Search Results (372)

Alpine grassland degradation in permafrost regions seriously affects the provision of ecosystem services, posing a threat to ecological security. Revegetation is a key strategy for the restoration of alpine grassland ecosystems on the Qinghai–Tibetan Plateau (QTP). However, there is a lack of comprehensive research evaluating ecosystem services after revegetation, especially in permafrost regions. In this study, we assessed the changes in ecosystem services following revegetation in the alpine permafrost regions of the QTP through on-site monitoring and sampling, using extremely degraded alpine grassland as a control. In addition, we analyzed trade-offs among ecosystem services and identified key drivers. Our results indicate that (1) revegetation significantly increased forage supply, carbon storage, and soil retention values (p < 0.05), while water retention and permafrost stability showed no significant changes (p > 0.05); (2) vegetation restoration effectively reduced the trade-offs among ecosystem services; and (3) the main drivers were vegetation coverage, precipitation, belowground biomass, and restoration duration. Overall, this study demonstrates that revegetation improves ecosystem services. The enhancement in these services provides valuable data for future research on ecosystem services in alpine grassland. Full article

The functional relationship between soil permittivity and soil water content serves as the theoretical foundation for electromagnetic wave-based techniques used to determine soil moisture levels. However, the response of permittivity to changes in soil water content varies significantly across different soil types. Current models that utilize soil permittivity to estimate soil water content are often based on empirical statistical relationships specific to particular soil types. Moreover, existing physical models are hindered by an excessive number of parameters, which can be difficult to measure or calculate. This study introduces a universal model, termed the Soil Refractive Index (SRI) model, to describe the relationship between soil permittivity and soil water content. The SRI model is derived from the propagation velocity of electromagnetic waves in various soil components and the functional relationship between electromagnetic wave velocity and relative permittivity. The SRI model expresses soil water content as a linear function of the square root of the relative permittivity for any soil type with the slope and intercept as the two undetermined parameters. The slope is primarily influenced by the relative permittivity of soil water, while the intercept is mainly affected by both the slope and the soil porosity. The applicability of the SRI model is validated through tested soil samples and comparison with previously published empirical statistical models. For dielectric lossless soil, the theoretical value of the slope is calculated to be 0.126. The intercept varies across different soil types and increases linearly with soil porosity. The SRI model provides a theoretical basis for calculating soil water content using permittivity across various soil types. Full article

The accurate modeling of complex freeze–thaw processes and hydrothermal dynamics within the active layer is challenging. Due to the uncertainty in hydrothermal simulation, it is necessary to thoroughly investigate the parameterization schemes in land surface models. The Noah-MP was utilized in this study to conduct 23,040 ensemble experiments based on 11 physical processes, which were aimed at improving the understanding of parameterization schemes and reducing model uncertainty. Next, the impacts of uncertainty of physical processes on land surface modeling were evaluated via Natural Selection and Tukey’s test. Finally, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to identify the optimal combination of parameterization schemes for improving hydrothermal simulation. The results of Tukey’s test agreed well with those of Natural Selection for most soil layers. More importantly, Tukey’s test identified more parameterization schemes with consistent model performance for both soil temperature and moisture. Results from TOPSIS showed that the determination of optimal schemes was consistent for the simulation of soil temperature and moisture in each physical process except for frozen soil permeability (INF). Further analysis showed that scheme 2 of INF yielded better simulation results than scheme 1. The improvement of the optimal scheme combination during the frozen period was more significant than that during the thawed period. Full article

