Search Results (247)

We develop a high-order space-time spectral method for nonlinear convection–diffusion equations with a Riemann–Liouville time-fractional derivative and a spectrally defined space-fractional Laplacian. The spatial discretization uses a Fourier spectral method that diagonalizes the fractional Laplacian under periodic boundary conditions. The temporal discretization employs a Petrov–Galerkin method based on generalized Jacobi functions which capture the initial singularity exactly. The nonlinear convection term is treated pseudo-spectrally, and the resulting algebraic system is solved with a damped Newton iteration. Rigorous error analysis proves exponential convergence in both space and time. Numerical experiments for various fractional orders confirm the spectral accuracy. Simulations of the fractional Burgers equation demonstrate that increasing the viscosity enhances diffusion and stabilizes the solution, while a nonlinear coefficient that significantly exceeds the viscosity leads to error growth over long time intervals. The method provides an efficient and accurate tool for simulating anomalous transport phenomena. Full article

Against the backdrop of intensified climate change and increasingly prominent imbalances in resource supply and demand, achieving multi-objective collaborative optimization of the Water-Energy-Food-Carbon (WEFC) nexus under uncertain conditions has become a pivotal task for regional sustainable development. Taking the Heihe River Basin, a typical arid inland river basin in northwest China with a complex WEFC nexus, as the research area, this study develops a multi-objective collaborative optimization model for the WEFC nexus, targeting three core goals: maximizing crop irrigation water productivity, minimizing carbon emissions, and enhancing low-carbon agricultural competitiveness. The model embeds constraints of regional water security, food security, land policy, and total water resource availability, introduces the uncertainty parameter τ to quantify fluctuations in available surface water, and adopts the ideal point method to convert the multi-objective problem into a single-objective optimization task by minimizing the Euclidean distance between feasible solutions and the ideal solution, with a case application in the oasis area of the basin’s middle reaches. Results show the model exhibits excellent stability across varying uncertainty levels: crop irrigation water productivity stabilizes around 1.5 kg/m³, low-carbon agricultural competitiveness at approximately 0.1003 kg/yuan, and spatial differences in resource allocation are evident. Linze gains the most water resources (16.47 × 10⁸ m³) due to geographical advantages, while Gaotai obtains the least (6.51 × 10⁸ m³). In terms of planting structure, vegetables dominate the sown area owing to low carbon emissions and high water use efficiency, while wheat planting is relatively limited by climate adaptability and market demand. Carbon sink analysis confirms vegetables as the primary carbon sequestration contributor in Ganzhou and Linze, offering a practical pathway for agricultural carbon reduction. These findings provide tailored theoretical and practical support for balancing food security, efficient resource utilization, low-carbon development, and ecological protection in arid and semi-arid regions, facilitating regional carbon neutrality and sustainable agricultural development. Full article

High-spatiotemporal-resolution land surface temperature (LST) data are crucial for analyzing surface energy balance, modeling temperature-related processes, and monitoring thermal environments. However, despite advancements in multi-source fusion and reconstruction techniques, high-frequency LST data remain susceptible to anomalies such as abrupt changes and outliers due to retrieval uncertainties and varying observation conditions. Conventional statistical outlier detection methods risk misidentifying physically plausible rapid weather changes as data errors, introducing systematic biases. To address this, we propose a two-stage anomaly detection framework that follows a “temporal physical pre-screening first, spatial statistical verification later” logic. First, a piecewise empirical model, based on typical diurnal LST variation characteristics, is constructed to identify points violating physical patterns. Subsequently, a spatial consistency test using median absolute deviation (MAD) is introduced to distinguish real weather-driven fluctuations from genuine data anomalies from a spatial synergy perspective. This sequential design effectively reduces the risk of mis-correcting physically reasonable temperature variations. Validated using hourly seamless LST data (2016–2021) and ground observations in the Heihe River Basin, our method outperformed Seasonal-Trend decomposition using Loess (STL), double standardization methods, and robust Holt–Winters. For over 87% of the detected anomalies, the proposed method demonstrated positive improvement rates in RMSE, MAE, R, and R². The overall average improvement rates reached 23.61%, 18.79%, 16.46%, and 61.33%, respectively, indicating robust performance. The results underscore that explicitly incorporating physical constraints enhances the reliability and interpretability of quality control for high-temporal-resolution remote sensing LST data. Full article

