Search Results (100)

Assessment of crop water requirements (ETc) and their meteorological driving mechanisms are critical for irrigation management in arid inland river basins. Taking the Tailan River Irrigation District (Xinjiang, China) as a case study, temporal changes in cropping structure, crop-specific ETc, and irrigation-district–scale agricultural water demand, as well as the meteorological controls on ETc, were quantified for the period 2000–2024 using Google Earth Engine-based crop mapping, the CROPWAT model, and path analysis. The results demonstrated that the 2024 random forest classification model achieved high accuracy (overall accuracy = 0.902; Kappa = 0.876), and validation against statistical yearbook data confirmed the reliability of crop-area estimation. Cotton dominated the cropping structure (228.6–426.0 km²), while the orchard area expanded markedly from 206.5 km² in 2000 to 393.2 km² in 2024; wheat exhibited strong interannual variability, and maize occupied a relatively small area. Crop-specific ETc differed markedly among crop types, following the order orchard > cotton > maize > wheat, with orchards maintaining the highest water requirement across all growth stages. Total agricultural water demand, estimated by integrating crop-specific ETc with remotely sensed planting areas, increased from approximately 260 million m³ to over 500 million m³ after 2010, mainly due to orchard expansion and cotton cultivation. Path analysis indicated that interannual ETc variability exhibited a stronger statistical association with wind speed than with other meteorological variables. These results provide a quantitative basis for cropping-structure optimization and water-saving irrigation management under changing climatic conditions. Full article

Land use/cover change (LUCC) is a dominant driver of ecosystem service dynamics in arid inland basins. Focusing on the Yarkant River Basin (YRB), Xinjiang, we coupled the PLUS land-use simulation with the InVEST Habitat Quality Model to project 2040 land-use patterns under four policy scenarios—Natural Development (ND), Arable Protection (AP), Ecological Protection (EP), and Economic Development (ED)—and to quantify their impact on habitat quality. Model validation against the 2020 map indicated strong agreement (Kappa = 0.792; FOM = 0.342), supporting scenario inference. From 1990 to 2023, arable land expanded by 58.17% and construction land by 121.64%, while forest land declined by 37.45%; these shifts corresponded to a basin-wide decline and increasing spatial heterogeneity of habitat quality. Scenario comparisons showed the EP pathway performed best, with 32.11% of the basin classified as very high-quality habitat and only 8.36% as very low-quality. In contrast, under ED, the combined share of very low + low quality reached 11.17%, alongside greater fragmentation. Spatially, high-quality habitat concentrates in forest and grassland zones of the middle–upper basin, whereas low-quality areas cluster along the oasis–desert transition and urban peripheries. Expansion of arable and construction land emerges as the primary driver of degradation. These results underscore the need to prioritize ecological-protection strategies especially improving habitat quality in oasis regions and strengthening landscape connectivity to support spatial planning and ecological security in dryland inland river basins. Full article

As the impacts of climate change intensify, water resource conflicts are escalating globally, particularly in regions with uneven water distribution, such as Central Asia. Long-standing disputes over water allocation persist between Kyrgyzstan and Uzbekistan. This paper aims to examine the conflicts and challenges in water allocation between the two countries and explore the potential of Decision Support Systems (DSSs) as a viable solution. The paper begins by reviewing the historical evolution of water allocation in Central Asia, analyzing upstream–downstream disputes and notable cooperation efforts, with a focus on key water agreements. It then outlines the definitions, development, and classifications of DSSs in the context of water allocation and presents two illustrative case studies—the Tarim River Basin in Xinjiang, China, and the Nile River Basin in Africa. These cases demonstrate the applicability of DSSs in water-scarce regions with similar socio-ecological dynamics and complex multi-country, cross-sectoral water demands. Building on these insights, the paper analyzes the key challenges to implementing DSSs for transboundary water allocation in Central Asia, including limited data availability and sharing, insufficient technical capacity, chronic funding shortages, socio-political complexities, climate change impacts, and the inherent difficulty of modeling complex systems. In response, a set of targeted pragmatic recommendations is proposed. While acknowledging its limitations, the paper argues that establishing a structured, system-based decision-making framework—namely DSSs—can help stakeholders enhance climate-informed strategic planning and foster cooperation, ultimately contributing to more equitable and sustainable water resource allocation in the region. Full article

