Search Results (181)

The traditional Muskingum model has difficulty representing complex hydraulic behaviors under high-flow conditions because it relies on simplified assumptions and fixed parameters. To address the pronounced nonlinearity and non-stationarity of flood routing in the arid Tarim River Basin, a hybrid forecasting framework was developed by coupling the Muskingum method with multiple machine learning algorithms (Ridge, LASSO, RF, and LSTM) to predict and correct Muskingum residuals. Global Muskingum parameters were identified using the L-BFGS-B algorithm to represent basin-scale routing characteristics. For rolling forecast, a multidimensional feature space was constructed by integrating routing gradients and hydraulic interaction terms. The results indicated that all hybrid models outperformed the traditional Muskingum method across lead times. The Ridge-based hybrid model achieved the best performance at short lead times, with the Nash–Sutcliffe efficiency (NSE) at a 4 h lead time increasing from 0.56 for the physical baseline to 0.977. For longer lead times (12–24 h), the LASSO-based hybrid model demonstrated higher robustness, which was attributed to L1-regularization-based feature selection. The key scientific contribution of this work lies in proposing a lead-time-dependent adaptive modeling strategy, revealing the structural characteristics of the residuals of the Muskingum model, and demonstrating that, in the study basin, simple linear models outperform complex models in multi-step correction. Overall, the proposed framework alleviates systematic underestimation during high-flow periods and provides a predictive scheme for arid-region rivers that preserves physical interpretability while improving forecasting accuracy. Full article

Groundwater level (GWL) variations in the arid regions of Northwest China are driven by both natural processes and human activities. Identifying causal links between hydrological variables is fundamental to understanding groundwater evolution and conducting dynamic simulations. This study integrates the Mann–Kendall test, Seasonal-Trend decomposition using Loess, and the Peter and Clark Momentum-threshold and Momentary Conditional Independence (PCMCI) causal inference to analyze GWL variation characteristics and causal response processes across seven sub-basins in the Tarim Basin using multi-source remote sensing data. Results show an overall decline in GWL, primarily in the north-central part of the basin, with the Kaidu–Konqi River Basin reaching a maximum rate of 0.51 m/year. The trend components reveal localized depletion alongside broad stability, while seasonal components exhibit three types of temporal shifts in fluctuations. A mismatch exists between the prevalence of environmental influences and their causal strength. Daytime land surface temperature (LST_D), surface runoff (RO), and evapotranspiration (ET) show the highest detection frequencies, yet volumetric soil water in layers 2 (SWVL₂) and RO exhibit the largest ranges in strength and drive variations at specific sites. Response times are asymmetric. Negative effects from ET on GWL transmit quickly, while positive recovery is slow. Conversely, positive recharge from volumetric soil water in layer 1 (SWVL₁) is faster than its negative lag. At the basin scale, surface processes recharge GWL while mediating indirect influences from other variables. Climate and agricultural irrigation act as direct sinks. Depending on local conditions, three regional patterns emerge: direct climate-driven depletion, obstructed shallow water retention, and indirect compensation from agricultural water use. Causal networks indicate that RO and SWVL₁ have the highest centrality and dominate water output, whereas SWVL₂ acts as a passive receiver. Pathways from the surface to GWL are also asymmetric. The most frequent path involves step-by-step infiltration along RO → ET → SWVL₁ → SWVL₂ → GWL. In contrast, the paths with the highest cumulative strength are shorter and faster, specifically RO → ET → GWL and RO → SWVL₁ → GWL. The identified pathways and lag parameters provide a direct basis for groundwater dynamic modeling and water resource management in the basin. Full article

Triplophysa yarkandensis (Cypriniformes: Nemacheilidae), a rare endemic fish in the Tarim River Basin, Xinjiang, China, plays a pivotal role in maintaining the stability of plateau saline–alkaline aquatic ecosystems, yet its survival is increasingly threatened by habitat salinization. However, the multi-dimensional synergistic adaptation mechanisms linking its phenotypic variation, intestinal structure, and associated microbial communities to extreme saline–alkaline stress remain poorly understood. In this study, we innovatively integrated morphological/intestinal histological characterization, 16S rRNA gene sequencing, and microbial ecological analyses (co-occurrence networks and assembly processes) to systematically decode its adaptive strategies. Results revealed that T. yarkandensis exhibits a streamlined body shape, morphological variability, and elongated intestinal villi that may support locomotion and nutrient/ion uptake under osmotic stress. Its gut exerts a stringent selective filter, driving distinct differentiation between water and gut microbial communities—with gut-enriched core taxa (Aurantimicrobium and Aestuariivirga) and functional pathways (unsaturated fatty acid biosynthesis and ABC transporters) specialized for osmoregulation. Notably, the water microbial assembly is dominated by stochastic processes, while the gut assembly relies on host-driven deterministic selection, forming a habitat-specific adaptive pattern. These findings uncover the synergistic adaptation system of host phenotype and gut microbiota for survival in extreme saline–alkaline habitats, advancing our understanding of fish–microbe co-evolution in extreme ecosystems and providing critical theoretical support for the conservation of rare plateau fish, as well as guidance for the utilization of saline–alkaline water resources in aquaculture. Full article

