مجله مدیریت بیابان
پیاپی 27 (پاییز 1402)

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.

The temperature of the earth has been rising by about 0.74 degrees Celsius over the past century. A gradual increase in the average annual temperature has been reported by many researchers worldwide, while other reports suggest a decrease in this parameter. The assumption is that there will be more areas of the world experiencing higher temperatures. The climate changes are effectively represented by temperature changes, which is considered one of the main indicators in climate studies. The chemical composition of the atmosphere has changed because of the increase in human industrial activities, so it is responsible for unprecedented changes in the global climate in the past century. The increase in greenhouse gas concentration is the cause of this change. The evidence indicates that the increase in atmospheric gas concentration has caused a significant increase in global temperature. The use of thermal data from sensors is widely used in the study of terrestrial phenomena, as indicated by many studies. The temperature of the earth's surface is directly and indirectly linked to all human activities. It is still not possible to calculate the temperature of the earth's surface with perfect and accurate methods, but some sensors with suitable temporal, spectral, and spatial performance are able to take photos of the entire surface of the Earth. The study is more important due to the fact that various species of animals, such as Jebeer (belonging to the Bovidae), are exposed to climate changes in arid and desert areas. Due to its impact on humans, other creatures, and the entire environment, it is imperative to pay attention to climate change nowadays. In this regard, the main aim of the current study is to evaluate the LST (Land Surface Temperature) trends, changes, and temperature threats of the land surface in the Central Plateau of Iran. Time series remote sensing data of the MODIS (MOD11A2) sensor and Terra satellite, in 8 days with spatial resolution of 1km from 2002 to 2018 have been used.

 The current study has been focused on the central plateau of Iran. The central plateau of Iran lies within the arid lands belt of the northern hemisphere. The current study has been attempting to extract exact information from the images by employing specific techniques. To achieve this goal, the MOD11A2 product of Terra satellite MODIS sensor, the trend of temperature changes and time series construction of the significance of Man Kendall methods and linear correlation parameters such as maximum monthly temperature, maximum annually temperature based on maximum monthly temperature, median monthly temperature, maximum annually temperature based on median monthly temperature, minimum monthly temperature, minimum annually temperature for daily and nightly temperature were used in TerrSet software and Earth Trends Modeler section to extract significant increasing and decreasing areas. After identifying some parts of provinces with significant temperatures based on analysis and results, we can identify the vital numerical value of the temperature in each pixel of those significant parts in the next stage. This can be achieved by utilizing the difference between the final temperature and the initial temperature. Trend analysis was used to simulate daily and nightly temperature changes for parameters of maximum monthly temperature, maximum annually temperature based on maximum monthly temperature, median monthly temperature, maximum annually temperature based on median monthly temperature, minimum monthly temperature and minimum annual temperature.

Daily temperature data in the Central Plateau of Iran, which includes monthly minimum temperature, annual minimum temperature, monthly maximum temperature, annual maximum temperature based on monthly maximum temperature, monthly median temperature and annual maximum temperature based on monthly median temperature, common in Semnan and Isfahan provinces, showed a significant increase in linear correlation according to the results. In Isfahan province, the linear correlation decreased significantly between the maximum annual temperature based on the maximum monthly temperature and the median monthly temperature. There was no significant trend in other provinces. The linear correlation between temperature data in Isfahan and Semnan provinces, including the minimum monthly, minimum annual, maximum annual, and median monthly temperature, decreased significantly. The linear correlation between average annual temperature, average monthly temperature, maximum annual temperature determined by maximum monthly temperature, average monthly temperature, and maximum annual temperature determined by median monthly temperature increased significantly in Yazd and Isfahan provinces. No significant trends were observed in other provinces. To estimate the amount and approximate number of significant increases and decreases, simulations of temperature changes were conducted. The range and approximate range of numbers for significant increase and decrease in temperature were calculated in degrees Celsius. In all analyses, the parts with higher temperatures had a reddish color. The intensity of the red color increased as the temperature increased, and as the temperature decreased, the red color became fainter and turned blue. The central plateau of Iran recorded a maximum temperature of 44C°and a minimum temperature of -7C°according to this study. The central plateau of Iran has three main provinces, which include Isfahan, Semnan, and Yazd. Considering the temperatures mentioned for these three provinces, the temperatures obtained from this study are very similar, which means that the conducted study is approved to a large extent. Animals are considered to be at risk due to temperature changes. Future research should emphasize the impact of climate change and temperature increase on the living conditions of various animals, particularly those found on the central plateau of Iran.

