Desert
Volume:27 Issue: 1, Winter - Spring 2022

Groundwater is known as the most important source of fresh water and its management is extremely important in arid and semi-arid regions, where there is a scarcity of surface water due to the lack of enough rainfall. Excessive water harvesting and improper water management can cause a decline in groundwater levels, which can lead environmental, social and economic crises. Therefore, this valuable resource must be exploited correctly and accurately. To achieve this aim, it is necessary to know the extent of its changes. Hence, in this study, the groundwater level changes in Semnan and Damghan plains, Iran have been investigated. For this purpose, Piezometric well data from 1994 to 2018 were used. Groundwater level zoning in two study regions was carried out using Inverse Distance Weighting (IDW), Kriging, and Co-kriging methods and the best zoning method was selected by Taylor diagram and Nash-Sutcliffe Model Efficiency Coefficient (NSE). Results of these two methods indicated that IDW and Kriging models are the most accurate way to zoning the groundwater level in Semnan and Damghan plains, respectively. The results of groundwater level maps showed that both plains have a decreasing trend in groundwater level over the time. Most of the water level dropping has been occurred in the east and south of Semnan plain and the eastern parts of Damghan plain which may be due to the concentration of agricultural areas in these parts. In Semnan plain, the depletion of groundwater is from 1.59 to 33.56 meters in April and from 1.55 to 35.40 meters in October, while in Damghan plain is from 3.76 to 30.97 and from 3.85 to 30.60 in April and October, respectively.

Climate change is known as one of the fundamental challenges of mankind, which has affected all aspects of human life. The present study aimed to evaluate the changes in precipitation and predict extreme precipitation in Lake Urmia basin. For this purpose, we evaluated the indices associated with changes in precipitation of eight synoptic stations of Lake Urmia basin for three time periods, namely near (2021-2040), middle (2041-2060) and far future (2061-2080), under two scenarios (RCP4.5 and RCP8.5). Using the CanESM2 model, we compared these periods to the observational period (1986-2005). To this end, after examining the capability of the SDSM model in simulating the reference period climate (1986-2005), future daily precipitation was downscaled. Subsequently, using the RClimDex, extreme precipitation indices were calculated for future periods. The results of the spatial distribution of precipitation variations showed that the average precipitation increased in the following decades, based on both scenarios. Investigation of the changes in extreme indices also revealed that percentile indices (R95p and R99p), Rx1day, SDII, and PRCPTOT rose based on both scenarios and in most future periods; meanwhile, the Rx5day, CWD, and CDD were reduced compared to the baseline period. Among the threshold indices, R10 increased based on RCP4.5 and decreased based on RCP8.5 whereas R20 and R25 did not change significantly compared to the reference period. However, in the far future, all the indices, except for CWD, had a decreasing trend. Although there is a great deal of uncertainty concerning precipitation and extreme precipitation forecasting, the results of such research can be conducive to the future policy making of a country, such as risk assessment.

Dust concentration, as the level of particulate matter (PM10), has become an important indicator of air pollution, and has attracted a great deal of attention from environmental agencies and organizations, public health, and science worldwide. Over recent years, dust storms with intense drought have had numerous adverse effects on human health and socio-economic situation in arid and semi-arid countries. Despite the inevitability of their occurrence, natural and human activities could exacerbate this phenomenon. Imagery data analyses have improved our understanding of dust detection and monitoring. Previous research has extensively studied the dust storms in day time. Meanwhile, there are a few studies investigating dust detection over-night. For dust detection over-night, several algorithms were utilized herein, including brightness temperature difference (BTD) for 20, 23, 31, and 32 MODIS bands and artificial neural network (ANN). The obtained results revealed that BTD indices have ood performance for dust detection in the southwest of Iran and their accuracy will be better with an increase in the concentration and density of dust and a reduction in cloud cover in the region. The BTD and ANN methods were evaluated using different indices. Our findings revealed that ANN method was more accurate than BTD indices. This finding is probably attributed to the complex properties of dust; artificial neural network is an appropriate method to model nonlinear and complex dust and surface properties.

120-day wind of Sistan is a regional wind current that blows on average from June to September. This wind, which is accompanied by dust, has carved its role on the face of the Sistan region due to its continuity and speed. This study aimed to investigate the pressure gradient during the activity of 120-day winds and its relationship with dust  and wind speed in Helmand Endorheic basin. For this purpose, the dust and wind speed data of 11 synoptic stations were daily extracted from the Iran Meteorological Organization since 1986 for Helmand basin. After making a complete database, the frequency of dusty days and wind speed during the activity of 120-day winds of Sistan was extracted. Atmospheric pressure data corresponding to the duration of the activity of 120-day winds was extracted from the National Center of Environmental Prediction (NCEP) and National Center of Atmospheric Research (NCAR) databases with a spatial resolution of 2.5° × 2.5°. Following the extraction of pressure gradient via Pearson correlation coefficient, the relationship between the speed and dust of the basin, and the pressure gradient was investigated. The study of wind speed showed that strong winds during the activity of 120-day winds had an increasing trend. The results also revealed that the pressure gradient had a direct relationship with wind speed and dust storms in Helmand basin. In addition, the speed and dusts of the mid-eastern part of the basin were mostly influenced by the pressure gradient.

