نشریه مهندسی اکوسیستم بیابان
پیاپی 36 (پاییز 1401)

Due to locating most of Iran in arid and semi-arid climates and the consequences of drought, assessing the trend of drought change is very important. An overview of past researches indicates the occurrence of severe and long-term droughts in recent years, which highlights the effects of drought in Iran and other parts of the world. Climate change due to global warming, severe and prolonged droughts and consequently water scarcity is one of the major challenges, especially in arid and semi-arid regions. Therefore, it is necessary to study the trend of drought change as one of the consequences of climate change, which leads to severe limitation of water resources, so that in the presence of increasing drought changes, necessary measures will be taken to manage water resources. Therefore, in this study, the trend of meteorological drought changes in Kerman province is evaluated by SPEI index. Kerman province, like other arid and semi-arid regions of IRAN, is no exception to the phenomenon of drought and so far no study has been conducted to study the fluctuations of meteorological drought by existing indicators (SPI, SPEI, RDI, PDSI) in the whole province.

The study area of this paper is Kerman province which located in the southeast of Iran. The climatology data, including mean monthly precipitation and mean monthly temperature, of 7 synoptic stations were used to conduct this study for the period of 1990-2018. These data were obtained from Meteorological Organisation of Kerman.
To conduct this study, first, Standardized Precipitation Evapotranspiration Index (SPEI) was calculated to extract drought periods in three time scales i.e., 3, 9 and 12 month. SPEI estimates dry and wet periods based on precipitation and temperature data. In order to evaluate the trend of time series changes, the first step is to examine the existence of autocorrelation between time series. For this purpose, the Pre-Whitening method (Von Storch, 1999) was used to determine the presence or absence of autocorrelation. After confirming the absence of autocorrelation between data, Mann-Kendall test was applied to SPEI values. In the next step, the Sen’s slope test for the time series is calculated and the significance of the slope at different levels of confidence is obtained. In other words, the assessment of upward and downward trend of SPEI series was evaluated using two both Mann-Kendall and Sen' Slope trend tests

Considering the impact of climate change on precipitation and temperature in Iran and also the significant impact of precipitation and its fluctuations on agricultural production, it is important to assess the fluctuations of phenomena such as drought that are directly affected by precipitation and temperature. Therefore, in this study, drought changes were assessed by SPEI3, SPEI9 and SPEI12 indices in Kerman province as one of the regions with arid and semi-arid climate. The results of SPEI time series analysis showed that the highest number of drought events occurred in Shahrbabak station with a frequency of 80% during the statistical period 1990 to 2018. Also, the most severe drought occurred at this station in 2016, according to the annual SPEI average. The average annual SPEI values ​​showed that the study area experienced the most severe drought events between 1998 and 2010. The results of evaluating the monthly changes of drought by Mann-Kendall and Sen’s slope method showed that in almost all months of the year except May, an increasing and decreasing trend is observed, so that in January (increasing and decreasing trend) and October (increasing trend) there are the most significant drought changes. Seasonal changes of drought showed that drought changes in spring, autumn and winter have an upward slope that in some stations this increase is significant at the level of 99 and 95%, while the summer season during the study period was faced with increasing and decreasing changes. The increasing trend of drought in spring, summer and autumn has been reported by Malekinejad et al. (2012) in Tehran province. In general, the evaluation of annual changes in drought in the study stations showed that the whole area during the statistical period (1990-2018) had an upward trend. In other words, the severity of drought is increasing in Kerman province, which is one of the main reasons for climate change and consequently increase in temperature (Mirakbari and Ebrahimi, 2021; Mesbahzadeh et al., 2020). Also, the increasing trend of drought in Iran has been reported by several researchers, including Ghorbani et al. (2020), Mozaffari et al. (2021), Amani et al. (2021). The study of changes in drought periods showed that Kerman province has experienced three periods with different trend slopes during the statistical period under study. The first period (1996-1990) has a decreasing slope, the second period (1997-2010) and the third period (2018-2011) have an increasing slope, which is relatively consistent with changes in precipitation and average temperature throughout Kerman province. The results of this part of the research are consistent with the results of Ebrahimi et al. (2021), which reported the existence of three periods of drought changes with different trend slopes based on Domarten drought index in the whole country during the statistical period of 2011-2018. In general, the results of this study showed that drought in Kerman province is increasing, which requires more attention of experts and planners of water resources for proper management of water shortages caused by severe droughts.