Extreme precipitation has a significant impact on the frequency, severity, and duration of natural hazards, such as floods, droughts, and landslides. This has a significant impact on human life, the economy, natural ecosystems, and agriculture (Song et al, 2015: 34). Between 1880 and 2012, there was a 0.85 °C increase in the average global temperature, with a general increase in precipitation in the mid-latitudes of the Northern Hemisphere (IPCC, 2013: 2; Lio et al, 2017: 822). In addition, there is a possibility of a rise in extreme precipitation in the future (Klein Tank et al, 2006: 1), and so far, the reason for these changes and their relationship with the general circulation of the atmosphere have not been considered. The aim of this study is to analyze the trend of changes in extreme precipitation indices in northwestern Iran and its association with the general circulation of the atmosphere.

In order to analyze the changes in extreme precipitation events in northwestern Iran, daily precipitation data was collected from 20 synoptic stations in the region between 1986 and 2010. The region that is being studied encompasses West Azerbaijan, East Azerbaijan, Ardabil, Zanjan, and Kurdistan. In assessing limit events, high quality and reliable long-term climate data with daily (or higher) resolution is required (Clintanak et al., 2009: 9). The first step was to examine the quality control and homogeneity of data. The RClimDex software package, introduced as a standard tool by ETCCDI, was used to perform quality control and evaluate data homogeneity in this research. The Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) introduced 11 indexes to examine changes in precipitation level indices in northwest Iran. RClimDex software calculates these indicators with a significance level of 0.05. This process seeks to establish a standard set of indicators to examine and compare the characteristics of different regions. The software was used to calculate precipitation indices and display the trend and rate of change on a map.

The extreme precipitation indices were calculated to determine the regional trend and percentage of stations with positive and negative trends for the studied stations in northwestern Iran. Afterward, a map was created showing the spatial distribution of the slope for each of the indices. All precipitation indexes, except for the maximum growth period index (CDD), are declining according to the results. The probability of precipitation has decreased due to the more stable winter atmosphere in the region from the point of view of general atmospheric circulation. The region's spring atmosphere, similar to that of winter, shows an increase in stability, which will result in less rainfall. In summer, except for the coastal provinces of the Caspian Sea and the coasts of the Oman Sea, the rest of the country has recorded a decrease in rainfall of 1 mm per day. Most parts of the country experienced an increase in atmosphere thickness to 6 meters in autumn in the study area. Autumn in the region is typically stable and barotropic, but the study area is experiencing less rainfall. This study examines the trend of changes in extreme precipitation indices in northwestern Iran and its relation to a large-scale general circulation of the atmosphere. According to the results, 75% of stations in the region are experiencing a decrease in the maximum daily rainfall (RX1day) and 80% are experiencing a decrease in the maximum five-day rainfall (RX5day). While both the very wet (R95P) and ultra-wet (R99P) day indices are experiencing a downward trend, the R95P index is experiencing a more pronounced downward trend. All three indices R10, R20, and R25 have been declining for the past 25 years, but the R10 index has fallen more rapidly than the other two indices. Sarab station has a positive CWD trend alone, while other stations have a negative and decreasing trend of this index. In most stations throughout the region, the CDD index is increasing. In 85% of stations in the region, the PRCPTOT index is decreasing and there is a noticeable increase in rainfall. The SDII index is experiencing a decrease in 60% of the stations in northwestern Iran, while an increase is being observed in 40%. All precipitation indices, except for the CDD index, have a decreasing trend in general. Drawing and analyzing combined difference maps for geopotential height parameters of 500 hPa, relative rotation of 500 hPa, vertical velocity (omega), rainwater and precipitation rate to study the general atmospheric circulation of the region indicates an increase in altitude has led to a 500 hPa increase in climate stability in the study area (northwest of Iran). The study of omega and relative rotation shows that the region is experiencing a decrease in upward currents and positive rotation. The lack of atmospheric moisture load and rainfall in all seasons can be seen in rainfall water difference maps and rates. Precipitation indices and the general circulation model of the region's atmosphere are compared, indicating that the moisture load of the region's atmosphere has decreased, resulting in drought.

