand particles in the sub-basins of Iran varies between 28 and 65, and on the other hand, the average for the whole country is 44%, the minimum of which is related to the sub-basin of Karkheh and the maximum is related to the sub-basin of Kavir Lut. In Gavkhouni and Kavir Lut sub-basins, the reason the low soil erodibility factor is the high percentage of sand in these sub-basins, so that the percentage of sand in the Lut and Gavkhouni basins is 65 and 47% of the total soil particles, respectively. Considering that the average percentage of organic carbon in the sub-basins of Iran varies between 0.3 and 3.9, respectively, these values are related to the sub-basins of Hamun-e-Hirmand and Sefidroud-Haraz, so it can be said that the majority of the country's sub-basins are in poor conditions in terms of the percentage of organic matter. The results show that the southwestern, western and northeastern parts of the country have the maximum amount of soil erodibility factor, and the central Iran and desert parts of Iran have lower erodibility due to having a higher percentage of sand particles. Also, the results show that the lowest average amount of soil erodibility at the sub-basin scale using SoilGrids data with a value of 0.033 (ton*h/Mj*mm) is related to Lut Desert and also its maximum value is 0.045 (ton*h/ Mj*mm) related to it is the sub-basin of Haleh. In addition, the maximum and minimum values of the erodibility index with the HWSD data as an average of the basins of Iran are corresponding to the Mand and Gavkhouni sub-basins, respectively, with values of 0.042 and 0.033 (ton*h/ Mj*mm). So, the results showed that the average soil erodibility factor in Iran using two the HWSD and SoilGrids databases was 0.036 and 0.038 (ton*h/ Mj*mm) respectively.

Conclusion and Suggestions:

The study of soil erodibility with the data of SoilGrids and HWSD at the sub-basins scale showed that the maximum and minimum RE in Atrak and South Balochestan sub-basins are 21 and 1 percent, respectively, and the amount of RE is about 5% for the country average; Therefore, it can be concluded that although the RE between the two databases is not high, SoilGrids data is a more suitable source for soil and water resource modeling due to its continuity and better spatial resolution. Finally, it should be said that although this database is modeled using a larger number of profiles (about 150,000 soil profiles in the world), so they have appropriate accuracy. However, it is necessary to state that investigating the uncertainty of these data in order to improve the results of this database in order to improve the results of that in different parts of the country is recommended to researchers and researchers. Also, it is noteworthy that the EPIC model was used to estimate soil erodibility in this study, while its evaluation and comparison with other soil erodibility estimation models in the country is suggested to other researchers and experts.

Detecting the potential of groundwater resources is one of the most important methods of water exploitation management to deal with water shortage, which is inevitable due to the growing need for water in the country. The first step in managing groundwater resources is to recognize the potential of groundwater. Fuzzy-Analytic Hierarchy Process (AHP) is also recognized as an important tool in decision-making about natural resources management, especially water resources. Bam-Narmashir plain, in Kerman province, is one of the most important plains of the country. This plain plays a very important role in the region in terms of supplying the water resources needed by agriculture, industry and drinking parts.

In this study, AHP-fuzzy model and GIS were used to identify suitable areas for potential groundwater resources in Bam-Narmashir plain. Criteria such as rainfall, temperature, geographical direction, slope, land use, vegetation cover, distance from the road, distance from the river, distance from the fault, distance from the city and village, distance from wells and springs, soil texture and the permeability of the formation were selected for decision making and the weight of each of them was calculated and prioritized using AHP model. Then, the desired layers were fuzzy and indicator maps were prepared using Arc GIS software. Then, the groundwater potential map was prepared. Finally, the ROC curve was used to validate the groundwater potential map in the region.

The result of ROC curve indicated the high accuracy of Fuzzy-AHP method in preparing the groundwater potential map in the study area. The results were consistent with the results of researchers such as Rezaei Moghadam et al. (2017) and Faraji Sabokbar et al. (2011). Also, the results showed that the regions with geological sub-criteria have the highest weight of 0.614 and the climatic sub-criteria have the lowest weight of 0.117. The value of inconsistency ratio was 0.07, which is smaller than 0.1, and it indicates consistency in the opinions and judgments of research decision-makers. Finally, the results showed that about 7.77% of the total area of the plain, equivalent to 755.54 square kilometers, is suitable for the implementation of underground water resources potential detection systems. Also, 1.57, 31.71, 58.98 and 7% of plain lands have very weak, weak, medium, and high potential for finding the potential of underground water resources, respectively.The results showed a sparse distribution of suitable areas for groundwater potential in Bam-Narmashir plain, which is compatible with the results of Sekar and Randir (2007), who stated that the groundwater recharge potential at the scale of the watershed has non-uniform spatial distribution. The results showed that in the northern and western part of the plain, where the soil is mostly low capacity for groundwater potential and infiltration, which is in line with the results of Akbarpour et al. (2015), who stated in west and north-west of Birjand basin, the soil has a low capacity for groundwater penetration due to the low depth of the soil and high slope.

In recent years, the use of geographic information system (GIS) and its combination with multi-criteria and decision-making methods to obtain more accurate results have increased and played a key role in studies of the finding potential of groundwater resources. Even though it is very difficult and complicated to accurately identify suitable places for the potential of underground water resources, this study showed that the use of GIS makes it possible to identify these suitable places with minimal facilities, which is consistent with the findings of Khairkhah et al. (2013). Overall, according to the results of this study, about 7% of the region has high groundwater potential. Therefore, it is necessary to adopt scenarios to reduce the over-exploitation of groundwater and to apply the measures to improve the irrigation systems, the methods to reduce evaporation and improve the cultivation system.

The dust storm is recognized as a global problem which has a large-scale negative impact on the world. Dust strong winds transport them long distances. The dust storm caused a lot of damage to the economy, health and the environment. Therefore, an adequate understanding of the origin and time of dust storms can be effective in reducing dust damage. The purpose of the ongoing research is to identify the source of the dust storm events. For this purpose, the dust events in a period of 11 years were analyzed using the synoptic meteorological data of Kermanshah. The number of 646 dust storms were identified, and their detection operations and identifying of the areas affected by dust storm and areas of origin were performed using MODIS and Deep Blue. The HYSPLIT was used to route the dust storm and the entry routes of dust storm into the Kermanshah city. Based on the dust codes, the highest number of dust storms per year was registered in 2008 and 2009, respectively.  Most dust storms were observed in summer and during May, June and January. The results of DRS, HYSPLIT have shown that the western directions have the largest amount of dust input in Kermanshah, and the MODIS and Deep Blue images also confirm this fact. Overall, the findings showed that most of the routes come from the north and centre of Iraq and the Syrian Desert.  Dust storms originate at the border and arid lands of northern and central Iraq and Syria.