Arid region ecosystems are among the most fragile ecological types worldwide. They depend heavily on limited water resources and are strongly influenced by intensive human activities, leading their ecosystem services to exhibit nonlinear and threshold responses to driving factors. Identifying the thresholds of ecosystem services under the combined influence of natural and socio-economic interactive drivers is of great significance for regional ecological risk warning and differentiated management. Taking the Tarim River Basin as a case study, this research establishes an integrated analytical framework that combines causal inference, interaction term construction, interpretable machine learning (XGBoost-SHAP), and piecewise linear regression. The framework is used to evaluate the variations in four types of ecosystem services in 2000, 2010, and 2023, to analyze the interactive effects of driving factors, and to identify their thresholds influencing ecosystem service functions. The results indicate that (1) different types of ecosystem service functions exhibited distinct trends from 2000 to 2023, with habitat quality and water yield showing declining tendencies, while soil conservation and Windbreak and sand fixation demonstrated gradual increases; (2) Causal Screening and interaction modeling revealed that the interaction between precipitation and population density (Pre × Pop) served as the key synergistic driver of changes in the four ecosystem service functions. Both the ecosystem services and the coupled natural–social driving processes exhibited pronounced nonlinear characteristics, with evident trend shifts occurring within specific threshold intervals. (3) The precise coupling thresholds of different ecosystem services under natural–social drivers were identified, intuitively revealing the coupling threshold characteristics of various ecosystem services; (4) The integration of causal inference with interpretable machine learning enhances the reliability of threshold identification, revealing the heterogeneous response mechanisms of different services and providing a quantitative basis for the zoning regulation and differentiated management of regional ecosystems. The findings offer a transferable methodological framework to support ecological governance in arid regions. Full article

The Aksu River Basin, the main headwater of the Tarim River, contributes more than 70% of the main stream’s runoff and is therefore critical in maintaining hydrological stability in this arid river system. In recent decades, rapid oasis expansion and growing agricultural water withdrawals have intensified competition for surface and groundwater, posing increasing ecological risks to the downstream Tarim River Basin. To quantitatively characterize river–groundwater hydrological responses under intensive water use, we combined statistical analysis, field observations, and distributed hydrological modeling within a basin-scale conceptual framework. Multiple lines of evidence—water level monitoring, hydrochemical tracers, stable isotopes, and the integrated surface–groundwater model MIKE SHE—were used to identify river–groundwater interaction mechanisms in the Aksu alluvial plain. Results reveal a typical three-stage spatial exchange pattern: river recharge to groundwater in the upstream reach, groundwater discharge to the river in the midstream, and renewed river infiltration to groundwater downstream. The patterns inferred from water levels, hydrochemistry, and isotopes are broadly consistent, while water-level data better resolve left–right bank asymmetry. The MIKE SHE model supports the seasonal bidirectional exchange dynamics and reproduces runoff behavior with acceptable performance (RMSE and residual standard deviation within 20% of observed means and R² > 0.7 during both calibration (2010–2017) and validation (2018–2021)). The proposed multi-evidence framework captures the spatio-temporal variability of river–groundwater interactions in arid regions and provides spatially differentiated guidance for conjunctive surface–groundwater regulation and integrated water resources management in the Tarim River Basin. Full article

