Search Results (237)

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

The Mw 7.8 Gorkha Earthquake of 25 April 2015 triggered over 25,000 landslides across central Nepal, with 4775 events concentrated in Gorkha District alone. Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. Using a balanced dataset of 4775 landslide and non-landslide instances, we evaluated the performance of Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models through spatial cross-validation, SHapley Additive exPlanations (SHAP) explainability, and ablation analysis. The RF model outperformed all others, achieving an accuracy of 87.9% and a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) value of 0.94, while XGBoost closely followed (AUC = 0.93). Ensemble models collectively classified over 95% of observed landslides into High and Very High susceptibility zones, demonstrating strong spatial reliability. SHAP analysis identified elevation, proximity to fault, peak ground acceleration (PGA), slope, and rainfall as dominant predictors. Notably, the inclusion of post-seismic rainfall substantially improved recall and F1 scores in ablation experiments. Spatial cross-validation revealed the superior generalizability of ensemble models under heterogeneous terrain conditions. The findings underscore the value of integrating post-seismic hydrometeorological factors and spatial validation into susceptibility assessments. We recommend adopting ensemble models, particularly RF, for operational hazard mapping in earthquake-prone mountainous regions. Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience. Full article

As an integral component of humanity’s cultural heritage, traditional villages universally confront challenges such as population loss and cultural discontinuity amid rapid urbanization. Cluster-based protection models have increasingly become the international consensus for addressing the survival crisis of such settlements. This study selects the Central Yunnan region of Southwest China—characterized by its complex geography and multi-ethnic habitation—as the research area. Employing ArcGIS spatial analysis techniques alongside clustering algorithms, we examine the spatial distribution characteristics and clustering patterns of 251 traditional villages within this region. The findings are as follows. In terms of spatial distribution, traditional villages in Central Yunnan are unevenly dispersed, predominantly aggregating on mid-elevation gentle slopes; their locations are chiefly influenced by rivers and historical courier routes, albeit with only indirect dependence on waterways. Regarding single-cluster attributes, the spatial and geomorphological features exhibit a composite “band-and-group” pattern shaped by river valleys; culturally, two dominant modes emerge—“ancient-route-dependent” and “ethnic-symbiosis”—reflecting an economy-driven cultural mechanism alongside latent marginalization risks. Concerning construction characteristics, the “Qionglong-Ganlan” and Han-style “One-seal” residential features stand out, illustrating both adaptation to mountainous environments and the cumulative effects of historical culture. Based on these insights, we propose a three-tiered clustering classification framework—“comprehensive-element coordination”, “feature-led”, and “potential-cultivation”—to inform the development of contiguous and typological protection strategies for traditional villages in highland, multi-ethnic regions. Full article

Reliable gridded precipitation products (GPPs) are essential for effective hydrological simulations, particularly in mountainous regions with limited ground-based observations. This study evaluates the performance of two widely used GPPs, CHIRPS and ERA5, in estimating precipitation and supporting runoff generation using the Soil and Water Assessment Tool (SWAT) across three headwater catchments (Sill, Drava and Isel) in the Austrian Alps from 1991 to 2018. The region’s complex topography and climatic variability present a rigorous test for GPP application. The evaluation methods combined point-to-point comparisons with gauge observations and assessments of generated runoff and runoff trends at annual, seasonal and monthly scales. CHIRPS showed a lower precipitation error (RMAE = 25%) and generated more consistent runoff results (RMAE = 12%), particularly in smaller catchments, whereas ERA5 showed higher spatial consistency but higher overall precipitation bias (RMAE = 37%). Although both datasets successfully reproduced the seasonal runoff regime, CHIRPS outperformed ERA5 in trend detection and monthly runoff estimates. Both GPPs systematically overestimate annual and seasonal precipitation amounts, especially at lower elevations and during the cold season. The results highlight the critical influence of GPP spatial resolution and its alignment with catchment morphology on model performance. While both products are viable alternatives to observed precipitation, CHIRPS is recommended for hydrological modelling in smaller, topographically complex alpine catchments due to its higher spatial resolution. Despite its higher precipitation bias, ERA5’s superior correlation with observations suggests strong potential for improved model performance if bias correction techniques are applied. The findings emphasize the importance of selecting GPPs based on the scale and geomorphological and climatic conditions of the study area. Full article

