Mountainous Regions Research Articles

Trends in climate adaptation solutions for mountain regions

This study addresses the critical need for documented adaptation progress in mountain regions by reviewing recently implemented or ongoing adaptation solutions collected from the Adaptation at Altitude Solutions Portal (A@A Solution Portal). Using a data driven approach, the research explores the characteristics, feasibility, and transformative potential of these solutions. Findings reveal a predominant focus on addressing droughts and floods, aligning with the IPCC’s emphasis on water-related impacts in mountains. Notably, watershed management practices emerge as popular solutions, showcasing their capacity to address multiple concerns beyond climate impacts. Education and awareness, along with land use practices, dominate the types of solutions, reflecting their positive impact on project acceptability and low associated risk of maladaptation. Agricultural land and forests are the main ecosystems where solutions are reported, with an evident association with education and awareness and land use change solutions. Most SDGs and Sendai targets are found to be addressed by the solutions emphasising the importance of documenting project experiences as way to bridge previously reported gaps between policy frameworks and on-the-ground implementation. Despite community involvement being high in many of the solutions, challenges such as gender inequality persists. While solutions often demonstrate local relevance and depth of change, upscaling remains challenging, with limited evidence of mainstreaming and replication. Sustainability criteria are moderately met, incorporating inclusive decision-making but with uncertainty regarding long-term plans. Furthermore, findings underscore the significance of co-developing and maintaining adaptation solution portals, illustrating how this approach enriches our understanding of adaptation progress in mountains. Moreover, this research contributes to broadening the scope of systematic adaptation assessments by providing a nuanced perspective that integrates local needs and diverse knowledge systems. In essence, this study makes a valuable contribution to the evolving landscape of adaptation research, emphasizing the importance of practical insights and collaborative efforts to address the complex challenges posed by climate-related impacts and corresponding adaptation efforts.

Application of fallout radionuclide—137Cs for estimating soil erosion in steep hillslopes with diverse land use of North-western Indian Himalayas

Hilly and mountainous regions are significantly impacted by soil erosion, primarily due to rainfall-runoff processes occurring on the hillslope scale. Assessing soil erosion is crucial for quantifying the loss of soil carbon and nutrients, which diminishes the potential of soil ecosystem services and is critical for mitigating the impacts of climate change on food security. However, the Himalayan landscape poses serious challenges for assessing soil erosion due to its steep and rugged terrain, which hinders the use of conventional and modelling methods. The fallout radionuclide—137Cs has been extensively utilized as an environmental marker for investigating soil redistribution processes. Despite its potential, there is a notable lack of 137Cs-based soil erosion studies in the Himalayan region. In this context, we assessed the applicability of the fallout radionuclide—137Cs method in quantifying soil erosion rates and identifying erosion hotspots on two hillslopes of the Higher Himalayas. On the hillslope scale, we observed that soil erosion rates vary based on slope gradient and land use/land cover. Forested areas exhibited the lowest soil erosion rates compared to cultivated areas, while flat hillslope positions experienced lower erosion rates than steeper positions. The average net erosion rate for the Harsil hillslope varied among different hillslope positions, ranging from – 2.9 to – 15.6 t ha−1 yr−1. Similarly, in the Gangnani hillslope, the net erosion rates varied across different positions, ranging from – 5.6 to – 39 t ha−1 yr−1. Our findings confirm that the middle and lower hillslope positions are the most critical source areas with higher soil erosion rates, while hillslope positions with forest cover demonstrate the lowest erosion rates, identified as helpful in controlling soil erosion. The study has demonstrated the applicability of FRN as a soil erosion measurement method in the complex, rugged, and steep terrain of the Himalayas, highlighting the need for targeted conservation efforts to mitigate soil erosion and preserve ecosystem integrity.

