High-latitude Areas Research Articles

Ladder electrodialysis: Efficient up-concentration of lithium ion and its mechanisms behind

The development of lithium extraction technology from salt lakes has seen significant demand in recent years, driven by the surge in energy storage needs for lithium-ion batteries used in electric vehicles and renewable power plants. Currently, evaporation technologies such as Mechanical Vapor Recompression (MVR) and Multi-Effect Distillation (MED) are commonly employed to concentrate lithium chloride for subsequent lithium carbonate precipitation. However, these evaporation methods limit lithium yield and increase capital and operational costs, particularly in high latitude areas. Pressure-driven membrane processes like reverse osmosis are hindered by concentration polarization and cannot significantly increase lithium chloride concentration. This study proposes a new membrane stack configuration with a laddered compartment design, termed Ladder Electrodialysis (LED), which addresses the concentration polarization issue and achieves a lithium salt (LiCl) concentration of 16.39 % (196 g·L−1). Economic analysis shows that the energy consumption is only 0.42 kWh per kilogram of LiCl. Ladder electrodialysis is a novel salt concentration technology, with applications in brine valorization or disposal.

An improved framework for quantifying the contribution of climatic and anthropogenic factors to vegetation dynamics - A case study of China

Accurately quantifying the impact of climatic and anthropogenic factors on vegetation change is critical for developing and evaluating ecological management strategies. However, most presented studies typically ignore the climate temporal effects and assume that all pixels are affected by both climate change (CC) and human activities (HA), which often introduce uncertainties. In this study, Leaf area index (LAI), temperature, precipitation, solar radiation, and land cover data from 1982 to 2020 were used to detect and attribute vegetation dynamics in China. We used partial correlation analysis, generalize linear model, trend analysis, and improved RESTREND to analyze the spatiotemporal variation characteristics of vegetation LAI from 1982 to 2020 and to quantify the effects of CC and HA on vegetation dynamics since the implementation of the Grain for Green Project (GGP). The results indicate that significant vegetation greening appeared in most areas (66.2%) between 2000 and 2020. Both CC and HA have positive effects on vegetation greening. In arid and semi-arid regions, precipitation was the primary driver of vegetation change, while in high-latitude areas of southern and southwestern China, temperature was the primary determinant. After considering the temporal effects, the explanatory power of climate variables for vegetation dynamics increased by 4.0% compared to ignoring the temporal effects, accounting for 72.81%. After dividing the pixels into those affected by CC and those affected by both CC and HA, the contribution of HA was decreased from 31.19% to 25.96%. Although the contribution of HA is lower than that of CC, ecological engineering has effectively promoted vegetation greening. These research findings provide scientific data and theoretical basis for ecological environment protection and natural resource management.

Analysis of CSP/PV Hybrid Power Plants in High Latitude Regions

This study aims to explore the potential of combined CSP and PV systems in high latitude areas. Several performance metrics are evaluated and compared with standalone CSP or PV plants to identify the benefits and challenges of deploying such hybrid plants. Six different sites have been selected and the ASDELSOL hybrid power plant simulation tool, developed by the Thermodynamics and Renewable Energies Group at the University of Seville, has been used. This simulation tool allows performing dynamic performance simulations of hybrid solar plants under various operation strategies, along with conducting economic evaluations. We have analyzed a hybrid solar plant composed of 50 MW PTC and 75 MW PV, with 10 hours of TES and a 15 MW electric heater to transfer excess PV energy to the TES tanks in a 50 MW base load operation strategy, where the main objective of the PV plant is to cover the self-consumption of the PTC plant. Results obtained show that PV/PTC hybridization reduces LCOE and increases CF compared to standalone PV or PTC plants. These improvements are more pronounced in regions where the contribution of PTC plants is lower. In high latitude regions, an N-S orientation of PTC fields achieves higher production than an E-W orientation. However, a combination of both orientations can optimize their production and performance. Finally, it is concluded that it is necessary to carry out feasibility studies in high latitude locations before dismissing them outright, as it has been observed that, in certain regions, hybrid PV/PTC solar plants can offer an effective alternative.