The northern permafrost regions are increasingly experiencing frequent and intense extreme events, with a rise in the occurrence of compound extreme events. Many climate-related hazards in these areas are driven by such compound events, significantly affecting the stability and functionality of vegetation ecosystems. However, the cumulative and lagged effects of compound extreme events on vegetation remain unclear, which may lead to an underestimation of their actual impacts. This study provides a comprehensive analysis of the spatiotemporal variations in compound extreme events and the vegetation response to these events in the northern permafrost regions from 1982 to 2022. The primary focus of this study is on examining the cumulative and lagged effects of compound extreme climate events on the Kernel Normalized Difference Vegetation Index (kNDVI) during the growing seasons. The results indicate that in high-latitude regions, the frequency of extreme high temperature–precipitation compound events and high temperature–drought compound events have increased in 58.0% and 67.0% of the areas, respectively. Conversely, the frequency of extreme low temperature–drought compound events and extreme low temperature–precipitation compound events has decreased in 70.6% and 57.2% of the areas, with the high temperature–drought compound events showing the fastest increase. The temporal effects of compound extreme events on kNDVI vary with vegetation type; they produce more cumulative and lagged effects compared with single extreme high-temperature events and fewer effects compared with single extreme precipitation events, with compound events significantly affecting forest and grassland ecosystems. Notably, extreme high temperature–precipitation compound events exhibit the strongest cumulative and lagged effects on vegetation, while extreme low temperature–drought compound events influence wetland and shrubland areas within the same month. This study underscores the importance of a multivariable perspective in understanding vegetation dynamics in permafrost regions. Full article

In permafrost regions, vegetation growth is influenced by both climate conditions and the effects of permafrost degradation. Climate factors affect multiple aspects of the environment, while permafrost degradation has a significant impact on soil moisture and nutrient availability, both of which are crucial for ecosystem health and vegetation growth. However, the quantitative analysis of climate and permafrost remains largely unknown, hindering our ability to predict future vegetation changes in permafrost regions. Here, we used statistical methods to analyze the NDVI change in the permafrost region from 1982 to 2022. We employed correlation analysis, multiple regression residual analysis and partial least squares structural equation modeling (PLS-SEM) methods to examine the impacts of different environmental factors on NDVI changes. The results show that the average NDVI in the study area from 1982 to 2022 is 0.39, with NDVI values in 80% of the area remaining stable or exhibiting an increasing trend. NDVI had the highest correlation with air temperature, averaging 0.32, with active layer thickness coming in second at 0.25. Climate change plays a dominant role in NDVI variations, with a relative contribution rate of 89.6%. The changes in NDVI are positively influenced by air temperature, with correlation coefficients of 0.92. Although the active layer thickness accounted for only 7% of the NDVI changes, its influence demonstrated an increasing trend from 1982 to 2022. Overall, our results suggest that temperature is the primary factor influencing NDVI variations in this region. Full article

Climate change is resulting in significant transformations in mountain areas all over the world, causing the melting of glacier ice, reduction in snow accumulation, and permafrost loss. Changes in the mountain cryosphere are not only modifying flora and fauna distributions but also affecting the stability of slopes in those regions. For all these reasons, and because of the risks these phenomena pose to the population, the dentification of dangerous areas is a crucial step in the development of risk reduction strategies. While several methods and examples exist that cover the assessment and computation of single sub-components, there is still a lack of application of risk assessment due to glacier melting over large areas in which the final result can be directly employed in the design of risk mitigation policies at regional and municipal levels. This research is focused on landslides and gravitational movements on slopes resulting from rapid glacier melting phenomena in the Valle d’Aosta region in Italy, with the aim of providing a tool that can support spatial planning in response to climate change in Alpine environments. Through the conceptualization and development of a GIS-based and multi-criteria approach, risk is then estimated by defining hazard indices that consider different aspects, combining the experience acquired from studies carried out in various disciplinary fields, to obtain a framework at the regional level. This first assessment is then deepened for the Lys River Valley, where the mapping of hazardous areas was implemented, obtaining a classification of buildings according to their hazard score to estimate the potential damage and total risk relating to possible slope instability events due to ice melt at the local scale. Full article

