Search Results (59)

Accurately monitoring terrestrial water storage (TWS) variations is essential due to global climate change and growing water demands. This study investigates TWS changes in Oregon, USA, using Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory, Gravity Recovery and Climate Experiment (GRACE) level-3 mascon data from the Jet Propulsion Laboratory (JPL), and Noah model data from the Global Land Data Assimilation System (GLDAS) data. The results show that the GNSS inversion offers superior spatial resolution, clearly capturing a water storage gradient from 300 mm in the Cascades to 20 mm in the basin and accurately distinguishing between mountainous and basin areas. However, the GRACE data exhibit blurred spatial variability, with the equivalent water height amplitude ranging from approximately 100 mm to 145 mm across the study area, making it difficult to resolve terrestrial water storage gradients. Moreover, GLDAS exhibits limitations in mountainous regions. The GNSS can provide continuous dynamic monitoring, with results aligning well with seasonal trends seen in GRACE and GLDAS data, although with a 1–2 months phase lag compared to the precipitation data, reflecting hydrological complexity. Future work may incorporate geological constraints, region-specific elastic models, and regularization strategies to improve monitoring accuracy. This study demonstrates the strong potential of GNSS technology for monitoring TWS dynamics and supporting environmental assessment, disaster warning, and water resource management. Full article

Landslide hazard chains pose significant threats in mountainous areas worldwide, yet their cascading effects remain insufficiently studied. This study proposes an integrated framework to systematically assess the landslide-landslide dam-outburst flood hazard chain in mountainous river systems. First, landslide susceptibility is assessed through a random forest model incorporating 11 static environmental and geological factors. The surface deformation rate derived from SABS-InSAR technology is incorporated as a dynamic factor to improve classification accuracy. Second, motion trajectories of rock masses in high-risk zones are identified by Rockfall Analyst model to predict potential river blockages by landslide dams, and key geometric parameters of the landslide dams are predicted using a predictive model. Third, the 2D HEC-RAS model is used to simulate outburst flood evolution. Results reveal that: (1) incorporating surface deformation rate as a dynamic factor significantly improves the predictive accuracy of landslide susceptibility assessment; (2) landslide-induced outburst floods exhibit greater destructive potential and more complex inundation dynamics than conventional mountain flash floods; and (3) the outburst flood propagation process exhibits three sequential phases defined by the Outburst Flood Arrival Time (FAT): initial rapid advancement phase, intermediate lateral diffusion phase, and mature floodplain development phase. These phases represent critical temporal thresholds for initiating timely downstream evacuation. This study contributes to the advancement of early warning systems aimed at protecting downstream communities from outburst floods triggered by landslide hazard chains. It enables researchers to better analyze the complex dynamics of such cascading events and to develop effective risk reduction strategies applicable in vulnerable regions. Full article

The Yellow River (YR), China’s second-largest river, is rich in water resources, particularly in its upper reaches, which are characterized by mountainous canyons and considerable hydropower potential. Since the 1950s, 24 reservoirs have been constructed along a 918 km stretch of the upper Yellow River (UYR), creating the highest concentration of cascade reservoirs. This development has had significant ecological impacts on the surrounding environment. This study examines the relationship between reservoir attributes and climate factors to evaluate the environmental effects of reservoirs in the UYR. (1) Following reservoir construction, the average annual temperature and precipitation increased by 3–10%, though seasonal and spatial distributions varied. Temperature increases were most pronounced in winter, while precipitation decreased in some regions during spring and summer, although the overall trend remained positive. (2) The ecosystem experienced significant post-construction changes, including reductions in arable land, grassland, and unused land, while water bodies, construction land, and forests expanded. Consequently, the ecosystem within the reservoir area now accounts for 5.2–12.5% of the total area of the region. (3) Temperature and precipitation were closely linked to reservoir attributes, with storage volume (CAP) and long-term average flow (DIS) significantly affecting precipitation, while surface area (AREA) and normal storage level (FSL) had a greater influence on temperature. In conclusion, the dual impacts of reservoir construction on local climate and land use highlight the complex environmental mechanisms involved, providing valuable insights for future reservoir development and ecological protection in the Yellow River Basin and similar regions. Full article