Assessment of a hydrologic-land surface model to simulate thermo-hydrologic evolution of permafrost regions

Hydrologic-land surface models (H-LSMs) offer a physically-based framework for representing and predicting the present and future states of the extensive high-latitude permafrost areas worldwide. Their primary challenge, however, is that soil temperature data are severely limited, and traditional model validation, based only on streamflow, can show the right fit to these data for the wrong reasons. Here, we address this challenge by (1) collecting existing data in various forms including in-situ borehole data and different large-scale permafrost maps in addition to streamflow data, (2) comprehensively evaluating the performance of an H-LSM with a wide range of possible process parametrizations and initializations, and (3) assessing possible trade-offs in model performance in concurrently representing hydrologic and permafrost dynamics, thereby pointing to the possible model deficiencies that require improvement. As a case study, we focus on the sub-arctic Liard River Basin in Canada, which typifies vast northern sporadic and discontinuous permafrost regions. Our findings reveal that different process parameterizations tend to align with different data sources or variables, which largely exhibit inconsistencies among themselves. We further observe that a model may fail to represent permafrost occurrence yet seemingly fit streamflows adequately. Nonetheless, we demonstrate that accurately representing essential permafrost dynamics, including the active soil layer and insulation effects from snow cover and soil organic matter, is crucial for developing high-fidelity models in these regions. Given the complexity of processes and the incompatibility among different data sources/variables, we conclude that employing an ensemble of carefully designed model parameterizations is essential to provide a reliable picture of the current conditions and future spatio-temporal co-evolution of hydrology and permafrost.

A high-altitude population of Hyperboreomyzon polaris (Hirudinea: Glossiphoniidae) in Altai Mountains, South Siberia, Russia.

Hyperboreomyzon polaris Bolotov, Eliseeva, Klass & Kondakov, 2022 (Hirudinea: Glossiphoniidae) is an enigmatic freshwater leech that was recently described based on three specimens from two remote regions of the Eurasian Arctic, that is, the Kolguev Island and Putorana Plateau, Russia. Later on, misidentified historical samples of this species were discovered in the Hirudinea collection of Zoological Institute of the Russian Academy of Sciences (St. Petersburg, Russia). These samples were collected by Pavel G. Ignatov, a prominent Russian geographer and traveler, from a remote high-altitude area of the Altai Mountains (South Siberia) in 1901. To collect new samples from this population, we organized a field expedition to the same area (Lake Dzhulukul) in 2023. Molecular genetic data (COI and 18S rRNA) obtained from newly collected specimens revealed that they belong to H. polaris. Hence, this leech could be considered an Arctic-alpine species, the disjunctive range of which covers Arctic areas of Eurasia and Central Asian (South Siberian) mountains. We describe and illustrate living individuals of H. polaris for the first time, update its morphological diagnosis, present a life cycle reconstruction based on the frequency of four size classes in available samples. It is shown that this species prefers small lentic water bodies such as small lakes and lakelets. The crop content of a well-fed specimen from Altai contains DNA of the northern pintail Anas acuta Linnaeus (Aves: Anatidae), indicating that H. polaris feeds on blood of waterfowl. Our results indicate that Hyperboreomyzon may be considered a species adapted to cold high-latitude areas and mountain refugia.

Spatiotemporal evolution of air-sea CO2 flux in the Northwest Pacific and its response to ENSO

Global warming, driven by human activities since the Industrial Revolution, has significantly elevated atmospheric carbon dioxide (CO2) levels, leading to higher global temperatures and a rise in extreme weather events. The ocean, as a major carbon sink, has absorbed about 30% of human-induced carbon emissions, helping mitigate global warming’s impacts. This study examines the spatiotemporal distribution of air-sea CO2 flux in the Northwest Pacific from 1982 to 2021 and its response to El Niño-Southern Oscillation, using Empirical Orthogonal Function and composite analysis. The seasonal patterns of air-sea CO2 flux and the influence of environmental factors were further evaluated. The results show that air-sea CO2 flux in the Northwest Pacific exhibits clear seasonal fluctuations. In winter, high-latitude areas act as significant carbon sources. Strong winds deepen the mixed layer, promoting CO2 release from the ocean into the atmosphere. In contrast, in summer, longer daylight hours, rising SST, and melting sea ice lead to upwelling, which brings nutrients to the surface and stimulates phytoplankton growth. This process turns the region into a carbon sink as phytoplankton growth, driven by intense sunlight, enhances the ocean’s CO2 absorption. The mid-latitude region consistently acts as a carbon sink year-round. During El Niño events, more negative air-sea CO2 flux anomalies appear in the eastern Northwest Pacific, enhancing carbon uptake. La Niña events have the opposite effect in the eastern regions. These findings highlight the Northwest Pacific’s critical role in modulating regional and global carbon cycles under varying climatic conditions. Understanding these dynamics is crucial for improving predictions of future climate impacts and for developing effective strategies to mitigate global warming.

