Amu Darya River Basin Research Articles

ОЦЕНКА СОВРЕМЕННОГО СОСТОЯНИЯ ВОДНО-СОЛЕВОГО РЕЖИМА ОРОШАЕМЫХ ЗЕМЕЛЬ В СРЕДНЕМ ТЕЧЕНИИ Р.АМУДАРЬИ (на примере Бухарской области)

The article examines the current state, the problem of the formation and preservation of saline soils on irrigated lands in the middle reaches of the Amudarya River basin (within the Bukhara region of the Republic of Uzbekistan). Based on the calculation of water-salt balances in the context of the region and characteristic areas, a quantitative assessment of the ratio of water intakes and drainage flow, the quality of irrigation water and salt removal was carried out, and cause-and-effect relationships between the ratio of the sizes of water intakes and drainage flow were identified.

Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

A comprehensive assessment indicator of the water-energy-food nexus system based on the material consumption relationship

The input–output relationships among material, energy, and information within the water, energy, and food subsystems are intricately interwoven, constituting an integrated system vital for sustainable development. Various water-energy-food (WEF) nexus indicators have been applied to evaluate the system's internal relationships and state. Nevertheless, these methods fail to aggregate the internal relationships into an indicator. This study proposes a WEF nexus indicator that incorporates the interconsumption into the system using the material flow analysis. The interconsumption consists of energy consumption in the food and water subsystems, water consumption in the energy and food subsystems, and food consumption in the energy subsystem. Moreover, the major factors were identified by the sensitivity factor method. The three countries (Tajikistan, Uzbekistan and Turkmenistan) in the Amu Darya River (ADR) basin exhibit strong interactions in the WEF nexus system, are chosen as a case study. Our results show that in 1992–2018, Uzbekistan's food subsystem consumed the most energy among the three countries; the energy consumption within the water subsystem displays an upward trend across all three countries; the water consumption in the energy subsystem of Tajikistan and Uzbekistan is 2–4 times greater than that in Turkmenistan; and the water withdrawals for agriculture within the three countries have remained relatively stable. The composite index in three countries exhibits an upward trend, with the most pronounced increase occurring in Turkmenistan. Uzbekistan has the strongest WEF nexus relationship, with a WEF nexus index that was 2–3 times greater than that of Tajikistan and Turkmenistan. The most important subsystem of Tajikistan and Turkmenistan is the energy subsystem; however, water resources are the major factors in Uzbekistan. The introduced indicator enables a comparison of the WEF system between regions and temporal variations in a certain region. It can provide intuitive and effective data support for WEF nexus system management.

Hydrogeospatial analysis and predictive modelling of the dynamics of the Amu Darya River basin under evolving climate scenarios

Hydrogeospatial analysis and predictive modelling of the dynamics of the Amu Darya River basin under evolving climate scenarios

Study on the Aral Sea crisis from the risk assessment of polycyclic aromatic hydrocarbons and organochlorine pesticides in surface water of Amu Darya river basin in Uzbekistan

The Amu Darya River, as one of the important flows of the Aral Sea located in the semi-arid area of Central Asia, also performed as the primary water supply for Uzbekistan. Therefore, the residues and risks of anthropogenic-related persistent organic pollutants (POPs) in waters of the Amu Darya River were conducted in the present study to elucidate their possible effects on the water safety in such a specific area as well as on the Aral Sea Crisis from a new perspective. Thirty-nine water samples distributed along the Amu Darya River to the coastal of the Aral Sea were analyzed for both polycyclic aromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs) occurrence, showing the total concentrations of ΣOCPs and ΣPAHs in the range of 1.16–22.75 ng/L and 3.18–506.26 ng/L, respectively. Spatial differences showed higher levels for both OCPs and PAHs along the lower reaches of the Amu Darya River due to intense human activities. Source identification performed by isomer ratios indicated that dichlorodiphenyltrichloroethanes (DDTs) probably originated from recent use, while hexachlorocyclohexanes (HCHs), chlordanes, and endosulfans originated mainly from historical usage. Furthermore, the principal component analysis showed PAHs were from coal and petroleum combustion (65.2%), biomass combustion (27.2%), and industrial chemical combustion (7.64%). The human health risk assessment demonstrated no carcinogenic or non-carcinogenic risks at present. However, moderate to high ecological risks to aquatic organisms especially were observed along the lower reaches, especially the delta area. The results obtained would not only provide important basic data for such a semi-arid area but also show us the possible toxic effects induced by such pollutants, which should attract more attention in the shrinking case of the Aral Sea.