Rocky land cover provides vital habitat for many different species, including endemic, vulnerable, or threatened plants and animals; thus, various land management organizations prioritize the conservation of rocky habitat. Despite its importance, land cover classification maps rarely classify rocky land cover explicitly, and if they do, they are limited in spatial resolution or extent. Consequently, we used random forest models in Google Earth Engine (GEE) to classify rocky land cover at a high spatial resolution across a broad spatial extent in the Cascade Mountains and Columbia River Gorge in Washington, USA. The spectral indices derived from Sentinel-2 satellite data and NAIP aerial imagery, the specialized multi-temporal predictors formulated using time series of normalized burn ratio (NBR) and normalized difference in vegetation index (NDVI), and topographical predictors were especially important to include in the rocky land cover classification models; however, the predictors’ relative variable importance differed regionally. Beyond evaluating random forest models and developing classification maps of rocky land cover, we conducted three case studies to highlight potential avenues for future work and form connections to land management organizations’ needs. Our replicable approach relies on open-source data and software (GEE), aligns with the goals of land management organizations, and has the potential to be elaborated upon by future research investigating rocky habitats or other rare habitat types. Full article

Approximately 24% of the global land area consists of mountainous regions, with 10% of the population relying on these areas for their cultivated land. Accurate statistics and monitoring of cultivated land in mountainous regions are crucial for ensuring food security, creating scientific land use policies, and protecting the ecological environment. However, the fragmented nature of cultivated land in these complex terrains challenges the effectiveness of existing extraction methods. To address this issue, this study proposed a cascaded network based on an improved semantic segmentation model (DeepLabV3+), called Cascade DeepLab Net, specifically designed to improve the accuracy in the scenario of fragmented land features. This method aims to accurately extract cultivated land from remote sensing images. This model enhances the accuracy of cultivated land extraction in complex terrains by incorporating the Style-based Recalibration Module (SRM), Spatial Attention Module (SAM), and Refinement Module (RM). The experimental results using high-resolution satellite images of mountainous areas in southern China show that the improved model achieved an overall accuracy (OA) of 92.33% and an Intersection over Union (IoU) of 82.51%, marking a significant improvement over models such as U-shaped Network (UNet), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3+. This method enhances the efficiency and accuracy of monitoring cultivated land in mountainous areas and offers a scientific basis for policy formulation and resource management, aiding in ecological protection and sustainable development. Additionally, this study presents new ideas and methods for future applications of cultivated land monitoring in other complex terrain regions. Full article

In mountainous regions, forested areas, and other zones prone to natural disasters, power equipment faces heightened risks of shutdown. Such disruptions significantly elevate the risk of secondary cascading failures within the power grid. Consequently, devising cascading failure mitigation strategies from an operational perspective is of paramount importance for containing the spread of cascading failures in the power system during disasters and minimizing the losses incurred from disaster incidents. Firstly, based on the severity of natural disaster accident risks, this paper establishes a risk index for power equipment for the first time, providing a new perspective for the refined analysis of the development model of cascading failures in power systems. Subsequently, a new collaborative mitigation strategy for system cascading failures is proposed at the operational control level. This strategy, in conjunction with proactive prevention and control measures, aims to promptly sever potential cascading failure paths upon the occurrence of a disaster, thereby ensuring that the area of power outage is minimized to the greatest extent possible. The effectiveness of the proposed strategy is verified through simulation cases. The results show that in the scenario set in this article, the risk of cascading failures under natural disasters is nearly five times higher than that without natural disasters. At the same time, the cascading failure control method proposed in this study can reduce the risk of cascading failure by about 80%. Full article

Hydrodynamic models (HMs) are tools for simulating flow behavior through the solution of conservation equations. These equations can have different degrees of simplification, which influence the model structure. One-dimensional (1D) HMs are still popular due to their simplicity. A crucial parameter for obtaining accurate 1D HM outputs is the effective Manning roughness factor (EMRF). The EMRF reflects additional numerical and dissipative aspects beyond boundary roughness. Although generalized likelihood uncertainty estimation (GLUE) is an important method for uncertainty analysis, it requires the selection of a likelihood function and a cutoff threshold. The goal of this study was to determine the effect of the likelihood function on the EMRF characteristics for mountain river morphologies, considering a certain cutoff threshold. The results show that the error model and the treatment of the residual in the objective function affect the EMRF range and limits in the studied reaches with a cascade or step pool. Furthermore, the analysis shows that these morphologies deviate from the model structure, which may affect the likelihood curve shape. Notably, the EMRF and measured roughness did not intersect in the studied reach with a plane bed, which is attributed to the presence of vegetation on the banks of that reach. Full article