One of the most important components of the water requirement is the reference evapotranspiration (ET0), which is one of the most important components of the hydrological cycle that dependent to climate variables such as: wind speed near ground, air temperature, radiation Solar and relative humidity. Consequently, ET0 values can be estimated and simulated using meteorological models based on physical equations or the empirical relationship of meteorological variables. There are various methods for calculating reference evapotranspiration, each of which has different results depending on the different meteorological assumptions and data they consider. Worldwide, the FAO model is used as a reference method for estimating evapotranspiration. But this model requires a lot of input information such as: max air temperature, min air temperature, max relative humidity, min relative humidity, solar radiation and wind speedy, which sometimes is not possible to access all the information especially in arid and semi-arid regions like Iran and this necessitates the selection of models with less input data and appropriate accuracy. In this study, seven reference evapotranspiration estimation models that require less data including: Kimberley penman, FAO radiation model, Hargreaves Samani, Makkink, belany Kridle FAO 24, Turc and Peristly Teylor, were evaluated than the FAO model. The reference evapotranspiration was then modeled using Gene Expression Programming Model (GEP) and the results were compared with experimental methods. Multivariate regression was used to determine the model input patterns and to investigate the effect of climatic parameters on ET0. Multivariate regression in fact expresses the relationship between several predictor variables with the response variable in question. Such models have assumptions. The assumption that distinguishes multivariate regression from simple regression is that: 1) The number of predictor (independent) variables in the regression should be less than the number of observations. 2) There is a complete linear correlation between predictor and response variables. If the two assumptions are violated, the regression equation cannot be estimated. Gene expression planning is a combination of the GA and GP methods developed by Ferreira in 1999. In this method, linear and simple chromosomes of constant length, similar to the genetic algorithm and branch structures of different sizes and shapes, are combined, similar to the decomposition trees in genetic programming. In short, it can be stated that in this way the genotype and phenotype are separated and the system will be able to enjoy all the evolutionary benefits. Although the phenotype in GEP is similar to the branched structure of GP, the branched structure in GEP, also called tree expression, represents all independent genomes. In summary, it can be briefly stated that in GEP refinement takes place in a linear structure and then expressed as a tree structure, which will result in only the modified genome being transferred to the next generation. It does not need heavy structures to reproduce and mutate. For this purpose, daily maximum and minimum temperature, maximum and minimum relative humidity, Wind Speed and sunlight hours of the 35-year period (1983–2017) were used for the Aligodarz Synoptic Station. 70% of the data were used for training and 30% of the data were used for testing the model. Also, two types of mathematical operators including four-element operations and default model operators were used in the GEP method. The results showed that the Kimberley penman and FAO radiation models are more accurate than the other experimental models. Multivariate regression results showed acceptable modeling accuracy with R2 = 0.95. The analysis of model coefficients showed the highest effect of maximum Temperature with a coefficient of 0.58 on reference evapotranspiration. After that, respectively, wind speed, sunshine hours, minimum temperature, maximum and minimum relative humidity have the most influence on prediction and estimation of evapotranspiration. Therefore, six models for model inputs were determined. In Gene Expression Programming, model 2 with model default operators with RMSE = 0.843 and R2 = 0.932 at training stage and RMSE = 0.76 and R2 = 0.941 Performed better in the test phase. Comparison of the reference evapotranspiration estimation models indicated that the Gene Expression Programming model outperformed the other models. The results of this study showed that the GEP model has acceptable ability to estimate reference evapotranspiration under Aligodarz climatic conditions and introduced it as a usable model in this field.

Soil salinity is a major cause of desertification. It can be caused by several factors including and not limited to improper irrigation, low irrigation water quality, saline groundwaters, saline geological formations, and improper land management. Since direct measurement of soil salinity is costly and time-intensive, the main purpose of this research is verifying whether satellite remote sensing data can be used as a proxy to estimate soil salinity.

In the present study, soil salinity changes in Torbat Heydariyeh city of Khorasan Razavi province were investigated using MODIS satellite data in the period of 2005, 2010, 2015 and 2020. We used a combination of MODIS visual images band 4 and 6 to produce the salinity index. In order to evaluate the alternative indices and the effect of salinity on other environmental variables, the correlation between salinity, altitude, slope and EVI (Enhanced Vegetation Index) was also examined. To prepare the ground truth map in 2020, a field sampling was conducted in late June of 2020 from the top 0-5 cm of soil surface in a random sampling design from 80 points. Soil salinity was determined in saturated extract. The obtained salinity values were compared with the equivalent values in the 2020 salinity map based on the kappa index to determine the accuracy of the calculations.