TRANSFORMATION OF THE AMUDARYA RIVER BASIN IN THE CONTEXT OF CLIMATE CHANGE

The article assesses the impact of climate change on the water resources of the Amudarya River basin. The Amudarya River is fed by thawed snow and glacial waters. Glaciers have a significant role in the water supply of agriculture, as well as hydropower. Due to the intensive melting of glaciers in the short term, the flow of some rivers will increase. However, in the long term, the runoff will decrease, and some glaciers may disappear altogether. An important task of this study was to take into account the work of researchers working on environmental problems in the Amudarya basin. Since the ecology of the transboundary Amudarya River is in the sphere of national interests of several countries: Tajikistan, Uzbekistan, Turkmenistan and Kazakhstan, certain conditions for cross-border cooperation are being formed. The main problem of cooperation is unstable river regimes, which require continuous planning and negotiations on the allocation of resources, and which are exacerbated by climate change. The main consequences of the influence of climate change on the formation of water resources and water use in the Amudarya River basin are highlighted.

Developing an integrated PCE-ANOVA-RF method for uncertainty quantification of hydrological model – The Amu Darya River Basin in Central Asia

Uncertainty quantification of hydrological model parameters plays an important role in the streamflow prediction of river basins. It is still a challenge to evaluate the effect of parameter uncertainty on hydrological models in association with moderate computation and high accuracy. In this study, a PCE-ANOVA-RF (abbreviated as PAR) method is developed to analyze the individual and interactive effects of multiple uncertain parameters in the Soil and Water Assessment Tool (SWAT) model as well as generate probabilistic forecasts of daily streamflow. The proposed PAR method can not only unveil the effect of parameter uncertainty and save computation time without reducing the simulation accuracy, but also expand PCE’s ability to predict future streamflow processes. The Amu Darya River Basin in Central Asia is introduced to verify the feasibility and applicability of PAR. The results disclose that (i) compared with the Sequence Uncertainty Fitting ver-2 (SUFI-2) algorithm, the simulation accuracy of PAR is acceptable (e.g., NSE = 0.85 in calibration period and 0.81 in validation period); (ii) in terms of variance analysis and streamflow simulation, PAR is more efficient and can largely reduce the number of model runs (about 88%); (iii) the parameters related to snow melting process have significant effects on both average flow and peak flow; besides, the interaction among these parameters are more obvious than that of other sensitive parameters.

Impact assessment of climate change on water resources of the Kokcha watershed: a sub-basin of the Amu Darya river basin in Afghanistan

Abstract Climate change is one of the most concerning issues which mostly impacts water resources. This study aimed to investigate the Kokcha watershed under the effects of climate change. The study was performed utilizing the Soil and Water Assessment Tool (SWAT) considering the Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios in the periods of 2050–2059 and 2090–2099. The Model for Interdisciplinary Research on Climate (MIROC5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) was used to prepare future climate data. The temperature indicated a rising of +2.47 and +2.85 °C in 2050–2069 considering RCP4.5, and +3.38 and +5.51 °C based on the RCP8.5 scenario through 2080–2099. Precipitation showed a −30 and −17.17% decrease based on RCP4.5, and a decrease of −9.28 and −4.52% considering RCP8.5 in the mentioned periods, respectively. The historical runoff peak shifted a month earlier with a −54.56 and −25.98% decrease considering RCP4.5 and a −29.18 and −6.45% based on the RCP8.5 scenario in the mid and end of the century accordingly. Alternatively, a second river flow peak takes shape due to rainfall in July. This study's result can be used to adapt water management to climate change in the Kokcha watershed and similar regions.

