Lakes in Arid Land and Saline Dust Storms

Saline dust storms are typical mainly for the arid and semiarid Central Asia induced by environmental change of tail-end lake basin. Although not the dominant type of global dust, saline dusts from playas may be important with respect to atmospheric chemistry, windborne nutrients and human health because of their high salt content. Saline dust storms in Central Asia occur frequently; this is not only a local issue, but also a regional ecological disaster. A complete understanding of the mechanism and diffusion characteristics are urgently required, and control measurements are urgently needed to lessen the occurrence of saline dust storms, which has been an ignored and serious environmental issue in the context of climate change in arid and semi-arid regions

Grain Size Characteristics of Sediments Found in Typical Landscapes in the Playa of Ebinur Lake, Arid Central Asia

A playa usually refers to a salt desert landscape mainly composed of loose and fine lacustrine sediments. Severe wind erosion on a playa causes the playa to become a source of dust and salt dust and poses a threat to vast areas downwind. Currently, little is known about the impact of wind erosion on the particle size distribution of sediments in different landscapes in the playa. In the present study, six dominant different landscapes in a natural state with the same sedimentary environment in the playa of Ebinur Lake were selected to provide insights into the different characteristics of particle size distribution under the effect of long-term wind erosion. The results reveal that the grain-size composition clearly differed among different landscapes. All samples had a common dominant size group consisting of very fine sand and sand. The very fine sand and sand content of Haloxylon ammodendron desert zone (LS5) was the lowest, while the clay and silt content was the highest at both depths among the six landscapes. The lowest clay and silt fraction and highest sand fraction appeared in the herbal desert zone (LS3) at both depths. Almost all of the sediment samples were of a bimodal distribution mode, with significant differences. The cumulative curve showed a similar S-shape, while the probability cumulative curve showed an inverted S-shape with three subpopulations of granularity characteristics. The smallest mean particle diameter appeared in LS5. The majority of the sediments were moderately to poorly sorted. The mean particle size of the sediments from the six landscapes was significantly different (p < 0.05), while no significant difference was observed among the other three parameters. Generally, it can be inferred that LS5 can reduce wind speed effectively, probably due to the smaller leaves and dense branches of Haloxylon ammodendron, which results in a high level of coverage. The results of the present study will have some implications for the grain size characteristics for changes in intensity in regional wind erosion environment and will also have some basis for wind erosion prevention and control in the playa of Ebinur Lake

Grain Size Characteristics of Sediments Found in Typical Landscapes in the Playa of Ebinur Lake, Arid Central Asia

A playa usually refers to a salt desert landscape mainly composed of loose and fine lacustrine sediments. Severe wind erosion on a playa causes the playa to become a source of dust and salt dust and poses a threat to vast areas downwind. Currently, little is known about the impact of wind erosion on the particle size distribution of sediments in different landscapes in the playa. In the present study, six dominant different landscapes in a natural state with the same sedimentary environment in the playa of Ebinur Lake were selected to provide insights into the different characteristics of particle size distribution under the effect of long-term wind erosion. The results reveal that the grain-size composition clearly differed among different landscapes. All samples had a common dominant size group consisting of very fine sand and sand. The very fine sand and sand content of Haloxylon ammodendron desert zone (LS5) was the lowest, while the clay and silt content was the highest at both depths among the six landscapes. The lowest clay and silt fraction and highest sand fraction appeared in the herbal desert zone (LS3) at both depths. Almost all of the sediment samples were of a bimodal distribution mode, with significant differences. The cumulative curve showed a similar S-shape, while the probability cumulative curve showed an inverted S-shape with three subpopulations of granularity characteristics. The smallest mean particle diameter appeared in LS5. The majority of the sediments were moderately to poorly sorted. The mean particle size of the sediments from the six landscapes was significantly different (p &lt; 0.05), while no significant difference was observed among the other three parameters. Generally, it can be inferred that LS5 can reduce wind speed effectively, probably due to the smaller leaves and dense branches of Haloxylon ammodendron, which results in a high level of coverage. The results of the present study will have some implications for the grain size characteristics for changes in intensity in regional wind erosion environment and will also have some basis for wind erosion prevention and control in the playa of Ebinur Lake

