Search Results (34)

The mountainous rivers of Central Asia host diverse ichthyofauna threatened by increasing anthropogenic pressures, particularly water pollution, abstraction, and hydropower development. This study provides valuable morphometric and ecological data for Schizothorax eurycephalus (snow trout) and Triplophysa ferganaensis (stone loach) in the Shakhimardan River basin, Uzbekistan. S. eurycephalus exhibited positive allometric growth, while T. ferganaensis showed negative near-isometric growth. The mean Fulton’s Condition Factor was 1.0 for S. eurycephalus and 0.7 for T. ferganaensis, with site-specific variations. Strong correlations among morphometric parameters, particularly length–height relationships, support non-invasive monitoring techniques. Dietary analysis revealed S. eurycephalus was predominantly herbivorous, with around 70% algae consumption. Early sexual maturity was observed in S. eurycephalus males, whereas T. ferganaensis showed no clear maturity signs, but swollen bellies suggested ongoing or recent reproductive activity. These baseline morphometric and ecological data establish a solid foundation for future ecological assessments, conservation strategies, and the design and monitoring of mitigation measures to address anthropogenic impacts in this vulnerable region. Full article

Central Asia (CA) faces growing Water–Energy–Food (WEF) Nexus challenges, due to its complex transboundary water management, legacy Soviet-era water infrastructure, and increasing climate and socio-economic pressures. This study presents the development of a System Dynamics Model (SDM) to evaluate WEF interdependencies across the Aral Sea Basin (ASB), including the Amu Darya and Syr Darya river basins and their sub-basins. Different downscaling strategies based on the area, population, or land use have been applied to process open-access databases at the national level in order to match the scope of the study. Climate and socio-economic assumptions were introduced through the integration of already defined Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The resulting SDM incorporates more than 500 variables interacting through mathematical relationships to generate comprehensive outputs to understand the WEF Nexus concerns. The SDM was successfully calibrated and validated across three key dimensions of the WEF Nexus: final water discharge to the Aral Sea (Mean Absolute Error, MAE, <5%), energy balance (MAE = 4.6%), and agricultural water demand (basin-wide MAE = 1.2%). The results underscore the human-driven variability of inflows to the Aral Sea and highlight the critical importance of transboundary coordination to enhance future resilience. Full article

Two freshwater rivers, the Amu Darya and Syr Darya, flow into the Aral Sea, but they began to diminish in the early 1960s, and by the 1980s, the lake had nearly ceased to exist due to excessive water consumption for agriculture and the unsustainable management of water resources from rivers, which transformed the Aral Sea into a hypersaline lake. Despite this, the flora and fauna of the region began to evolve in the high-salinity seabed soil, which has received little attention in studies. In this study, we isolated approximately 1400 bacterial strains from the rhizosphere and phyllosphere of plant species of distinct families. Bacterial isolates were examined for antifungal activities against a range of pathogenic fungi such as Rhizoctonia gossypii, Trichothecium ovalisporum, Fusarium annulatum, F. oxysporum, F. culmorum, F. brachygibbosum, F. tricinctum, F. verticillioides, Alternaria alternata, A. terreus, Aspergillus niger, and As. flavus. Eighty-eight bacterial isolates exhibited varying antagonistic ability against pathogenic fungi. Furthermore, DNA barcoding of isolates using the 16S rRNA gene indicated that most antagonistic bacteria belonged to the Bacillus and Pseudomonas genera. The study also explored the activity of hydrolytic and cell-wall-degrading enzymes produced by antagonistic bacteria. The findings revealed that antagonistic bacteria can be utilized to widely protect seabed plants and plants growing in saline areas against pathogenic fungi, as well as agricultural crops. Full article