Dust and sand storms occurring in northern China are strongly controlled by near-surface aerodynamics, yet the spatial heterogeneity of these processes remains poorly understood. We obtained field measurements of the wind above gobis, sandy surfaces, and dry lakebeds in the Hexi Corridor Desert and Heihe River Basin, and sandy surfaces in northern China. First, the slope of wind profile (a₁) reveals distinct drag reversal with increasing wind speed: under low winds, a₁ increases from sandy to dry lakebed to gobi surfaces, whereas under high winds, actively saltating sandy surfaces exhibit the highest a₁, surpassing gobi and dry lakebed. Second, the dynamic feedback between sediment transport and aerodynamics is clear: at below-threshold winds, friction velocity ($u_{*}$) and aerodynamic roughness length (z₀) are lowest for sand; however, as wind speed increases to initiate significant saltation, the sandy surface develops the highest $u_{*}$ and z₀, highlighting the dominant role of grain-borne roughness. Third, the focal height (z_f) shows regional disparity, varying by up to two orders of magnitude for both sandy and gobi surfaces, with a strong correlation to local gravel coverage. This work provides spatially explicit parameterizations of surface type, offering a physical basis for modeling dust emission and transport in northern China and similar arid regions globally. Such parameterizations are essential for developing reliable early warning systems and evidence-based land management strategies. These advances contribute directly to ecosystem sustainability and community resilience in vulnerable arid and semi-arid regions under climate change. Full article

Soil moisture is a critical variable in the eco-hydrological processes of arid regions; however, the vertical stratified mechanisms of soil moisture response to meteorological factors in artificial grassland remain inadequately quantified. Based on 10-min interval monitoring data from 2015 to 2024 in the middle reaches of the Heihe River, this study investigated the dynamics of soil moisture within a 0–160 cm depth profile in an arid artificial grassland. By integrating the Mann–Kendall trend test, Pearson correlation, time-lagged cross-correlation, multiple regression analysis and redundancy analysis, we systematically investigated the changing relationships between meteorological factors and soil moisture. The results revealed the following: (1) main meteorological factors driving surface processes (e.g., net radiation, air temperature, vapor pressure deficit) showed significant increasing trends with strong variability, while relative humidity decreased significantly, and these findings collectively point to a general trend of warming and drying in the region; (2) WS, Ta, rainfall, and RH are the principal factors explaining soil moisture variations, wherein temperature and humidity exhibit positive correlations with soil moisture; (3) RDA results showed that shallow soil moisture (0–20 cm) was primarily governed by air temperature and rainfall, whereas deep soil moisture was increasingly regulated by vapor pressure deficit; (4) time-lagged cross-correlation analysis showed that the response time of soil moisture to rainfall almost increased with soil depth, while the correlation coefficient gradually weakened from 0.43 to 0.06. This study quantitatively elucidates the stratified control mechanism of meteorological factors on the vertical pattern of soil moisture, contributing to a deeper understanding of the response of eco-hydrological processes under climate change and providing a scientific basis for water resource management, agricultural planning, and climate prediction. Full article

Oasis regions in arid northwestern China represent critical interfaces for watershed ecological security and rural sustainable development. However, under escalating resource constraints and intensifying human–land conflicts, the disorderly expansion of rural settlements has increasingly constrained high-quality territorial development. Liangzhou District, located in the transitional zone of the upper Heihe River Basin at the eastern end of the Hexi Corridor, provides a representative case for examining the spatial evolution of rural settlements in oasis environments. Using multi-temporal land-use data from 2000 to 2023, this study integrates landscape pattern metrics, kernel density estimation, and nearest-neighbor analysis to characterize the spatiotemporal evolution of rural settlements. The Markov–CLUE-S model is further applied to simulate land-use changes under three scenarios for 2035: natural development, new urbanization, and ecological protection. Results indicate that the number of rural settlement patches increased from 1598 to 3009, while their total area expanded from 10,321.83 hm² to 20,828.34 hm², demonstrating a sustained expansion trend and a transition from scattered distribution to increasingly clustered patterns along urban centers and major transportation corridors. Scenario simulations suggest that rural settlement areas will decline by 5.27 km², 12.13 km², and 11.68 km² under the three respective scenarios, predominantly converting to cropland, grassland, and urban construction land. Model validation yields a Kappa coefficient of 0.88, confirming high simulation accuracy. This study develops an integrated “pattern evolution–driving mechanism–scenario response” analytical framework for rural settlement dynamics in arid oasis regions, highlighting the combined influences of environmental constraints and socio-economic drivers. The findings provide a scientific basis for rural spatial optimization and watershed-scale territorial governance in arid regions. Full article