Morphological problems for distinguishing between glacier ice, glacier ice with a debris cover (debris-covered glaciers), and rock glaciers are outlined with respect to recognising and mapping these features. Decimal latitude–longitude [dLL] values are used for geolocation. One model for rock glacier formation and flow discusses the idea that they consist of ‘mountain permafrost’. However, signs of permafrost-derived ice, such as flow features, have not been identified in these landsystems; talus slopes in the neighbourhoods of glaciers and rock glaciers. An alternative view, whereby rock glaciers are derived from glacier ice rather than permafrost, is demonstrated with examples from various locations in the mountain domain, 𝔻𝕞. A Google Earth and field examination of many rock glaciers shows glacier ice exposed below a rock debris mantle. Ice exposure sites provide ground truth for observations and interpretations stating that rock glaciers are indeed formed from glacier ice. Exposure sites include bare ice at the headwalls of cirques and above debris-covered glaciers; additionally, ice cliffs on the sides of meltwater pools are visible at various locations along the lengths of rock glaciers. Inspection using Google Earth shows that these pools can be traced downslope and their sizes can be monitored between images. Meltwater pools occur in rock glaciers that have been previously identified in inventories as being indictive of permafrost in the mountain domain. Glaciers with a thick rock debris cover exhibit ‘hidden ice’ and are shown to be geomorphological units mapped as rock glaciers. Full article

This article focuses on forecasting morphological changes in small rivers, using the Ulken Almaty River, located on the northern slope of the Ile Alatau range in the Tien Shan mountain system, as a case study. One of the key components of river morphology is the dynamics of channel processes, including erosion, accretion, and the shifting of channel forms. Understanding these processes in rivers flowing through urbanized areas is essential for mitigating environmental and infrastructural risks. Despite their importance, studies of this nature in Kazakhstan remain at a formative stage and are largely fragmentary, underscoring the need for modern approaches to river morphology analysis. Three representative sections of the Ulken Almaty River (upstream, midstream, and downstream) were selected for analysis. Satellite imagery from 2012 to 2021 was used for manual digitisation of river channel outlines. Annual erosion and accretion areas were calculated based on these data. The DSAS 5.1 module, integrated into ArcGIS 10.8.1, was applied to determine the rates of erosion and accretion over the ten-year period. To forecast future channel changes, the Kalman filter model was employed, enabling projections for 10 and 20 years into the future. A comparative analysis of the intensity of the erosion and accretion processes was conducted for each river section. Spatial and temporal variations in bank dynamics were identified, with the most significant changes occurring in the middle and lower reaches. Forecasted scenarios indicate the possible deformation pathways of the river channel influenced by both natural and anthropogenic factors. The results provide valuable insights into the spatiotemporal dynamics of fluvial processes in small mountain rivers under the pressure of urban development and climatic variability. The methodology employed in this study offers practical applications for urban planning, river management, and the mitigation of geomorphological hazards. Full article

Arid grasslands exhibit high proportions of belowground biomass (BGB), yet the climatic influence on BGB carbon density remains poorly understood. Accurately estimating BGB carbon density in arid grassland vegetation presents a significant challenge. Using extensive field sampling, multi-source remote sensing data, and machine learning methods, the spatial distribution of BGB carbon density across Xinjiang grasslands was estimated, and its environmental drivers across different geomorphological regions were revealed. The results show that BGB carbon density accounts for 93.8–97.2% of total carbon density in Xinjiang grassland, with notably high proportions exceeding 97% in the Junggar Basin, Kunlun Mountains, and Altun Mountains regions. From 2000 to 2023, BGB carbon density increased significantly (p < 0.01) from 1175.18 gC·m⁻² to 1379.09 gC·m⁻², with significant increases observed in the Junggar Basin, Tarim Basin, Kunlun Mountains, and Altun Mountains. In addition, environmental factor analysis revealed distinct soil moisture threshold effects governing BGB carbon density-precipitation relationships: carbon density increases linearly with precipitation when soil moisture remains below 0.2 m³·m⁻³, shows a parabolic relationship between 0.2 and 0.4 m³·m⁻³, and decreases with increasing precipitation when soil moisture exceeds 0.4 m³·m⁻³. Soil moisture and precipitation emerge as dominant factors influencing BGB carbon density changes, with regional variations in their relationships. These findings provide critical insights into carbon sequestration dynamics in arid grassland ecosystems and their response mechanisms under climate change. Full article