Changing land use from rangeland and forest to agricultural land and orchard can greatly affect the characteristics and fertility of the soil, especially in arid and semi-arid regions. In this study, seven major land-use types in Kohgiluyeh-and-Boyer-Ahmad province of southwest Iran were selected; these land types were orchards (grape), forests, rangelands, and agricultural lands which cultivated corn, beans, and rainfed and irrigated wheat containing five soil orders, namely Entisols, Inceptisols, Mollisols, Alfisols, and Vertisols. The samples were collected from the soil depths of 0-30 cm. Based on the results, the highest average content of organic carbon (OC) was detected in the forest (3.3%). It may thus be stated that there is a balance in forest soils between the rapid decomposition of soil organic matter and the rapid accumulation of litter due to plantation and also an abundance of litter. In all soil samples, the highest percentage of Fe and Mn were found in the residual (Res) fraction and the lowest percentage in the exchangeable (Ex) fraction. The highest and lowest amounts of Fe and Mn carbonate (Car) form were associated with forest and rangeland land uses. Different land uses had an important influence on the amount of the Fe form bound to organic compounds. In this way, the maximum amount of this form belonged to forest use which contained the highest amount of organic matter; the lowest amount of organic matter was related to rangeland use.

This research was conducted to quantify the direct and indirect effects of surface shear resistance (SSR) on dust hotspot soils in the southeast of Ahvaz city, Khuzestan, Iran. For this purpose, we measured certain parameters, including the mean weight diameter (MWD) of dry aggregates, particle size distribution (PSD), permanent wilting point percentage, soil moisture percentage, sodium absorption ratio (SAR), organic matter, calcium carbonate (CaCO3), and soil electrical conductivity (EC). SSR was measured in 100 different locations of the field conditions using the modified shear device (MSD), specifically designed and manufactured to perform this project. The effects of soil properties on SSR were investigated employing path analysis and multi-linear regression approaches. SSR values (0.32-0.98 kPa) in the dust hotspot soils indicated that these soils are highly susceptible to wind erosion and have a high variability (4.26%). The best regression pedotransfer model accounted for 42% of SSR variations by soil estimating parameters. MWD and CaCO3 were identified as the most sensitive parameters in SSR estimation in the dust hotspot soils in southwestern Iran. MWD and CaCO3 (via PSD) showed the highest direct and indirect effects on SSR, respectively. In general, SAR on SSR represented no significant effects in this region due to high EC values.

Particulate matter emission is an important threat to sustainable development due to its various effects on the atmosphere. Particulate matter originates from natural sources or human activities. Since Birjand is located between Sistan plain and Karakum desert, numerous dust events have been reported in this area. In the present study, concentration of PM10 was divided into annual, seasonal, monthly, weekly, and hourly time scales from 2014 to 2018. HYSPLIT model and AOD products were used for examining the movement pattern and origins of the particles. Pearson correlation was calculated between the frequency of dusty days and climate variables. The results revealed that PM10 concentration and dusty days frequency trends were similar. Additionally, the mean temperature and wind speed had a similar trend as PM10 concentration. Furthermore, PM10 was significantly related to dust and most of the climate variables. The closest correlation of PM10 was with dusty days in seasonal (Pearson correlation = 0.494) and monthly (Pearson correlation = 0.619) time scales. Based on PM10 daily concentration, 34 unhealthy days were identified. To track the particles on unhealthy days, HYSPLIT model was employed. Except in spring, the wind roses showed that the main direction of the wind was to the west. Meanwhile, based on AOD images, the particles originated from dust sources. A big amount of PM10 concentration originated from the surrounding regions, and the majority of dust particles came from the north. Therefore, the local climate variables as well as dust events of the surrounding regions had crucial roles in the rise in PM10 concentration, which should be taken into consideration by managers so that PM10 concentration could be taken under control.

