Topographic Factors Research Articles

Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach.

Leptospirosis is a neglected zoonotic disease prevalent worldwide, particularly in tropical regions experiencing frequent rainfall and severe cyclones, which are further aggravated by climate change. This bacterial zoonosis, caused by the Leptospira genus, can be transmitted through contaminated water and soil. The Pacific islands bear a high burden of leptospirosis, making it crucial to identify key factors influencing its distribution. Understanding these factors is vital for developing targeted policy decisions to mitigate the spread of Leptospira. This study aims to establish a precise spatio-temporal risk map of leptospirosis at a national scale, using binarized incidence rates as the variable to predict. The spatial analysis was conducted at a finer resolution than the city level, while the temporal analysis was performed on a monthly basis from 2011 to 2022. Our approach utilized a comprehensive strategy combining machine learning models trained on binarized incidences, along with descriptive techniques for identifying key factors. The analysis encompasses a broad spectrum of variables, including meteorological, topographic, and socio-demographic factors. The strategy achieved a concordance metric of 83.29%, indicating a strong ability to predict the presence of contamination risk, with a sensitivity of 83.93%. Key findings included the identification of seasonal patterns, such as the impact of the El Niño Southern Oscillation, and the determination that rainfall and humidity with a one-month lag are significant contributors to Leptospira contamination. Conversely, soil types rich in organic matter may reduce bacterial presence and survival. The study highlights the significant influence of environmental factors on the seasonal spread of Leptospira, particularly in tropical and subtropical regions. These findings are crucial for public health planning, providing insights for targeted policies to reduce leptospirosis, while advanced machine learning models serve as a robust tool for improving disease surveillance, and risk assessment, which ultimately supports the development of an early warning system.

Analysis of Regional Characteristics of Climate Change Factors Affecting Water Distribution Pipe Leakage

Understanding the factors behind urban water leakage is crucial for developing a sustainable climate and protecting civil infrastructure. Water leaks not only waste essential resources but also increase urban vulnerabilities to climate-induced disasters. This study investigates the teleconnection between leakage incidents and climate change indices to establish predictive insight for water management. It focuses on climate phenomena such as El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), which significantly influence global climate dynamics, affecting temperature and precipitation in South Korea. Using Pearson correlation analysis and Granger causality tests, this research examines climate indices and leakage data across South Korea’s inland regions from 2009 to 2022. The results indicate that ENSO indices exhibit a lead time of 6 to 30 months, with significant correlations in coastal areas, particularly Chungnam (west coast) and Gyeongnam (east coast). Inland regions such as Gimcheon and Chuncheon also showed notable correlations influenced by topographical factors. The findings highlight the importance of integrating climate teleconnection indices into risk management strategies. This approach allows for targeted monitoring and predictive modeling, enabling proactive responses to water leakage risks and contributing to sustainable urban development.

Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML

Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims to evaluate the flash flood susceptibility in the Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by the H2O automated ML platform. The best-performing model was used to generate a flash flood susceptibility map, and its interpretability was analyzed using the Shapley Additive Explanations (SHAP) tree interpretation method. The results revealed that the top four models, including both single and ensemble models, demonstrated high accuracy in the tests. The flash flood susceptibility map generated by the best-performing eXtreme Randomized Trees (XRT) model showed that 8.92%, 12.95%, 15.42%, 31.34%, and 31.37% of the study area exhibited very high, high, moderate, low, and very low flash flood susceptibility, respectively, with approximately 74.9% of the historical flash floods occurring in areas classified as moderate to very high susceptibility. The SHAP plot identified topographic factors as the primary drivers of flash floods, with the importance analysis ranking the most influential factors in such descending order as DEM, topographic wetness index, topographic position index, normalized difference vegetation index, and average multi-year precipitation. This study demonstrates the benefits of interpretable machine learning, which can provide guidance for flash flood mitigation.

