Search Results (5,667)

Albedo is a primary driver of the glacier surface energy balance and consequent melting. As glacier albedo decreases, it further accelerates glacier melting. Over the past 20 years, glaciers on the Tibetan Plateau have experienced significant melting. However, our understanding of the variations in glacier albedo and its driving factors in this region remains limited. This study used MOD10A1 data to examine the average characteristics and variations in glacier albedo on the Tibetan Plateau from 2001 to 2022; the MOD10A1 snow cover product, developed at the National Snow and Ice Data Center, was employed to analyze spatiotemporal variations in surface albedo. The results indicate that the albedo values of glaciers on the Tibetan Plateau predominantly range between 0.50 and 0.60, with distinctly higher albedo in spring and winter, and lower albedo in summer and autumn. Glacier albedo on the Tibetan Plateau decreased at an average linear regression rate of 0.06 × 10⁻² yr⁻¹ over the past two decades, with the fastest declines occurring in autumn at an average rate of 0.18 × 10⁻² yr⁻¹, contributing to the prolongation of the melting period. Furthermore, significant variations in albedo change rates with altitude were found near the snowline, which is attributed to the transformation of the snow and ice surface. The primary factors affecting glacier albedo on the Tibetan Plateau are temperature and snowfall, whereas in the Himalayas, black carbon and dust primarily influence glacier albedo. Our findings reveal a clear decrease in glacier albedo on the Tibetan Plateau and demonstrate that seasonal and spatial variations in albedo and temperature are the most important driving factors. These insights provide valuable information for further investigation into surface albedo and glacier melt. Full article

The Grain for Green Program (GFGP) plays a critical role in enhancing watershed vegetation cover. Analyzing changes in vegetation cover provides significant practical value in guiding ecological conservation and restoration in vulnerable regions. This study utilizes MOD13Q1 NDVI data to construct the Kernel Normalized Difference Vegetation Index (kNDVI) and analyzes the spatiotemporal evolution and future trends of vegetation cover from 2000 to 2020, covering key periods of the GFGP. The study innovatively combines the optimal parameter geographic detector with constraint lines to comprehensively reveal the nonlinear constraints, intensities, and critical thresholds imposed by various driving factors on the kNDVI. The results indicate that the following: (1) The vegetation cover of the Luo River Basin increased significantly between 2000 and 2020, with a noticeable increase in the percentage of high-quality vegetation. Spatially, the vegetation cover followed a pattern of being “high in the southwest and low in the northeast”, with 73.69% of the region displaying improved vegetation conditions. Future vegetation degradation is predicted to threaten 59.40% of the region, showing a continuous or future declining trend. (2) The primary driving factors for changes in the vegetation cover are evapotranspiration, elevation, population density, and geomorphology type, with temperatures and GDP being secondary factors. Dual-factor enhancement or nonlinear enhancement was observed in interactions among the factors, with evapotranspiration and population density having the largest interaction (q = 0.76). (3) The effects of driving factors on vegetation exhibited various patterns, with thresholds existing for the hump-shaped and concave-waved types. The stability of the kNDVI in 40.23% of the areas showed moderate to high fluctuations, with the most significant fluctuations observed in low-altitude and high-temperature areas, as well as those impacted by dense human activities. (4) By overlaying the kNDVI classifications on the GFGP areas, priority reforestation areas totaling 68.27 km² were identified. The findings can help decisionmakers optimize the next phase of the GFGP and in effective regional ecological management. Full article

Land use and land cover (LULC) changes driven by ecological restoration and protection projects play a pivotal role in reshaping landscape patterns. However, the specific impacts of these projects on landscape structure remain understudied. In this research, we applied geographically weighted regression (GWR) to analyze the spatial relationships between typical land use expansion and landscape pattern characteristics in the Lesser Khingan Mountains–Sanjiang Plain region between 2017 and 2022. Our results indicate three key findings: (1) Significant spatial heterogeneity exists in the relationship between landscape patterns and land use expansion, which varies across geographic locations; (2) Ecological restoration projects generally reduce fragmentation, dominance, and heterogeneity while enhancing connectivity, particularly in forest and farmland regions. However, excessive land use expansion in certain areas may reverse these positive effects; (3) Landscape complexity increases in high-altitude mountainous regions due to land use expansion but decreases in plains, particularly in forest-to-farmland conversions. These findings provide new insights into how landscape patterns respond to ecological restoration efforts and offer actionable guidance for improving future land use planning and policy decisions. Our study highlights the need to consider local geomorphological factors when designing ecological projects, ensuring that restoration efforts align with regional landscape dynamics to maintain landscape integrity. Full article