Although the salinity level in the region is not very high, but the trend of change indicates an increase in salinity in the region, mainly in the southern and central parts and parts of the north of the region. In terms of topography, most of the area is flat with minor elevations in the middle and northern section. Soil salinity is believed to be correlated with the topography of a region. Salts tend to accumulate more in the lowlands and highlands become saline only if their parent formation is saline. To investigate the effect of this factor on soil salinity distribution in Torbat-e Heydarieh, an accurate digital altitude map of 12.5 meters resolution from the ALOS satellite was used. The correlation between salinity, altitude and slope was evaluated at different times. However, this relationship was not significant in any of the studied periods. But the interesting point was the higher correlation between salinity and altitude, which seems reasonable considering the distribution of evaporitic and Marine formations in the plain areas. We did not obtain significant changes in vegetation in the region between 2005 and 2020. If one would divide the EVI index results into three cover classes, 60% of the area would be classified as without coverage or with very poor cover (2235 square kilometers), 20% as medium (745 square kilometers) and high (745 square kilometers), respectively. Most of the vegetation of the region is scattered in the northern plains on the alluvial sediments. Accordingly, ​​approximately 60% (2235 square kilometers) of the total areas fell in the class of no salinity (0-0.17), 35% (1304 square kilometers) in light salinity (0.17-1.2) and only 5% of the area (186 square kilometers) in the extreme class (more than 0.2). To confirm the validity of the salinity index, the salinity measured in the field was compared with the corresponding values ​​from the produced map and the kappa coefficient was equal to 0.65%, indicating the validity of the measurements performed. However, the correlation between different parameters and salinity showed no correlation with slope, altitude and EVI, which is conceivable due to the scattered vegetation of the area. Interestingly, the values ​​of height and slope correlations were higher than vegetation. It seems that the severe lack of vegetation and the accumulation of evaporative formations in a few spots have led to a lack of correlation between these factors.

The results of soil salinity estimation in Torbat Heydarieh showed that remote sensing data is a viable alternative to direct soil salinity measurements. However, soil salinity did not correlate with other environmental factors including vegetation cover, altitude and slope gradient. Therefore, these factors cannot help us as proxies of soil salinity in these arid areas. According to the accuracy of the salinity index, it can be used to monitor salinity in the region. These results can help land managers to deal with salinity and ultimately desertification.

As one of the most significant and sensitive landforms to wind erosion, sand dunes vary in their extent of activities based on the influence exerted on them by various climatic and terrestrial factors.The study area comprises a Busher province’s county located in southern Iran, where sand dunes activity causes great problems for the local community. Therefore, the identification of active sand dunes by using wind regime of the region and the factors involved in their activity based on the Lancaster index, the aim of this research is.

First, the trends of climate factors, including precipitation, temperature, potential evapotranspiration, wind velocity, and local and non-local-originated dust were investigated at an annual scale using the data concerning the hourly-based wind velocity, weather codes, monthly precipitation, temperature, and potential evapotranspiration data collected from Dayer meteorological station over a 30-year period (1989–2018). Then, the sand dunes’ mobility status was examined by calculating the frequency of erosive wind and the aridity index over the whole study period using the Lancaster Index. Moreover, Sensitivity analysis was performed to predict the effect of meteorological parameters’ potential changes on sand dunes' mobility.

The study’s results indicated that according to the Lancaster index, sand dunes were fully active during the 30-year period. Furthermore, the aridity index was found to be subjected to real, extremely serious, and serious risks of desertification in three classes hyper-arid, arid, and semi-arid, respectively. On the other hand, the results of sensitivity analysis suggested that if the frequency of upper-erosive-threshold winds would increase by 30% in the future, the sand dunes' activity and mobility would increase by 30 %. However, if the precipitation rate increases by 30 %, sand dunes' mobility, and activity will decrease by 23%. Moreover, no significant trend was observed in meteorological parameters, including precipitation, temperature, and potential evapotranspiration throughout the whole study period.

In general, an increasing trend was found in sand dunes activity during the 30-year study period, suggesting a direct correlation between such an increase and precipitation. Moreover, the study’s results indicated that the management measures carried out over the past 30 years (for instance, planting seedlings, hand-planting afforestation, and grazing management) have proved insufficient, necessitating the application of new management policies in this regard.