Inventory and Spatial Distribution of Landslides on the Eastern Slope of Gongga Mountain, Southwest China

The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it is necessary to identify the spatial distribution of landslides in the region. In this paper, the Google Earth platform and GF-1 and GF-6 satellite imagery were used to construct new pre-earthquake and co-seismic landslides. Then, we analyzed the relationship between the conditioning factors of the pre-earthquake and co-seismic landslide inventories and the spatial distribution of landslides, as well as the main controlling factors of landslide development. The main conclusions are as follows: (i) Through remote-sensing interpretation and field investigation, 1198 and 4284 landslides were recognized before and after the earthquake, respectively, and the scale was mainly small- and medium-sized. (ii) In two kinds of inventories, landslides are primarily distributed along the banks of the Dadu River basin, within elevations of 1200–1400 m and slopes of 30–50°. (iii) The distribution of pre-earthquake and co-seismic landslides was influenced by engineering geological layer combinations and earthquake intensity, with these two factors being the most significant. This paper plays an important role in hazard prevention and reconstruction planning in the Gongga Mountains.

Low-Grade Glioma Clinical Trials in the United States: A Systematic Review.

Low-grade glioma (LGG) is a malignancy of the central nervous system that is often treatable with surgical resection and chemoradiation. However, despite an initial positive response to standard therapy, most LGG eventually progress to high-grade gliomas which are nearly uniformly fatal. There is a pressing need for more clinical trials and greater clinical trial accessibility to improve the standard of care of LGG to delay or prevent its progression. In this study, we systematically examined the scope and inclusion of clinical trials for LGG based in the United States. This cross-sectional study analyzes trends in trial design and reported demographic data from completed LGG trials registered on ClinicalTrials.gov between 2010 to 2023. Inclusion criteria, investigational therapies, primary outcomes, and preliminary results were compared and summarized. A total of 14 trials with 1067 participants were included in the study. Most of the trials were not exclusive to LGGs and 14% had mutation-specific inclusion criteria. To date, two of the trials have led to new FDA-approved treatments. All trials reported age and sex, while only 57% reported race and 43% reported ethnicity. Individuals identifying as Black or African American and Asian or Pacific Islander were statistically underrepresented. Lastly, we investigated the geographic distributions of trial sites across the United States, which demonstrated several coverage gaps in the Rocky Mountain and Southeast regions. These findings suggest specific areas for improvement in LGG clinical trial reporting and accessibility.

Ambiguous and Complex – Rural Revival in a Peripheral Mountain Region (SW Poland)

AbstractThe general development conditions of rural areas have been addressed by many authors; however, the conditions and factors that allow the depopulated problem rural areas to re‐enter the development path are an understudied phenomenon. This article examines the types and drivers of rural revival, evaluates the transformations of rural areas and forecasts their future development. It focuses on a representative region for the key transformations of mountainous rural areas in the Polish‐Czech borderland. This study offers a complex, interdisciplinary approach using quantitative and qualitative methods to examine the in‐depth mechanisms of rural transformation and to vocalise local knowledge and inhabitants' perceptions. The results demonstrate that the depopulated and marginalised peripheral rural regions have the potential to return to the development path, but the symptoms, drivers and perceptions of rural transformations vary greatly across the region. The study highlights the complexity and ambiguity of assessing, evaluating and forecasting rural transformations.

Cold and Wet Island Effect in Mountainous Areas: A Case Study of the Maxian Mountains, Northwest China

The Maxian Mountains, characterized by high altitudes and abundant vegetation, create a cooler and more humid environment compared to the surrounding areas, and are highly susceptible to climate change. In order to study the cold and wet island effects in the Maxian Mountains, air temperature and relative humidity (RH) were analyzed using meteorological station data. Additionally, spatial variations were examined by retrieving Land Surface Temperature (LST) and the Temperature Vegetation Dryness Index (TVDI) from 2001 to 2021. The most pronounced cold island effect was observed in the mountainous area during summer, mainly in May and July. The most significant wet island effect was observed from March to May, with an average relative humidity difference of 24.72%. The cold island area index, as an indicator of the cold island effect, revealed an increasing trend in the summer cold island effect in recent years. The cooling intensity ranged from 5 to 10 °C, with variations observed between 500 and 1000 m. A 30% increase in wet island effects in summer was observed, with a humidification intensity within a range of 500 m. Geodetector analysis identified vegetation cover as the primary factor affecting the thermal environment in mountainous areas. The increase in vegetation in mountainous areas was identified as the main reason for enhancing the cold and wet island effects. The findings emphasize the role of vegetation in enhancing cold and wet island effects, which is crucial for understanding and preserving mountainous regions.