In the face of climate change and human activities, Central Asia’s (CA) terminal lake basins (TLBs) are shrinking, leading to deteriorating natural environments and serious soil wind erosion (SWE), which threatens regional socio-economic development, human health, and safety. Limited research on SWE and population exposure risk (PER) in these areas prompted this study, which applied the RWEQ and a PER model to assess the spatiotemporal changes in SWE and PER in TLBs in CA, including the Ili River Basin (IRB), Tarim River Basin (TRB), Syr Darya River Basin (SRB), and Amu Darya River Basin (ARB), from 2000 to 2020. We analyzed the driving factors of SWE and used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate dust event trajectories. The findings from 2000 to 2020 show a spatial reduction trend in SWE and PER, with primary SWE areas in the Taklamakan Desert, Aral Sea Basin, and Lake Balkhash. Significant PER was observed along the Tarim River, near Lake Balkhash, and in the middle and lower reaches of the ARB and SRB. Over the past 21 years, temporal trends in SWE have occurred across basins, decreasing in the IRB, but increasing in the TRB, SRB, and ARB. Dust movement trajectories indicate that dust from the lower reaches of the SRB and ARB could affect Europe, while dust from the TRB could impact northern China and Japan. Correlations between SWE, NDVI, temperature, and precipitation revealed a negative correlation between precipitation and NDVI, suggesting an inhibitory impact of precipitation and vegetation cover on SWE. SWE also varied significantly under different LUCCs, with increases in cropland, forestland, and desert land, and decreases in grassland and wetland. These insights are vital for understanding SWE and PER in TLBs and offer theoretical support for emergency mitigation in arid regions. Full article

Climate change is affecting the snow cover conditions on a global scale, leading to changes in the extent and duration of snow cover as well as variations in the start and end of snow cover seasons. These changes can have a paramount impact on runoff and water availability, especially in catchments that are characterized by nival runoff regimes, e.g., the Syr Darya in Central Asia. This time series analyses of daily MODIS snow cover products and in situ data from hydrological stations for the time series from 2000 through 2022 reveal the influences of changing snow cover on the runoff regime. All catchments showed a decrease in spring snow cover duration of −0.53 to −0.73 days per year over the 22-year period. Catchments located farther west are generally characterized by longer snow cover duration and experience a stronger decreasing trend. Runoff timing was found to be influenced by late winter and spring snow cover duration, pointing towards earlier snowmelt in most of the regions, which affects the runoff in some tributaries of the river. The results of this study indicate that the decreasing snow cover duration trends lead to an earlier runoff, which demands more coordinated water resource management in the Syr Darya catchment. Further research is recommended to understand the implications of snow cover dynamics on water resources in Central Asia, crucial for agriculture and hydropower production. Full article

Agriculture in Central Asia and in the Fergana Valley in general strongly depends on irrigation and drainage of agricultural lands. The Fergana Valley includes about 45% of the irrigated area in the Syr Darya River basin. Active use of irrigation in agriculture can lead to changes in the soil’s natural composition, as well as pollution and changes in the soil’s physical and chemical properties. Soil degradation in the process of irrigation can lead to a decrease in crop yields and, as a consequence, to a decrease in food security in the region. In this study, a comparative analysis of three main types of Calcisols (Dark, Light, and Typical) before (uncultivated soil) and after agricultural use (surface-irrigated agricultural soil) was carried out. Irrigation leads to increment of SOC stocks in Typical (from 113.8 to 126.3 t/ha) and Light (from 62.8 to 100.1 t/ha) Calcisols and to decreasing of SOC stocks in Dark Calcisols (from 160.1 to 175.3 t/ha). In general, the content of biophilic elements (SOC and TN) is lower in irrigated soils, and their distribution in the soil profile is close to the functional relationship (r² 0.98 to 0.99). In uncultivated Calcisols, the profile distribution of SOC and TN is more heterogeneous (r² 0.67 to 0.97). Changes in the humification processes of soil organic matter are also identified; in soils after irrigation the carbon ratio of humic/fulvic acids (CHA/CFA) is lower (<1) compared to their uncultivated counterparts (~1). The alteration of the soil water regime also resulted in transformation of the individual compositions of amino acids. All studied types of Calcisols are characterized by changes in particle-size distribution of soils especially in the number of the silt fraction (0.01–0.05 mm) and the difference between uncultivated and irrigated soils, 10–20%, which is associated with the processes of colmatage by accumulation of a fine fraction and replacement of sub-fractions in the fraction of sand. The highest concentrations of nutrients are characteristic of the upper soil horizons (P up to 231, K up to 2350 mg/kg), which indicate their pedogenic and agrogenic origins rather than inheritance from the parent material. Soil P and K availability is rather high, with non-labile forms prevailing, although of near reserve. The surface irrigation results in apparent accumulation of water-soluble Mg²⁺ (1.6–2.1 meq/100 g) and K⁺ (0.6–0.9 meq/100 g), but the cation of Ca²⁺ predominates in the base cations’ composition, which is the most favorable in terms of soil agrogenic property formation. Data obtained will be useful for development of strategies for effective land use in arid, subtropical, overpopulated regions of Central Asia that have deficient water sources and intensive soil degradation. Full article