Populus euphratica is a critical constructive species in arid desert regions, serving as a “natural barrier” for oasis protection. The sustainable management of Populus euphratica forests is directly related to regional ecological security, and the fine identification of sparse Populus euphratica forests is essential for the conservation of natural Populus euphratica forests. Currently, most mapping studies on Populus euphratica distribution focus on the extraction of dense, contiguous Populus euphratica forests, with insufficient attention paid to the identification of sparse Populus euphratica forests. This study utilizes Gaofen-2 (GF-2) satellite imagery as the data source and takes a typical sparse Populus euphratica forests distribution area in the Tarim River Basin as the study site. It systematically evaluates the performance of nine mainstream deep learning models, including U-Net, DeepLabV3+, and SegFormer, in the task of sparse Populus euphratica forests identification. The results indicate that: (1) The false-color sample set, synthesized from near-infrared, red, and green bands, contributes to improved model accuracy. Compared to the true-color (red, green, blue bands) dataset, the average Intersection over Union (IoU) of the nine models shows a relative improvement of approximately 20%. (2) For the sparse Populus euphratica forests identification task based on the false-color dataset, four models—U-Net, U-Net++, MA-Net, and DeepLabV3+—exhibited excellent performance, with IoU exceeding 75%. (3) Using U-Net as the baseline model, this study integrated the max-pooling indices mechanism, atrous spatial pyramid pooling, and residual connection modules to construct a semantic segmentation network tailored for sparse Populus euphratica forests, named Sparse Populus euphratica Segmentation Network (SPS-Net). This model achieved an IoU of 80%, a relative improvement of approximately 6.3% over the baseline model, and demonstrated good stability in large-scale classification tests. The identification scheme for sparse Populus euphratica forests constructed using GF-2 imagery and deep learning models proposed in this study can provide effective technical support for the refined monitoring and protection of natural Populus euphratica forests. Full article

Situated at the core of the Asian arid zone, the Tarim River Basin (TRB) serves as a critical indicator of regional hydroclimatic responses to global warming. Utilizing 27 extreme climate indices recommended by the Expert Team on Climate Change Detection and Indices, this study analyzes the spatiotemporal evolution of climate extremes in the TRB from 1960 to 2022 and explores their correlations with primary large-scale atmospheric circulation factors. The results indicate that, at the temporal scale, extreme warm indices (TX90P, TN90P, SU25, TR20) and most extreme precipitation indices (except for CDD) exhibited increasing trends, accompanied by pronounced abrupt changes and periodic characteristics. The changes were characterized by stronger warming at low temperatures than at high temperatures, greater nighttime warming than daytime warming, and larger increases in warm days than cold days. Extreme temperature and precipitation indices underwent abrupt changes in the mid-to-late 1990s and 1980s, respectively. The former exhibits pronounced “cold-warm” oscillations at 10–15-year and 25–35-year scales, while the latter displays distinct “wet-dry” cyclic alternations at 8–9-year and 30–32-year scales. Spatially, extreme temperature indices showed consistent warming across most stations. In contrast, the change trends of extreme precipitation indices displayed obvious spatial heterogeneity, with growth rates generally decreasing from west to east. Further analyses demonstrate that most extreme climate indices exhibit significant linear correlations with the AMO, PDO, NAO, and NOI. Notably, the AMO emerges as the dominant driver of variations in both extreme temperature and precipitation. In the context of accelerated global warming, these insights are pivotal for enhancing regional climate risk management and water resource adaptability. Full article

In order to improve the robustness and internal consistency of evapotranspiration estimation in arid regions and to reveal the characteristics of water consumption structure within a river basin, this study focused on the Aksu River Basin. Multiple data sources, including the Penman–Monteith model, MODIS remote sensing products, GRACE terrestrial water storage change data, and the GLDAS–Noah model, were integrated to establish a Bayesian Model Averaging (BMA)-based framework for fusing actual evapotranspiration (ETa) estimates. The results indicate that the BMA integration effectively mitigated model-dependent biases and improved the consistency and robustness of basin-scale ETa estimates. During the period 2000–2020, ETa in the basin exhibited an overall increasing trend (approximately 4.04 mm/a), with a spatial distribution pattern characterized by higher values in the northwest and lower values in the southeast. In terms of water consumption effectiveness, low-effectiveness water consumption predominated in the basin (accounting for 61.24%), while high-effectiveness water consumption accounted for a relatively smaller proportion (26.01%). These results suggest that the current water consumption structure remains dominated by low-effectiveness components, indicating potential room for optimization in balancing irrigation activities and ecosystem water use. The multi-source data fusion and water consumption effectiveness evaluation framework proposed in this study provides a scientific basis for water resource management and ecological water security assessment in arid river basins. Full article