With the increasing world's population, the need for food and medicine, and their continuous supply, are essential for mankind. Medicinal plants are one of the important factors for human life, as they can be used both as food and medicine. The ancients had a long history of using medicinal plants, and they used their many properties extensively. The scientific name for Caper plant is Capparis spinosa L. Due to its resistance to environmental stress and its ability to act as a protector against soil destruction, this valuable medicinal plant is suitable for growing in arid and desert areas. Commercial cultivation of this plant is very valuable because it is rich in bioactive compounds. This plant's compounds can be extremely useful and effective in protecting humans from various diseases or enhancing the treatment of diseases. This study aimed to examine the total phenol and flavonoid composition, antioxidant activity, and extraction efficacy of various parts of this plant in two desert locations in Yazd and Isfahan to select the best cultivation region from these two sites for expanding cultivation. To determine the most effective compounds and the optimal extraction method, various organs of the plant were examined separately in this research. The best extraction efficiency can be achieved by growing the plant in the region and harvesting the desired organ, and the extracted materials can be used to prevent and treat diseases.

 The caper plant was collected from desert sites in Yazd and Isfahan in different parts, which included leaves, stems, buds, flowers, fruits, and roots. Plants were collected from four different locations on the site, and finally, the plants from four different locations were combined. After the botany expert's approval, extraction was done from different parts of the plant with hydroalcoholic solvent (80% ethanol) by the Soxhlet method. In this research, different experiments of total phenol, total flavonoid, antioxidant content, and extraction efficiency were performed using standard methods. Different dilutions of extract and standard material were made for all experiments. Folin Ciocalto's reagent was used to measure the total phenol, and it was reported according to the Gallic Acid standard per gram dry weight of the plant. Different tests like ABTS, DPPH, CUPRAC, and FRAP were employed to measure the plant's antioxidant content. The standard for milligrams of Trolox per gram of dried plant weight was used to calculate all of them. The number of flavonoids was measured based on the Aluminium Chloride colorimetry method and was expressed in the standard form of milligrams of Quercetin per gram of weight of the dried plant. To determine the extraction efficiency, the weight of the powder extract obtained from the extraction of different organs was calculated with a scale, and then the ratio of the weight of the powder extract of the weight of dried plant was reported as extraction method efficiency. For each of the experiments, 3 repetitions were performed. A two-way analysis of variance was utilized to examine the data's normality, and finally, IBM SPSS Statistics 26 software was utilized to analyse them.

The findings demonstrated that phenolic and flavonoid compounds were abundant in various parts of the caper plant from both locations. The leaves demonstrated a higher concentration of phenolic and flavonol compounds, with 48.611 mgGA/gDW and 19.842 mgQE/gDW, respectively. The antioxidant activity of roots was the highest among all parts of the plant. The Yazd site's caper plants were found to have a higher total phenolic and flavonoid content, with 35.572 mgGA/gDW and 14.14.164 mgQE/gDW, when compared to the other regions. The highest antioxidant activity was found in the fruit and root of the caper plant using the DPPH method. The ABTS method's measurement of antioxidant activity resulted in the same results and indicated that fruits had the highest activity. A positive correlation was observed between the amount of phenol and flavonoids. Furthermore, the Yazd site had a higher extraction efficiency than the Isfahan site, measuring 16.754%. The best region between two desert sites is also the best organ for extraction, as per the results of the current investigation. These results can be utilized to cultivate caper plants that contain more effective substances. These findings emphasize the status of the Caper plant as a rich source of secondary metabolites and show its potential as a potent healing agent with highly beneficial compounds, the site of Yazd is a suitable site for the cultivation of this plant. By cultivating this plant, in addition to helping to reduce desertification and prevent soil erosion, it is possible to have a source of secondary metabolites, especially phenol and flavonoid compounds, and use them in many medicinal applications.