The Ebinur Lake Basin, a key ecological security barrier for windbreak and sand control in northern Xinjiang, is crucial to the ecological safety of western China and the northern sand-prevention belt. Combining the basin’s geographical characteristics, this study comprehensively evaluated ecosystem service functions from four dimensions: water conservation, soil and water conservation, windbreak and sand-fixation, and biodiversity maintenance. Simultaneously, it conducted an ecological sensitivity assessment from four aspects: soil erosion, desertification, land use, and salinization sensitivity. The assessments of the importance of ecosystem service function and ecological sensitivity results were combined to create a tiered zoning plan for the basin. The basin was divided into four first-level zones: the Ebinur Lake Water Area and Wetland Biodiversity Protection Zone, the Desert Vegetation Windbreak and Sand Fixation Ecological Restoration Zone, the Oasis Agricultural Ecological Function Protection Zone, and the Mountain Water Conservation Zone. Six second-level zones were also delineated: the Ebinur Lake Wetland National Nature Reserve, Gobi Vegetation Distribution and Soil Erosion Sensitive Zone, Desert Vegetation Restoration Zone, Jinghe-Bortala Valley Oasis Agricultural Ecological Function Zone, Mountain Water Conservation and Forest-Grass Protection Zone, and Sayram Lake Water Body. This assessment and zoning plan provide support and scientific basis for the basin’s comprehensive ecological management, integrated protection and governance of mountains, rivers, forests, farmlands, lakes, grasslands and deserts, as well as regional ecological development. Full article

Groundwater constitutes a vital freshwater resource essential for sustaining agricultural productivity, industrial processes, and domestic water supply. Quantifying spatiotemporal dynamics of Groundwater Storage (GWS) across China provides a critical scientific basis for sustainable water resource management and conservation. Employing a unified methodology combining Gravity Recovery and Climate Experiment (GRACE) observations and global hydrological models (GLDAS, WGHM), this study investigates spatiotemporal variations in Groundwater Storage Anomalies (GWSA) across China and its nine major river basins from February 2003 to December 2019. The results indicate an overall declining trend in China’s GWSA at −2.27 to −0.38 mm/yr. Significant depletion hotspots are identified in northern Xinjiang, southeastern Tibet, and the Haihe River Basin. Conversely, statistically significant increasing trends are detected in the Endorheic Basin of the Tibetan Plateau and the middle reaches of the Yangtze River Basin. Although GWSA inversions derived from different Global Land Data Assimilation System (GLDAS) models show general consistency, there are still pronounced regional heterogeneities in model performance. The findings offer critical scientific foundations for water resources managers and policymakers to formulate sustainable groundwater management strategies in China. Full article

Accurate detection of cattle and sheep is a core task in precision livestock farming. However, the complexity of agricultural settings, where visible light images perform poorly under low-light or occluded conditions and infrared images are limited in resolution, poses significant challenges for current smart monitoring systems. To tackle these challenges, this study aims to develop a robust multimodal fusion detection network for the accurate and reliable detection of cattle and sheep in complex scenes. To achieve this, we propose YOLO-MSLT, a multimodal fusion detection network based on YOLOv10, which leverages the complementary nature of visible light and infrared data. The core of YOLO-MSLT incorporates a Cross Flatten Fusion Transformer (CFFT), composed of the Linear Cross-modal Spatial Transformer (LCST) and Deep-wise Enhancement (DWE), designed to enhance modality collaboration by performing complementary fusion at the feature level. Furthermore, a Content-Guided Attention Feature Pyramid Network (CGA-FPN) is integrated into the neck to improve the representation of multi-scale object features. Validation was conducted on a cattle and sheep dataset built from 5056 pairs of multimodal images (visible light and infrared) collected in the Manas River Basin, Xinjiang. Results demonstrate that YOLO-MSLT performs robustly in complex terrain, low-light, and occlusion scenarios, achieving an mAP@0.5 of 91.8% and a precision of 93.2%, significantly outperforming mainstream detection models. This research provides an impactful and practical solution for cattle and sheep detection in challenging agricultural environments. Full article