Present-day stress field, strain rate field and seismicity of the Pannonian region: overview and integrated analysis

We present an overview of recent findings on the seismicity, stress field and strain rate field of the Pannonian region. We furthermore show new inferences for deformation mechanisms and lithospheric rheology via an integrated analysis of the datasets. The N-NW motion of the Dinarides and the opposite, SW motion of the E-and S-Carpathians induce shortening in the western and central Pannonian Basin while leading to regional shearing around the basin's SE boundary. This induces moderate seismic activity related to strike-slip and reverse faulting. Maximum horizontal stress and geodetic shortening directions generally agree, implying that upper crustal stresses and surface deformation correspond to the same forces. 2D rheological models confirm that the shallow upper crust is the only brittle layer in the Pannonian lithosphere, while the brittle-ductile transition zone could be as shallow as 6-9.5 km in the Danube Basin. Comparison of seismic moment rates and moment rate predictions from geodetic strain rates show that deep Pannonian sub-basins accumulate major seismic deficits. This could be explained by dominantly aseismic deformation of the weak upper crust, as supported by a comparison with case studies from different geodynamic environments. Stronger mountainous regions in the study area show no to moderate seismic deficits.

Spatiotemporal Variations in MODIS EVI and MODIS LAI and the Responses to Meteorological Drought across Different Slope Conditions in Karst Mountain Regions

Based on monthly MODIS EVI and LAI data from 2001 to 2020, combined with the Standardized Precipitation Evapotranspiration Index (SPEI), this study employs Theil–Sen trend analysis, Mann–Kendall (MK) test, Hurst index analysis, and correlation analysis to comparatively analyze the overall vegetation trends, spatial distribution characteristics, and future trends of different vegetation types in Guizhou Province under varying slope conditions. The study also explores the response of vegetation to SPEI at different time scales across different slopes. The results indicate the following: (1) From 2001 to 2020, the average values of EVI (0.34%/a) and LAI (1.4%/a) during the growing season exhibited an increasing trend, with the improved vegetation areas primarily concentrated in the western region of Guizhou, while the degradation areas were mainly located in the central and eastern regions. (2) Under different slope conditions, EVI generally showed slight improvement, while LAI exhibited significant improvement, with dry-lands experiencing the largest changes. Future trends indicate continuous improvement, but the proportion of vegetation improvement area decreases with increasing slope. When the slope is less than 5°, the proportion of vegetation improvement area is the highest. (3) The positive correlation between EVI, LAI, and SPEI at different time scales is stronger than the negative correlation, with the strongest correlations observed when the slope is less than 5°. When the slope exceeds 35°, the relationship between vegetation and drought response is almost unaffected by the slope. These findings provide a scientific basis for vegetation growth monitoring and the study of climate change and vegetation interactions in Guizhou Province.

A Method for Cropland Layer Extraction in Complex Scenes Integrating Edge Features and Semantic Segmentation

Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge detail loss and limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with the temporal characteristics of medium-resolution remote sensing images for identifying cultivated land. The methodology involves geographically zoning and stratifying the study area, and then integrating semantic segmentation and edge detection to analyze remote sensing images and generate initial extraction results. These results are refined through post-processing with medium-resolution imagery classification to produce a detailed map of the cultivated land distribution. The method achieved an overall extraction accuracy of 95.07% in Tongnan District, with specific accuracies of 92.49% for flat cultivated land, 96.18% for terraced cultivated land, 93.80% for sloping cultivated land, and 78.83% for forest intercrop land. The results indicate that, compared to traditional methods, this approach is faster and more accurate, reducing both false positives and omissions. This paper presents a new methodological framework for large-scale cropland mapping in complex scenarios, offering valuable insights for subsequent cropland extraction in challenging environments.