Research on the Effect of Topographic Factors on Extreme Rainfall in South China

Research on the Effect of Topographic Factors on Extreme Rainfall in South China

Effects of Topography on Vegetation Coverage Changes in Rural Areas Under Multi-scale Scenarios

Revealing the characteristics of vegetation coverage change and the effects of topography on vegetation coverage change in rural areas since the reform and opening up has direct implications for further understanding of human-land coupling processes and resource and environmental changes and provides a reference for ecological environment protection in rural revitalization. Using the Taihua Town in Yixing City, Jiangsu Province, as a rural case study, we revealed the basic features of rural vegetation coverage change from 1986 to 2020. Furthermore, using 0.25 km2（scenario 1）, 0.50 km2（scenario 2）, and 1.00 km2 （scenario 3） as scale scenarios in the study area, both the single and comprehensive topographic effects on vegetation coverage change were explored. The study produced several important results： ① Vegetation coverage in the study area generally maintained an upward trend from 1986 to 2020, during which the spatial pattern of vegetation coverage distribution was generally high in the southeast and southwest and low in the north （northwest）. During this period, the vegetation coverage in the study area increased from 74.15% to 83.44%, and the spatial pattern of vegetation coverage distribution generally remained similar. ② Under the multi-scale scenarios, the vegetation coverage distribution in the study area generally maintained a similar spatial pattern from 1986 to 2020, excepting a few local differences. Under the same-scale scenario, Moran's index of vegetation coverage in the study area was higher in 2020 than in 1986. Taking scenario 1 as an example, Moran's index of vegetation coverage in the study area increased from 0.603 to 0.652 between 1986 and 2020. ③ Under the multi-scale scenarios, from 1986 to 2020, vegetation coverage in the study area generally maintained an upward trend with increase of elevation, slope, and topographic relief. As an example of the vegetation coverage change with elevation, in scenario 1 in 2020, the vegetation coverage in the study area increased from 64.50% in the area with elevations of 0-50 m to 98.77% in the area with elevations above 350 m. ④ Under the multi-scale scenarios, the comprehensive topographic effects on mean vegetation coverage change in the study area from 1986 to 2020 showed that elevation, slope, and topographic relief all had positive effects, and their effects were greater in scenario 3 than in scenario 2 and then in scenario 1 for the same topographic factor. According to the comprehensive topographic effects on mean vegetation coverage change in the study area from 1986 to 2020, the equation simulation coefficients of elevation under scenarios 1, 2, and 3 were 0.271, 0.281, and 0.283, respectively. The respective slope values were 0.283, 0.289, and 0.294, and the topographic relief values were 0.283, 0.292, and 0.295. This suggests that the scale variation has impacts on the comprehensive topographic effects. The results of this study are valuable for understanding the changes in the rural ecological environment under multi-scale scenarios since the reform and opening up.

QUANTITATIVE ANALYSIS OF GEOMORPHIC RESPONSES AND TOPSIS APPROACHE ANALYSIS TO ACTIVE TECTONIC PROCESS: A CASE STUDY OF THE NORTHEAST OF THE AURES (TEBESSA)

This study combines GIS and remote sensing technologies to analyze geomorphological changes in the Tebessa basin, focusing on key indices such as hypsometric integral (HI) and stream sinuosity (SS). The objective was to assess how drainage anomalies and tectonic activity influence flow and relief patterns. Asymmetry and topographic factors were incorporated into a geographic information system, and the region was classified into four IRAT classes: high, medium, low, and rank. Results identified highly deformed areas near active tectonic anomalies, highlighting subsurface fault activity and its influence on river sinuosity. A new diagram illustrates the spatial evolution of these structures, providing insight into the region’s geological dynamics. The study also applied the TOPSIS method to prioritize watershed areas based on tectonic activity, offering a comprehensive analysis of the basin's geomorphological changes.

Global assessment of vegetation patterns along topographic gradients

ABSTRACT The complex topography in mountainous regions, exemplified by factors like slope aspect, leads to noticeable variations in vegetation patterns, which are fundamental for understanding mountain ecosystems. However, a consistent global-scale quantification of topography's influence on vegetation patterns is still lacking. Here, we utilize two phenological metrics as proxies for vegetation-maximum vegetation greenness and seasonal greenness amplitude-computed from Sentinel-2 images, to quantify differences across three topographic factors: slope aspect, steepness, and elevation within each 0.15°×0.15° mountain grid. Our mapping reveals clear geographic patterns indicating that topography strongly influences vegetation in arid and polar ecosystems, with an influence approximately 1.9 times higher than in temperate ecosystems. Topography is also important in humid regions, as demonstrated by diverse vegetation types growing on different slope aspects, steepness levels, and elevations. Additionally, the impacts of slope aspect, steepness, and elevation vary within the same region. In 25.9% of mountain grids, slope aspect causes the largest difference in vegetation patterns, while elevation and steepness account for 43.1% and 31%, respectively. Our study highlights the hotspot areas where topographic effects on vegetation patterns are most pronounced, enabling researchers to focus on these regions for better parameterization of Earth system models.

