Desert
Volume:28 Issue: 2, Summer -Autumn 2023

Linear dunes are widespread in the Earth’s deserts and also in some extraterrestrial locations such as Titan and Mars. They show great variety in their forms, and occur in three main classes: simple, compound and complex. Using Google Earth this paper illustrates this diversity. Among the topics covered are simple dunes, linear dunes with tuning fork junctions, linear dunes with complex crests, linear dunes associated with stars, with domes, with zibars, with barchans and with parabolics, and linear dunes controlled by topography.

In this study, the Entropy Weighted Water Quality Index (EWQI) was used to assess the groundwater suitability for drinking purposes in Damghan Plain, Iran. This index has been known as the most unbiased model for assessing drinking water quality. Additionally, physicochemical indices including Sodium Adsorption Ratio (SAR), Magnesium Hazard (MH), Kelley’s Ratio (KR), Salinity Hazard (SH), Synthetic Harmful Coefficient (K), Potential Salinity (PS), Total Dissolved Solids (TDS), Chloride (Cl-), Permeability Index (PI) and Soluble Sodium Percentage (SSP) were used to evaluate the suitability of groundwater for irrigation purposes at August 2018 (dry season) and February 2019 (wet season). The results indicated that sodium (Na+) and chloride (Cl-) are exceeding the permissible limits based on WHO standards and Cl- has the highest entropy weight. EWQI maps illustrated that the groundwater has moderate quality in the western parts and poor quality in the eastern parts of the study area. The mean value of this index has decreased from 149.47 in August 2018, to 147.26 in February 2019, which reflects that the groundwater quality has been improved for drinking purposes. The values of SAR, KR, PI and SSP indices slightly increased, which indicated that the quality of groundwater has more deteriorated in terms of these indices. The mean value of MH, SH, K, PS, TDS and Cl- indices have slightly decreased during the study period. Finally, Land Use-Land Cover (LULC) map was used to show which groundwater consumption is appropriate with its quality. Groundwater in the urban areas has moderate and poor quality for drinking purpose and suitable quality in terms of SAR, K and PI and unsuitable in terms of MH, KR, SH, PS, TDS, Cl- and SPP in agricultural lands. The suitable condition in terms of SAR, K and PI is because of the high concentration of Mg2+ and Ca2+. Thus, groundwater is not suitable for irrigation in the agriculture sector.

Dust is one of the major crises in the Middle East. In recent years, the frequency and intensity of this crisis in the region has increased and this has increased the need to study this phenomenon. In this article, we have studied the temporal and spatial changes of the dust storm phenomenon in Iran, Khuzestan (south-west) in the period 2000 to 2018. For this purpose, ground dust data and horizontal visibility and (Aerosol Optical Depth( data of MODIS satellite have been used. The AOD Optical Visibility Index has been declining from 2003 to 2007 and then increasing again. In general, we can see a gradual but increasing trend in the AOD index in the study area, from an average of 0.35 at the beginning of the period to nearly 0.6 at the end of the period, which indicates the predominance of dust storms in the region. The relationship between dust and climatic parameters was also examined. In terms of external storms, precipitation had a significant (P <0.001) and negative effect. This means that on days when there is less rain, the horizontal visibility index decreases and more dust storms were formed. In terms of domestic storms, only the wind direction had a significant direct effect (P <0.05), which means that some directions had higher dust storm frequencies. Finally, it can be concluded that the trend of domestic and foreign dust storms is increasing. The frequency of their occurrence is also increasing and with the decrease of regional rainfall due to climate change, we should expect more severe storms in the near future. For this reason, it is necessary for the authorities to act as soon as possible in controlling the internal dust centers, and to provide the ground for controlling the regional centers through regional consultations.

The upward movement of water through capillary rise from the shallow water-table (1–1.5 m) to the root zone (0.4–0.6 m) is an important incoming flux in the soil system. UPFLOW is a specialized model for determining capillary rise, which is a simple model with a small amount of input data. This research was conducted to assess the efficiency of the UPFLOW model in the simulation of capillary rise in shallow water-table conditions, based on the observed capillary rise using a lysimeter in the agro-industry of Amirkabir, Khuzestan. The input data of the model include: crop(root-water uptake rate at different sections of root zone, crop cover type, crop coefficient), soil(number of soil layers and their thickness, mean soil-water content of the profile, anaerobiosis point of the soil, saturated hydraulic conductivity of the soil profile), weather(maximum and minimum temperature, solar radiation, wind speed, and relative humidity, ET0) and water-table(depth to water-table from the soil surface, salt content of the groundwater) were measured. Then, the capillary rise was calculated for each month during the stages of sugarcane growth. The performance of this model was evaluated by the statistical indices including determination coefficient (R2), mean absolute error (MAE), root mean square error (RMSE), relative error (RE), and model efficiency (EF). The results of the field measurement showed that the highest amount of capillary rise (1.6 mm) was observed in July, with the maximum evapotranspiration (400 mm) and the maximum growth of sugarcane. The lowest value (0 mm) also was observed in the months when sugarcane growth stopped, i.e., November, December, January, and February. The results of this research have shown that the UPFLOW model in the studied area has not shown suitable efficiency (based on statistical indicators) in the simulation of the upward flow in specific conditions of water-table, crop, soil, climate, and management measures (R2:0.23, MAE:8.71, RMSE:9.10, RE:180.50, EF: -136.40). Therefore, this model, in shallow water-table conditions, to accurately estimate capillary rise, requires comprehensive evaluations of various effective factors and, if possible, to modify and adjust this model in this area.