This study examines the contribution of rainfall data (RF) in improving the streamflow-forecasting accuracy of advanced machine learning (ML) models in the Syr Darya River Basin. Different sets of scenarios included rainfall data from different weather stations located in various geographical locations with respect to the flow monitoring station. Long short-term memory (LSTM)-based models were used to examine the contribution of rainfall data on streamflow-forecasting performance by investigating five scenarios whereby RF data from different weather stations were incorporated depending on their geographical positions. Specifically, the All-RF scenario included all rainfall data collected at 11 stations; Upstream-RF (Up-RF) and Downstream-RF (Down-RF) included only the rainfall data measured upstream and downstream of the streamflow-measuring station; Pearson-RF (P-RF) only included the rainfall data exhibiting the highest level of correlation with the streamflow data, and the Flow-only (FO) scenario included streamflow data. The evaluation metrics used to quantitively assess the performance of the models included the RMSE, MAE, and the coefficient of determination, R². Both ML models performed best in the FO scenario, which shows that the diversity of input features (hydrological and meteorological data) did not improve the predictive accuracy regardless of the positions of the weather stations. The results show that the P-RF scenarios yielded better prediction accuracy compared to all the other scenarios including rainfall data, which suggests that only rainfall data upstream of the flow monitoring station tend to make a positive contribution to the model’s forecasting performance. The findings evidence the suitability of simple monolayer LSTM-based networks with only streamflow data as input features for high-performance and budget-wise river flow forecast applications while minimizing data processing time. Full article

This study presents a comprehensive analysis of the length-weight relationship, condition factors, and age of Schizothorax eurystomus in the Shakhimardan River basin in Central Asia, along with a comparative perspective to other Schizothorax species in the region. The study found that S. eurystomus exhibits positive allometric growth, which is consistent with similar patterns observed in this species from the Syr Darya River basin. The two analyzed condition factors showed mean values within the normal range, indicating good feeding and environmental conditions. However, significant disparities between minimum and maximum values of these factors indicated varied growth conditions which may be influenced by anthropogenic factors. Age estimation using opercular bones showed variations in the total length among fish of the same age, and a clear age distribution pattern across different sites. Younger fish predominantly inhabited the shallower, warmer, and lower sections of the river, which is impacted by agricultural water diversion, while older specimens were found in areas with higher discharge and deeper pools. Overall, this research provides valuable insights into the life history traits of S. eurystomus, underlining the need for sustainable fishery management and conservation strategies in the Shakhimardan River basin. The findings also emphasize the importance of considering habitat quality and anthropogenic pressures regarding understanding both fish population dynamics and growth patterns. Full article