Continuous and long-term measurement of vegetation is required to obtain basic and timely information concerning rangelands. The important point to consider in this regard is the correct choice and the proper number of representative places to be taken into account when measuring vegetation density in different ecological areas. On the other hand, monitoring rangeland ecosystems is exessively costly, requiring a great number of personnel and technical facilities. However, failing to monitor the rangelands in those areas that are representative of a wide range of vegetation prevents the achivement of desired results concerning vegetation monitoring. Therefore, as measuring and monitoring the vegetation of macro rangeland ecosystems of Iran requires the investigation of several landmarks / sites over a specific period, this study sought to monitor the vegetation of a number of select representative sites along the elevation gradient of Taftan’s southern slope.
 

 First, several places representing the habitats of the region and the distribution of Taftan Mountain’s vegetation along the altitude gradient were selected by examining the maps of Iran’s ecological zones (Khash region). Then, vegetation characteristics of the places, including percentage of canopy cover, number of bases per hectare, and amount of species forage production were measured, followed by the classification of sampling units based on similarities in species composition. To this end, four 450-meter transects with a distance of 100 meters from each other were used in the first place, which is dominated by Hammada salicornia and Zygophyllum atriplicoides. On each transect, 15 four-square meter plots (2× 2 m), that is 60 plots in total, were placed 30 meters away from each other. The second place that was dominated by Hammada salicornia, Zygophyllum atriplicoides, Artemisia sieberi and Artemisia santolinifolia species resembled the first place in terms of sampling network and the number of sampling units. On the other hand, three 100-meter transects were used in the third place with 50 meters’ distance from each other, on each of which 10 two square-meter plots (2×1 m) were placed 10 meters away from each other (30 plots in total). The lower vestibule of the third place was dominated by Artemisia quettensis, Artemisia sieberi, and Artemisia deserti (herbaceous species), and the upper vestibule of the place was dominated by Amygdalus scoparia (the woody element). Moreover, in the fourth place dominated by Artemisia quettensis and perennial herbaceous species such as Ferula ovina, four 100-meter transects were used with 50 meters’ distance from each other, on each of which 10 one-square meter plots were placed 10 meters away from each other (40 plots in total). To classify sampling units based on species compositional similarities, the sites’ vegetation was first classified into ecological groups using Twinspan analysis. Then, the correlation between the groups and topographic factors (slope, direction, and height) was investigated via principal component analysis (PCA), and the significance of the model was evaluated by P-value. Furthermore, thee collected data were analyzed using the Pc-OIRD software version 5.1.
 

 The study’s results indicated that the average canopy cover in the studied sites were 17%, 28.9%, 52.6%, and 40.8%, respectively, during the growing season at 2021, with the sites’ average forage production being 32.8 kg/ha, 46.0 kg/ha, 107.7 kg/ha, and 208.8 kg/ha. Moreover, compared to the potential forage production in steppe areas (200 kg/ha), the sites’ producation capacity (against the ultimate production limit) was found to be 16.4%, 23.0%, 53.5%, and 100%. Based on the results of the classification, the first axis had the highest positive correlation with the dominant species of the first and second places, that is, the Hammada salicornia and Zygophyllum atriplicoides, suggesting that the similarities within the species of the two places are the same. The second axis was also found to have the highest positive correlation with Artemisia quettensis and Ferula ovina (the dominant species in the fourth place), indicating the distinction of the similarities within the species of the place form other three places, especially from the first and second places. On the other hand, compared to other sampling units, those of the third place where Artemisia quettensis, Artemisia sieberi, and Amygdalus scoparia are dominant were found to have an intermediate distribution, with their species mainly having a negative correlation with the first and second coordinate axes.
 

 According to the results of this study, selecting at least three representative locations (site) along a region’s altitude gradient to monitoring vegetation on a regional scale, it is necessary to select. A site can represent desert ossuaries (altitude range 1500-1700 m) dominated by Hammada salicornia and Zygophyllum atriplicoides. The other two sites can represent both middle-altitude mangroves (located at altitude range of 1700-2500 m) dominated by Artemisia quettensis, Artemisia sieberi, Artemisia deserti, and high-altitudes above the tree line (more than 2800 m) whre Artemisia quettensis, Ferula ovina, and cushion plants are predominant. Therefore, it could generally be argued that applying the results of vegetation monitoring and measurement to a specific scale on a wider scale requires the consideration of temporal and spatial scales of monitoring (customary order scale, landscape scale, and regional/national scale), taking into account the existence of different issues and data.