In this current paper, we propose to study a three-dimensional Moran model (Xn(1),Xn(2),Xn(3)), where each random walk (Xn(i))∈{1,2,3} increases by one unit or is reset to zero at each unit of time. We analyze the joint law of its final altitude Xn=max(Xn(1),Xn(2),Xn(3)) via the moment generating tools. Furthermore, we show that the limit distribution of each random walk follows a shifted geometric distribution with parameter 1−qi, and we analyze the maximum of these three walks, also giving explicit expressions for the mean and variance. Full article

Accurate forecasting of high-resolution particulate matter 2.5 (PM_2.5) levels is essential for the development of public health policy. However, datasets used for this purpose often contain missing observations. This study presents a two-stage approach to handle this problem. The first stage is a multivariate spatial time series (MSTS) model, used to generate forecasts for the sampled spatial units and to impute missing observations. The MSTS model utilizes the similarities between the temporal patterns of the time series of the spatial units to impute the missing data across space. The second stage is the high-resolution prediction model, which generates predictions that cover the entire study domain. The second stage faces the big N problem giving rise to complex memory and computational problems. As a solution to the big N problem, we propose a Gaussian Markov random field (GMRF) for innovations with the Matérn covariance matrix obtained from the corresponding Gaussian field (GF) matrix by means of the stochastic partial differential equation (SPDE) method and the finite element method (FEM). For inference, we propose Bayesian statistics and integrated nested Laplace approximation (INLA) in the R-INLA package. The above approach is demonstrated using daily data collected from 13 PM_2.5 monitoring stations in Jakarta Province, Indonesia, for 1 January–31 December 2022. The first stage of the model generates PM_2.5 forecasts for the 13 monitoring stations for the period 1–31 January 2023, imputing missing data by means of the MSTS model. To capture temporal trends in the PM_2.5 concentrations, the model applies a first-order autoregressive process and a seasonal process. The second stage involves creating a high-resolution map for the period 1–31 January 2023, for sampled and non-sampled spatiotemporal units. It uses the MSTS-generated PM_2.5 predictions for the sampled spatiotemporal units and observations of the covariate’s altitude, population density, and rainfall for sampled and non-samples spatiotemporal units. For the spatially correlated random effects, we apply a first-order random walk process. The validation of out-of-sample forecasts indicates a strong model fit with low mean squared error (0.001), mean absolute error (0.037), and mean absolute percentage error (0.041), and a high R² value (0.855). The analysis reveals that altitude and precipitation negatively impact PM_2.5 concentrations, while population density has a positive effect. Specifically, a one-meter increase in altitude is linked to a 7.8% decrease in PM_2.5, while a one-person increase in population density leads to a 7.0% rise in PM_2.5. Additionally, a one-millimeter increase in rainfall corresponds to a 3.9% decrease in PM_2.5. The paper makes a valuable contribution to the field of forecasting high-resolution PM_2.5 levels, which is essential for providing detailed, accurate information for public health policy. The approach presents a new and innovative method for addressing the problem of missing data and high-resolution forecasting. Full article

This study builds on previous research into the basic flow structure of a separated curved swept compression ramp shock wave/turbulence boundary layer interaction (CSCR-SWBLI) at the leading edge of an inward-turning inlet. We employ the ice-cluster-based planar laser scattering (IC-PLS) technique, which integrates multiple observation directions and positions, to experimentally investigate a physical model with typical parameter states at a freestream Mach number of 2.85. This study captures the fine structure of some sections of the flow field and identifies the presence of Görtler vortices (GVs) in the CSCR-SWBLI. It is observed that due to the characteristics of variable sweep angle, variable intensity interaction, and centrifugal force, GVs exhibit strong three-dimensional characteristics in the curved section. Additionally, their position is not fixed in the spanwise direction, demonstrating strong intermittence. As the vortices develop downstream, their size gradually increases while the number decreases, always corresponding to the local boundary layer thickness. When considering the effects of coupling of bilateral walls, it is noted that the main difference between double-sided coupling and single-sided uncoupling conditions is the presence of a large-scale vortex in the central plane and an odd number of GVs in the double-sided model. Finally, the existence of GVs in CSCR-SWBLI is verified through the classical determine criteria Görtler number (G_T) and Floryan number (F) decision basis. Full article