Snow Detection in Gaofen-1 Multi-Spectral Images Based on Swin-Transformer and U-Shaped Dual-Branch Encoder Structure Network with Geographic Information

Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary segmentation, as well as issues like detail snow information loss and mountainous snow omission, this paper presents a novel snow detection network based on Swin-Transformer and U-shaped dual-branch encoder structure with geographic information (SD-GeoSTUNet), aiming to address the above issues. Initially, the SD-GeoSTUNet incorporates the CNN branch and Swin-Transformer branch to extract features in parallel and the Feature Aggregation Module (FAM) is designed to facilitate the detail feature aggregation via two branches. Simultaneously, an Edge-enhanced Convolution (EeConv) is introduced to promote snow boundary contour extraction in the CNN branch. In particular, auxiliary geographic information, including altitude, longitude, latitude, slope, and aspect, is encoded in the Swin-Transformer branch to enhance snow detection in mountainous regions. Experiments conducted on Levir_CS, a large-scale cloud and snow dataset originating from Gaofen-1, demonstrate that SD-GeoSTUNet achieves optimal performance with the values of 78.08%, 85.07%, and 92.89% for IoU_s, F1_s, and MPA, respectively, leading to superior cloud and snow boundary segmentation and thin cloud and snow detection. Further, ablation experiments reveal that integrating slope and aspect information effectively alleviates the omission of snow detection in mountainous areas and significantly exhibits the best vision under complex terrain. The proposed model can be used for remote sensing data with geographic information to achieve more accurate snow extraction, which is conducive to promoting the research of hydrology and agriculture with different geospatial characteristics.

A Semantic Knowledge Graph of European Mountain Value Chains

The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas’ cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods. The present paper describes a geospatially explicit semantic knowledge graph containing information on 454 European mountain value chains. It is the first large-size, structured collection of information on mountain value chains. Our graph, structured through ontology-based semantic modelling, offers representations of the value chains in the form of narratives. The graph was constructed semi-automatically from unstructured data provided by mountain-area expert scholars. It is accessible through a public repository and explorable through interactive Story Maps and a semantic Web service. Through semantic queries, we demonstrate that the graph allows for exploring territorial complexities and discovering new knowledge on mountain areas’ environmental, societal, territory, and economic aspects that could help stem depopulation.

Characteristics of Precipitation and Mesoscale Convective Systems Over the Peruvian Central Andes in Multi 5‐Year Convection‐Permitting Simulations

AbstractUsing the Weather Research and Forecasting model with two planetary boundary layer schemes, ACM2 and MYNN, convection‐permitting model (CPM) regional climate simulations were conducted for a 6‐year period, including a one‐year spin‐up period, at a 15‐km grid spacing covering entire South America and a nested convection‐permitting 3‐km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4‐km simulation covering South America produced by National Center for Atmospheric Research (NCAR), three gridded precipitation products, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian central Andes region. Results show that all km‐scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low‐level jet and terrain‐induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests model improvements and better model configurations for future regional climate projections.