Understanding a catchment’s geomorphological and erosion processes is essential for sustainable land management and soil conservation. This study investigates the Xynias drained lake catchment in Central Greece using a twofold geospatial modelling approach that combines morphometric analysis with the Revised Universal Soil Loss Equation (RUSLE) to evaluate the area’s landscape evolution, surface drainage features, and soil erosion processes. The catchment exhibits a sixth-order drainage network with a dendritic and imperfect pattern, shaped by historical lacustrine conditions and the carbonate formations. The basin has an elongated shape with steep slopes, high total relief, and a mean hypsometric integral value of 26.3%, indicating the area is at an advanced stage of geomorphic maturity. The drainage density and frequency are medium to high, reflecting the influence of the catchment’s relatively flat terrain and carbonate formations. RUSLE simulations also revealed mean annual soil loss to be 1.16 t ha⁻¹ y⁻¹ from 2002 to 2022, along with increased erosion susceptibility in hilly and mountainous areas dominated by natural vegetation. In comparison to these areas, agricultural regions displayed less erosion risk. These findings demonstrate the effectiveness of combining GIS with remote sensing for detecting erosion-prone areas, informing conservation initiatives. Along with the previously stated results, more substantial conservation efforts and active land management are required to meet the Sustainable Development Goals (SDGs) while considering the monitored land use changes and climate parameters for future catchment management. Full article

Sedimentary basins developed in mountain belts are natural traps of catchment erosion products and can produce comprehensive palaeoflood records that extend beyond instrumental or historical data. This study investigates the Lake Moo plain (1120 m a.s.l.), located in the Mt. Ragola (1712 m a.s.l.) ophiolitic massif in the Northern Apennines (Italy), which serves as an excellent case study for inferring the chronology of past flood events due to its position relative to the dominant atmospheric flow and its favorable geological and geomorphological characteristics. The Northern Apennines is a relatively understudied region regarding the reconstruction of past Holocene flood activity through the analysis of lake sediments and peat bogs, compared with areas like the Alps. The main objective of this research was to analyze sediment cores taken from a lake situated in a catchment area dominated by ultramafic rock lithologies and associated residual weathering cover deposits. This allowed us to detect and characterize past flood events in the Ligurian–Emilian Apennines. A multidisciplinary approach, integrated with reference data on geology, geomorphology, pedology, and petrography, enabled a more detailed description of the changes in the hydrologic cycle. Collectively, these data suggest that periods of increased past flood activity were closely linked to phases of rapid climate change at the scale of the Ligurian–Emilian Apennines. The preliminary results suggest that floods occurring during periods of temperature drops have distinct characteristics compared with those during temperature rises. Full article

The Qinghai–Tibet Plateau is one of the most geologically active regions in the world, characterized by significant geomorphic variation and a wide range of geological hazards. The multifactorial coupling of tectonic movements, geomorphological evolution, climate variability, and lithological characteristics contributes to the pronounced spatial heterogeneity of the disaster-inducing environment. Identifying key controlling factors and their driving mechanisms is crucial for effective regional disaster prevention and mitigation. This study adopts a systematic framework based on regional disaster systems theory, integrating tectonic activity, engineering geology, topography, and precipitation to construct a multi-factor zoning system. Using the Random Forest model, we quantify factor contributions and delineate eight distinct disaster-inducing environment zones. Zones I–III (Himalayas–Hengduan Mountains–Qilian Mountains) are characterized by a dominant coupling mechanism of “tectonic fragmentation—topographic relief—precipitation erosion” and account for the majority of large-scale disasters. In contrast, Zones IV–VIII, primarily located in the central–western Plateau basins, are constrained by limited material sources, resulting in lower disaster densities. The findings indicate that geological structures and lithological fragmentation provide the material foundation for hazard occurrence, while topographic potential and hydrodynamic forces serve as critical triggering conditions. This nonlinear coupling of factors shapes a disaster geographic pattern characterized by “dense in the east and sparse in the west”. Based on these results, the targeted recommendations proposed offer valuable theoretical insights and methodological guidance for disaster mitigation and region-specific management across the Qinghai–Tibet Plateau. Full article