Trend Analysis of Annual and Seasonal River Runoff by Using Innovative Trend Analysis with Significant Test

This study investigated the temporal patterns of annual and seasonal river runoff data at 13 hydrological stations in the Lake Issyk-Kul basin, Central Asia. The temporal trends were analyzed using the innovative trend analysis (ITA) method with significance testing. The ITA method results were compared with the Mann-Kendall (MK) trend test at a 95% confidence level. The comparison results revealed that the ITA method could effectively identify the trends detected by the MK trend test. Specifically, the MK test found that the time series percentage decreased from 46.15% in the north to 25.64% in the south, while the ITA method revealed a similar rate of decrease, from 39.2% to 29.4%. According to the temporal distribution of the MK test, significantly increasing (decreasing) trends were observed in 5 (0), 6 (2), 4 (3), 8 (0), and 8 (1) time series in annual, spring, summer, autumn, and winter river runoff data. At the same time, the ITA method detected significant trends in 7 (1), 9 (3), 6(3), 9 (3), and 8 (2) time series in the study area. As for the ITA method, the &ldquo;peak&rdquo; values of 24 time series (26.97%) exhibited increasing patterns, 25 time series (28.09%) displayed increasing patterns for &ldquo;low&rdquo; values, and 40 time series (44.94%) showed increasing patterns for &ldquo;medium&rdquo; values. According to the &ldquo;low&rdquo;, &ldquo;medium&rdquo;, and &ldquo;peak&rdquo; values, five time series (33.33%), seven time series (46.67%), and three time series (20%) manifested decreasing trends, respectively. These results detailed the patterns of annual and seasonal river runoff data series by evaluating &ldquo;low&rdquo;, &ldquo;medium&rdquo;, and &ldquo;peak&rdquo; values

Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia

The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud&ndash;Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin

Spatial and Vertical Variations and Heavy Metal Enrichments in Irrigated Soils of the Syr Darya River Watershed, Aral Sea Basin, Kazakhstan

In the Syr Darya River watershed, 225 samples from three different layers in 75 soil profiles were collected from irrigated areas in three different spatial regions (I: n = 29; II: n = 17; III: n = 29), and the spatial and vertical variation characteristics of potentially toxic elements (Cd, Co, Cu, Ni, and Zn) and a metallic element (Mn) were studied. The human health risks and enrichment factors were also evaluated in the Syr Darya River watershed of the Aral Sea Basin in Kazakhstan. There were significant differences in the contents of heavy metals in the different soil layers in the different sampling regions. Based on element variation similarity revealed by hierarchical cluster analysis, the elemental groupings were consistent in the different layers only in region I. For regions II and III, the clustered elemental groups were the same between surface layer A and B, but differed from those in the deep layer C. In sampling region I, the heavy metals in surface soils were significantly correlated with the ones in deep layers, reflecting that they were mainly affected by the elemental composition of parent materials. In region II, the significant correlations only existed for Cu, Mn, and Zn between the surface and deep layers. The similar phenomenon with significant correlation was also observed for heavy metals in sampling region III, except for Cd. Finally, enrichment factor was used to study the mobilization and enrichment of potentially toxic elements. The enrichment factors of Zn, Cu, and Cd in surface layer A that were greater than 1.5 accounted for 1.16%, 6.79%, and 24.36% of sampling region I, respectively. In sampling region II, the enrichment factors of Zn, Cu, Cd, and Co that were greater than 1.5 accounted for 0.03%, 4.76%, 0.54%, and 9.03% of the total area, respectively. In sampling region III, only the enrichment factors of Zn, Cu, and Cd that exceeded 1.5 accounted for 0.24%, 4.90%, and 6.89% of the total area, respectively. Although the contents of the heavy metals were not harmful to human health, the effects of human activities on the heavy metals in the irrigated soils revealed by enrichment factors have been shown in this study area