Season-dependent climate sensitivity of the surface runoff of major rivers in Changbai Mountain

Due to the sensitivity of runoff to changes in seasonal snowpack, the changes in surface runoff within high-latitude and high-elevation areas are more complex than in other areas. However, this sensitivity is often overlooked when correlating changes in runoff with meteorological variables. Here, we analyze changes in surface runoff during snowmelt and snowless periods and their sensitivity to climate change from 1960 to 2019 in the upstream watersheds of the Songhua, Yalu, and Tumen Rivers in Changbai Mountain. Most runoff indices, except for low flow in the Yalu River, exhibited decreasing trends during snowmelt and snowless periods. Precipitation was the primary variable responsible for surface runoff during snowless and snowmelt periods. Notably, rainfall replenished all runoff indices during snowless periods, particularly under high flow conditions. The average and high flows were also primarily supplemented by rainfall during snowmelt periods. However, low flow was positively correlated with snowmelt in the Songhua and Tumen Rivers and with temperature in the Songhua, Yalu and Tumen Rivers during snowmelt periods. Additionally, extreme runoff events were closely associated with extreme precipitation. These findings demonstrate the seasonal and index-dependent climate sensitivity of surface runoff within Changbai Mountain, offering insights into the surface runoff response mechanism to climate change in high-altitude mountainous areas. Under future climate change conditions, frequent and intense extreme precipitation events may increase the risk of flooding, owing to the high sensitivity of high flows to precipitation, posing a serious threat to regional ecological security.

Precise Positioning of Distributed Power Supply in Smart Microgrids for High-latitude Areas

This paper focuses on the precise micro-location of distributed power sources in smart microgrids at high latitudes. It first clarifies the strategic position of microgrids in power systems, especially in complex environments at high latitudes, and emphasizes the urgency of their location. Subsequently, it systematically reviews the research trends of smart microgrid and distributed power supply domestically and internationally, revealing the limitations of existing research and problems to be solved. The unique climate adaptability and power supply-demand characteristics of microgrids in this region are deeply analyzed according to the characteristics of high latitude, clarifying the key role of distributed power supply. The core discussion revolves around examining the application potential and limitations of mainstream distributed energy sources such as solar energy, wind energy, and energy storage devices in a specific environment. The aim is to provide a theoretical basis and practical guidance for scientific planning and optimal layout of smart microgrids in high-latitude regions while promoting innovation and development in this field.

Transcriptome analysis of Sesuvium portulacastrum L. uncovers key genes and pathways involved in root formation in response to low-temperature stress.

Sesuvium portulacastrum L., a perennial facultative halophyte, is extensively distributed across tropical and subtropical coastal regions. Its limited cold tolerance significantly impacts both the productivity and the geographical distribution of this species in higher-latitude areas. In this study, we employed RNA-Seq technology to delineate the transcriptomic alterations in Sesuvium plants exposed to low temperatures, thus advancing our comprehension of the molecular underpinnings of this physiological adaptation and root formation. Our findings demonstrated differential expression of 10,805, 16,389, and 10,503 genes in the low versus moderate temperature (LT vs. MT), moderate versus high temperature (MT vs. HT), and low versus high temperature (LT vs. HT) comparative analyses, respectively. Notably, the gene categories "structural molecule activity", "ribosome biogenesis", and "ribosome" were particularly enriched among the LT vs. HT-specific differentially expressed genes (DEGs). When synthesizing the insights from these three comparative studies, the principal pathways associated with the cold response mechanism were identified as "carbon fixation in photosynthetic organisms", "starch and sucrose metabolism", "plant hormone signal transduction", "glycolysis/gluconeogenesis", and "photosynthesis". In addition, we elucidated the involvement of auxin signaling pathways, adventitious root formation (ARF), lateral root formation (LRF), and novel genes associated with shoot system development in root formation. Subsequently, we constructed a network diagram to investigate the interplay between hormone levels and pivotal genes, thereby clarifying the regulatory pathways of plant root formation under low-temperature stress and isolating key genes instrumental in root development. This study has provided critical insights into the molecular mechanisms that facilitate the adaptation to cold stress and root formation in S. portulacastrum.