THE CHARACTERIZATION OF HIGH-ALTITUDE GRASSLANDS IN THE ALMAJ MOUNTAINS

Grasslands are essential for providing animal feed and ecosystem services, having important roles in regulating the climate and maintaining biodiversity and natural cycles. This study analyzed the distribution and characteristics of the grasslands in the Almăj Mountains, highlighting the influence of topographic and climatic factors on them. The research was carried out in the Almăj Mountains, a mountainous area in southwestern Romania, using geospatial techniques in ArcGIS 10.4. The grasslands were delimited and analyzed based on a Digital Elevation Model (DEM), slopes, slope exposure, temperature and precipitation. Climate data were obtained from local weather stations and spatial interpolation provided a detailed analysis of environmental variables. The grasslands of the Almăj Mountains are concentrated between altitudes of 500 - 800 m (76% of the surface). Lands with slight and moderate slopes (5.1 - 15°) host 77% of them, and the dominant exposure is south and southeast, an orientation that favours grassland vegetation. Multiannual average temperatures are high, as an effect of global warming, which also affects mountainous areas with low altitudes. The distribution of grasslands in the Almăj Mountains is significantly influenced by altitude, slope, exposure and climatic conditions. Grasslands are used agro-pastorally, having a crucial importance for local communities. The geospatial methodology facilitated a detailed analysis, contributing to the development of sustainable management practices for these ecosystems.

An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost

Soil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. To compare the performance of different machine-learning models, this study conducted a soil sampling experiment on saline soils along the south bank of the Yellow River in Dalate Banner. The experiment lasted two years (2022 and 2023) during the spring bare soil period, collecting 304 soil samples. The soil salinity was estimated with the multi-source remote sensing satellite data by combining the extreme gradient boosting model (XGBoost), Optuna hyper-parameter optimization, and Shapley addition (SHAP) interpretable model. Correlation analysis and continuous variable projection were employed to identify key inversion factors. The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. The optimal model was selected to estimate soil salinity in the study area from 2019 to 2023. The results showed that the XGBoost model fitted optimally, the test set had high R2 (0.76) and the ratio of performance to deviation (2.05), and the estimation results were consistent with the measured salinity values. SHAP analysis revealed that the salinity index and topographic factors were the primary inversion factors. Notably, the same inversion factor influenced varying soil salinity estimates at different locations. The saline soils of the study area in 2019 and 2023 were 65% and 44%, respectively, and the overall trend of soil salinization decreased. From the viewpoint of spatial distribution, the degree of soil salinization showed a gradually increasing trend from south to north, and it was most serious on the side near the Yellow River. This study is of great significance for the quantitative estimation of salinized soil in the irrigated area on the south bank of the Yellow River, the prevention and control of soil salinization, and the sustainable development of agriculture.

Assessing Wildfire Risk in South Korea Under Climate Change Using the Maximum Entropy Model and Shared Socioeconomic Pathway Scenarios

For effective management and prevention, wildfire risk prediction needs to consider the substantial impacts of climate change on wildfire patterns. This study analyzed the probability of wildfire occurrence in South Korea using the Maximum Entropy (MaxEnt) model and predicted future wildfire occurrence under shared socioeconomic pathway (SSP) climate change scenarios. The model utilized historical fire occurrence data and was trained using 12 environmental variables encompassing climate, topography, vegetation, and socioeconomic factors. Future wildfire risk was predicted under the SSP2-4.5 and SSP5-8.5 scenarios for 2041–2060 and 2081–2100. Increased average temperature and solar radiation were key drivers of elevated wildfire risk, whereas increased precipitation and relative humidity reduced this risk. Under current conditions, 367,027 ha (6.52%) within the study area were classified as high-risk based on the MaxEnt model output (p > 0.6). Under both SSP scenarios, a decline in the at-risk area was observed over time. This study provides fundamental data for wildfire management and prevention strategies in South Korea and provides quantitative evidence on the potential impact of climate-related environmental changes on wildfires.