Rational allocation and coordinated operation of water resources in arid inland river basins are crucial for sustaining irrigated agriculture, maintaining ecological baseflow and ensuring reservoir safety. To address this need, this study develops and evaluates joint-operation schemes for the Jingou River-Hongshan Reservoir irrigation system in Xinjiang, northwestern China, to improve coordination among irrigation water supply, ecological baseflow maintenance and reservoir safety. A monthly reservoir-canal-irrigation operation model is formulated with irrigation demands, ecological flow constraints and key engineering limits. Using this model, operating schemes are generated to explore trade-offs among three objectives: shortages, reliability and non-beneficial reservoir releases. The non-dominated schemes obtained from multi-objective optimization are then ranked using an entropy-weighted TOPSIS framework, from which representative solutions are selected for further interpretation. The results indicate that the top-ranked schemes deliver comparable and relatively well-balanced performance across the objectives. Under the preferred compromise scheme, annual irrigation shortages amount to about 39% of total demand, the mean satisfaction level of irrigation and ecological requirements reaches roughly 57%, and the combined index of spill losses and end-of-year storage deviation remains low. Schemes that push shortage reduction or reliability enhancement to extremes tend to increase spill losses, compromise storage security or both, thereby degrading overall performance. The proposed optimization-ranking framework offers a transparent basis for identifying robust operating strategies that reflect local management priorities and is transferable to other reservoir-supported irrigation systems in arid regions. Full article

The Tarim River Basin, a core region for economic development and ecological security in China’s inland arid areas, faces the pressing challenge of synergistically improving land use efficiency to resolve human-land conflicts under water resource constraints and achieve sustainable development. Based on the “economic-social-ecological” benefit coordination theory, this study constructs a land use efficiency evaluation system with 16 indicators and integrates the coupling coordination degree model and gravity model to quantitatively analyze the spatiotemporal differentiation patterns and coupling mechanisms of land use efficiency in the basin from 1990 to 2020. Results show that economic and social benefits of land use increased during this period, exhibiting a “high-north, low-south” spatial pattern, while ecological benefits remained relatively high but declined gradually. The coupling coordination degree of subsystem benefits displayed significant spatial heterogeneity, with an overall upward trend, where composite factors emerged as the primary constraint. Spatially, land use efficiency coupling coordination evolved from “core polarization” to “axial expansion” and finally “networked synergy,” with stronger linkages concentrated in oasis irrigation districts. These findings provide theoretical support for ecological conservation, water management, and policy-making in southern Xinjiang, offering pathways to synergize the “economic-social-ecological” system and promote sustainable development in arid regions. Full article

Model-based simulation of farmland evapotranspiration and crop growth facilitates precise monitoring of crop and farmland dynamics with high efficiency, real-time responsiveness, and continuity. However, there are still significant limitations in using crop models to simulate the dynamic process of evapotranspiration and cotton growth in mulched drip-irrigated cotton fields under different irrigation gradients. The SWAP crop growth model effectively simulates crop growth. However, the original SWAP model lacks a dedicated module to consider the impact of mulching on cotton field evapotranspiration and cotton dry matter mass. Therefore, in this study, the source codes of the soil moisture, evapotranspiration, and crop growth modules of the SWAP model were improved. The evapotranspiration and cotton growth data of the mulched drip-irrigated cotton fields under three irrigation treatments (W1 = 3360 m³·hm⁻², W2 = 4200 m³·hm⁻², and W3 = 5040 m³·hm⁻²) in 2023 and 2024 at the Xinjiang Modern Water-saving Irrigation Key Experimental Station of the Corps were used to verify the simulation accuracy of the improved SWAP model. Research shows the following: (1) The average relative errors of the simulated evapotranspiration, leaf area index, and dry matter weight of cotton in the improved SWAP crop growth model are all <20% compared with the measured values. The root means square errors of the three treatments (W1, W2, and W3) ranged from 0.85 to 1.38 mm, from 0.03 to 0.18 kg·hm⁻², and 55.01 to 69 kg·hm⁻², respectively. The accuracy of the improved model in simulating evapotranspiration and cotton growth in the mulched cotton field increased by 37.49% and 68.25%, respectively. (2) The evapotranspiration rate of cotton fields is positively correlated with the irrigation water volume and is most influenced by meteorological factors such as temperature and solar radiation. During the flowering stage, evapotranspiration accounted for 62.83%, 62.09%, 61.21%, 26.46%, 40.01%, and 38.8% of the total evapotranspiration. Therefore, the improved SWAP model can effectively simulate the evaporation and transpiration of the mulched drip-irrigated cotton fields in the Manas River Basin. This study provides a scientific basis for the digital simulation of mulched farmland in the arid regions of Northwest China. Full article