As a strategic crop, wheat is important to the world’s food supply. Salinity is a major threat to the food supply in the whole world and Iran. The research was carried out for identifying morpho-physiological and biochemical markers to select salt-tolerant wheat genotypes. The research was done on two salt-tolerant (Pishgam) and susceptible (Shahryar) bread wheat cultivars under saline conditions (250 mM NaCl). The results showed a high genetic diversity for most traits. The traits of harvest index, root-to-shoot dry weight ratio, root dry weight, plant height, potassium-to-sodium ion ratio, total protein, superoxide dismutase, catalase, peroxidase, and total carbohydrates showed the highest alignment with increasing grain yield under salt stress conditions. The traits of potassium ion accumulation (K+), superoxidase dismutase (SOD), proline (Pr), and relative water content (RWC) were entered into the regression model, as the most important traits affecting grain yield under salinity conditions, respectively. According to the results, it is possible to suggest two groups of markers to select salt-tolerant genotypes. The first group includes some morpho-physiological markers namely a high amount of harvest index, main spike weight, relative water content, water consumption, root-to-shoot dry weight ratio, total carbohydrates, proline, and root dry weight; and the second group includes the ionic and biochemical markers namely a high amount of K+ accumulation, K+/Na+ accumulation ratio, SOD, catalase, peroxidase, and a low amount of Na+ accumulation, malondialdehyde, and hydrogen peroxide. Therefore, the above-introduced markers can be useful indicators to select salt-tolerance genotypes in future wheat breeding programs.

Many regions of Iran lack digital map of soil properties. The Chahardowli plain in western Iran is one of these areas. Due to the importance of sand, silt, and clay components, having quantitative and continuous data on abrupt changes in these two properties in this area is very critical. Therefore, to study sand, silt, and clay, samples were taken at depths of 0–5, 5–15, 15–30, 30–60, and 60–100 cm, according to GlobalSoilMap. Finally, 145 samples were collected from 30 soil profiles. The significant covariates were selected by Random Forest Recursive Feature Elimination (RF-RFE). Relationships between these characteristics and environmental predictors were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. The accuracy and precision of the models used for all three particles showed that the RF model had the most accurate prediction with R2 and RMSE of 0.82 and 2.34 for clay, 0.80 and 3.87 for sand, and 0.85 and 2.89 for silt, respectively. In this study, terrain-based variables had a greater impact on improving accuracy than remote-sensing variables. The current study showed that even with limited information, digital mapping of sand, silt, and clay particles under GlobalSoilMap and the use of environmental factors can provide acceptable results.

Wind erosion plays an essential role in the production of sediment for dust storms and occurs when strong and continuous wind interacts with dry, fine-grained and loose soil. Identifying dust production centers is the first step in prioritizing different areas for executive operations to reduce dust and determine its control methods. Jazmurian area is one of the areas where the intensity and frequency of dust events have increased in recent years and caused a lot of damage. The purpose of this research is to determine the areas prone to dust production and sensitive to wind erosion using the ILSWE model in the Jazmurian wetland basin. This model is based on five effective factors of climate erosivity, soil erodibility, soil crust, vegetation cover, and surface roughness. Maps of temperature, precipitation, wind speed, percentage of sand, silt, clay, calcium carbonate, EVI and land use were used to calculate these factors. After calculating each factor, ILSWE index was calculated by multiplying them. Finally, sensitive areas were identified by classifying this index in Arc GIS software. The ILSWE classification map showed that 46.72% of the studied area is in very low sensitivity class, 16.56% in low class, 13.67% in medium class, 12.41% in severe class and 10.64% in class Very severe sensitive to wind erosion. Severe and very severe sensitivity class was considered as the center of dust generation. The results showed that the southern (wetland area and its surroundings), west, southwest, east and southeast areas of the Jazmurian wetland basin are prone to dust production and wind erosion. These areas are mostly located in areas without vegetation (barren areas), salt marshes and sand dunes; which shows the importance of vegetation in reducing producing dust. On the other hand, the topography, the presence of wind and the characteristics of the soil in these areas help to produce dust. In general, the results of this research showed that the ILSWE model has a suitable efficiency for determining areas prone to wind erosion and dust production.