 Considering the fact that evaporation affects the planning and operations of water resources as a key process in the hydrologic cycle, predicting its magnitude and patterns, particularly in arid, semi-arid, and hyper-arid environments such as Sistan plain (in northern Sistan-Baluchistan Province, Iran) is of great importance. On the other hand, as accurate estimation of Pan evaporation is regarded as one of the main aspects of water management in such regions, it is crucially important to accurately simulate the pan evaporation based on the available regional meteorological parameters. Therefore, this study sought to investigate the capabilities of soft computing techniques for estimating monthly evaporation in Sistan plain. The results of the study could be helpful for the management of water resources in the Sistan area, allowing the policymakers to develop future projects of water resource management/development plans for the region based on Evaporation estimations.

 various meteorological parameters, including maximum, minimum, and average temperature rates, relative humidity, wind speed, and precipitation rate were used to predict monthly Evaporation using the consistent and uninterrupted historical time series data (1994–2021) collected from three meteorological stations (Zabol, Zahak, and Chahnimeh).
The main purpose of this study was to assess the performance of a soft computing model in simulate pan evaporation. To this end, nine soft computing models, including Model Tree (MT), Random Forest (RF), Support Vector Machines (SVM), Bayesian Ridge Regression (BRR), Gaussian Process (GP), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), and Multivariate Adaptive Regression Splines (MARS) were used to predict evaporation at the meteorological stations selected for this research.
On the other hand, the model’s performance was assessed using statistical measures, including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Taylor diagram. Moreover, to construct predictive models, the dataset was divided into training (70%) and validation (30%) data. Then, eight combinations of input parameters were selected for Zabol and Zahak sites based on the Pearson correlation coefficient between the individual input parameters and evaporation. Finally, the best input combination and the optimal values for different models were determined using the R programming language.

 The different input combinations were determined for the two sites independently based on the inclusion of the weather parameters with the highest coefficient Pearson with evaporation. Then, each model was run using various fixed sets of parameters. For the Zabol station, the minimum temperature rate indicated the greatest correlation coefficient with (0.96), followed by average temperature (0.95) and smallest by rainfall (-0.42). It was also found that the outputs of Zabol and Zahak stations were very close and similar to each other, which could partly be attributed to the proximity of the two stations and their same topography and climatic conditions in the Sistan plain.
On the other hand, the results of assessing the models’ performance indicated that in the validation stage, MT (whose R2 = 0.97 and RMSE=57.3) delivered the best performance in Zabol station under Scenario 1, RF (with its R2 and RMSE being 0.98 61.7, respectively) performed the best under Scenario 2, MT with its R2 and RMSE being 0.97 and 61.27, respectively, showed the best performance under Scenario 3, the ANN and MT (whose R2 and RMSE reported as being 0.96 and 59.9, respectively) put in the best performance under Scenario 4 , and RF with R2 = 0.97 and RMSE=59, 59.24, 58.23, and 58.3 delivered the best performance under scenarios 5 to 8, respectively.
Moreover, the results suggested that adding the number of input variables to the models made no difference in their accuracy level. It was also found that out of eight scenarios investigated in this study, the RF model delivered the performance under five scenarios (2, 5, 6, 7, and 8), and that the MT performed well under two scenarios (1 and 3). Therefore, RF and MT could be introduced as the best soft computing models for simulating and estimating the pan evaporation of the Sistan plain.

 The results showed that with increasing the input of variables to the model, there was not much difference in the accuracy of the models. For example, R2 of scenario 1 with only the minimum monthly temperature input is equal to scenario 8 with eight inputs equal to 0.98 in the validation stage. Therefore, the findings showed that the model with only the minimum monthly temperature input has the same performance as the model with eight inputs The main contribution of this study was introducing a soft computing model to accurately estimate the pan evaporation of the Sistan plain using a meteorological parameter (minimum monthly temperature).

 Known as the dominant landform in arid and semi-arid regions, Playa offers the only evidence of past environmental conditions in semi-arid regions. in other words, considering the fact that Playa constitutes a region with negative water balance for more than half of the year and capillary properties close to the surface and sediments, playa sediments are, in some cases, considered as the only evidence of past environmental conditions in arid and semi-arid regions. On the other hand, geochemical methods are used to examine geochemical processes, tectonics, and the origin of playa sediments, the most important aspect of which is to discover the origins of changes in rocks, relief, climate, tectonic setting, transport history, and diagenesis. It should also be noted that the Quaternary period is considered responsible for the escalation of eolian processes, desert formation, and dryness of lakes, being characterized by drastic changes in climatic conditions worldwide.