Five decades of freshwater salinization in the Amu Darya River basin

Study regionThe Amu Darya River (ADR) basin in Central Asia. Study focusTo understand the spatiotemporal patterns and underlying driving mechanisms of river salinization in arid environments, this study gathered 50 years (1970–2019) of water chemistry data from 12 locations along the ADR. The variations in discharge and salinity were assessed by a linear regression model and violin plot. The salinity-discharge relationships were evaluated by a general hyperbolic model and Spearman’s rank correlation coefficient. Random forest models were also constructed to identify the predominant drivers of river water salinization. Finally, a conceptual model of river water salinization was constructed. New hydrological insights for the regionThe water salinity (S) in the upper stream of the ADR was 541–635 mg/L. Salinity showed an increasing trend along the river course, reaching 751–1560 mg/L downstream. In the downstream, the river salinity before the 1990 s (751–1128 mg/L) was slightly lower than that after the 1990 s (983–1560 mg/L). Generally, water salinity was notably correlated with river discharge (Q) in upstream, exhibiting a relationship of S= 17,497Q−0.62, p < 0.05, before the 1990 s. Interannual variation in river salinity is mainly controlled by secondary salinization, and intra-annual variation is controlled by river flow. From upstream to downstream, the controlling salinization process changes from primary salinization to secondary salinization. Specifically, secondary salinization has accelerated due to intensified agricultural activities in recent years.

Extreme Hydrometeorological Conditions and Changes in the Amu Darya River Basin in Central Asia

Abstract The Amu Darya contributed 70% of the flow to the Aral Sea in central Asia before the 1960s, when the Amu Darya streamflow to the Aral Sea started to dwindle. The severe environmental and socioeconomic disaster happened mainly due to intensified water abstraction with the backdrop of climate change. However, knowledge of up to the most recent extreme climate conditions and their changes, as well as their relations to streamflow in the basin, is still lacking. This study aims to understand extreme hydrometeorological conditions and their changes, as well as their relations in the past several decades, especially in the upper Amu Darya basin. The spatial patterns of the means of all extreme temperature indices followed the elevation gradient. The majority of the basin showed an increasing trend in extreme warm events but a decreasing trend in extreme cold events. The north of the upper basin had over 1000 mm annual precipitation, and the east had less than 300 mm annual precipitation. Overall, the upper Amu Darya basin underwent a wetting and warming annual trend. Annual streamflow in the upper subbasins was less than 750 m3 s−1, but together they produced over 1500 m3 s−1 flow in the middle reach and basin outlet. Streamflow change varied among subbasins. Correlations between climatic factors and streamflow at annual time steps were weak but distinct at monthly time steps with lagged effects. In highland subbasins with high coverage of glaciers and snow, temperature minima and maxima impacts were opposite and overwhelmed precipitation, whereas in lowland subbasins, precipitation was more important.

Kyrgyz transboundary rivers’ runoff assessment (Syr-darya and Amu-darya river basins) in climate change scenarios

The research was carried out as part of the preparation “Water Resources” section of the 4th National Communication of the Kyrgyz Republic to the UNFCCC. The article presents extensive information on the current state and changes over a long period of observations of water resources, climate and glaciation in Kyrgyzstan (Syr-Darya and Amu-Darya river basins). The representative rivers were selected as the main objects, which are transboundary between Kyrgyzstan, Uzbekistan, Kazakhstan and Tajikistan. The study aims to assess the inter-annual and intra-annual dynamics of the flow of the rivers for the period from 2020 to 2080, based on the climate projections CMIP5 (RCP4.5 and RCP8.5) and CMIP6 (SSP2-4.5 and SSP5-8.5) (IPCC WG II Report, 2022). The hydrological modeling method in the HBV light and HBV EHT models and the inertial mean annual flow change method were used to estimate changes in water resources. On the rivers of the northern part of the Fergana Valley, the average annual runoff is expected to increase by 1-19%, in the Amudarya river basin (Kyzyl-Suu river) – by 27-64% of the values for 2006-2019. In the Naryn river, water discharge for this period will remain within the current values for 2006-2019. The method of inertial change in the average annual runoff showed an increase in runoff for 2030 and 2040 for all the studied catchment areas by 6-19% of the current values for 2006-2020. The results of the study are intended for decision-makers on the rational use and long-term planning of water resources under climate change.