This study describes the implementation of Internet of Things (IoT) sensors in flow meters installed in drinking water systems and in fog catchers built in low-income, high-altitude communities in the Andes region of Ecuador, taking studies at the University de las Fuerzas Armadas ESPE as our reference. The influence and management of these intelligent sensors are analyzed, as well as a basic review of the materials and methods used in their implementation. The importance of validating the accuracy and reliability of IoT sensors compared to professional devices is highlighted, especially in mountain areas with difficult access. Additionally, the cost–benefit of using IoT sensors in fog catchers and drinking water distribution networks is mentioned, which depends on several factors such as the scale of the project, the objectives to be achieved concerning monitoring, and the available resources. Finally, it is highlighted that using Internet of Things (IoT) sensors in construction and water collection systems has proven beneficial in detecting possible effects on its operation and determining consumption and supply flows for a given population. Full article

The interaction between the natural environmental and socioeconomic factors is crucial for assessing the dynamics of plateau ecosystems. Therefore, the remote sensing ecological index (RSEI) and CatBoost-SHAP model were employed to investigate changes in the ecological quality and their driving factors in the Diqing Tibetan Autonomous Prefecture, China, from 2001 to 2021. The results showed an increase from 0.44 in 2001 to 0.71 in 2021 in the average RSEI for the Diqing Prefecture, indicating an overall upward trend in the ecological quality. Spatial analysis shows the percentage of the area covered by different levels of RSEI and their temporal changes. The results revealed that “good” ecological quality accounted for the largest proportion of the study area, at 42.77%, followed by “moderate” at 21.93%, and “excellent” at 16.62%. “Fair” quality areas accounted for 16.11% and “poor” quality areas only 2.57%. The study of ecological and socioeconomic drivers based on the CatBoost-SHAP framework also indicated that natural climate factors have a greater impact on ecological quality than socioeconomic factors; however, this effect differed significantly with altitude. The findings suggest that, in addition to strengthening climate monitoring, further advancements in ecological engineering are required to ensure the sustainable development of the ecosystem and the continuous improvement of the environmental quality in the Diqing Prefecture. Full article

In low-altitude, GNSS-denied scenarios, Unmanned aerial vehicles (UAVs) rely on sensor fusion for self-localization. This article presents a resilient multi-sensor fusion localization system that integrates light detection and ranging (LiDAR), cameras, and inertial measurement units (IMUs) to achieve state estimation for UAVs. To address challenging environments, especially unstructured ones, IMU predictions are used to compensate for pose estimation in the visual and LiDAR components. Specifically, the accuracy of IMU predictions is enhanced by increasing the correction frequency of IMU bias through data integration from the LiDAR and visual modules. To reduce the impact of random errors and measurement noise in LiDAR points on visual depth measurement, cross-validation of visual feature depth is performed using reprojection error to eliminate outliers. Additionally, a structure monitor is introduced to switch operation modes in hybrid point cloud registration, ensuring accurate state estimation in both structured and unstructured environments. In unstructured scenes, a geometric primitive capable of representing irregular planes is employed for point-to-surface registration, along with a novel pose-solving method to estimate the UAV’s pose. Both private and public datasets collected by UAVs validate the proposed system, proving that it outperforms state-of-the-art algorithms by at least 12.6%. Full article