Evidential Belief Function (EBF) Model for Landslide Susceptibility Analysis in Idukki District, Kerala, India

A Geo-statistical Dempster-Shafer Theory Evidential Belief Function (DST-EBF) model is selected for Landslide susceptibility evaluation. The aim of the study is to delineate zones prone to landslides within the Idukki district, Kerala using the EBF model and Geographical Information System (GIS) Technique. The objective is to integrate diverse datasets to assess and predict the landslide-prone areas, providing valuable insights for risk management and mitigation startgies. Topographical, Anthropogenic, and Geological factors are considered Landslide Conditioning Factors (LCFs), and Landslide Inventory data are used to establish and validate the Suscebtlity zones. Landslide inventory data is randomly divided into Training(70%) and Testing (30%) data. The resultant Landslide Susceptibility map is categorized into five zones using natural breaking classification method: Very low (20.96%), Low (29.71%), Medium (24.50%), High (18.63%), and Very high (6.20%), respectively, within the study area. The success rate and prediction rate were calculated using the AUC_ROC method and the EBF Model achieved the highest precision with a success rate of 0.935, and a prediction rate of 0.943 in the current study.

Spatial Prediction of Soil Total Phosphorus in a Karst Area: Comparing GWR and Residual-Centered Kriging

Accurate soil total phosphorus (TP) prediction is essential to support sustainable agricultural practices and formulate ecological conservation protection policies, particularly in complex karst landscapes with high spatial variability and high phosphorus and cadmium content and interactions, complicating nutrient management. This study uses GIS and geostatistical methods to analyze the spatial distribution, influencing factors, and predictive modeling of soil TP in the karst region of northern Mashan County, Guangxi, China. Using 427 surface soil samples, we developed five predictive models: ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK) combined with environmental variables such as land uses, soil types, and topographic factors; residual mean-centered kriging (MM_OK), and residual median-centered kriging (MC_OK). Our results indicate that higher TP levels were observed in agricultural lands (paddy fields and dry land, at 766 and 913 mg·kg−1, respectively) may due to fertilization, while forests and shrublands showed lower TP levels (383 and 686 mg·kg−1, respectively), reflecting natural phosphorus cycling. The high-value areas of soil TP concentration are in the karst areas in the west and east of the study area, and the low-value area is in the Hongshui River valley in the north of Mashan. The spatial distribution of soil TP is affected by land use, soil type, and topography. The GWRK model exhibited superior accuracy (80.6%), with predicted concentration of TP closely aligning with observed TP values, effectively capturing fine spatial variations, and showing the lowest mean standardized error, average standard error, and mean absolute error. GWRK also achieved the highest R2 (0.67), demonstrating robust predictive capability. MM_OK and MC_OK models performed well and showed smoother spatial transitions, while the OK model displayed the lowest predictive accuracy (62%). By utilizing spatially adaptive weighting, GWRK and its residual-centered kriging method improve soil TP’s prediction accuracy and smoothness in karst areas, providing a reference for targeted soil conservation and sustainable agricultural practices in spatially complex karst environments.

A variable increase of debris cover on Pyrenean glaciers from 2000 to 2022

AbstractThe last remaining very small glaciers (< 0.5 km2) of the Pyrenees are the southernmost glaciers in Europe and respond rapidly to climate variability. Most of them are also influenced by local topographic factors and geomorphological processes impacting the energy and mass balance. This paper presents the first temporal study on the changes in debris cover on Pyrenean glaciers from 2000 to 2022 at a regional scale. The data allowed for the first analysis of the lithological characteristics of each glaciarised cirque in order to identify possible factors that determine the evolution of debris input. We manually mapped the extent of supraglacial debris with corresponding glacier outlines using very high-resolution aerial imagery and the existing glacier inventories from 2000, 2011, 2020 and 2022. The results show that debris cover on Pyrenean glaciers has increased significantly in number and extent over the study period whilst glaciers continue to decline and shrink. In 2022, 14 of the 18 remaining glaciers have debris cover greater than 10% of their area, and six have debris cover greater than 40%. The observed increase in debris cover is much stronger for glaciers determined by topoclimatic factors and located on metamorphic and sedimentary cirques, which underlines the important role of paraglacial processes in their development. Meanwhile, glaciers on granitic cirques have lower debris cover and have shown a lesser increase compared to initial measurements conditions. Future work should focus on understanding debris sources and their characteristics to determine the role of debris cover in the response of Pyrenean glaciers to climate change.