Assessment and forecast of water resources use in Uzbekistan

The article examines the assessment of the state and problems of irrigation water use in the republic in the past and in the near future. A comparative analysis of the state of water resources use for the period of the last 30 years from 1989 to 2019 was carried out. According to the estimates, own water withdrawal for 5 years (2014 / 15-2018/19) shows that an average of 53.2 billion m3 were used for irrigation, including 50.5 billion m3 from rivers. At present (2018-2019), an average of 57.06 billion m3 of water is taken for irrigation of 4.289 million hectares of land, against 63.02 billion m3 approved in 1989. At the same time, the actual water consumption amounted to 48.9 billion m3. Specific water consumption (in the years of average water content) in the Amudarya river basin is 12.5 thousand m3/ha, in the Syrdarya basin - 10.4 thousand m3/ha. Forecast calculations show that there is a tendency to reduce water from rivers to agriculture by an average of 0.104 billion m3/year, with an increase in other sectors from 11.53% (communal and household sector) to 100% (fisheries). The volume of discharges into CDS is decreasing due to the use of collector-drainage waters throughout the region, while specific water consumption has increased to 11.50 thousand m3/ha, i.e. by 450 m3/ha or 28.1%. The reasons for the lack of water for irrigating agricultural crops during the period of vegetative irrigation are stated. The issues of water saving and ways of transition to modern technologies for the use of surface and ground waters, redistribution of water and solutions for the interstate use of river resources are touched upon.

Impact Assessment of climate change on water resources of the Amu Darya River basin

Water consumption and water use have been continuously increasing, and the impact of economic activities on the hydrological regime of water flow, especially in irrigation, population and industry, has increased significantly. In particular, it is significantly increasing in the countries of Central Asia, including the Republic of Uzbekistan. Today's global problem is the severity, scale and unusualness of anthropogenic climate change, especially in developing countries, creates the need for action with an urgent scientific approach. Undoubtedly, the assessment of the observed climate change and the current state of water resources will require many years of in-depth scientific research. One of the factors limiting the development of the countries of the Amu Darya basin is the lack of water resources. The world is experiencing intensive climate warming, which leads to the melting of glaciers, which are the main sources of fresh water for the river basins that make up the water resources of the countries of Central Asia. The article gives an assessment of regional and local climate change and gives recommendations for reducing water shortages in the countries of the province

Selection of CMIP6 GCM with projection of climate over the Amu Darya River Basin

Selection of CMIP6 GCM with projection of climate over the Amu Darya River Basin

Calibration and evaluation of Hargreaves-Samani equation for estimating reference evapotranspiration: A case study in the Amu Darya River Basin, Central Asia