Oropharyngeal microbiome profiling and its association with age and heart failure in the elderly population from the northernmost province of China.

Respiratory tract infections are the most common triggers for heart failure in elderly people. The healthy respiratory commensal microbiota can prevent invasion by infectious pathogens and decrease the risk of respiratory tract infections. However, upper respiratory tract (URT) microbiome in the elderly is not well understood. To comprehend the profiles of URT microbiota in the elderly, and the link between the microbiome and heart failure, we investigated the oropharyngeal (OP) microbiome of these populations in Heilongjiang Province, located in the North-East of China, a high-latitude and cold area with a high prevalence of respiratory tract infection and heart failure. Taxonomy-based analysis showed that six dominant phyla were represented in the OP microbial profiles. Compared with young adults, the OP in the elderly exhibited a significantly different microbial community, mainly characterized by highly prevalent Streptococcus, unidentified_Saccharibacteria, Veillonella, unidentified_Pre votellaceae, and Neisseria. While unidentified_Prevotellaceae dominated in the young OP microbiome. There was competition for niche dominance between Streptococcus and member of Prevotellaceae in the OP. Correlation analysis revealed that the abundance of unidentified_Saccharibacteria was positive, while Streptococcus was negatively correlated to age among healthy elderly. The bacterial structure and abundance in the elderly with heart failure were much like healthy controls. Certain changes in microbial diversity indicated the potential OP microbial disorder in heart failure patients. These results presented here identify the respiratory tract core microbiota in high latitude and cold regions, and reveal the robustness of OP microbiome in the aged, supplying the basis for microbiome-targeted interventions.IMPORTANCETo date, we still lack available data on the oropharyngeal (OP) microbial communities in healthy populations, especially the elderly, in high latitude and cold regions. A better understanding of the significantly changed respiratory tract microbiota in aging can provide greater insight into characteristics of longevity and age-related diseases. In addition, determining the relationship between heart failure and OP microbiome may provide novel prevention and therapeutic strategies. Here, we compared OP microbiome in different age groups and elderly people with or without heart failure in northeastern China. We found that OP microbial communities are strongly linked to healthy aging. And the disease status of heart failure was not a powerful factor affecting OP microbiome. The findings may provide basic data to reveal respiratory bacterial signatures of individuals in a cold geographic region.

Prevalence and risk factors for refractive error in older adults in eight ethnicities in China: The China national health survey

PurposeThis study aimed to investigate the prevalence of refractive error (RE) and risk factors for myopia among older adults in the Han and various minority ethnic groups across seven provinces in China. MethodsThis cross-sectional study forms a part of the ophthalmic dataset of the China National Health Survey (CNHS). Face-to-face interviews and ophthalmic examinations were conducted in seven provinces located in western and northern China. The age- and sex-adjusted prevalence of RE among Han and seven other ethnic groups aged 50–80 years were compared. A mixed-effects model was used to identify the risk factors associated with RE. ResultsA total of 12,902 participants, including 8800 Han and 4102 from ethnic minorities, were included in the study. The age- and sex-adjusted prevalence of myopia, high myopia, hyperopia, and astigmatism ranged from 15.3 % (Manchu) to 22.9 % (Han), 0.2 % (Yugur) to 2.8 % (Han), 21.6 % (Tibetan) to 48.9 % (Uyghur), and 38.7 % (Yi) to 57.5 % (Manchu) across different ethnicities, respectively. Compared to the Han population, the Mongolian (odds ratios (OR) 0.62, 95 % confidence interval (CI) 0.46–0.84, p = 0.002), Tibetan (OR 0.66, 95 % CI 0.52–0.85, p = 0.001), Uyghur (OR 0.63, 95 % CI 0.49–0.80, p < 0.001), Yi (OR 0.65, 95 % CI 0.46–0.92, p = 0.014), and Yugur (OR 0.65, 95 % CI 0.50–0.85, p = 0.001) ethnicities were less likely to have myopia. There was no significant difference in the prevalence of myopia between the Manchu, Korean, and Han ethnic groups. Factors associated with a lower prevalence of myopia included rural residence (p < 0.001), a body mass index (BMI) > 18.5 kg/m2 (all p < 0.001), residence in higher latitude areas (p = 0.020), and a history of smoking (p = 0.002 in the past smoking group, p = 0.031 in the current smoking group). The Mongolian (p = 0.006) and Yugur (p = 0.007) populations, participants living in rural areas (p = 0.012), and those with a BMI >24 kg/m2 (p = 0.038 in the >24.0 ≤ 27.0 kg/m2 group or p = 0.041 in the >27.0 kg/m2 group) were less likely to have high myopia. Factors associated with a higher prevalence of hyperopia included older age (all p < 0.001), rural residence (p = 0.039), higher latitude areas (p = 0.031), smoking history (p = 0.040), and Mongolian (p = 0.001), Uyghur (p < 0.001), Yi (p < 0.001), and Yugur (p = 0.002) ethnicities. Conversely, the Manchu population (p = 0.004) and individuals with higher education levels than illiteracy (p = 0.024 or p < 0.001) were less likely to have hyperopia. ConclusionsMyopia affected more than one-fifth of the older adults in the Han population in this survey. Significant differences in the prevalence of RE were observed between minority ethnicities and Han individuals, except for the Manchu and Korean groups.