Traditional villages contain rich natural and humanistic information, and exploring the spatial distribution characteristics and cultural landscape zoning of traditional villages can provide scientific support for their centralized and continuous protection and renewal and sustainable development. In this study, 326 traditional villages in the northern Henan region were taken as the research object, followed by analyzing their spatial distribution characteristics by using geostatistical methods, such as nearest-neighbor index, imbalance index, geographic concentration index, etc., combining the theory of cultural landscape to construct the traditional villages’ cultural factor index system, extracting the cultural factors of the traditional villages to form a database, and adopting the K-means clustering method to divide the region. The results show that the spatial distribution of traditional villages in northern Henan tends to be concentrated overall, with an uneven distribution throughout the region. The density is highest in the northwestern part of Hebi City and lower in the central and southern parts of Xinxiang City, Neihuang County, and Puyang City. Based on the cultural factor index system, the K-means algorithm divides the traditional villages in northern Henan into six clusters. Among them, the five cultural factors of topography and geomorphology, building materials, courtyard form, structural system, and altitude and elevation are the most significant, and they are the cultural factors that dominate the landscape of the villages. There is a significant correlation between topography, altitude, and other cultural factors, while the correlation between the street layout and other factors is the lowest. Based on the similarity between the clustering results and the landscape characteristics, the traditional villages in northern Henan can be divided into the stone masonry building culture area along the Taihang Mountains, the brick and stone mixed building culture area in the low hills of the Taihang Mountains, the brick and wood building culture area in the North China Plain, and the raw soil building culture area in the transition zone of the Loess Plateau. Full article

The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. This study developed a spatial–temporal adaptive fusion model integrating Landsat 30-m data with MODIS daily observations to generate continuous high-precision dNBR datasets. Using a typical karst fire region in Guizhou and Yunnan, China, as a case study, we validated the method’s effectiveness for fire trace extraction in fragmented landscapes. The proposed fusion technique addresses cloud cover limitations in humid climates by constructing continuous NBR time series, enabling precise fire boundary delineation and severity quantification. We comparatively implemented multiple fusion approaches (FSDAF, STARFM, and STDFA) and evaluated their performance through both spectral (RMSE, AD, and PSNR) and spatial (Edge, LBP, and SSIM) metrics. Key findings include the following: (1) FSDAF outperformed other methods in spectral consistency and spatial adaptation, particularly for heterogeneous mountainous terrain with fragmented vegetation. (2) Comparative experiments demonstrated that pre-calculating vegetation indices before temporal fusion (Strategy I) produced superior results to post-fusion calculation (Strategy II). Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. This study advances fire monitoring in karst regions by significantly improving both the spatio-temporal resolution and accuracy of burn scar detection compared to conventional approaches. The framework provides a viable solution for fire impact assessment in topographically complex landscapes under cloudy conditions. Full article

Terraces have long transformed steep slopes into gradual steps, reducing erosion and enabling agriculture on marginal land. In France’s Roya Valley, these dry stone structures, neglected for decades, demonstrated remarkable resilience during storm Alex in October 2020. This prompted civil society and researchers to identify terraces that could support food security and agri-tourism initiatives. This study aimed to develop a semi-automatic method for detecting and mapping terraced areas using LiDAR and orthophoto data from French repositories, processed with GIS and analyzed through a Support Vector Machine (SVM) classification algorithm. The model identified 18 terraces larger than 1 hectare in Saorge and 35 in La Brigue. Field visits confirmed evidence of abandonment in several areas. Accuracy tests showed a user accuracy (UA) of 97% in Saorge and 72% in La Brigue. This disparity reflects site-specific differences, including terrain steepness, vegetation density, and data resolution. These results highlight the value of machine learning for terrace mapping while emphasizing the need to account for local geomorphological and data-quality factors to improve model performance. Enhanced terrace detection supports sustainable land management, agricultural revitalization, and risk mitigation in mountainous regions, offering practical tools for future landscape restoration and food resilience planning. Full article