Study regionAmu Darya River Basin (ADRB), Central Asia. Study focusReference-evapotranspiration (ETo) is the most prominent variable for agriculture water-resource management. The Penman-Monteith (PM) equation has been accepted as universal-standard for estimating ETo but it requires extensive climatic-variables. The PM alternative Hargreaves-Samani (HS) equation inadequately estimates ETo with an average error of RMSE 1.84 mm/day, NSE 0.27, and MAE 0.61 mm/day, respectively, in the ADRB. New hydrological insight for the regionThe present study proposes the calibration of original HS-equation based on actual vapor pressure and linear regression to estimate daily ETo at 114 meteorological stations in the ADRB. The locally-calibrated HS-equation coefficient (C) was found variated-widely across the different climatic-conditions and months, ranging from 0.0025 to 0.0186, due to hyper-aridity conditions in the study area. Compared with proposed methods by other studies, the modified-HS equation and empirical-coefficient using newly-proposed variable leads to a significant reduction of the average root means square error and mean absolute error by about 55 % and 52 %, respectively, demonstrates their feasibility and accuracy in the ADRB, Central Asia. In-addition, the modified-HS-equation based on climatic zones were found to be more accurate and comprehensive than geographical localizations. The modified-HS equation and empirical-coefficient using newly-proposed variable and approach is suitable to estimate ETo in a data scarce region (e.g., ADRB), when only temperatures, solar radiation and vapor pressure data are available.

A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region

Statistical downscaling of daily precipitation is important for assessing the impact of climate change. However, some issues (e.g., the underestimation of extremes) limit statistical downscaling methods to project daily precipitation accurately. This study develops an integrated hybrid statistical downscaling model combined with bias correction and Bayesian model averaging (named as SDBC-BMA) for improving the ability of downscaling methods. The performance of SDBC is compared with regression based statistical downscaling methods (RSDM) and hybrid statistical downscaling methods (HSDM) based on different performance metrics and extreme indices. SDBC-BMA is applied to the Amu Darya River Basin (ADRB) to demonstrate its feasibility and capability, where three GCMs and two CMIP6 scenarios are considered. Several findings can be summarized: (1) compared to RSDM and HSDM, SDBC is more robust with a higher computation ability of daily precipitation and a lower mean bias (i.e., +0.10 mm/day); (2) compared with the simple model average, the biases of extreme indices and mean daily precipitation obtained from SDBC-BMA could decrease by 49 % and 88 %, respectively; (3) the mean precipitation values would increase by 56 ± 41 mm/year (under SSP245) and 126 ± 55 mm/year (under SSP585) during 2076–2100, higher than mean precipitation in the historical period (1979–2005); (4) extreme indices under multi-GCMs (except for consecutive wet days) would increase by 20 % (under SSP245) and 22 % (under SSP585), implying that extreme events in ADRB are more frequent in the 21st century. The increasing risk of precipitation extremes would exacerbate threats to agricultural and ecological security.

Estimation of green and blue water evapotranspiration using machine learning algorithms with limited meteorological data: A case study in Amu Darya River Basin, Central Asia

Green-water evapotranspiration (GWET) and blue-water evapotranspiration (BWET) are much frequently discussed variables in the recent debates of water resources management and water productivity in water-scarce regions. But the deficiency of long-term, on-site records and limited observation stations is a critical challenge in determining the veracity of these variables. The GWET and BWET estimations rely considerably on extensive climate data, water fluxes data, soil parameters, crop distribution, and crop management data. However, obtaining accurate data by on-site observations or by remote sensing products is a difficult task in a data-scarce region and fewer variables are not sufficient to empirically estimate GWET and BWET. Machine learning (ML) is a modern artificial intelligence decision-making tool based on the analysis of fed-data and computer algorithms. This study reported the enormous potential of ML algorithms for estimating BWET and GWET using different sets of available climate variables. Wheat crop BWET and GWET were estimated at 114 meteorological stations in the Amu-Darya River Basin (ADRB) in Central Asia, using four most widely used ML algorithms: artificial neural network (ANN), supported vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN). ML algorithms were trained with 75 % of the data, while tested and validated with 25 % of the data. A set of 24 models of different unique combinations of available variables were attempted to reasonably estimate GWET and BWET, and satisfying results were achieved. RF was found to be the most-promising ML algorithm to estimate BWET and GWET with limited available climate data. The estimated BWET and GWET can be considered in agriculture water resources policies to minimize further risks to the agroecosystem in ADRB.