Improving global soil moisture prediction through cluster-averaged sampling strategy

Understanding and predicting global soil moisture (SM) is crucial for water resource management and agricultural production. While deep learning methods (DL) have shown strong performance in SM prediction, imbalances in training samples with different characteristics pose a significant challenge. We propose that improving the diversity and balance of batch training samples during gradient descent can help address this issue. To test this hypothesis, we developed a Cluster-Averaged Sampling (CAS) strategy utilizing unsupervised learning techniques. This approach involves training the model with evenly sampled data from different clusters, ensuring both sample diversity and numerical consistency within each cluster. This approach prevents the model from overemphasizing specific sample characteristics, leading to more balanced feature learning. Experiments using the LandBench1.0 dataset with five different seeds for 1-day lead-time global predictions reveal that CAS outperforms several Long Short-Term Memory (LSTM)-based models that do not employ this strategy. The median Coefficient of Determination (R2) improved by 2.36 % to 4.31 %, while Kling-Gupta Efficiency (KGE) improved by 1.95 % to 3.16 %. In high-latitude areas, R2 improvements exceeded 40 % in specific regions. To further validate CAS under realistic conditions, we tested it using the Soil Moisture Active and Passive Level 3 (SMAP-L3) satellite data for 1 to 3-day lead-time global predictions, confirming its efficacy. The study substantiates the CAS strategy and introduces a novel training method for enhancing the generalization of DL models.

Research on the ice-resistance performance of conical wind turbine foundation in brash ice fields

Due to the significantly higher wind energy density in high-latitude sea areas compared to other regions, the construction of offshore wind farms is shifting towards these areas. However, in high-latitude sea areas, large amounts of sea ice float on the surface during winter, making ice loads a critical factor affecting the structural safety of wind turbines. For monopile flexible wind turbines, installing an ice-breaking cone near the waterline is an effective measure to withstand the threat of sea ice. To clarify the ice resistance performance of conical wind turbine foundations in brash ice zones, this study proposes a coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM). This method is used to calculate the ice load results for a cylindrical riser, and the results were found to be in good agreement with the ice load experiment results from China University of Petroleum's wave flume (CUPWF). This validation confirms the rationality of the method. Taking a 5 MW monopile wind turbine installed with an ice-breaking cone as the research object, the time history of dynamic ice loads and the displacement response of ice-induced vibration during the interaction between the wind turbine and brash ice are simulated. Considering the water level variations caused by tides, the differences in ice resistance performance between the upright cone and the inverted cone of the ice-breaking cone are analyzed. Finally, the impact of changes in cone angle on the ice resistance performance of the wind turbine foundation is determined. The results indicate that under the impact of brash ice, the upright cone of the ice-breaking cone has a higher ice resistance capacity compared to the inverted cone. With changes in cone angle, the trends of ice loads in the horizontal and vertical directions vary oppositely. However, the overall ice resistance capacity of the wind turbine foundation decreases as the cone angle increases. This study can provide a reference for the rational design of conical wind turbines in cold regions.