Dust is a phenomenon that mainly occurs in arid and semi-arid areas as a result of high wind speed and turbulence on the surface of the soil without vegetation and prone to erosion. Various factors such as wind speed, vegetation cover, soil characteristics, climatic factors, etc. are involved in the creation of wind erosion and the resulting dust, all of which are related to each other and lead to an increase or decrease in wind erosion and dust storms. The problems caused by dust storms are due to the lack of sufficient information about the prevailing conditions in the region, the way these conditions change, and the lack of knowledge of sensitive and prone areas to dust storms. To deal with this phenomenon and provide appropriate management solutions, it is necessary to know the areas prone to dust and the effective factors in the occurrence of this phenomenon. In this regard, remote sensing and modeling can be very effective in investigating the dust phenomenon. Numerous studies have also been conducted to investigate dust storms and dust sources and to model areas sensitive to this phenomenon using remote sensing data and machine learning.Isfahan province is considered one of the most important geographical regions of the country, which is susceptible to successive drought, desertification, and dust storms due to its special geographical location, low rainfall, and proximity to the desert. So, it is necessary to carry out studies that will lead us to a correct understanding of dust-prone areas in this province. Therefore, In the current research, zoning of dust-prone areas in Isfahan province was done using meteorological codes related to dust, Aerosol optical depth values of MODIS sensor of Terra satellite (2001-2022), and machine learning algorithms including RF, BRT, SVM, and CART.

Study area Isfahan Province with an area of nearly 107017 km2 (6.4% of Iran area) is located between 30° 43′ to 34° 30′ N and 49° 38′ to 55° 31′ E in central Iran (Fig. 1). The mean annual precipitation of this province is between 40 mm and more than 800 mm and its mean annual temperature varies from 10 °C to 20 °C (Iran Meteorological Organization). According to the Torrent White method, the climate of Isfahan province is dry in 58.73% of its area (eastern, northeastern, and sub-central parts of the province), semi-arid in 28% of its area (central and northern parts of the province), and humid and semi-humid in 13.27% of its area (western and southern parts of the province).

First, using AOD values, the occurrence and non-occurrence points of dust were determined. Ten various factors including land use, temperature, rainfall, erosive wind speed, slope, altitude, albedo, EVI, NDSI, and NDMI were determined as predictive factors. n the next step, the correlation between the predictive factors was calculated using the variance inflation factor (VIF). Using machine learning algorithms, spatial modeling of susceptible areas to dust was done and the importance of predictive factors in zoning was determined using the jackknife test. Finally, using the value of the area under the ROC curve (ROC-AUC), the validation of the model was done.

The zoning map of dust-prone areas in Isfahan province showed that the low-altitude and flat parts of the north, parts of the northeast, southeast of the province, and the central areas towards the southwest and west of Isfahan province are vulnerable areas against the occurrence of dust.The highest percentage of areas prone to dust in the RF model was in the very low class with a value of 21.36%, in the BRT model was in the medium class with a value of 22.66%, and in the SVM and CART models, it was in very high and low classes with values of 23.92% and 37.6%, respectively.The results of validation illustrated that the RF model with AUC = 0.86 was the most efficient, followed by BRT, CART, and SVM models with AUC values of 0.82, 0.79, and 0.77 respectively. According to the results of the Jackknife test, in RF, BRT, and CART models, rainfall had the most effect in modeling while in the SVM model, temperature and then rainfall had the most effect in the modeling.