Livestock represent a significant source of methane (CH₄) emissions, particularly in pastoral regions. However, in Xinjiang—a pivotal pastoral region of China—the spatiotemporal patterns of livestock CH₄ emissions remain poorly characterized, constraining regional mitigation actions. Here, a detailed CH₄ emissions inventory for livestock in Xinjiang spanning the period 2000–2020 is compiled. Eight livestock categories were covered, gridded livestock maps were developed, and the dynamic emission factors were built by using the IPCC 2019 Tier 2 approaches. Results indicate that the CH₄ emissions increased from ~0.7 Tg in 2000 to ~0.9 Tg in 2020, a 28.5% increase over the past twenty years. Beef cattle contributed the most to the emission increase (59.6% of total increase), followed by dairy cattle (35.7%), sheep (13.9%), and pigs (4.3%). High-emission hotspots were consistently located in the Ili River Valley, Bortala, and the northwestern margins of the Tarim Basin. Temporal trend analysis revealed increasing emission intensities in these regions, reflecting the influence of policy shifts, rangeland dynamics, and evolving livestock production systems. The high-resolution map of CH₄ emissions from livestock and their temporal trends provides key insights into CH₄ mitigation, with enteric fermentation showing greater potential for emission reduction. This study offers the first long-term, high-resolution CH₄ emission inventory for Xinjiang, providing essential spatial insights to inform targeted mitigation strategies and enhance sustainable livestock management in arid and semi-arid ecosystems. Full article

Groundwater level (GWL) prediction in arid regions faces two fundamental challenges in conventional numerical modeling: (i) irreducible parameter uncertainty, which systematically reduces predictive accuracy; (ii) oversimplification of nonlinear process interactions, which leads to error propagation. Although machine learning (ML) methods demonstrate strong nonlinear mapping capabilities, their standalone applications often encounter prediction bias and face the accuracy–generalization trade-off. This study proposes a hybrid TCN–Transformer–LSTM (TTL) model designed to address three key challenges in groundwater prediction: high-frequency fluctuations, medium-range dependencies, and long-term memory effects. The TTL framework integrates TCN layers for short-term features, Transformer blocks to model cross-temporal dependencies, and LSTM to preserve long-term memory, with residual connections facilitating hierarchical feature fusion. The results indicate that (1) at the monthly scale, TTL reduced RMSE by 20.7% (p < 0.01) and increased R² by 0.15 compared with the Groundwater Modeling System (GMS); (2) during abrupt hydrological events, TTL achieved superior performance (R² = 0.96–0.98, MAE < 0.6 m); (3) PCA revealed site-specific responses, corroborating the adaptability and interpretability of TTL; (4) Grad-CAM analysis demonstrated that the model captures physically interpretable attention mechanisms—particularly evapotranspiration and rainfall—thereby providing clear cause–effect explanations and enhancing transparency beyond black-box models. This transferable framework supports groundwater forecasting, risk warning, and practical deployment in arid regions, thereby contributing to sustainable water resource management. Full article

Wetlands function as crucial transitional zones between land and water ecosystems worldwide, contributing significantly to the stability of local ecosystems. However, there is limited research on landscape changes in Xinjiang’s arid interior regions and the factors driving these changes. This study uses data reanalysis techniques to examine the spatial and temporal evolution and landscape patterns of wetlands, as well as their driving forces, in Xinjiang between 1990 and 2023. The results show that over the past three decades, the wetland area in Xinjiang has grown from 18,427 km² in 1990 to 21,532 km² in 2023, with an annual increase of about 94 km². The greatest growth in wetlands, particularly lakes, marshes, and rivers, has occurred around the periphery of the Tarim Basin and the Ili River Basin, while mountainous areas have seen slight reductions. The distribution pattern shows higher wetland coverage in southern Xinjiang and less coverage in the north, with the largest proportion of wetlands found in the south. Additionally, wetland expansion has led to improvements in the number, density, aggregation, and connectivity of wetland patches, while the complexity of their shapes has decreased. The overall habitat quality of wetlands has also improved over time. Attribution analysis highlights that the rise in runoff due to temperature increases over the past 30 years is a major driver of wetland expansion, with warming accounting for the largest share of expansion in lakes (36%) and in rivers (47.9%). Furthermore, the implementation of large-scale engineering measures, such as ecological water diversion, water-saving irrigation, and reservoir management, has contributed significantly to wetland expansion and ecological restoration. These results provide useful insights for the long-term conservation and management of wetland resources in the arid areas of Xinjiang. Full article