In oxygen production on plateaus, pressure swing adsorption (PSA) oxygen production is currently the most commonly used oxygen production method. In plateau regions, low pressure leads to a decrease in adsorbent nitrogen–oxygen separation performance, which affects the performance of PSA oxygen production, so it is particularly important to enhance adsorbent nitrogen–oxygen separation performance. In this paper, Li-LSX (lithium low-silicon aluminum X zeolite molecular sieve) adsorbents were modified using the liquid phase ion exchange method, and five kinds of modified adsorbents were obtained, namely AgLi-LSX, CaLi-LSX, ZnLi-LSX, CuLi-LSX, and FeLi-LSX, respectively. The influences of different metal ions and modification time lengths on the adsorbent nitrogen adsorption and nitrogen–oxygen separation coefficients were analyzed. Through theoretical calculations, the nitrogen and oxygen adsorption and separation performances of the modified adsorbents at different altitudes and low adsorption pressures were investigated. It is shown that the nitrogen adsorption capacity of the AgLi-LSX-1 adsorbent obtained from the modification experiment reaches 27.92 mL/g, which is 3.24 mL/g higher than that of Li-LSX; the nitrogen–oxygen separation coefficients of S1 and S2 are 19.24 and 7.54 higher, respectively; and the nitrogen–oxygen separation coefficients of S4 are 20.85 and 7.54 higher than those of Li-LSX, respectively. With the increase in altitude from 50 m to 5000 m, the nitrogen–oxygen separation coefficient of the AgLi-LSX-1 adsorbent increased rapidly from 20.85 to 57, and its nitrogen–oxygen separation coefficient S4 exceeded that of the Li-LSX adsorbent to reach 47.61 at an altitude of 4000 m. Therefore, the modified adsorbent AgLi-LSX-1 in this paper can enhance the performance of the PSA oxygen process for oxygen production in plateau applications. Full article

For advanced aircraft, the temperature environment inside the cabin is very severe due to the high flight speed and the compact concentration of the electronic equipment in the cabin. Accurately predicting the temperature environment induced inside the cabin during the flight of the aircraft can determine the temperature environment requirements of the onboard equipment inside the cabin and provide an accurate input for the thermal design optimization and test verification of the equipment. The temperature environment of the whole aircraft is divided into zones by the cluster analysis method; the heat transfer mechanism of the aircraft cabin is analyzed for the target area; and the influence of internal and external factors on the thermal environment is considered to establish the temperature environment prediction model of the target cabin. The coefficients of the equations in the model are parameterized to extract the long-term stable terms and trend change terms; with the help of the measured data of the flight state, the model coefficients are determined by a stepwise regression method; and the temperature value inside the aircraft cabin is the output by inputting parameters such as flight altitude, flight speed, and external temperature. The model validation results show that the established temperature environment prediction model can accurately predict the change curve of the cabin temperature during the flight of the aircraft, and the model has a good follow-up performance, which reduces the prediction error caused by the temperature hysteresis effect. For an aircraft, the estimated error is 2.8 °C at a confidence level of 95%. Engineering cases show that the application of this method can increase the thermal design requirements of the airborne equipment by 15 °C, increase the low-temperature test conditions by 17 °C, and avoid the problems caused by an insufficient design and over-testing. This method can accurately predict the internal temperature distribution of the cabin during the flight state of the aircraft, help designers determine the thermal design requirements of the airborne equipment, modify the thermal design and temperature test profile, and improve the environmental worth of the equipment. Full article

Archaeological investigations worldwide have focused on when and how humans permanently settled in high-altitude environments. Recent evidence from Xiada Co, Qusongguo, and Dingzhonghuzhuzi in western Tibet, where lithic artifacts and radiocarbon dates with original deposits were first accessed, provides new insights into human activities in this extreme environment during the early Holocene. This paper examines the mobility and land-use patterns of foragers in western Tibet from the perspectives of lithic analysis. Assemblages from three sites suggest homogenous technologies and raw material use, as well as potential interaction network of hunter-gatherers within the plateau during the early Holocene. It further argues that the material exponents and travel cost models of site location supported permanent occupation of the western Tibetan Plateau in this period. Full article