Precipitation plays a critical role in the hydrological cycle and significantly influences the biodiversity of the Earth’s ecosystems. It also regulates socioeconomic systems by impacting agricultural production and water resources. Analyzing climate-driven changes in precipitation patterns is essential for understanding the hydrological cycle’s response to global warming. This study analyzed the projections of five general circulation models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to evaluate variations in the seasonal and annual patterns of future precipitation over the northern highlands of Pakistan (NHP). The analysis focused on precipitation variations projected for the near future (2021–2050), in comparison to the historical climate (1985–2014), utilizing two combined scenarios from the Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP2-4.5 and SSP5-8.5). This study employed the multi-model ensemble (MME) approach, which demonstrated notable seasonal and annual variations in precipitation across the NHP. The average annual precipitation is expected to decrease in both scenarios, with SSP2-4.5 expecting a reduction of −21.42% and SSP5-8.5 expecting a decrease of −22.43%, compared to the historical average precipitation. In both scenarios, the seasonal precipitation patterns are similar. However, the changes are more noticeable in the spring and summer. Both SSPs predict a 15% decrease in summer precipitation, while SSP2-4.5 and SSP5-8.5 predict a 5% and 4% decrease in spring precipitation, respectively. These changes can result in more frequent and intense periods of drought, which might adversely impact agriculture, human health, the environment, hydropower generation, and the surrounding ecosystem. This study provides important insights into projected seasonal and annual precipitation changes over the NHP, which is particularly susceptible to the effects of climate change. Thus, it is crucial to understand these predicted changes in precipitation in order to develop strategies for adapting to the climate, assuring water security, and promoting sustainable agricultural practices in this area. Full article

Due to carbon dioxide (CO₂) levels, driven by our reliance on fossil fuels and deforestation, the challenge of global warming looms ever larger. The need to keep the global temperature rise below 1.5 °C has never been more pressing, pushing us toward innovative solutions. Enter carbon capture, utilization, and storage (CCUS) technologies, our frontline defense in the fight against climate change. Imagine a world where CO₂, once a harbinger of environmental doom, is transformed into a tool for healing. This review takes you on a journey through the realm of CCUS, revealing how these technologies capture CO₂ from the very sources of our industrial and power activities, repurpose it, and lock it away in geological vaults. We explore the various methods of capture—post-combustion, oxy-fuel combustion, and membrane separation—each with their own strengths and challenges. But it is not just about science; economics play a crucial role. The costs of capturing, transporting, and storing CO₂ are substantial, but they come with the promise of a burgeoning market for CO₂-derived products. We delve into these financial aspects and look at how captured CO₂ can be repurposed for enhanced oil recovery, chemical manufacturing, and mineralization, turning waste into worth. We also examine the landscape of commercial-scale CCS projects, highlighting both global strides and regional nuances in their implementation. As we navigate through these advancements, we spotlight the potential of Artificial Intelligence (AI) to revolutionize CCUS processes, making them more efficient and cost-effective. In this sweeping review, we underscore the pivotal role of CCUS technologies in our global strategy to decarbonize and forge a path toward a sustainable future. Join us as we uncover how innovation, supportive policies, and public acceptance are paving the way for a cleaner, greener world. Full article

Accurate surface soil moisture (SM) data are crucial for agricultural management in Jiangsu Province, one of the major agricultural regions in China. However, the seasonal performance of different SM products in Jiangsu is still unknown. To address this, this study aims to evaluate the applicability of four L-band microwave remotely sensed SM products, namely, the Soil Moisture Active Passive Single-Channel Algorithm at Vertical Polarization Level 3 (SMAP SCA-V L3, hereafter SMAP-L3), SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), and SMAP-INRAE-BORDEAUX (SMAP-IB) in Jiangsu at the seasonal scale. In addition, the effects of dynamic environmental variables such as the leaf vegetation index (LAI), mean surface soil temperature (MSST), and mean surface soil wetness (MSSM) on the performance of the above products are investigated. The results indicate that all four SM products exhibit significant seasonal differences when evaluated against in situ observations between 2016 and 2022, with most products achieving their highest correlation (R) and unbiased root-mean-square difference (ubRMSD) scores during the autumn. Conversely, their performance significantly deteriorates in the summer, with ubRMSD values exceeding 0.06 m³/m³. SMOS-IC generally achieves better R values across all seasons but has limited temporal availability, while SMAP-IB typically has the lowest ubRMSD values, even reaching 0.03 m³/m³ during morning observation in the winter. Additionally, the sensitivity of different products’ skill metrics to environmental factors varies across seasons. For ubRMSD, SMAP-L3 shows a general increase with LAI across all four seasons, while SMAP-IB exhibits a notable increase as the soil becomes wetter in the summer. Conversely, wet conditions notably reduce the R values during autumn for most products. These findings are expected to offer valuable insights for the appropriate selection of products and the enhancement of SM retrieval algorithms. Full article