A comparative study of intelligent prediction models for landslide susceptibility: random forest and support vector machine

Colluvial landslides widely developed in mountainous and hilly areas have the characteristics of mass occurrence and sudden occurrence. How to reveal the spatial distribution rules of potential landslides quickly and accurately is of great significance for landslide warning and prevention in the study area. Landslide susceptibility prediction (LSP) modeling provides an effective way to reveal the spatial distribution of regional landslides, however, it is difficult to accurately divide slope units and select prediction models in the processes of LSP modeling. To solve these problems, this paper takes the widely developed colluvial landslides in Dingnan County, Jiangxi Province, China as the research object. Firstly, the multi-scale segmentation (MSS) algorithm is used to divide Dingnan County into 100,000 slope units, to improve the efficiency and accuracy of slope unit division. Secondly, 18 environmental factors with abundant types and clear meanings, including topography, lithology and hydrological environment factors, were selected as input variables of LSP models. Then, a widely representative Support Vector Machine (SVM) and Random Forest (RF) models were selected to explore the difference characteristics of various machine learning models in predicting landslide susceptibility. Finally, the comprehensive evaluation method is proposed to compare the accuracy of various slope unit-based machine learning methods for LSP. The results show that the MSS algorithm can divide slope units in Dingnan County efficiently and accurately. The RF model (AUC = 0.896) has a higher LSP accuracy than that of the SVM model (AUC = 0.871), and the landslide susceptibility indexes (LSI) predicted by the RF model have a smaller mean value and a larger standard deviation than those of the SVM model. Conclusively, the overall performance of RF model in predicting landslide susceptibility is higher than that of SVM model.

Spatial Sediment Erosion and Yield Using RUSLE Coupled with Distributed SDR Model

Estimating sediment yield in a river is a challenging task in the water resources field. Different methods are available for estimating sediment erosion and yield, but generally they are not spatially distributed in nature. This paper presents the application of the Revised Universal Soil Loss Equation (RUSLE) for estimating soil erosion and integrates it with spatially distributed Sediment Delivery Ratio (SDR) to calculate sediment yield in a Himalayan river. The study area is Kabeli sub-catchment, located upstream of the Koshi River Basin in the eastern part of Nepal. The Kabeli River is where numerous hydropower projects are envisaged, and sediment-related issues are of major concern. With the use of the RUSLE, the mean annual soil erosion is estimated at 35.96 tons/ha/yr. The estimated specific sediment yield (SSY) from the distributed SDR method is 6.74 tons/ha/yr, which is close to the observed SSY of 7.26 tons/ha/yr using the data records of ~8 years. Based on correlation analysis, the topographic factor (LS) is the most sensitive RUSLE parameter with respect to sediment erosion. The sloping areas near the river hillslope are particularly vulnerable to soil erosion. The results indicate that the approach employed in this study may be potentially applied in other catchments with similar physiographic characteristics for the estimation of sediment yield.

Spatiotemporal Variation and Driving Mechanisms of the Global Production-Living-Ecological Space Coupling Coordination Degree

The coupling coordination degree (CCD) of the production-living-ecological space (PLES) functional index is an indicator of regional sustainable development potential. However, previous studies have failed to reveal the driving mechanisms of the CCD of PLES functional index on a global scale. Therefore, this study employed the CCD model to evaluate the CCD of the PLES functional index and spatial regression models to measure the heterogeneous drivers using multi-source data in 2000, 2010, and 2020. The results demonstrated that ecological spaces dominated (85%) globally, while living spaces comprised the smallest share (3%). The ecological functional index was higher than the production and living functional indices. Further analyses revealed that topographic factors were the main restricting factors for PLES; the proportion of production and living spaces decreased with increasing altitude and slope, whereas ecological spaces showed the opposite trend. The global CCD values of the PLES functional index in 2000, 2010, and 2020 were 0.186, 0.189, and 0.198, respectively, showing an increasing trend. High CCD was generally observed in areas with dense population and industry where human activity systems interact with natural ecosystems. The formation of the CCD of the global PLES functional pattern results from the joint action of natural and socioeconomic factors, with pronounced spatial heterogeneity. Our findings can help optimize global territorial space utilization, improve territorial space utilization efficiency, and realize global sustainable development goals.

Canine Vector-Borne Diseases (CVBDs) in Liguria, North-West Italy: A Retrospective Study over an 11-Year Period (2013-2023).