SEDIMENTATION IN HYDROELECTRIC POWER TURBINES

The primary cause of erosive wear and energy loss is friction, and reportedly, one-third of the world’s energy resources go to overcome friction by several methods. The presence of deposits in water is a common problem in mountainous regions and some Ukrainian hydroelectric power stations. Hydro turbines face serious issues related to erosion from deposits during operation. Erosive wear occurs due to the impact of solid particles against a solid surface. The current environment contains particles whose velocity is sufficient to damage metal surfaces. Various researchers have conducted numerous studies to minimise the effect of deposit erosion in multiple locations of Francis turbine components (FT). For this purpose, they performed field investigations, experimental measurements, empirical modelling, computational fluid dynamics (CFD) work, and other methods. Various empirical models have been used to describe erosive wear in terms of material and fluid properties. They established that, for ductile materials, maximum erosion occurs at an impact angle of 30°, whereas for brittle materials, it occurs in the range of 80–90°. In the literature, various experimental methods are available to quantitatively assess slurry erosion, such as the rotating disk apparatus (RDA), slurry tank testing, jet erosion testing, and rotating drum testing. Due to manufacturing technologies used in mechanical processing processes, the exposed surface has a certain absolute roughness that increases during machine operation due to abrasion or erosion. Therefore, surface texture leads to increased energy efficiency losses during operation. Technological advancements have facilitated the widespread use of computational tools to address deposit erosion issues. In recent decades, numerical methodology has been a widely used and effective tool, yielding tangible and reliable results. This study examines potential methods for detecting and reducing deposits. This type of erosion depends on flow characteristics, surface properties, and properties of the eroding material. The article provides comprehensive information on sedimentation (deposits) causing wear in hydraulic turbines. Keywords: hydroelectric turbine, sedimentation erosion, computational fluid dynamics, fluid-structure interaction.

GENETIC DIVERSITY AND POPULATION STRUCTURE OF JUNIPERUS SERAVSCHANICA KOM. IN CENTRAL ASIA REVEALED BY MICROSATELLITE MARKERS

Juniperus seravschanica Kom. is a widely distributed and significant tree species in Central Asia, thriving in the mountain ranges from Oman to Uzbekistan, Kyrgyzstan, and Kazakhstan. It plays a vital role in forming shrub-forest massifs in mountainous regions, contributing to soil drainage and stabilization at medium to high altitudes. To gain a detailed understanding of the current status of J. seravschanica and to develop effective conservation strategies, a comprehensive study of the species' genetic diversity and population structure was conducted. Leaf samples were collected from 15 J. seravschanica populations across Uzbekistan, Kyrgyzstan, and Kazakhstan to assess genetic diversity and population structure. Using 11 polymorphic simple sequence repeat (SSR) markers, genetic diversity parameters such as the number of alleles, the number of effective alleles, the percentage of polymorphic loci, and Nei's genetic diversity index were evaluated. Nei's genetic diversity index for J. seravschanica populations averaged 0.450, ranging from 0.407 to 0.566. The AMOVA revealed that 90.3% of the total genetic variation was within populations, indicating a high level of genetic diversity within each population. Gene flow, calculated using alleles from all populations, was estimated at 4.654. Population structure analysis showed weak clustering among the studied populations, supporting the AMOVA findings. This study provides valuable insights into the genetic diversity and population structure of J. seravschanica in Central Asia. The high genetic diversity within populations suggests a strong adaptive capacity to environmental changes. These findings can be effectively utilized to develop conservation strategies for J. seravschanica, ensuring the long-term sustainability of this important tree species in Central Asia. This work was supported by Grant No.AP09259027 of the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan.

Identification of influencing factors and risk assessment of underground space flooding in the mountain city

As a consequence of the process of urbanization, there is an increasing reliance on and exploitation of underground spaces. Moreover, these areas are susceptible to urban waterlogging, which can be difficult to drain, potentially leading to significant risks. At present, there are few simulations in hydrodynamic models that consider underground spaces. The risk of inundation of underground spaces remains an uncertain phenomenon. This study conceptualized underground spaces as storage areas, with the aim of simulating the spatial distribution of flood risk for different return periods. We studied the flooding processes and characteristics of three underground space types. The results indicate that the flood evolution simulated by the simplified modeling method that incorporates underground space is more aligned with the actual situation. To further analyze the risk distribution of urban waterlogging, we choose the XGBoost model and SHapley Additive exPlanations (SHAP) method to assess the influence of specific indicators on the occurrence of urban waterlogging in mountainous regions. Our analysis revealed that key factors include density of road (DR), flow velocity (V), density of pipes (DP), elevation (DEM), and inundation height (H), with contribution rates of 23.13 %, 17.06 %, 14.67 %, 10.12 %, and 9.04 %, respectively. This analysis clarified the risk of underground inundation within the study area, which can serve as a reference point for the formulation of flood control and drainage strategies.

GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)

Abstract Landslides and slope processes constitute one of the most frequent natural hazards in valleys near major rivers and mountainous regions. The surface layer, characterized by its relatively loose composition, is prone to sliding due to a combination of distinct natural and human-related factors. Specific sections along the right bank of the Danube River in Smederevo city exhibit significant susceptibility to landslide activation, often leading to substantial material losses and posing a risk to the local population. The initial step in the provided research involves analyzing existing literature and mapping landslides within the study area. The initial analysis covers both natural conditions and anthropogenic activities. The second step includes establishing a geospatial database in the Geographic Information System and generating eight thematic maps. In the third step, different weight coefficients were assigned to the criteria, which facilitated the creation of the Landslide Susceptibility Index using the Best–Worst Method. Subsequently, in the fourth step, a composite map illustrating landslide susceptibility was produced. According to this research, about 4% of the territory of Smederevo, or 19.3 km2, is highly or very highly susceptible to landslides. These localities are located on the right bank of the Danube River and around the Ralja River. Receiver operating characteristic-area under the curve value indicates very high predictive power (approximately 1), thus suggesting the reliability of the used methodology. This visualization of areas highly prone to such occurrences empowers policymakers to implement more effective environmental protection measures and institute sustainable management practices for agricultural parcels in this region. Also, the provided research represents the inaugural integration of advanced remote sensing techniques and interdisciplinary investigations, offering deeper insights into landslide activity in the study area and yielding more comprehensive results.

Advances in InSAR Analysis of Permafrost Terrain

ABSTRACTDifferential interferometric synthetic aperture radar (InSAR) is a remote sensing technique for measuring surface displacements with precision down to millimeters, most commonly from satellites. In permafrost landscapes, InSAR measurements can provide valuable information on geomorphic processes and hazards, including thaw subsidence and frost heave, thermokarst, and permafrost creep. We first review recent progress in InSAR data availability, InSAR processing and uncertainty analysis methods relevant to permafrost studies. These technical advances have contributed to our understanding of surface deformation in flat and sloping terrain in polar and mountainous regions. We emphasize two emerging trends. First, InSAR increasingly enables insight into the mechanisms, controls, and drivers of permafrost landscape dynamics on subseasonal to decadal time scales. Second, InSAR observations in conjunction with models enable novel ways to infer subsurface parameters, such as near‐surface ground ice content and active layer thickness. We anticipate that in the coming decade, InSAR will mature into a widely used operational tool for monitoring, modeling, and planning across rapidly changing permafrost landscapes.

Spatial Estimation of Soil Organic Carbon Content Utilizing PlanetScope, Sentinel-2, and Sentinel-1 Data

The accurate prediction of soil organic carbon (SOC) is important for agriculture and land management. Methods using remote sensing data are helpful for estimating SOC in bare soils. To overcome the challenge of predicting SOC under vegetation cover, this study extracted spectral, radar, and topographic variables from multi-temporal optical satellite images (high-resolution PlanetScope and medium-resolution Sentinel-2), synthetic aperture radar satellite images (Sentinel-1), and digital elevation model, respectively, to estimate SOC content in arable soils in the Wuling Mountain region of Southwest China. These variables were modeled at four different spatial resolutions (3 m, 20 m, 30 m, and 80 m) using the eXtreme Gradient Boosting algorithm. The results showed that modeling resolution, the combination of multi-source remote sensing data, and temporal phases all influenced SOC prediction performance. The models generally yielded better results at a medium (20 m) modeling resolution than at fine (3 m) and coarse (80 m) resolutions. The combination of PlanetScope, Sentinel-2, and topography factors gave satisfactory predictions for dry land (R2 = 0.673, MAE = 0.107%, RMSE = 0.135%). The addition of Sentinel-1 indicators gave the best predictions for paddy field (R2 = 0.699, MAE = 0.114%, RMSE = 0.148%). The values of R2 of the optimal models for paddy field and dry land improved by 36.0% and 33.4%, respectively, compared to that for the entire study area. The optical images in winter played a dominant role in the prediction of SOC for both paddy field and dry land. This study offers valuable insights into effectively modeling soil properties under vegetation cover at various scales using multi-source and multi-temporal remote sensing data.