Permafrost peatlands are sensitive to changes in nitrogen levels because they are largely nitrogen-limited ecosystems. However, the microbial mechanisms by which the addition of nitrogen increases the emission of greenhouse gasses from permafrost peatlands remain unclear. This study was conducted to decipher the relationship between greenhouse gas emissions and soil microorganisms under nitrogen addition. Here, we performed a 154-day experimental investigation in order to assess the release of greenhouse gasses such as CO₂, CH₄, and N₂O from the soils. Additionally, we examined the correlation between the rates of these gas emissions and the presence of crucial microbial functional genes in the soil. The results showed that the addition of low (0.01 g kg⁻¹), medium (0.02 g kg⁻¹), and high (0.04 g kg⁻¹) levels of nitrogen increased the cumulative CO₂ emissions by 2.35%–90.42%, respectively. The cumulative emissions of CH₄ increased by 17.29%, 25.55% and 21.77%, respectively. The cumulative emissions of N₂O increased 2.97, 7.49 and 7.72-fold. The addition of nitrogen increased the abundance of functional genes in the bacteria, fungi, methanogens, denitrifying bacteria, and nitrogen-fixing bacteria in soil by modifying abiotic soil variables and providing sufficient substrates for microorganisms. The results indicated that the addition of nitrogen can significantly promote the emission of greenhouse gasses by increasing the abundance of functional microbial genes in the soil of permafrost peatlands. These findings highlight the importance of considering nitrogen deposition and the nitrogen released from thawing permafrost when predicting the future greenhouse gasses emitted from permafrost peatlands. Full article

Soil particle distribution is one of the basic parameters for many Earth system models, while the soil texture data are largely not available. This is especially true for complex terrains due to the difficulties in data acquisition. Here, we selected an area, Wenquan area, with rolling mountains and valleys, in the eastern Qinghai–Tibet Plateau (QTP) as the study area. Using the random forest model, we established quantitative models of silt, clay, and sand content, and environmental variables, including elevation, slope, aspect, plane curvature, slope curvature, topographic wetness index, NDVI, EVI, MAT, and MAP at different depths based on the survey data of 58 soil sample points. The results showed that sand content was the highest, accounting for more than 75% of the soil particles. Overall, the average values of clay and silt gradually decreased with increasing soil profile depth, while sand showed the opposite pattern. In terms of spatial distribution, clay and silt are higher in the southeast and lower in the northwest in each standard layer, while sand is just the opposite. The random forest regression model showed that vegetation condition was a controlling factor of soil particle size. These results showed that random forest applies to predicting the spatial distribution of soil particle sizes for areas with complex terrains. Full article

Understanding the stability of soil organic matter (SOM) is essential for making accurate predictions regarding carbon release rates. However, there is limited information on the role of chemical composition of dissolved organic matter (DOM) in SOM stability. To address this gap, the peatland soil profile in the discontinuous frozen soil region of Northeast China was selected as the focus of this research, and a comprehensive analysis was conducted on the differences between the molecular composition of DOM and the stability of SOM. The results indicate a significant carbon accumulation phenomenon near the permafrost table. Through analyses using TG-50, δ¹³C, and δ¹⁵N, it was determined that SOM near the permafrost table exhibits high stability, whereas SOM within the permafrost layer demonstrates poor stability. Investigations utilizing UV-vis, 3D-EEM, FT-IR, and ¹H-NMR technologies revealed that DOM near the permafrost table is of high quality and highly aromatic. Furthermore, compared to near the permafrost table, humic acid materials in the permafrost layer decreased by 17%, while protein materials increased by 17%. These findings offer a novel perspective on the understanding of SOM stability in peatland soil profiles within discontinuous permafrost regions. Full article