A spatiotemporal, multi-task learning (MTL) model for simulating surface water–groundwater (SW-GW) dynamics is developed and applied to the Heihe River Basin, Northwest China. The Transformer-based model (MT-TFT) jointly forecasts surface runoff and groundwater levels, outperforming MTL models built on gated recurrent unit (GRU) and long short-term memory (LSTM) architectures. Compared with single-task learning, adding a coupled groundwater-level task markedly improves surface runoff prediction, achieving a Nash–Sutcliffe efficiency (NSE) of 0.73 and a coefficient of determination (R²) of 0.75. Attention-based interpretability shows that the model assigns the highest weights to time steps with elevated precipitation; as lead time shortens, attention further concentrates on these periods, improving the accuracy of near-term, multi-step forecasts. These results highlight the value of inductive transfer across hydrologic targets and demonstrate that MT-TFT provides an effective, interpretable framework for SW–GW coupling. Full article

Obtaining evapotranspiration (ET) estimates at high spatiotemporal resolution is a fundamental prerequisite for clarifying the patterns and controlling factors of agricultural water consumption in drylands. However, most existing ET products are provided at daily or coarser spatial–temporal scales, which limits the ability to capture short-term variations in crop water use. This study developed a novel hourly 10-m ET estimation method that combines remote sensing with machine learning techniques. The approach was evaluated using agricultural sites in two arid regions: the Heihe River Basin in China and the Bekaa Valley in Lebanon. By integrating hourly eddy covariance measurements, Sentinel-2 reflectance data, and ERA5-Land reanalysis meteorological variables, we constructed an XGBoost-based modeling framework for hourly ET estimation, and incorporated the SHapley Additive exPlanations (SHAP) method for model interpretability analysis. Results demonstrated that the model achieved strong performance across all sites (R² = 0.86–0.91, RMSE = 0.04–0.05 mm·h⁻¹). Additional metrics, including the Nash–Sutcliffe efficiency coefficient (NSE) and percent bias (PBIAS), further confirmed the model’s robustness. Interpreting the model with SHAP highlighted net radiation (Rn), 2-m temperature (t2m), and near-infrared reflectance of vegetation (NIRv) as the dominant factors controlling hourly ET variations. Significant interaction effects, such as Rn × NIRv and Rn × t2m, were also identified, revealing the modulation mechanism of energy, vegetation status and temperature coupling on hourly ET. The study offers a practical workflow and an interpretable framework for generating high-resolution ET maps, thereby supporting regional water accounting and land–atmosphere interaction research. Full article

Understanding the spatiotemporal dynamics of vegetation and their driving mechanisms is essential for ecological assessment and management. However, current assessments of the Heihe River Basin (HRB) vegetation dynamics remain uncertain due to reliance on single indices without cross-validation and oversimplified attribution of residual variations. Here, we integrated four complementary vegetation indices (NDVI, EVI, kNDVI, and NIRv) with trend and abrupt change detection analyses to establish a framework for assessing vegetation changes in the HRB from 2004 to 2023. Given that the dominance of non-climatic factors is widely attributed to human water management and land use policies, land use change and other anthropogenic factors were incorporated together with topographic/edaphic factors into the optimal parameter-based geographical detector (OPGD), where vegetation changes induced by non-climatic factors were first isolated through residual trend analysis, thereby quantifying their explanatory power on vegetation index variations. The results demonstrate that vegetation in the HRB experienced a fluctuating upward trend (0.0013/yr) from 2004 to 2023, with significant improvement in 43% and degradation in 3% of the region. Climatic and non-climatic factors explained 26% and 74% of spatial variation, dominated by precipitation and land use change, respectively. Notably, the interaction of land use change and elevation accounted for 56% of NIRv variation, markedly exceeding single factors, as determined by the interaction detector in the OPGD. Additionally, large-scale ecological restoration projects and effective water resource management policies have played a pivotal role in facilitating vegetation recovery across the basin. This study enhances insight into vegetation dynamics and supports both sustainable restoration and development in the HRB. Full article