Black locust (Robinia pseudoacacia L.), one of the major afforestation species adopted in vegetation restoration, is notable for its rapid root growth and drought resistance. It plays a vital role in improving the natural environment and soil fertility, contributing significantly to soil and water conservation and biodiversity protection. However, compared with natural forests, due to the low diversity, simple structure and poor stability, planted forests including Robinia pseudoacacia L. are more sensitive to the changing climate, especially in the aspects of growth trend and adaptive range. Studying the ecological characteristics and geographical boundaries of Robinia pseudoacacia L. is therefore important to explore the adaptation of suitable niches to climate change. Here, based on 162 effective distribution records in China and 22 environmental variables, the potential distribution of suitable niches for Robinia pseudoacacia L. plantations in past, present and future climates was simulated by using a Maximum Entropy (MaxEnt) model. The results showed that the accuracy of the MaxEnt model was excellent and the area under the curve (AUC) value reached 0.937. Key environmental factors constraining the distribution and suitable intervals were identified, and the geographical distribution and area changes of Robinia pseudoacacia L. plantations in future climate scenarios were also predicted. The results showed that the current suitable niches for Robinia pseudoacacia L. plantations covered 9.2 × 10⁵ km², mainly distributed in the Loess Plateau, Huai River Basin, Sichuan Basin, eastern part of the Yunnan–Guizhou Plateau, Shandong Peninsula, and Liaodong Peninsula. The main environmental variables constraining the distribution included the mean temperature of the driest quarter, precipitation of driest the quarter, temperature seasonality and altitude. Among them, the temperature of the driest quarter was the most important factor. Over the past 90 years, the suitable niches in the Sichuan Basin and Yunnan–Guizhou Plateau have not changed significantly, while the suitable niches north of the Qinling Mountains have expanded northward by 2° and the eastern area of Liaoning Province has expanded northward by 1.2°. In future climate scenarios, the potential suitable niches for Robinia pseudoacacia L. are expected to expand significantly in both the periods 2041–2060 and 2061–2080, with a notable increase in highly suitable niches, widely distributed in southern China. A warning was issued for the native vegetation in the above-mentioned areas. This work will be beneficial for developing reasonable afforestation strategies and understanding the adaptability of planted forests to climate change. Full article

As a key component of the ecosystem, soil seed banks (SSBs) play a vital role in the evolution and renewal of plant communities. Although the pattern and mechanisms of influence of SSBs along the altitudinal gradient have been reported, most studies have focused on forest, grassland and alpine meadow ecosystems. The pattern and factors of SSBs across the altitudinal gradient in sedge peatlands remain largely unknown. Through vegetation surveys and seed germination experiments, we studied the changes in aboveground vegetation and SSBs in sedge peatlands at altitudes ranging from 300 m to 1300 m in the Changbai Mountains, China, and discussed the direct and indirect effects of climatic factors, soil properties and aboveground vegetation on SSBs. The results showed that the richness and density of the SSBs of sedge peatlands decreased with the altitude. Similarly, aboveground vegetation richness and density declined with altitude. A Spearman correlation analysis showed that SSB richness and density were mainly correlated with mean annual temperature, soil total phosphorus and ammonia nitrogen and the plant composition and richness of aboveground vegetation. A structural equation model analysis showed that climatic factors and aboveground vegetation directly affected seed bank richness, while soil properties indirectly affected it by directly affecting aboveground vegetation. Climatic factors, soil properties and aboveground vegetation directly affected SSB density, and soil properties indirectly affected it by directly affecting aboveground vegetation. This finding enhances our understanding of the altitude patterns of the SSBs in sedge peatlands and the response to future climate and environmental changes. Full article

The East Kunlun Orogenic Belt is an essential part of the Qin-Qikun composite orogenic system, the most crucial orogenic belt in Qinghai Province, and an important gold ore-producing area in China. The Gouli gold field in its eastern section is one of the most important gold fields discovered in the belt in recent years. The Mailong mining area is an important gold mining area in the Gouli ore-concentrated area. The area has experienced frequent and intense magmatic activity, with intrusive rock bodies extensively exposed and intersected by a complex network of fault structures, providing excellent geological conditions for the formation of gold deposits. However, it is difficult to explore due to high altitude, poor transportation, and shallow coverage. This study used an audio magnetotelluric sounding method to track the deep direction and inclination of known mineral belts in the Mailong mining area, and identified mineral exploration targets, providing a basis for mineral exploration. Subsequently, a gold ore body was discovered through drilling verification, achieving a breakthrough in deep mineral exploration. The electromagnetic exploration method works well for exploring structurally altered rock-type gold deposits in plateau desert areas, and combined with the results of this electromagnetic exploration, a metallogenic geological model and genesis process of the Mailong mining area has been constructed. Full article