Tugai wetlands, including the forests of Populus euphratica Oliv. and P. pruinosa Schrenk, are major biodiversity hotspots within semi-arid and arid ecozones. However, for over a century, Central Asian river systems have been severely affected by dam regulation, water withdrawals for large-scale irrigated agriculture, and deforestation. To support sustainable use and protection of this threatened forest type, we provide information on the distribution and degradation status of Tugai wetlands in the Syr Darya floodplain using Normalized Difference Vegetation Index (NDVI) time series from Landsat 7 and Moderate Resolution Imaging Spectroradiometer (MODIS). An accuracy assessment confirmed the validity of the MODIS-based wetland map, with an overall accuracy of 78.6%. This was considerably better than the Landsat product, mainly due to the greater temporal frequency of the MODIS time series. We further calculated trends and breakpoints between 2001 and 2016 using the BFAST algorithm. We found negative trends for nearly a third of the wetlands. Breakpoint detection showed major stress events in the years 2001, 2009, and 2016. Our study revealed the temporal and spatial distribution and vitality of an endangered forest ecosystem that has rarely been studied thus far. Climate change may accelerate the destabilization of the Tugai forests at the Syr Darya floodplain. Full article

The objective of the study was a pre-screening of the microplastic (MP) content in surface water and benthic sediments of Kara Darya and Chirchiq rivers, the first-order tributaries of the Syr Darya River (Uzbekistan). For the first time, surface water and benthic sediment samples were taken from this region, and quantitative screening of MPs 0.15–5.00 mm in size was performed. A combined visual and μRaman-based methodology was used to quantify and characterize artificial polymer microparticles from the surface water and bottom sediments of two rivers. The average abundance of MPs in the Kara Darya River and Chirchiq River waters was found to be 4.28 ± 0.09 and 0.95 ± 0.36 items per m³, and that in benthic sediments attained 244 ± 28.9 and 333 ± 11.5 items per kg of dry soil, respectively. MP concentration in surface water and benthic sediments of the Kara Darya River significantly exceeded (p-value < 0.01) that in the Chirchiq River. Microfibers were most abundant; the proportion of MP fibers in the water of the Kara Darya and Chirchiq rivers amounted to 89 and 95%, respectively, and that in benthic sediments of the rivers was 86 and 84%, respectively. The dominance of microfibers may indicate the route of entry to the rivers through domestic wastewater treatment plant discharges. The polymer microparticles in the surface water and benthic sediments of the Kara Darya and Chirchiq rivers were mainly represented by polyethylenterephtalate (PET), which accounted for half of all MPs detected in the Kara Darya River. Microparticles of textile origin were particularly abundant in the Kara Darya River, where viscose and nylon fibers were also found, which suggests the leading role of synthetic textiles in the pollution. The reported data are the first experimental evidence of MP pollution of the Syr Darya tributaries, but the distribution and circulation of MPs in surface water in Central Asia requires further comprehensive studies. Full article

Populus pruinosa is listed in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species and the Red Data Book of Kazakhstan. The habitats of the species are associated with the floodplains of desert rivers, including the Syr Darya and the Ili. The reduction in tugai forests is associated with changes in the hydrological regimes of rivers, the reduction in groundwater, fires, and grazing. The aim of this research is to advance our knowledge of the woodlands and forests of P. pruinosa in Kazakhstan by analyzing the cenoflora and forestry characteristics, conducting ecological niche modeling to select suitable habitats for ecological restoration, and identifying rehabilitation strategies. The vegetation was studied using traditional methods of geobotanic field research in 2021 and 2022. Ecological niche modeling includes the calculation of all the environmental variables that are significant for the species, taken in their optimal ranges, and identifying the intersection of all the favorable conditions. To identify these variables, geographically linked meeting points of the studied species, climatic and soil conditions, relief characteristics identified by the SRTM and raster sets of the environmental variables (WorldClim), and distances to watercourses and settlements were accepted for consideration. A total of 102 environmental variables were used. Full article