 Stretching over an area of approximately 120 km, the Sabzevar Playa is one of the most elongated depressions in the Khorasan Razavi province in northeastern Iran. The Playa is located between 35°55' – 36°25' north latitude and 56°15' – 57°45' east longitude, covering an area of 2648 km2, which is typically classified along with Great Kavir and its surrounding playas (e.g. Damghan Kavir, Bajestan playa, Haj Aligholi Kavir) under the name of the “Dasht-e Kavir” basin. In addition, seasonal hydrological currents flows in the playa. However, according to krinsley (1970), the playa had been a closed basin throghout the Pliocene, which has then been converted to a semi-closed basin under the influence of the fault activities.
The geological nature of the playa comprises of alluvial and evaporation sediments belonging to the Quaternary period, including Windborne dunes, tertiary igneous rocks, and Cretaceous carbonates (dolomite and limestone) which are mainly found in neighboring mountain flanks. Moreover, some areas in the periphery of Sabzevar Playa constitute ophiolite sequences called Sabzevar ophiolites. On the other hand, while intrusive and volcanic units are mainly found around the northern and eastern parts of the Playa, carbonates and detrital sedimentary units containing conglomerates and sandstone are scattered along the playa, possessing abundant outcrops. It should be noted that there exists a metamorphic complex with pre-Jurassic sedimentary sequences near the western half of the playa, being characterized by a varying degree of metamorphism from green-schist to amphibolite.
This study examined one hundred sixty air-dried powder samples to identify both bulk and clay mineralogy using the X-ray diffraction (XRD) technique at the central laboratory of the Ferdowsi University of Mashhad and Razi Applied Science Foundation in Tehran. Moreover, the major concentrations of oxide and trace elements were determined via X-ray Fluorescence (XRF) method based on the procedure introduced by Abdi et al., (2018). Finally, the elemental ratios were, as major representatives of environmental changes (wet/dry periods), calculated at surface and depth levels. 

 The mineralogical results obtained via the application of the X-ray diffraction (XRD) technique and scanning electron microscopy (SEM-EDS) method revealed that silica oxide, clay, carbonate, and evaporite minerals were the most abundant minerals identified in the sediments of Sabzevar playa (Table 1). On the other hand, the results of the X-ray fluorescence (XRF) technique showed that silicon oxide (SiO2) had the highest abundance (between 39.8 and 45.5% by weight) among the main oxides identified in the sediments. Furthermore, the amount of L.O.I (Loss on ignition) was 1.8 to 16.9 percent of the sediments’ weight in the samples . It was also found that minor elements in the above-mentioned samples comprised of As Ba Ce, Co, Cr, Cu, Nb, Ni, Pb, Rb, Sr, V, Y, Zr, Zn, and Cl. 

 Taking the Fe2O3/K2O oxide values ​​and the presence of clay minerals into consideration, it could be argued that according to Herron's classification (Herron, 1988), Sabzevar playa’s sediments are equivalent to wackes. On the other hand, compared to the values ​​of the upper continental crust and the negative trend of Na2O versus SiO2 and Al2O3, the enriched amounts of CaO, MgO, and Na2O oxides could be attributed to carbonate minerals such as dolomite, calcite, and the presence of halite minerals in the playa’s sedimentation environment. Moreover, the enriched amounts of Na2O, Al2O3, and K2O3 could be justified by the presence of feldspars and clay minerals. It was also found that the high amounts of Fe2O3 oxide in the sediments were due to the presence of magnetite. Also, compared to the playa’s other sediments and clay minerals, the relatively small amounts of siliceous sediments were found to be responsible for the positive correlation between SiO2 and Al2O3, the SiO2/Al2O3 ratio, and the depletion of these two oxides.
On the other hand, the low values ​​of TiO2 could be attributed to their derivation from intermediate rocks. Moreover, the enrichment of the Sr element could have occurred due to the replacement of Sr with K and Ca in potassium and calcium minerals, respectively. In addition, taking the positive trend of TiO2 into consideration, this study found that compared to Zr and Al2O3 and the ratios of minor elements such as Cr/V and Y/Ni, Sabzevar playa’s sediments are of intermediate to mafic igneous origin similar to ophiolites.
It was also revealed that based on the lithology around Sabzevar playa and the geochemical evidence, the Sabzevar ophiolite series in the northeast of the playa and the metamorphic complex in its western part had played a major role in forming the sediments of Sabzevar playa, whose geochemical data in the original area showed evidence of a dry climate. Furthermore, it was found that the sediments had been left in an active tectonic setting such as oceanic to continental magmatic arcs, which is consistent with the ophiolitic origin of the region and other results found in this regard.