Accurately predicting soil particle size fractions (PSFs) and classifying soil texture types are essential for soil resource assessment and sustainable land management. PSFs, comprising clay, silt, and sand, form a compositional dataset constrained to sum to 100%. The practical implications of incorporating compositional data characteristics into PSF mapping remain insufficiently explored. This study applies a two-point machine learning (TPML) model, integrating spatial autocorrelation and attribute similarity, to enhance both the quantitative prediction of PSFs and the categorical classification of soil texture types in the Heihe River Basin, China. TPML was compared with random forest regression kriging (RFRK), random forest (RF), XGBoost, and ordinary kriging (OK), and a novel TPML-C model was developed for multi-class classification tasks. Results show that TPML achieved R² values of 0.58, 0.55, and 0.64 for clay, silt, and sand, respectively. Among all models, the ALR_TPML predictions showed the most consistent agreement with the observed variability, with predicted ranges of 2.63–98.28% for silt, 0.26–36.16% for clay, and 0.64–96.90% for sand. Across all models, the dominant soil texture types were identified as Sandy Loam (SaLo), Loamy Sand (LoSa), and Silty Loam (SiLo). For soil texture classification, TPML with raw, ALR-, and ILR-transformed data reached right ratios of 61.09%, 55.78%, and 60.00%, correctly identifying 25, 26, and 27 types out of 43. TPML with raw data exhibited strong performance in both regression and classification, with superior ability to separate ambiguous boundaries. Log-ratio transformations, particularly ILR, further improved classification performance by addressing the constraints of compositional data. These findings demonstrate the promise of hybrid machine learning approaches for digital soil mapping and precision agriculture. Full article

Climate change intensifies the challenges surrounding water cycling and vegetation dynamics in arid desert ecosystems, calling for detailed observations to decode adaptive plant strategies and support restoration efforts. This study analyzes interannual variations in water budgets and vegetation coverage in two distinct desert systems—K. foliatum (midstream) and R. songarica (downstream)—within the Heihe River Basin from 2016 to 2021. We uncover a pronounced ecohydrological contrast: the K. foliatum ecosystem displays substantial soil moisture variability alongside high precipitation and evapotranspiration rates, leading to a soil water deficit. In contrast, the R. songarica ecosystem maintains minimal moisture fluctuation under extreme aridity, yet records a slight water surplus. Notably, vegetation coverage in K. foliatum closely correlates with soil water storage, precipitation, and evapotranspiration, whereas R. songarica exhibits no significant hydrological coupling, implying a pulsed response to episodic rainfall. Groundwater recharge emerges as a key compensatory mechanism against rainfall shortages in midstream regions. These findings underscore the need for region-specific management—prioritizing groundwater conservation downstream and intelligent irrigation regulation midstream—offering a science-backed pathway for restoring and managing water resources in arid inland basins under climate change. Full article

Oases are the core carriers of societal development in arid regions, and their spatial patterns have changed significantly, driven by climate change and anthropogenic activities. This study integrates historical documents, archeological materials, maps, and remote sensing data. The changes in the temperature, precipitation, settlements, war frequency, and oasis area were identified by combining quantitative and qualitative methods, and the partial least squares path model (PLS-PM) was utilized to quantify the natural and human driving factors. The results show that the oasis development in the Tarim and Heihe River Basins exhibits distinct spatio-temporal variability and phased characteristics and is comprehensively shaped by both natural and anthropogenic drivers. The Tarim Basin’s natural oases demonstrate a “fluctuating recovery” pattern. The cultivated oases gradually expanded. The natural oases within the Heihe River Basin have persistently decreased, and cultivated oases show a “U”-shaped evolution pattern. This reflects the strong intervention of human reclamation in the cultivated oases. The introverted social ecosystem has endowed the Tarim River Basin with the ability to self-repair and achieve a periodic recovery. The Heihe River Basin serves as a strategic corridor for national external engagement, relying on regime stability. A regime collapse led to its lack of a stable recovery period. The PLS-PM reveals that the Tarim River Basin oasis evolution is predominantly driven by climate fluctuations. The path coefficient of natural factors for artificial oases is 0.63, and extreme drought leads to natural oasis contraction. The human influence dominates the Heihe River Basin, with a −0.93 path coefficient linking the cultivated oasis area to human factors. The frequency of wars (load 0.74) and changes in settlements (load −0.92) are the key factors. This study provides a powerful case for the analysis of the evolution and driving mechanism of future oases in drylands. Full article

Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table dynamics, and their governing factors. This study develops a novel remote sensing framework to quantify factor controls on groundwater–climate interaction characteristics in the Heihe River Basin (HRB). High-resolution (0.005° × 0.005°) maps of groundwater response time (GRT) and water table ratio (WTR) were generated using multi-source geospatial data. Employing Geographical Convergent Cross Mapping (GCCM), we established causal relationships between GRT/WTR and their drivers, identifying key influences on groundwater dynamics. Generalized Additive Models (GAM) further quantified the relative contributions of climatic (precipitation, temperature), topographic (DEM, TWI), geologic (hydraulic conductivity, porosity, vadose zone thickness), and vegetative (NDVI, root depth, soil water) factors to GRT/WTR variability. Results indicate an average GRT of ~6.5 × 10⁸ years, with 7.36% of HRB exhibiting sub-century response times and 85.23% exceeding 1000 years. Recharge control dominates shrublands, wetlands, and croplands (WTR < 1), while topography control prevails in forests and barelands (WTR > 1). Key factors collectively explain 86.7% (GRT) and 75.9% (WTR) of observed variance, with spatial GRT variability driven primarily by hydraulic conductivity (34.3%), vadose zone thickness (13.5%), and precipitation (10.8%), while WTR variation is controlled by vadose zone thickness (19.2%), topographic wetness index (16.0%), and temperature (9.6%). These findings provide a scientifically rigorous basis for prioritizing groundwater conservation zones and designing climate-resilient water management policies in arid endorheic basins, with our high-resolution causal attribution framework offering transferable methodologies for global groundwater vulnerability assessments. Full article

Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 m, leveraging auxiliary variables including vegetation indices, terrain parameters, and land surface reflectance. By establishing non-linear relationships between LST and predictive variables through eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, the proposed framework was rigorously validated using in situ measurements across China’s Heihe River Basin. Comparative analyses demonstrated that integrating multiple vegetation indices (e.g., NDVI, SAVI) with terrain factors yielded superior accuracy compared to factors utilizing land surface reflectance or excessive variable combinations. While slope and aspect parameters marginally improved accuracy in mountainous regions, including them degraded performance in flat terrain. Notably, land surface reflectance proved to be ineffective in snow/ice-covered areas, highlighting the need for specialized treatment in cryospheric environments. This work provides a reference for LST downscaling, with significant implications for environmental monitoring and urban heat island investigations. Full article

Land Surface Temperature (LST), as a core variable in the coupling of land–atmosphere energy transfers and ecological responses, relies heavily on the global coverage capacity of thermal infrared remote sensing (TIR-LST) for dynamic monitoring. Currently, the time reconstruction method of the TIR-LST products from China’s Fengyun polar-orbiting satellite under dynamic cloud interference remains under exploration. This study focuses on the Heihe River Basin in western China, and addresses the issue of cloud coverage in relation to the Fengyun-3C (FY-3C) satellite TIR-LST. An innovative spatiotemporal reconstruction framework based on multi-source data collaboration was developed. Using a hybrid ensemble learning framework of random forest and ridge regression, environmental parameters such as vegetation index (NDVI), land cover type (LC), digital elevation model (DEM), and terrain slope were integrated. A downscaling and multi-factor collaborative representation model for land surface temperature was constructed, thereby integrating the passive microwave LST and thermal infrared VIRR-LST from the FY-3C satellite. This produced a seamless LST dataset with 1 km resolution for the period of 2017–2019, with temporal continuity across space. The validation results show that the reconstructed data significantly improves accuracy compared to the original VIRR-LST and demonstrates notable spatiotemporal consistency with MODIS LST at the daily scale (annual R² ≥ 0.88, RMSE < 2.3 K). This method successfully reconstructed the FY-3C satellite’s 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking. Full article