Plants in arid and semi-arid climates face limitations in accessing water resources. In addition, roots have a significant role in water uptake, nutrient absorption, hormone regulation, and mechanical anchoring. Therefore, we conducted a randomized complete block design study with four replications to investigate the root systems of four Quinoa cultivars (Rosada, Black, Titicaca, and Multi-hued Bulk) in arid and semi-arid climates. This research aimed to identify the most efficient cultivars based on their root length, root width, root fresh and dry weight, root density, root surface area, root volume, and shoot length in response to environmental stresses. No significant difference was observed between the Black and Rosada cultivars, but they outperformed the Titicaca and Multi-hued Bulk cultivars regarding root development, showing a better balance of dry matter allocation between the roots and aerial parts. The wavy and large root surface areas were observed in the Rosada cultivar, leading to improved yield. The Rosada and Black cultivars were more efficient (p<0.01) in water absorption compared to the other two studied cultivars based on various critical factors, including root-to-shoot weight ratio (0.09, 0.07), root diameter (0.66, 0.46mm), specific root length (18.6, 32.2cm.g-1), root surface area (42.6, 34.6cm3), root length (16.2, 17.3cm), root penetration and expansion into deeper soil layers (with root width and volume, 13.9, 15.3cm, and 8.3, 5.3cm3, respectively), and root surface area density (32.18, 30.16cm2). Therefore, Rosada and Black cultivars are well-suited for breeding programs in regions with moisture limitations.

The current research aims to assess the feasibility of groundwater resources in two soft and hard formations of the Shamil-Takht basin using logic and operators Fuzzy and Boolean in the GIS environment. For this purpose, eight and seven thematic layers in the soft and the hard formations were investigated and analyzed separately. The layers were prepared in a raster format in the GIS environment and then each of the layers was classified according to the values of usefulness obtained with Fuzzy membership degree and 0 and 1 Boolean values. In the next step, AND and Gamma operators for Fuzzy layers and Algebraic Multiplication operators for Boolean logic were used in combining layers. The groundwater potential map of the studied area was obtained from the desired pixels resulting from performing the operations at the output. Based on this, in Fuzzy logic, 77.1% of the area of soft formation and 11% of the area of hard formation, and in Boolean logic, 24.1% of the area of soft formation and 1.74% of the area of hard formation had the possibility of water sources. Areas with high potential of groundwater resources were in areas with low slope and cultivated and areas with weak potential of water resources were in areas with alluvial and sedimentary formations. Also, the field investigations confirm that the emerging springs and existing wells in the Shamil-Takht basin are located in the areas with the lowest slope and high density of the waterway.

The use of accurate and up-to-date information is crucial for sustainable resource management in agriculture. Satellite imagery provides the opportunity for the comprehensive monitoring of resources, and enables precise planning not only for rural development and agricultural sectors but also for national development programs and the implementation of food and water security policies in the country. It also helps prevent land use changes and their degradation. In this research, an attempt has been made to calculate the cultivable land area in Shahr-e-Kord Plain located in Chaharmahal-va-Bakhtiari Province. The imagery data of Landsats 7-8 and Sentinel-2 satellites were obtained in the form of NDVI index during the period from 2013 to 2022, which processed and validly classified using unsupervised algorithms in GIS environment. All existing features was differentiated based on digital number values and green area surfaces identified for different time periods. The sound and secure areas of cultivation were considered based on the frequencies of plantation during the 2013-2022. Average of the agricultural lands approximated 9,392 ha. Due to recurrent water shortages in recent years, only about 2,603 ha of agricultural lands in the study region have been under permanent cultivation over the 8-year study period and the rest have been abandoned over time or left for fallow and livestock grazing. Accordingly, as a basis for the integrated land-water-crop system planning and recommending policy for conservation of permanently cultivable land resources in the agricultural system of the region, the map of their spatiotemporal distribution was prepared and presented with a pixel specific precision, by different years of cultivation.