River basins are not merely geographical spaces but also cultural-historical ecosystems, where the spatial patterns of Intangible Cultural Heritage (ICH) profoundly reflect the long-term interaction between human and environment, as well as contemporary transformations. While international research on ICH has evolved from conceptual clarification to interdisciplinary theory-building, and spatial quantitative methods have been widely applied to cultural heritage analysis, the spatial patterns, multi-scale structures, and “natural-human” driving mechanisms of ICH in continental arid river basins—particularly in the Tarim River Basin (TRB, China’s largest inland river and a key corridor of the Silk Road)—remain underexplored. To address this gap, this study takes 313 ICH items in the TRB as the research object. It uses ArcGIS 10.8.1 to visualize their spatial distribution and employs an integrated methodology—including global Moran’s I, kernel density estimation (KDE), DBSCAN spatial clustering, and geographical detector (Geodetector)—to systematically reveal their spatial characteristics and influencing factors. The findings indicate that: (1) The distribution of ICH exhibits a multi-scale feature of “global randomness with local clustering”: spatial autocorrelation is not significant at the county level, while at the micro-geographical scale, a dendritic structure characterized by “one axis, three cores, denser in the north and sparser in the south” emerges, which is highly coupled with the river network. DBSCAN clustering further identifies a “mainstem axis–tributary node” cluster system and a relatively high proportion of peripheral “noise” heritage points. (2) Agglomeration patterns vary significantly across different ICH categories, with traditional craftsmanship showing high clustering, while traditional sports, entertainment, and acrobatics display highly fragmented distributions. (3) The study reveals and validates a ternary “Water–Tourism–Urbanization” driving framework that predominantly shapes the spatial heterogeneity of ICH: water resources constitute a fundamental ecological threshold, whereas tourism development and urbanization have emerged as more explanatory social driving forces, with widespread nonlinear enhancement interactions between natural and human factors. This research moves beyond the traditional view of river basins as static cultural “containers,” providing empirical evidence for their dynamic nature as “cultural-ecological co-evolutionary systems.” The proposed ternary framework not only offers a new perspective for understanding the spatial resilience of ICH in arid regions and the potential risks of “spectacularization” and “spatial polarization” amid rapid changes, but also provides a scientific basis for spatial governance, culture-tourism integration, and the formulation of conservation strategies for ICH at the basin scale. Full article

The Three-River Connectivity Zone in the lower Tarim River Basin (TRCZ) is a typical area that has experienced decades of river cut-off, followed by artificial ecological water transfers and vegetation restoration. However, the long-term patterns of ecological water requirements and their response mechanisms to ecosystem services in this region remain unclear. This study aims to quantify the spatiotemporal dynamics and driving factors of ecological water requirements in the TRCZ from 1990 to 2020. We integrated multi-temporal remote sensing land cover data with the FAO Penman–Monteith equation to estimate vegetation evapotranspiration (as a proxy for ecological water requirement) and coupled the InVEST model with Random Forest modeling to identify key climatic and hydrological drivers. Unlike previous studies that focused primarily on precipitation inputs, our approach explicitly considers the ecosystem’s water yield function alongside water demand, offering new insights into the constraints on ecosystem services. Key findings reveal: (1) During the period of 2005–2010, the land cover types underwent significant changes, characterized by a marked expansion of sparse forest (14–21%) and a pronounced decline in forest land, which fundamentally reconfigured the ecosystem’s water demand structure. (2) Accordingly, the multi-year average ecological water requirement quota in the study area is 2.95 × 10⁷ m³, and the total ecological water requirement exhibited a fluctuating decline at a rate of −1.39 × 10⁵ m³/yr, yet sparse forest persisted as the dominant water-consuming component. (3) The Random Forest model (R² = 0.942) identified water yield (importance: 0.527) and precipitation (0.255) as the primary drivers, establishing the ecosystem’s water yield function rather than precipitation input alone as the critical constraint. (4) A widespread increase in the unit area ecological water requirement across vegetation types signaled escalating pressures from climate change. This research provides a quantitative framework and a transferable methodology for adaptive water resource management and ecological restoration in arid regions, emphasizing the balance between ecosystem water demand and supply functions. Full article