A novel hybrid sand and dust storm detection method using MODIS data on GEE platform

Accurate sand and dust storm (SDS) detection is important for assessing SDS disaster risk. Machine learning (ML) based SDS detection approaches have been widely used in recent years due to their higher accuracy and better detection results. However, this approach usually requires manual annotation of numerous training samples that are, in practice, laborious and time-consuming. To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. Based on 8 SDS events captured by MODIS over Arid Central Asia (ACA), the effectiveness and accuracy of this method were assessed and compared to traditional approaches. The experimental results indicate that the proposed method can distinguish between mixed pixels (thin cloud and land surface) and SDS pixels and that it minimizes misdetection more effectively. This method achieved more than 98% training accuracy and validation accuracy in SDS detection

Temporal characterization of sand and dust storm activity and its climatic and terrestrial drivers in the Aral Sea region

As the Aral Sea shrinks, the lakebeds are gradually drying up, and the newborn Aralkum Desert (AD) has become one of the most active sands and dust storms (SDS) sources in Arid Central Asia (ACA). However, the temporal characterization of SDS activity and its possible driving factors have yet to be thoroughly investigated. Here, we studied the temporal variations of the SDS activities in the Aral Sea during 2000–2020 based on the Enhanced Dust Index (EDI), then investigated the relative importance of different drivers of SDS activities during the different phases. The findings revealed that the SDS activities increased during the past 20 years (2000−2020). The spring season is the most active period of SDS, especially in April. Additionally, the peak date of SDS activities has arrived earlier in recent years (2019–2020). Both climate and terrestrial factors strongly influence the temporal characteristics of SDS. The main driving factors of SDS actives vary in different phases. From 2000 through 2005, wind speed is the primary driving factor (r = 0.867, p < 0.001). From 2006 to 2015, the SDS activities were dominated by soil characteristics and water area. Although SDS activity experienced a quiet period in 2016 due to lake recovery and changes in soil water content, the regional drought manifested by precipitation and relative humidity has played a vital role in the active SDS since 2016. The regional drought and continued falling water level will increase the SDS risk in 5–10 years. The Pre-Aral region—Amu Darya delta is the most vulnerable region to SDS because of its location downwind from the SDS source area. The study findings provide essential information for the prevention and mitigation of SDSs in the Aral Sea region. Given the growing uncertainty about the Aral Sea crisis, more attention should be paid to the SDS risk assessment, providing a scientific basis for regional sustainable development

Dimensionality-Transformed Remote Sensing Data Application to Map Soil Salinization at Lowlands of the Syr Darya River

The problem of saving soil resources and their reclamation measures under current climate change conditions attracts the world community’s close attention. It is relevant in the Syr Darya River’s lowlands, where the secondary soil salinization processes have intensified. The demand for robust methods to assess soil salinity is high, and the primary purpose of this study was to develop a quantitative analysis method for soil salinity estimation. We found a correspondence between the sum of salts in a topsoil layer to the Landsat 8 data in the Tasseled cap transformation of the image values. After testing several methods, we built a prediction model. The K-nearest neighborhood (KNN) model with a coefficient of determination equal to 0.96 using selected predictors proved to be the most appropriate for soil salinity assessment. We also performed a quantitative assessment of soil salinity. A significant increase in a salt-affected area and the mean soil sum expressing an intensification of secondary soil salinization from 2018 to 2021 was found. The increasing temperature values, decreasing soil moisture, and agricultural use affect the extension of salt-affected ground areas in the study area. Thus, the soil moisture trend in the Qazaly irrigation zone is negative and declining, with the highest peaks in early spring. The maximum temperature has a mean value of 15.6 °C (minimum = −15.1 °C, maximum = 37.4 °C) with an increasing trend. These parameters are evidence of climate change that also affects soil salinization. PCA transformation of the Landsat-8 satellite images helped to remove redundant spectral information from multiband datasets and map soil salinity more precisely. This approach simultaneously extends mapping opportunities involving visible and invisible bands and results in a smaller dataset