The freshwater discharge from catchments along the Gulf of Alaska, termed Alaska discharge, is characterized by significant quantity and variability. Owing to subarctic climate and mountainous topography, the Alaska discharge variations may deliver possible impacts beyond the local hydrology. While short-term and local discharge estimation has been frequently realized, a longer time span and a discussion on cascading impacts remain unexplored in this area. In this study, the Alaska discharge during 1982–2022 is estimated using the Soil and Water Assessment Tool (SWAT). The adequate balance between the model complexity and the functional efficiency of SWAT suits the objective well, and discharge simulation is successfully conducted after customization in melting calculations and careful calibrations. During 1982−2022, the Alaska discharge is estimated to be 14,396 ± 819 m³⋅s⁻¹⋅yr⁻¹, with meltwater contributing approximately 53%. Regarding variation in the Alaska discharge, the interannual change is found to be negatively correlated with sea surface salinity anomalies in the Alaska Stream, while the decadal change positively correlates with the North Pacific Gyre Oscillation, with reasonable time lags in both cases. These new findings provide insights into the relationship between local hydrology and regional climate in this area. More importantly, we provide rare evidence that variation in freshwater discharge may affect properties beyond the local hydrology. Full article

Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the materials involved. Indeed, rockfalls are generated when rocks fall from a very steep slope, while debris flow and/or mudslides are generated by fine materials like silt and clay after strong water imbibition. These events can amplify the damage caused by the initial trigger and propagate instability along a slope, often resulting in significant environmental and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici Mountains along the border of the Abruzzo and Lazio regions (Italy), serves as a notable example of the complexities and devastating consequences associated with such events. In March 2021, a substantial debris flow event obstructed the Liri River, marking the latest step in a series of landslide events. Conventional techniques such as geomorphological observations and geological surveys may not provide exhaustive information to explain the landslide phenomena in progress. For this reason, UAV image acquisition, InSAR interferometry, and pixel offset analysis can be used to improve the knowledge of the mechanism and kinematics of landslide events. In this work, the interferometric data ranged from 3 January 2020 to 24 March 2023, while the pixel offset data covered the period from 2016 to 2022. The choice of such an extensive data window provided comprehensive insight into the investigated events, including the possibility of identifying other unrecorded events and aiding in the development of more effective mitigation strategies. Furthermore, to supplement the analysis, a specific finite element method for slope stability analysis was used to reconstruct the deep geometry of the system, emphasizing the effect of groundwater-level flow on slope stability. All of the findings indicate that major landslide activities were concentrated during the heavy rainfall season, with movements ranging from several centimeters per year. These results were consistent with numerical analyses, which showed that the potential slip surface became significantly more unstable when the water table was elevated. Full article

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects. Full article

The recent mountain pine beetle outbreaks have caused widespread mortality among lodgepole pine trees in western North America, resulting in a reduced population of surviving trees. While previous studies have focused on the cascading impacts of these outbreaks on the physiology and growth of the surviving trees, there remains a need for a comprehensive study into the interactions among various physiological traits and the growth in post-outbreak stands. Specifically, the relationship between chemical (primarily terpenes) and anatomical (mainly resin ducts) defences, as well as the allocation of non-structural carbohydrates (NSCs) to support these defence modalities, is poorly understood. To address these gaps, we conducted a field survey of surviving lodgepole pine trees in post-mountain pine beetle outbreak stands in western Canada. Our retrospective analysis aimed at determining correlations between the post-outbreak concentrations of monoterpenes, diterpenes, and NSCs in the phloem and the historical resin duct characteristics and growth traits before and after the outbreak. We detected strong correlations between the post-outbreak concentrations of monoterpenes and historical resin duct characteristics, suggesting a possible link between these two defence modalities. Additionally, we found a positive relationship between the NSCs and the total concentrations of monoterpenes and diterpenes, suggesting that NSCs likely influence the production of these terpenes in lodgepole pine. Furthermore, historical tree growth patterns showed strong positive correlations with many individual monoterpenes and diterpenes. Interestingly, while surviving trees had enhanced anatomical defences after the outbreak, their growth patterns did not vary before and after the outbreak conditions. The complexity of these relationships emphasizes the dynamics of post-outbreak stand dynamics and resource allocations in lodgepole pine forests, highlighting the need for further research. These findings contribute to a broader understanding of conifer defences and their coordinated responses to forest insect outbreaks, with implications for forest management and conservation strategies. Full article