The Tianshan Expressway plays a crucial role in China’s “Belt and Road” strategy, yet the extreme climate of the Tianshan Mountains poses significant traffic safety risks, hindering local economic development. Efficient detection of hazardous road surface conditions (RSCs) is vital to address these challenges. The complexity and variability of RSCs in the region, exacerbated by harsh weather, make traditional surveillance methods inadequate for real-time monitoring. To overcome these limitations, a vision-based artificial intelligence approach is urgently needed to ensure effective, real-time detection of dangerous RSCs in the Tianshan road network. This paper analyzes the primary structures and architectures of mainstream neural networks and explores their performance for RSC recognition through a comprehensive set of experiments, filling a research gap. Additionally, T-Net, specifically designed for the Tianshan Expressway engineering project, is built upon the optimal architecture identified in this study. Leveraging the split-transform-merge structure paradigm and asymmetric convolution, the model excels in capturing detailed information by learning features across multiple dimensions and perspectives. Furthermore, the integration of channel, spatial, and multi-head attention modules enhances the weighting of key features, making the T-Net particularly effective in recognizing the characteristics of snow-covered and icy road surfaces. All models presented in this paper were trained on a custom RSC dataset, compiled from various sources. Experimental results indicate that the T-Net outperforms fourteen once state-of-the-art (SOTA) models and three models specifically designed for RSC recognition, with 97.44% accuracy and 9.79% loss on the validation set. Full article

Exploring the complex relationship between the freeze–thaw cycle and the surface energy budget (SEB) is crucial for deepening our comprehension of climate change. Drawing upon extensive field monitoring data of the Qinghai-Tibet Plateau, this study examines how surface energy accumulation influences the thawing depth. Combined with Community Land Model 5.0 (CLM5.0), a sensitivity test was designed to explore the interplay between the freeze–thaw cycle and the SEB. It is found that the freeze–thaw cycle process significantly alters the distribution of surface energy fluxes, intensifying energy exchange between the surface and atmosphere during phase transitions. In particular, an increase of 65.6% is observed in the ground heat flux during the freezing phase, which subsequently influences the sensible and latent heat fluxes. However, it should be noted that CLM5.0 has limitations in capturing the minor changes in soil moisture content and thermal conductivity during localized freezing events, resulting in an imprecise representation of the complex freeze–thaw dynamics in cold regions. Nevertheless, these results offer valuable insights and suggestions for improving the parameterization schemes of land surface models, enhancing the accuracy and applicability of remote sensing applications and climate research. Full article

This paper investigates the ability of a relatively low cost, commercially available uncrewed aerial vehicle (UAV), the DJI Mavic 3 Multispectral, to perform cryospheric research. The performance of this UAV, where applicable, is compared to a similar but higher cost system, the DJI Matrice 350, equipped with a Micasense RedEdge-MX Multispectral dual-camera system. The Mavic 3 Multispectral was tested at three field sites: the Lemon Creek Glacier, Juneau Icefield, AK; the Easton Glacier, Mt. Baker, WA; and Bagley Basin, Mt. Baker, WA. This UAV proved capable of mapping the spatial distribution of red snow algae on the surface of the Lemon Creek Glacier using both spectral indices and a random forest supervised classification method. The UAV was able to assess the timing of snowmelt and changes in suncup morphology on snow-covered areas within the Bagley Basin. Finally, the UAV was able to classify glacier surface features using a random forest algorithm with an overall accuracy of 68%. The major advantages of this UAV are its low weight, which allows it to be easily transported into the field, its low cost compared to other alternatives, and its ease of use. One limitation would be the omission of a blue multispectral band, which would have allowed it to more easily classify glacial ice and snow features. Full article