This study aimed to reveal intraspecific variations in two Dryomys species distributed in Türkiye, based on mitochondrial DNA cytochrome b gene sequences, and to discuss the factors driving these variations in the context of phylogeography and genetic species concepts. As a result of Maximum Likelihood, Bayesian Inference, and Network analyses, which included haplogroups or lineages from Italy, Russia, the Caucasus, and Iran identified in previous studies, along with Turkish haplotypes, three major clades (MC1, MC2, and MC3) were identified within Dryomys nitedula. These clades began to diverge evolutionarily in the middle of the Late Miocene (8.82 million years ago) and exhibit significant genetic differences from one another. The Turkish haplotypes were divided into five distinct lineages (N1–N5), each within five subclades (SC1–SC5), which were nested within these MCs. These lineages, their geographical distributions, and the subspecies defined in previous studies that correspond to these lineages are as follows: N1 from the Thrace region (Dryomys nitedula wingei), N2 from the Black Sea region (potentially a new subspecies), N3 from western and central Anatolia (Dryomys nitedula phrygius), N4 from northeastern Anatolia (Dryomys nitedula tichomirowi), and N5 from eastern Anatolia (Dryomys nitedula pictus). The N2 lineage, distributed in areas close to the coastal side of the Eastern Black Sea region and with a range close to both N3 (D. n. phrygius) and N4 (D. n. tichomirowi), exhibited high genetic differentiation from these two lineages and was a candidate to be treated as a new subspecies of Dryomys nitedula in Türkiye. The N5 lineage, which includes haplotypes from the distribution areas of the populations initially classified as Dryomys pictus and later as Dryomys nitedula pictus in previous studies, was found to be more closely related to Dryomys nitedula kurdistanicus from the Zagros Mountains than to D. n. pictus from the central regions of Iran. Combining the results of this study with previous research, it is clear that the D. nitedula lineages in Türkiye, along with haplogroups or subspecies in neighboring regions diverged between the middle Late Miocene and Middle Pleistocene. This divergence is believed to have been driven by climatic cycles and geomorphological processes that shaped the topography of their distribution range. The high genetic diversity observed in the lineages of Anatolia suggests that the region may have served as a glacial refuge for D. nitedula. Similarly to the processes and factors shaping the evolution of D. nitedula, Dryomys laniger was found to have diverged into two lineages, western (L1) and eastern (L2 or Dryomys anatolicus), within its distribution range during the Late Pliocene (2.94 Mya). To make a more accurate taxonomic assessment of D. laniger, a larger number of samples is needed, and the distribution limits should be more clearly defined. Full article

Golija Mountain, located in the southwestern part of Serbia, has been under protection as the Golija Nature Park since 2001. It is protected to preserve its forest ecosystems, diverse landscapes of exceptional beauty, and cultural heritage. Due to its natural and cultural values, the Golija Nature Park was declared a UNESCO Biosphere Reserve under the name “Golija-Studenica” in the same year. In addition to its ecosystem values, due to the complex geological and geomorphological past, there are a significant number of geodiversity objects on the mountains in the park. Research on these geodiversity objects has been the focus of the park’s administration in recent years. This protected natural area faces several challenges, with the sustainable development of tourism being one of the most significant. The construction of a large ski center is planned, which has already triggered the spontaneous development of unregulated weekend settlements near the mountain’s highest peaks. Geotourism provides an alternative to this development. Geosites, as the most representative landscapes and landforms, serve as key attractions for geotourists. The main goal of this work was to find appropriate geoactivities related to geosites that will enhance the geotourism offer, all with the aim of achieving the sustainable development of the Golija Nature Park. Full article