Geological mapping involves the identification of elements such as rocks, soils, and surface water, which are fundamental tasks in Geological Environment Remote Sensing (GERS) interpretation. High-precision intelligent interpretation technology can not only reduce labor requirements and significantly improve the efficiency of geological mapping but also assist geological disaster prevention assessment and resource exploration. However, the high interclass similarity, high intraclass variability, gradational boundaries, and complex distributional characteristics of GERS elements coupled with the difficulty of manual labeling and the interference of imaging noise, all limit the accuracy of DL-based methods in wide-area GERS interpretation. We propose a Transformer-based multi-stage and multi-scale fusion network, RSWFormer (Rock–Soil–Water Network with Transformer), for geological mapping of spatially large areas. RSWFormer first uses a Multi-stage Geosemantic Hierarchical Sampling (MGHS) module to extract geological information and high-dimensional features at different scales from local to global, and then uses a Multi-scale Geological Context Enhancement (MGCE) module to fuse geological semantic information at different scales to enhance the understanding of contextual semantics. The cascade of the two modules is designed to enhance the interpretation and performance of GERS elements in geologically complex areas. The high mountainous and hilly areas located in western China were selected as the research area. A multi-source geological remote sensing dataset containing diverse GERS feature categories and complex lithological characteristics, Multi-GL9, is constructed to fill the significant gaps in the datasets required for extensive GERS. Using overall accuracy as the evaluation index, RSWFormer achieves 92.15% and 80.23% on the Gaofen-2 and Landsat-8 datasets, respectively, surpassing existing methods. Experiments show that RSWFormer has excellent performance and wide applicability in geological mapping tasks. Full article

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work. Full article

The Dinaric karst in the north differs from the rest of the karst in Croatia in terms of karstification depth. The infiltrating precipitation drains in cascades from deeply karstified mountainous areas to the shallow or fluviokarst, forming the tributaries of the Kupa River. Time series analyses were conducted on a 5-year dataset to elucidate the hydrogeological conceptual model of the area and clarify disparate findings from tracer tests under varying hydrological conditions. The flow duration curve, autocorrelation functions, and recession curves were used to evaluate the spring discharge variability, the karstification degree, and the karst aquifer’s size. The crosscorrelation function and temperature dynamics were employed to assess the spring’s response to recharge and the hydrogeological system behavior. Comparative analysis with previous studies was conducted to contextualize the obtained results. The research outcomes delineated several key findings: (i) the deep karst zone is less developed than the shallow karst zone; (ii) groundwater exchange is significantly faster in shallow karst; (iii) groundwater divides in the Kapela Mountain are zonal; (iv) the homogenization of groundwater occurs during periods of high water levels; (v) fast water exchange transpires without concurrent groundwater temperature homogenization; and (vi) a definition of the boundary between deep and fluviokarst in Croatia. Full article

Climate change is increasing air temperatures and altering the precipitation and hydrological regime on a global scale. Challenges arise when assessing the impacts of climate change on the local scale for water resource management purposes, especially for low-mountain headwater catchments that not only serve as important water towers for local communities but also have distinct hydrological characteristics. Until now, no low-flow or hydrological drought studies had been carried out on the Lauter River. This study is unique in that it compares the Lauter River, a transboundary Rhine tributary, with a nearby station on the Rhine River just below its confluence at the French–German border. The Lauter catchment is a mostly natural, forested catchment; however, its water course has been influenced by past and present cultural activities. Climate change disturbances cascade through the hydrologic regime down to the local scale. As we are expecting more low-flow events, the decrease in water availability could cause conflicts between different water user groups in the Lauter catchment. However, the choice among different methods for identifying low-flow periods may cause confusion for local water resource managers. Using flow-rate time series of the Lauter River between 1956 and 2022, we compare for the first time three low-flow identification methods: the variable-threshold method (VT), the fixed-threshold method (FT), and the Standardized Streamflow Index (SSI). Similar analyses are applied and compared to the adjacent Maxau station on the Rhine River for the same time period. This study aims at (1) interpreting the differences amongst the various low-flow identification methods and (2) revealing the differences in low-flow characteristics of the Lauter catchment compared to that of the Rhine River. It appears that FT reacts faster to direct climate or anthropogenic impacts, whereas VT is more sensitive to indirect factors such as decreasing subsurface flow, which is typical for small headwater catchments such as the Lauter where flow dynamics react faster to flow disturbances. Abnormally low flow during the early spring in tributaries such as the Lauter can help predict low-flow conditions in the Rhine River during the following half-year and especially the summer. The results could facilitate early warning of hydrological droughts and drought management for water users in the Lauter catchment and further downstream along some of the Rhine. Full article