Inland waters in the endorheic basins of the arid zone are especially vulnerable to both climate-induced changes and anthropogenic influence. The North Aral Sea, which previously suffered a drastic shrinkage and partially recovered with the launch of the human-made Kokaral dam, is currently subject to significant inter-annual variability of its water volume. This study aimed to obtain insight into the modern water balance condition of the lake and to project the possible changes in it. A series of model simulation experiments were implemented based on three representative concentration pathway (RCP) scenarios with varying maximum lake surface levels, determined by the dam. Present-day dam conditions showed the possibility to retain the lake volume above 26 km${}^3$ under the RCP 2.6 and 6.0 scenarios. Simulations under the RCP 8.5 scenario revealed significant instability of the lake volume and a well-shown decrease in the outflow amount. A possible human-made increase in terms of the lake surface level up to 48.5 m.a.s.l. may allow for the retention of the volume in the range of 48–50 km${}^3$ in the RCP 2.6 case. The RCP 6.0 and 8.5 scenarios revealed a lake volume decrease and almost full cessation of the Kokaral outflow toward the end of the 21st century. Full article

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. Full article

The accurate calculation of sustainable development indicators is essential for the accurate assessment of the Sustainable Development Goals. This study develops a methodology that combines nighttime light indices, population distribution data, and statistics in order to examine changes and key drivers of SDG7 in the Aral Sea Basin from 2000–2020. In this study, the best-performing combination of four light indices and five simulation methods (two linear regression methods and three machine learning methods) was selected to simulate the spatial distribution of GDP in the Aral Sea Basin. The results showed that: (1) The prediction using the XGBoost model with TNL had better performance than other models. (2) From 2000 to 2020, the GDP of the Aral Sea Basin shows an uneven development pattern while growing rapidly (+101.73 billion, +585.5%), with the GDP of the lower Aral Sea and the Amu Darya River gradually concentrating in the middle Aral Sea and Syr Darya River basins, respectively. At the same time, the GDP of the Aral Sea Basin shows a strong negative correlation with the area of water bodies. (3) Although there is a small increase in the score (+6.57) and ranking (+9) of SDG7 for the Aral Sea Basin from 2000 to 2020, it is difficult to achieve SDG7 in 2030. Deepening inter-basin energy cooperation, enhancing investment in renewable energy, and increasing energy intensity is key to achieving SDG7. Full article

The uncertainties in soil erosion (SE) are further intensified by various factors, such as global warming, regional warming and humidification, and vegetation cover changes. Moreover, quantitative evaluations of SE in major basins of Central Asia (CA) under changing environments have rarely been conducted. This study conducted quantitative evaluation of SE in four major basins (Syr Darya Basin (SDB), Amu Darya Basin (ADB), Ili River Basin (IRB) and Tarim River Basin (TRB) using the Revised Universal Soil Loss Equation (RUSLE) and analyzed the main driving factors. SE quantities in the basins presented relatively consistent upward fluctuating trends from 1982 to 2017. Vegetation cover variation fluctuated significantly from 1982 to 2017. Specifically, vegetation cover decreased continuously in SDB, ADB, and IRB, but increased gradually in TRB. Pixels with positive spatial variation of vegetation mainly occurred around lakes and oases near rivers. The Normalized Difference Vegetation Index (NDVI) showed higher correlation with precipitation (80.5%) than with temperature (48.3%). During the study period, the area of arable land (AL) exhibited the largest change among all land use types in CA. Under long-term human activities, the proportion of NDVI of other land types converting to AL was the highest. In the structural equation model (SEM), precipitation, temperature, Shannon Diversity Index (SHDI), and NDVI strongly influenced SE. Overall, the major basins in CA were jointly affected by climate, human activities, and vegetation. Specifically, climatic factors exerted the strongest influence, followed by SHDI (human activities). SE was found to be relatively serious in ADB, SDB, and IRB, with SE in SDB even approaching that in the Loess Plateau. Under the background of global changes, appropriate water and land resource management and optimization configurations should be implemented in CA with reference to TRB in order to relieve local SE problems. Full article