Evaluation and Application of MSWEP in Drought Monitoring in Central Asia

Thanks to the large scope, high spatial resolution, and increasing data records, satellite-based precipitation products are playing an increasingly important role in drought monitoring. First, based on the data from ground sites, the long-term Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation product was evaluated in respect to drought monitoring. Then, based on the MSWEP product, the drought trends and the spatiotemporal characteristics of the drought events in four major basins (Amu Darya Basin, Syr Darya Basin, Chu-Talas River Basin, and Ili River Basin) in Central Asia, which have relatively dense gauge sites, were studied. The Standardized Precipitation Index (SPI) and the run theory were used to identify drought events and describe their characteristics. The results showed that MSWEP can effectively capture drought events and their basic characteristics. In the past 40 years, the study area experienced 27 drought events, among which the severest one (DS = 15.66) occurred from June 2007 to September 2008. The drought event that occurred from June 1984 to October 1984 had a drought peak value of 3.39, with the largest drought area (99.2%). Since 1881, there appeared a drying trend and a wetting trend in the Amu Darya River basin and the Ili River basin, respectively. No obvious wetting or drying trend was found in both the Chu-Talas River basin and the Syr Darya basin. Since 2016, the drought area has been on the increase.

Conjunctive Water Management under Multiple Uncertainties: A Case Study of the Amu Darya River Basin, Central Asia

With population growth, climate volatility, and economic expansion, the conjunctive management of surface–groundwater (SGW) faces great challenges. In this study, a hybrid factorial optimization programming (HFOP) method is developed through integrating factorial analysis, interval linear programming, flexible fuzzy programming, and two-stage stochastic programming into a general framework. HFOP can effectively reflect the multiple uncertainties and quantitatively identify the effects of multiple factors. Then, a HFOP-SGW model is formulated for the middle reaches of the Amu Darya River Basin, where 125 scenarios are analyzed. Some of the major findings are: (i) the improvement of surface-water transport efficiency and the proper use of groundwater can effectively alleviate regional water shortage; (ii) agricultural users have a high risk of water scarcity for all states, especially under a low-flow level; (iii) uncertainties of water-flow levels and risk-reverse attitudes of decision makers have significant impacts on the system’s benefits and water-allocation scheme; and (iv) the surface-water-transmission loss rate and risk perceptions of decision makers are the main factors affecting the system’s benefit’s and water-allocation scheme. These findings can help decision makers obtain desired water-allocation strategies to respond to the variations in water availability.

A fixed-mix stochastic fractional programming method for optimizing agricultural irrigation and hydropower generation in Central Asia

In this study, a fixed-mix stochastic fractional programming (FSFP) method is developed for balancing the water-allocation conflict between upstream hydropower generation and downstream agricultural irrigation. FSFP has advantages in dealing with ratio-objective problem under uncertainty, reflecting the dynamic and stochastic characteristics over a long-term planning context, as well as analyzing interrelationships between system efficiency and violation risk of water-allocation target. Then, FSFP is firstly applied to Tuyamuyun reservoir in the lower reach of Amu Darya River basin (Central Asia), where multiple scenarios based on different hydropower-generation targets and inflow levels are examined for identifying the complex relationship between hydropower generation and crop irrigation. Major findings and managerial insights can be summarized as: (i) with the reduction of reservoir inflow, water allocation for downstream agricultural irrigation would decrease by 30.4% once the minimum demand is satisfied, and hydropower generation should be higher priority for pursuing higher marginal benefit; (ii) with the shrinking water supply and rising hydropower-generation target, cotton planting should be firstly restrained due to its high water demand and grape planting is encouraged; (iii) under extreme water scarcity (i.e., low and very-low inflow levels), low-level hydropower generation target (i.e., α = 0.45) is desired for meeting the food requirement in the study basin; (iv) for alleviating the water shortage during dry seasons, it is recommended that water storage should be conducted in autumn and winter, and water release for crop irrigation should be implemented during spring and summer. These findings can help managers identify sustainable water-allocation schemes for agricultural irrigation and hydropower generation against water shortage, environmental destruction and energy insecurity in arid regions.