Chemical indices are widely used for characterizing the degree of weathering. In this study, we focused on evaluating the capability of chemical weathering indices to distinguish the sequence of aeolian-fluvial sediments in an arid region of Iran. Seven dominant geoforms were selected in Rafsanjan region, southeast of Iran, namely pediment, alluvial fan, margin of pediment and sand sheet, desert pavement, margin of fan and cultivated clay flat, active drainage, and margin of fan and uncultivated clay flat. One representative pedon was selected, described, and sampled for each geoform. The soil physicochemical properties of different horizons of each pedon were determined. We calculated various weathering indices, including weathering index of Parker (WIP), product index (PI), chemical index of alteration (CIA), silica-sesquioxide ratio (Kr), and CIA/WIP ratio (IR), and elucidated their depth distributions. The heterogeneity of the parent material within a given pedon was confirmed with field evidence, depth functions of clay-free sand fractions, and the uniformity value (UV) index. The horizon sequence with lithologic discontinuities (LDs) indicated that the studied pedons were formed by cyclic deposition of aeolian and fluvial sediments. The vertical variations of the weathering indices as well as the vertical trend of the Al2O3/SiO2 ratio (as a grain size index) were entirely consistent with the presence of the LDs. The results suggested that different factors, such as grain size, sedimentation cycling, and their interactive effects, should be considered in order to accurately assess the vertical trend of chemical weathering indices.

The southern slope of Al-Jabal Al-Akhdar in northeastern Libya is a model for the desertification process, as it witnesses a sharp deterioration due to the prevailing climatic conditions and irrational human activities.  We conducted the present research in the rangeland of southern Al-Jabal Al –Akhdar to investigate the effects of annual precipitation gradient on landscape function and soil surface condition. The study area was divided into three levels of annual precipitation (high, medium, and low precipitation). We randomly selected 10 sites for each precipitation level. Three line transects were taken for each site. Landscape Function Analysis methodology (LFA) was applied to assess soil surface condition. The Least Significant Difference (LSD) test was also used. The results showed the significant effect of annual precipitation on all the soil surface condition indices.  The highest SSI was at the high level with a mean of 49.8% and the lowest was at the medium level with an average of 44.6%, with a highly significant difference (P=0.000). The WII was low in all the precipitation levels, and highly significant differences were found; the same results could apply to NCI. LOIs of the three levels were reasonably close (0.063, 0.042, and 0.058) without any significant differences. A decrease was observed in PN with the direction to the south. The results revealed that landscape functioning in the study area was significantly different from the impact of rainfall.  It is necessary to develop different plans for each area according to its climatic conditions in order to combat erosion and conserve soil, as an essential natural resource.

This research seeks to investigate the consistency of satellite data and the information obtained from the ground meteorological stations in Iran. In this study, the Aerosol Optical Depth (AOD) data of Moderate Resolution Imaging Spectroradiometer (MODIS) deep blue algorithm of Terra satellite from 2000-2018 was used. The data of 390 meteorological stations during2000-2018 were used to evaluate and validate the satellite data. The aerosol optical depth (AOD) was studied and compared with the current weather codes of meteorological stations (codes 00 to 99). The frequency percentage and spatiotemporal matching methods were further used. Based on the results, the AOD at 550 nm data of the Terra satellite MODIS sensor had a significant relationship with the meteorological codes of 00 to 99 in Iran. This topic is useful in the study of meteorological phenomena. The present study evaluated the large values of aerosol optical depth (AOD) of meteorological phenomena in the boundary layer. The highest frequency percentage of the aerosol optical depth (AOD) between 0 and 3.5 belonged to the present weather codes No. 5 and 6. The amount of aerosol optical depth (AOD) was directly related to meteorological phenomena (short- or long-term) such as natural, industrial, and urban pollution, smoke, humidity changes, lightning, thunderstorms, and heavy rainfall. The amount of aerosol optical depth (AOD) varied depending on the season, place, and meteorological phenomena in Iran.

Various statistical techniques have been used for species distribution modeling that attempt to predict the occurrence of a given species with respect to environmental conditions. The current study was conducted to compare the performance of three regression-based models (multivariate adaptive regression splines, generalized additive models, and generalized linear models) with three machine-learning algorithms (random forest, artificial neural networks, and generalized boosted models). Also in this study, three sets of explanatory variables (climate-only, topography-only and combined topography-climate) for each species (i.e. Achillea millefolium, Festuca rupicola, and Centaurea jacea) were quantified and the effect of the interaction of the predictor variables with the modeling approaches on determining the accuracy of the predictions was tested. Model accuracy was evaluated using the area under the curve (AUC) of the receiver operating characteristics and true skill statistics (TSS). It was found that regression-based approaches, especially generalized additive model, performed better than those of machine-learning. The results showed that the topography-climate variables were the most important for mapping potentially suitable habitats of target species. The response curves associated with these variables indicate that there are ecological thresholds for favorable growth of all plant species studied.