Canine vector-borne diseases (CVBDs) pose a global threat to both canine and public health. This study evaluates the prevalence of Anaplasma spp., Ehrlichia spp., Borrelia burgdorferi sensu lato (s.l.), Rickettsia conorii, and Dirofilaria immitis in domestic dogs in Liguria, north-west Italy, a region where data were lacking. From 2013 to 2023, 8584 blood samples from shelter (74%) and owned dogs (26%) were submitted to the Istituto Zooprofilattico Sperimentale of Piemonte, Liguria and Valle d'Aosta (IZSPLV) for serological testing (indirect immunofluorescence and/or rapid immunochromatographic tests). Overall, 18.8% (95% CI: 18.0-19.7) of the dogs tested positive for at least one pathogen, with positivity against R. conorii antigen being the most frequently recorded (24.4%, 95% CI: 23.3-25.5). Lower prevalence levels were observed for Anaplasma spp. (1.82%, 95% CI: 1.47-2.23), Ehrlichia spp., (1.25%, 95% CI: 0.97-1.60), B. burgdorferi s.l. (0.22%, 95% CI: 0.11-0.39), and D. immitis (0.84%, 95% CI: 0.65-1.06). Positive cases for all pathogens increased over time, with prevalence differing significantly between owned and shelter dogs. Topographical factors, land use, and monthly relative humidity appeared to influence the positivity in the dogs. These results update the epidemiology of the investigated CVBDs in Liguria, indicating a widespread exposure to Rickettsia spp. among local dogs.

Study on Soil Freeze–Thaw and Surface Deformation Patterns in the Qilian Mountains Alpine Permafrost Region Using SBAS-InSAR Technique

The Qilian Mountains, located on the northeastern edge of the Qinghai–Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai–Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze–thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze–thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east–west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai–Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety.

Response of Topsoil Organic Carbon in the Forests of Northeast China Under Future Climate Scenarios

Soil organic carbon (SOC) serves as a highly sensitive indicator of climate change and plays a crucial role in terrestrial carbon cycles. Evaluating the impact of regional land use changes on SOC stocks is essential for assessing ecological and environmental effects. In this study, we utilized 157 soil samples and 11 environmental variables—including soil properties, topographic factors, and climatic conditions—to develop boosted regression tree (BRT) and random forest (RF) models to estimate topsoil SOC stocks for the year 2015. We used a 10-fold cross-validation approach, along with four validation metrics, to assess model performance. The BRT model demonstrated superior accuracy, with a higher R2 and Lin’s consistency correlation coefficient and a lower mean absolute error and root mean square error compared to the RF model. The key environmental factors influencing SOC stock variability in the BRT model included mean annual temperature, elevation, mean annual precipitation, the topographic wetness index (TWI), and catchment area. Based on this, we employed the space-for-time substitution approach and BRT model to forecast the spatial distribution of soil organic carbon (SOC) stocks in Northeast China’s forested regions under future climate scenarios for the 2050s and 2090s. Our findings indicate that, compared to the 2015 levels, the forecast indicates that SOC stocks will decrease by 122 Tg carbon and 123 Tg carbon under two different future scenarios, SSP245 and SSP585, respectively, by the 2050s. By the 2090s, these figures are expected to decrease further by 127 Tg C and 126 Tg C, respectively. Throughout both future periods, SOC stocks will predominantly be concentrated in the northwest region. This research highlights the necessity of thoroughly considering climatic factors in future studies of regional SOC stock dynamics. Moreover, the high-resolution maps produced in this study offer a scientific foundation for enhancing the implementation of ecological management practices in the forested regions of Northeast China, fostering environmental improvement and bolstering SOC and soil management strategies in response to future climate change.

Assessing the Global Sensitivity of RUSLE Factors: A Case Study of Southern Bahia, Brazil

Global sensitivity analysis (GSA) of the revised universal soil loss equation (RUSLE) factors is in its infancy but is crucial to rank the importance of each factor in terms of its non-linear impact on the soil erosion rate. Hence, the goal of this study was to perform a GSA of each factor of RUSLE for a soil erosion assessment in southern Bahia, Brazil. To meet this goal, three non-linear topographic factor (LS factor) equations alternately implemented in RUSLE, coupled with geographic information system (GIS) software and a variogram analysis of the response surfaces (VARSs), were used. The results showed that the average soil erosion rate in the Pardo River basin was 25.02 t/ha/yr. In addition, the GSA analysis showed that the slope angle which is associated with the LS factor was the most sensitive parameter, followed by the cover management factor (C factor) and the support practices factor (P factor) (CP factors), the specific catchment area (SCA), the sheet erosion (m), the erodibility factor (K factor), the rill (n), and the erosivity factor (R factor). The novelty of this work is that the values of parameters m and n of the LS factor can substantially affect this factor and, thus, the soil loss estimation.