Based on the results, the most vulnerable areas to dust are assigned to salt land, barren lands, and rangeland with poor quality. These areas, which are mainly located in the northern, central, and parts of the eastern sides of the province, have the lowest amount of surface soil moisture, the lowest amount of rainfall, and the highest temperature, and as a result, they face the lack of vegetation or weak vegetation. Therefore, these areas are exposed to the occurrence of dust, and with winds blowing at a speed exceeding the threshold speed of wind erosion, dust storms will happen.According to the validation results, the RF model has the best performance among the applied models, followed by the BRT, CART, and SVM models. Random forest algorithm is one of the advanced decision tree models used for classification and regression. This algorithm has a much more accurate performance compared to other simple regression trees or parametric statistical methods and is defined based on a large number of decision trees.The results of the jackknife test introduced rainfall as the most important factor in RF, BRT, and CART models. In the RF model, as the best model, after the rainfall factor, the temperature and altitude factors are more important than other factors. Considering the low amount of rainfall in dust-prone areas, it can be said that low rainfall, soil dryness, and, as a result, the reduction of vegetation, increase the conditions for creating wind erosion and dust.

Soil erosion is a serious threat to human well-being and life, especially in arid and semi-arid regions, and is one of the important issues in land management. Rill erosion is one of the most significant events in water erosion that affect soil loss, landscape, water resources, and land degradation can cause significant loss of soil in different climates. Identifying the effective processes that lead to the creation and expansion of rill erosion is necessary, and finding effective solutions to prevent rills is essential. In the meantime, one of the management solutions is determining the prone area to rill erosion. The high sensitivity of the lands of Nehbandan city (Kaji wetland watershed) to erosion is the reason for determining the prone areas to rill erosion. The maximum entropy method was used to identify the area that is susceptible to rill erosion.

The modeling process used 9 effective factors, including height, slope steepness, slope direction, rainfall, land use, land cover, soil texture, geomorphology, and geology, based on similar research. Factors affecting the occurrence of rill erosion were analyzed as independent variables. The first step in preparing a rill erosion sensitivity map was to determine the location of rill erosions in the Kaji wetland watershed using Google Earth and then to conduct field surveys. The basin was monitored in the field using GPS and 138 cases of rill erosion were recorded. The occurrence points were divided into two groups: training and validation, with a 70:30 ratio. The total occurrence points were divided into 97 incident points that were randomly selected for model training (validation stage) and 41 incident points that were used for validation purposes. The MaxEnt model relied on the data set used for training as independent variables. In order to use the maximum entropy model to determine rill erosion, first the independent variables (factors affecting the occurrence of rill erosion) and the dependent variable (identification of points with rill erosion) was converted to the required format and introduced to MaxEnt software. To evaluate the effectiveness of the model in detecting occurrence points (rill erosion) from pseudo-non-occurrence points, the area under the ROC curve was used. The Jack Knife test was utilized to investigate the identification and prioritization of 9 influential factors (independent variables) that influence the results. The model was implemented using the remaining variables as input factors after removing the independent variables separately for this purpose. The efficiency of the model built using all independent variables was measured in comparison to the case where the model was built based on other variables. To determine its effect on the output, the contribution of the omitted independent variable was examined.

According to the validation results, the sensitivity map for rill erosion has a high efficiency. The test stage should have a ROC curve of 0.859 (very good) and the test stage should have an average curve of 0.6 (moderate). The Jack Knife test revealed that the slope's steepness was the most significant environmental factor in the predicted sensitivity map for rill erosion in the study area. Geology and land cover were also recognized as other important factors. The MaxEnt model was found to be an effective model for preparing the rill erosion susceptibility map, according to the results. According to the findings, the slope steepness factor, which is 25%, is the main factor that affects the rill erosion of the Kaji wetland watershed. The high frequency of the slope class with a slope class below 20% suggests that this slope class is a significant factor in the development of rill erosion. The geological map of the region indicates that the majority of the region is dedicated to Quaternary formations, which is crucial for the development and creation of erosion in the region. The watershed's proneness to rill erosion is caused by poor rangeland usage. Management of vegetation and rangeland is necessary to reduce the potential for soil erosion in the region. According to the results, the soil texture of the region had less effect on the development of rill erosion; because most of the soil in the area is related to sand-gravel texture, which has a low effect on rill erosion. The MaxEnt model's high accuracy in modeling the sensitivity of rill erosion is evidenced by the results of the present study.