Under multiple stresses such as an arid climate, water scarcity, and desertification, inland river basins in arid regions represent a typically fragile ecosystem worldwide, and their ecological security faces increasingly complex and severe challenges. To address the limitations of traditional assessment methods characterized by single-perspective approaches, difficulties in quantifying indicators, and lack of a systematic framework for arid basins, this study constructed an innovative Health–Service–Risk–Sensitivity (HSRS) framework. Taking the Tarim River Basin (TRB) as a case study, the validity and necessity of this framework were validated through the Remote Sensing Ecological Index (RSEI) and correlation analysis. Furthermore, the XGBoost–SHAP model was further integrated to identify key threshold responses of multidimensional driving factors within the basin. The findings indicate that the ecological security of the TRB progressively improved, with approximately 11.64% of the area showing significant enhancement. The four most influential driving factors were land use, NDVI, human activity intensity, and soil moisture. Notably, the study identified critical environmental thresholds: when DEM ranged from 1500 to 3000 m and slope from 2° to 30°, constraining effects on the Comprehensive Ecological Security Index (CESI) increased. When annual precipitation exceeded 150 mm, NDVI was greater than 0.35, and soil moisture content exceeded 0.14 m³/m³, the constraint effect was further strengthened. Overall, the integration of the HSRS framework and the XGBoost-SHAP model offers a novel and effective approach for ecological security assessment in arid inland basins. Moreover, this approach has substantial practical implications for achieving precise coordination between regional ecological protection and sustainable development. Full article

The continuous depletion of global groundwater resources has posed a serious threat to the ecological stability of terminal lakes in arid regions. However, accurate ecological assessment and water resource management of these lakes face a long-term key bottleneck—the determination of an appropriate lake surface area. Previous research has primarily focused on identifying the minimum interannual suitable lake surface area, with limited exploration of the suitable area range for lakes experiencing significant annual surface area fluctuations. Taitema Lake is located in the southeastern Tarim Basin of arid northwest China and serves as the terminal lake for both the Tarim and Cherchen Rivers. This study examines Taitema Lake, a continental terminal lake in an arid region. We developed a comprehensive ecological security evaluation system based on landscape structure, steady-state conditions, and habitat elements to establish the minimum suitable lake surface area threshold. By combining this with the threshold for maximum suitable lake surface area—when ecological water use efficiency peaks—we determined the interannual suitable lake surface area for Taitema Lake to be 33.7–154.4 km². This study employed the MIKE 11 one-dimensional hydrodynamic model. Within the constraints of the lake surface area range determined by ecological water demand, we propose ecological dispatching plans for specific periods. During the green-up period (April to May), water is alternately transferred through either the Wenkuoer River or the old Tarim River at a flow rate of 24 m³/s, with a total conveyance volume of 1.3 × 10⁸ m³. For the sowing period (August to October), a dual-channel approach is used where both rivers transport water simultaneously at 27 m³/s each, resulting in a total conveyance volume of 4.3 × 10⁸ m³. This study offers valuable insights, supported by multi-scale models, for optimizing water resource allocation and ecological protection of lakes in arid areas. Full article

Terminal lakes in arid regions are highly vulnerable to climate variability and human water management, yet their long-term hydrological responses under multi-river regulation remain insufficiently quantified. Using Taitema Lake at the terminus of the Tarim Basin as a case study, this research integrates Landsat and Sentinel observations (2005–2025) with meteorological and river-inflow records to examine lake area dynamics and to identify river-specific hydrological controls. The results show pronounced intra- and interannual variability, with the lake expanding to a maximum of 461.52 km² in October 2017 and shrinking to 0.35 km² in October 2008. High-frequency permanent water (~43 km²) is concentrated in the deep central basin and largely influenced by the Qarqan River, whereas seasonal water (~300 km²) is broadly distributed and strongly affected by ecological releases from the Tarim River. Quantified inflow–area relationships indicate that the lake expands by 7–14 km² for each 0.1 × 10⁸ m³ of inflow. Based on frequency-based hydrological analysis, this study develops joint inflow strategies for wet, normal, and dry years, offering a practical hydrological basis for more precise and adaptive water allocation schemes in arid terminal lakes. 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

Earthquake-induced soil liquefaction poses significant geotechnical hazards, including sand boiling, loss of foundation bearing capacity, lateral spreading, pipeline flotation, uneven settlement, and slope instability. While cyclic liquefaction constitutive models can effectively simulate and predict site liquefaction behavior, their reliability hinges on the accurate calibration of constitutive parameters. Traditional calibration methods often fail to capture the comprehensive material response, are labor-intensive, time-consuming, and susceptible to subjective judgment. To overcome these limitations, this study develops an intelligent calibration framework for a cyclic liquefaction constitutive model by integrating a numerical solver for unit tests with the genetic algorithm (GA)-based optimization framework. The proposed method is rigorously evaluated in terms of calibration accuracy, convergence, repeatability, uncertainty, and computational efficiency. Validation via a series of laboratory unit tests on materials from an extremely high earth-rock dam project confirms the method’s effectiveness. Results demonstrate that the intelligent calibration approach achieves a high accuracy of 91.84%, offering a reliable, efficient, and robust solution for parameter determination. Full article