Changing seroprevalence of schistosomiasis japonica in China from 1982 to 2020: A systematic review and spatial analysis.

Schistosomiasis is a global public health issue. In China, while the seroprevalence of Schistosomiasis japonica has currently reduced to a relatively low level, risk of infection still exists in certain areas. However, there has been a lack of comprehensive research on the long-term trends of national seroprevalence, changes across age groups, and characteristics in spatial distribution, which is crucial for effectively targeting interventions and achieving the goal of eliminating schistosomiasis by 2030. Our study aimed to address this gap by analyzing the long-term trends of Schistosomiasis japonica seroprevalence in China from 1982 to 2020 based on the data from diverse sources spanning a period of 39 years. Seroprevalence data were collected from literature databases and national schistosomiasis surveillance system. Meta-analysis was conducted to estimate the seroprevalence. Joinpoint model was used to identify changing trend and inflection point. Inverse distance weighted interpolation was used to determine the spatial distribution of seroprevalence. The seroprevalence decreased from 34.8% in 1982 to 2.4% in 2020 in China. Before 2006, the seroprevalence was higher in the middle age group, and a pattern of increasing with age was observed afterwards. The areas with high seroprevalence existed in Dongting Lake, Poyang Lake, Jianghan Plain, the Anhui branch of the Yangtze River and some localized mountainous regions in Sichuan and Yunnan provinces. There was a significant decline in the seroprevalence of Schistosomiasis japonica from 1982 to 2020 in China. Nevertheless, schistosomiasis has not been eradicated; thus, implementing precise and personalized monitoring measures is crucial for the elimination of schistosomiasis, especially in endemic areas and with a particular focus on the elderly.

Altitude characteristics in the response of rain-on-snow flood risk to future climate change in a high-latitude water tower

Global warming is profoundly impacting snowmelt runoff processes in seasonal freeze-thaw zones, thereby altering the risk of rain-on-snow (ROS) floods. These changes not only affect the frequency of floods but also alter the allocation of water resources, which has implications for agriculture and other key economic sectors. While these risks present a significant threat to our lives and economies, the risk of ROS floods triggered by climate change has not received the attention it deserves. Therefore, we chose Changbai Mountain, a water tower in a high-latitude cold zone, as a typical study area. The semi-distributed hydrological model SWAT is coupled with CMIP6 meteorological data, and four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) are selected after bias correction, thus quantifying the impacts of climate change on hydrological processes in the Changbai Mountain region as well as future evolution of the ROS flood risk. The results indicate that: (1) Under future climate change scenarios, snowmelt in most areas of the Changbai Mountains decreases. The annual average snowmelt under SSP126, SSP245, SSP370, and SSP585 is projected to be 148.65 mm, 135.63 mm, 123.44 mm, and 116.5 mm, respectively. The onset of snowmelt is projected to advance in the future. Specifically, in the Songhua River (SR) and Yalu River (YR) regions, the start of snowmelt is expected to advance by 1–11 days. Spatially, significant reductions in snowmelt were observed in both the central part of the watershed and the lower reaches of the river under SSP585 scenario. (2) In 2021–2060, the frequency of ROS floods decreases sequentially for different scenarios, with SSP 126 > SSP 245 > SSP 370 > SSP 585. The frequency increments of ROS floods in the source area for the four scenarios were 0.12 days/year, 0.1 d/yr, 0.13 days/year, and 0.15 days/year, respectively. The frequency of high-elevation ROS events increases in the YR in the low emission scenario. Conversely, in high emission scenarios, YR high-elevation ROS events will only increase in 2061–2100. This phenomenon is more pronounced in the Tumen River (TR), where floods become more frequent with increasing elevation.