Identifying the impact of climate change and changes in underlying surface conditions on river runoff changes is critical for sustainable water resource use and watershed management in arid regions. The Kriya River is not only a key support for water resources in the arid environment of the Tarim Basin, but also a solid foundation for the survival and development of agricultural oases. In this study, the Kriya River Basin in Xinjiang, China, was taken as the research object, and the Mann–Kendall, Sen’s Slope, Cumulative Sum, and other methods were used to systematically analyze the temporal evolution law and multi-modal characteristics of runoff in the basin. Based on the Budyko hydrothermal coupling equilibrium equation, the contribution of temperature, evaporation, and the underlying surface to runoff variation was quantitatively interpreted. The study found that the annual runoff depth of the Kriya River Basin has shown a significant positive evolution trend in the past 60 years, with an increase rate of 0.5189 mm/a (p ≤ 0.01). Through the identification of mutation points, the runoff time series of the Kriya River was divided into the base period 1957–1999 and the change period 2000–2015. Without considering the supply of snowmelt runoff, the contribution rate of precipitation to runoff change was 75.23%, followed by the change in underlying surface (23.08%), and the potential evapotranspiration was only 1.69%. The results of this study provide a good scientific reference for water resources management and environmental governance in the Kriya River Basin. Full article

The oasis area of the Cherchen River Basin (OACRB) is located in the southeast edge of the Tarim Basin in Xinjiang, China. High boron (B) groundwater is observed in the OACRB according to 40 groundwater samples collected in May 2023. Identification of the chemical characteristics and B sources of groundwater in the OACRB is of great significance for the sustainable development and utilization of groundwater resources and the protection of animals, plants and human health. To explore the chemical characteristics and main B sources of groundwater, Piper three-line diagram, Gibbs diagram, correlation analysis, hydrogeochemical simulation and absolute principal component analysis (PCA-APCS-MLR) were used for analysis. The contribution of different factors to groundwater B was quantitatively evaluated. The results showed that the groundwater is weakly alkaline (with an average pH of 7.94) and mainly brackish water and saline water with Cl⁻ and Na⁺ as the main anions and cations. The groundwater is dominated by SO₄ · Cl-Na type. The average concentration (ρ) of groundwater B in the study area was 1.48 mg·L⁻¹ with the over-standard rate was 45.0%. The APCS-MLR receptor model analysis revealed that groundwater chemical components including B were mainly derived from leaching-enrichment, human activity, primary geological factors, and unknown sources. Groundwater B is obviously greater than the standard limit, which is mainly due to agricultural activities (fertilizers and pesticides) and unknown sources. Full article

Coarse-grained soils serve as essential fill materials in earth–rock dam engineering, where their mechanical properties critically influence dam deformation and stability, directly impacting project safety. Artificial intelligence (AI) techniques are emerging as powerful tools for predicting the mechanical properties of coarse-grained soils. However, AI-based prediction models for these properties face persistent challenges, particularly in parameter tuning—a process requiring substantial computational resources, extensive time, and specialized expertise. To address these limitations, this study proposes a novel prediction model that integrates Adaptive Simulated Annealing (ASA) with an improved Particle Swarm Optimization (PSO) algorithm to optimize the Elman Neural Network (ENN). The methodology encompasses three key aspects: First, the standard PSO algorithm is enhanced by dynamically adjusting its inertial weight and learning factors. The ASA algorithm is then employed to optimize the Adaptive PSO (APSO), effectively mitigating premature convergence and local optima entrapment during training, thereby ensuring convergence to the global optimum. Second, the refined PSO algorithm optimizes the ENN, overcoming its inherent limitations of slow convergence and susceptibility to local minima. Finally, validation through real-world engineering case studies demonstrates that the ASA-PSO-optimized ENN model achieves high accuracy in predicting the mechanical properties of coarse-grained soils. This model provides reliable constitutive parameters for stress–strain analysis in earth–rock dam engineering applications. Full article