Knowing the temporal and spatial changes of land use and the formation of heat islands over time is one of the most important issues in metropolitan planning and policy making. Thus, in this study heat islands and temperature changes and its relationship with land use changes have been monitored over a period of 35 years in two study areas, i.e. the cities of Mashhad and Sari, using the Google Earth Engine platform. For this purpose, the LST was computed and the land use maps of the studied periods were extracted during 8 time steps of 5 years from 1985 to 2020. The aim of this study is to investigate the spatial autocorrelation of heat islands and its relationship with land use in two studied regions with different climatic conditions. The results of temperature monitoring showed an increase in temperature between 1 to 2 °C in all types of land uses during 35 years. This increasing trend of temperature is proportional to the type of land use changes, so that the temperature increase in built-up lands was estimated to be 2 and 1.75 degrees Celsius in the cities of Sari and Mashhad, respectively. The average temperature of the three months of summer in Mashhad city in built-up areas has increased from 34.5°C to 36.25°C and in Sari city from 29.51°C to 31.51°C. while the minimum increase in temperature has occurred in the lands with forest coverage, which is 1.02 °C and 1.19 °C, respectively in the cities of Sari and Mashhad. Conclusively, in both climatic regions, the areas where the changes are in the direction of reducing or removing vegetation and creating residential areas, the temperature increase is the maximum, and the areas where the changes are in the direction of increasing forests and agricultural lands, the temperature increase is the minimum.

The associated impurities with sepiolite, mainly palygorskite, quartz, and dolomite, reduce the industrial quality of this mineral. The objectives of this study were to i) investigate the efficiency of particle size separation for purifying sepiolite and ii) determine the kinetic dissolution of sepiolite in HCl and sodium acetate - acetic acid buffer (SAB). Bulk sepiolite was provided from a mine located around the city of Fairman, in northeastern Iran. Three particle size fractions of 20-50, <20. and <2 µm were separated from the bulk sepiolite. The XRD results showed that the particle size separation method has a great impact on reducing the amount of quartz as well as the complete elimination of dolomite in particles <2 µm and palygorskite in 20-50 µm fractions. The <20 µm fraction composed ~70% of the sample which it’s impurities considerably decreased; Therefore, it is a cost-effective fraction for industrial use. Therefore, it is a simple and low-cost physical procedure, without applying chemical reagents to achieve suitable purified sepiolite. The results of dissolution kinetic showed that the data had the best fit with the rate equation of Kt=[(1-(1-X)1/2)2]. Increasing acid concentration, temperature, and time had a direct relation, while the solid to liquid ratio had an inverse relation with the apparent rate constant. This trend was observed for SAB, too. Dissolution of sepiolite in HCl was higher than that of SAB. Acid concentration and temperature were the most effective factors on sepiolite dissolution.

Attempt to evaluate soil fertility was and still is one of the most challenging public importance. Soil nutrients are the key factors in soil fertility. For this reason, when constructing soil fertility potential, many researchers prefer to investigate soil nutrient status or use and assessment of qualitative research methods. Quantifying soil fertility is challenging since various factors such as numerous physical and chemical characteristics of soil might affect it. The proper selection of factors that may more accurately describe soil fertility is another issue. So, in this study, we developed a regional soil fertility index (SFI) based on different soil nutrients for quantifying soil fertility. After receiving fertility, a comparative study of machine learning techniques was carried out to construct its distribution map, using digital soil mapping (DSM). The spatial distribution of the SFI map showed that 55% of the studied area had poor fertility, 27.25% had moderately fertile soils, and only a tiny area had fertile soils. The results indicated that heavy soil texture and high calcium carbonate content were the most limiting factor and phosphorus and zinc were the most limiting nutrients across the studied area. Comparing machine learning techniques yielded the finding that the Random forest model has the best performance for predicting SFI (R2= 0.86) compared with the Decision tree (R2= 0.53) and Multi-linear regression (R2= 0.35). Therefore, specific soil fertility management practices and training farmers on the proper use of soil fertility management practices are recommended.

Statistical assumptions are the basis of many univariate and multivariate statistical tests. Normality is the most basic assumption of multivariate analysis in plant ecology. If the normality assumption is violated, some specific statistical tests are not valid. Therefore, the present study compares the methods of normality assessment of some soil properties in the arid land of South Khorasan. It also examines the effect of increasing the number of soil samples from 25 to 50 on the normality results. Histogram, box plot, Q-Q plot, CV, skewness, and univariate and multivariate normality tests were used. The results showed that EC, K, Ca, Mg, Na, Cl, HCO3, and SAR data had a very high variation (CV 75–100%) and saturation moisture and pH had a low variation (CV <15%). Based on the results of most statistical tests and the skewness coefficient, saturation moisture, pH, N, P, CaCO3, sand, and silt were normal. EC, K, Ca, Mg, Na, Cl, HCO3 and SAR had the right skewed distribution. The results showed multivariate normality was violated, and the use of these data was not suitable for multivariate analysis. The results of the goodness-of-fit test showed that P, sand, and silt follow a normal distribution. Other soil properties do not follow any of the studied probability distributions (p≥0.05). Therefore, the use of nonparametric is recommended for the physical and chemical properties of the soil in the area. Although in general, the increase in the number of samples has a positive effect on the actual distribution of the community, but due to the high spatial variability of some soil properties such as salinity, the status of nutrients, particle size, etc., the CV and the range of variations in most of soil properties are wide.