Machine learning-based investigation of forest evapotranspiration, net ecosystem productivity, water use efficiency and their climate controls at meteorological station level

Evapotranspiration (ET), net ecosystem productivity (NEP), and ecosystem water use efficiency (EWUE) of forests are changing due to climate change. Traditional models using coarse-scale climate reanalysis data fail to capture local meteorological and hydrological conditions accurately. This study combines in situ meteorological observations, remote sensing, and advanced datasets (forest age, rooting depth, soil moisture) to estimate ET, NEP, and EWUE at forest meteorological stations via machine learning. About 60.6 % of stations showed a decrease in NEP from 2003 to 2010 to 2011–2019, while 63.9 % showed an increase in ET, and 58.9 % showed a decrease in EWUE. NEP and EWUE significantly declined in forests older than 60 years, with younger forests exhibiting higher NEP. EWUE in different forest types is driven by varying mechanisms, with DBF sites influenced by VPD and ENF sites by RSDN. EWUE of regions with inconsistent VPD data between site and reanalysis, such as northwestern North America, showed divergences from previous reanalysis-based studies but aligned more with atmospheric inversion findings. Slight summer VPD increases boosted NEP, especially in high-latitude areas, while early spring phenology and increased spring VPD reduced summer water availability. Incorporating more site-specific observations, such as plant traits, could enhance understanding of climate-plant-ecosystem relationships. This study underscores the potential of meteorological station-level data to improve forest carbon and water flux dynamics understanding, aiding forest management for carbon neutrality and climate adaptation.

Predicting the Current and Future Distribution of Monolepta signata (Coleoptera: Chrysomelidae) Based on the Maximum Entropy Model.

Monolepta signata is a polyphagous and highly destructive agricultural pest, currently only distributed in Asia. In its place of origin, it poses a serious threat to important economic crops, for instance, maize (Zea mays L.) and cotton (Gossypium hirsutum L.). Based on morphological and molecular data research, it has been found that M. quadriguttata (Motschulsky), M. hieroglyphica (Motschulsky), and M. signata are actually the same species. This discovery means that the range of this pest will expand, and it also increases the risk of it spreading to non-native areas worldwide. It is crucial for global agricultural production to understand which countries and regions are susceptible to invasion by M. signata and to formulate corresponding prevention, control, and monitoring strategies. This study uses the maximum entropy model, combined with bioclimatic variables and elevation, to predict the potentially suitable areas and diffusion patterns of M. signata worldwide. The results indicate that in its suitable area, M. signata is mainly affected by three key climatic factors: Precipitation of Wettest Month (bio13), Mean Temperature of Warmest Quarter (bio10), and Temperature Seasonality (bio4). Under the current status, the total suitable region of M. signata is 252,276.71 × 104 km2. In addition to its native Asia, this pest has potentially suitable areas in Oceania, South America, North America, and Africa. In the future, with climate change, the suitable area of M. signata will expand to high-latitude areas and inland areas. This study found that by the 2070s, under the SSP5-8.5 climate scenario, the change in the potentially suitable area of this insect is the largest. By identifying the potentially suitable areas and key climatic factors of M. signata, we can provide theoretical and technical support to the government, enabling them to more effectively formulate strategies to deal with the spread, outbreak, and invasion of M. signata.

Continuous wildfires threaten public and ecosystem health under climate change across continents

Wildfires burn approximately 3%–4% of the global land area annually, resulting in massive emissions of greenhouse gases and air pollutants. Over the past two decades, there has been a declining trend in both global burned area and wildfire emissions. This trend is largely attributed to a decrease in wildfire activity in Africa, which accounts for a substantial portion of the total burned area and emissions. However, the northern high-latitude regions of Asia and North America have witnessed substantial interannual variability in wildfire activity, with several severe events occurring in recent years. Climate plays a pivotal role in influencing wildfire activity and has led to more wildfires in high-latitude regions. These wildfires pose significant threats to climate, ecosystems, and human health. Given recent changes in wildfire patterns and their impacts, it is critical to understand the contributors of wildfires, focus on deteriorating high-latitude areas, and address health risks in poorly managed areas to mitigate wildfire effects.

Prediction of the potential distribution of Chimonobambusautilis (Poaceae, Bambusoideae) in China, based on the MaxEnt model.

Chimonobambusautilis is a unique edible bamboo species valued for its economic and nutritional benefits. However, its existence in natural habitats is at risk due to environmental shifts and human interventions. This research utilised the maximum entropy model (MaxEnt) to predict potential habitats for Ch.utilis in China, identifying key environmental factors influencing its distribution and analysing changes in suitable habitats under future climate conditions. The results show that the results of the MaxEnt model have high prediction accuracy, with an AUC (Area Under the receiver operating characteristic Curve) value of 0.997. Precipitation in the driest month (Bio14), altitude (Alt) and isothermality (Bio03) emerged as the primary environmental factors influencing the Ch.utilis distribution. Currently, the suitable habitats area for Ch.utilis is 10.55 × 104 km2. Projections for the 2050s and 2090s indicate potential changes in suitable habitats ranging from -3.79% to 10.52%. In general, the most suitable habitat area will decrease and shrink towards higher latitude areas in the future. This study provides a scientific basis for the introduction, cultivation and conservation of Ch.utilis.

Socioecological dynamics of diverse global permafrost-agroecosystems under environmental change

ABSTRACT Permafrost-agroecosystems include all cultivation and pastoral activities in areas underlain by permafrost. These systems support local livelihoods and food production and are rarely considered in global agricultural studies but may become more relevant as climate change is increasing opportunities for food production in high latitude and mountainous areas. The exact locations and amount of agricultural production in areas containing permafrost are currently unknown, therefore we provide an overview of countries where both permafrost and agricultural activities are present. We highlight the socioecological diversity and complexities of permafrost-agroecosystems through seven case studies: (1) crop cultivation in Alaska, USA; (2) Indigenous food systems and crop cultivation in the Northwest Territories, Canada; (3) horse and cattle husbandry and Indigenous hay production in the Sakha Republic, Russia; (4) mobile pastoralism and husbandry in Mongolia; (5) yak pastoralism in the Central Himalaya, Nepal; (6) berry picking and reindeer herding in northern Fennoscandia; and (7) reindeer herding in northwest Russia. We discuss regional knowledge gaps associated with permafrost and make recommendations to policy makers and land users for adapting to changing permafrost environments. A better understanding of permafrost-agroecosystems is needed to help sustainably manage and develop these systems considering rapidly changing climate, environments, economies, and industries.

Multi-year evaluation of CloudSat, ERA5, and IMERG global snowfall products

Snowfall products derived from satellite-based radar observations, reanalysis products and phase-classified precipitation products are the three primary sources of information on surface snowfall. This study compares snowfall rate products from CloudSat, ERA5 and IMERG using the Triple Collocation (TC) method on a global scale covering almost the entire period of CloudSat cloud-profiling radar (CPR) 2C products from 2006 to 2019, especially considering the precipitation phase classification field of IMERG (probability of Liquid Precipitation, pLP). The main conclusions include but are not limited to the following: (1) The pLP threshold significantly impacts the ability of IMERG to identify snowfall from precipitation, with an optimal pLP threshold of 25 percent determined by matching IMERG with CloudSat and ERA5 snowfall events globally. (2) CloudSat presents the highest performance and stability on a global scale, with average TC-estimated CC and RMSE of 0.472 and 1.465, respectively. (3) The TC-estimated CC of IMERG exhibit significant spatiotemporal heterogeneities, but IMERG product with the optimal pLP threshold show comparable performance with CloudSat, with an average TC-estimated RMSE of ∼ 1.533. (4) ERA5 performed worse than the other two products, except in some high-latitude and high-altitude areas. This study provides novel insights into the performances of snowfall rate products from different sources at the global scales and provides a foundation for improving satellite- and model-based snowfall rate retrievals.

Research on semantic segmentation algorithm of high latitude urban river ice based on deep transfer learning

ABSTRACT Automated observation methods for monitoring river ice in high-latitude urban areas are crucial for resource utilization, risk assessment, and navigation. However, current research lacks actual-scale river ice classification, such as low-altitude surveys. This study established a dataset of river ice in the Songhua River near Harbin, Northeast China, using UAV aerial photography and applied the RININet semantic segmentation algorithm for precise classification of different ice types in low-altitude aerial remote sensing images. To address environmental challenges, a feature extraction method integrating channel and spatial attention mechanisms was adopted, along with an improved pyramid pool structure to enhance feature recognition. Additionally, a two-stage transfer learning method established an ice recognition database, overcoming issues like small data volume and high annotation costs. Comparative evaluation metrics demonstrated the high accuracy of the semantic segmentation framework. Furthermore, a method for estimating ice blockage risk was proposed, applicable to various urban river ice management tasks, with practical significance.