Search Results (577)

The intensifying effects of climate change have led to increased flooding, even in desert regions, resulting in significant socio-economic and ecological impacts. This study analyzes the causes and consequences of flooding in the Zhem River basin using data from ground stations, including Kazhydromet, and satellite platforms such as USGS FEWS NET and Copernicus. Spatial analyses conducted in ArcGIS utilized classified raster data to map the dynamics of flooding, snow cover, vegetation, and soil conditions. This enabled a geoecological analysis of flood damage on the vital components of the local landscape. Results show that flooding in the Zhem River basin was driven by heavy winter precipitation, rapid snowmelt, and a sharp rise in spring temperatures. The flood damaged Kulsary city and also harmed the region’s soil, vegetation, and wildlife. In July 2024, the flooded sail area tripled compared to the same period in 2023. Additionally, the area of barren land or temporary water bodies (pools) formed three months after the water receded also tripled, increasing from 84.9 km² to 275.7 km². This study highlights the critical need for continued research on the long-term environmental effects of flooding and the development of adaptive management strategies for sustainable regional development. Full article

Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We developed a robust Flood Susceptibility Model (FSM) utilizing the Maximum Likelihood Classification (MLC) model and Analytical Hierarchy Process (AHP) incorporating 11 flood-influencing factors, including “Topographic Wetness Index (TWI), elevation, slope, precipitation (rain, snow, hail, sleet), rainfall, distance to rivers and roads, soil type, drainage density, Land Use/Land Cover (LULC), and the Normalized Difference Vegetation Index (NDVI)”. The model, trained on a dataset of 850 training points, 70% for training and 30% for validation, achieved a high accuracy (AUC = 90%), highlighting the effectiveness of the chosen approach. The Flood Susceptibility Map (FSM) classified high- and very high-risk zones collectively covering approximately 61.77% of the study area, underscoring significant flood vulnerability across Punjab. The Sentinel-1A data with Vertical-Horizontal (VH) polarization was employed to delineate flood extents in the heavily impacted cities of Dera Ghazi Khan and Rajanpur. This study underscores the value of integrating Multi-Criteria Decision Analysis (MCDA), remote sensing, and Geographic Information Systems (GIS) for generating detailed flood susceptibility maps that are potentially applicable to other global flood-prone regions. Full article

Mountain ecosystems, such as Nepal’s Annapurna Conservation Area (ACA), are highly vulnerable to climate change, which threatens biodiversity, water resources, and livelihoods. This study examines Land Use Land Cover (LULC) changes, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Snow Index (NDSI), climate variability, and community perception and adaptations over a 35-year period (1988–2023) using remote sensing, meteorological data, and community surveys. Vegetation expanded by 19,800 hectares, while barren land declined, reflecting afforestation and land reclamation efforts. NDVI showed improved vegetation health, while NDSI revealed significant snow cover losses, particularly after 1996. Meteorological analysis highlighted intensifying monsoonal rainfall and rising extreme precipitation events at lower elevations. Communities reported increased flooding, unpredictable rainfall, and reduced snowfall, driving adaptive responses such as water conservation, crop diversification, and rainwater harvesting. These findings demonstrate the value of integrating scientific data with local knowledge to inform sustainable adaptation strategies. Contributing to Sustainable Development Goals (SDGs) 6 and 13, the findings emphasize the importance of adaptive water management, resilient agriculture, and participatory conservation to enhance climate resilience in mountain ecosystems. Full article

High spatial resolution snow depth (SD) is crucial for hydrological, ecological, and disaster research. However, passive microwave SD product (10/25 km) is increasingly insufficient to meet contemporary requirements due to its coarse spatial resolution, particularly in heterogeneous alpine areas. In this study, we develop a superior SD downscaling algorithm based on the FT-Transformer (Feature Tokenizer + Transformer) model, termed FTSD. This algorithm fuses the latest calibrated enhanced resolution brightness temperature (CETB) (3.125/6.25 km) with daily cloud-free optical snow data (500 m), including snow cover fraction (SCF) and snow cover days (SCD). Developed and evaluated using 42,692 ground measurements across China from 2000 to 2020, FTSD demonstrated notable improvements in accuracy and spatial resolution of SD retrieval. Specifically, the RMSE of temporal and spatiotemporal independent validation for FTSD is 7.64 cm and 9.74 cm, respectively, indicating reliable generalizability and stability. Compared with the long-term series of SD in China (25 km, RMSE = 10.77 cm), FTSD (500 m, RMSE = 7.67 cm) provides superior accuracy, especially improved by 48% for deep snow (> 40 cm). Moreover, with the higher spatial resolution, FTSD effectively captures the SD’s spatial heterogeneity in the mountainous regions of China. When compared with downscaling algorithms utilizing the raw TB data and the traditional random forest model, the CETB data and FT-Transformer model optimize the RMSE by 10.08% and 4.84%, respectively, which demonstrates the superiority of FTSD regarding data sources and regression methods. Collectively, these results demonstrate that the innovative FTSD algorithm exhibits reliable performance for SD downscaling and has the potential to provide a robust data foundation for meteorological and environmental research. Full article

Snow avalanche is one of the major natural hazards in the mountain region, yet it has received less attention compared to other mountain hazards, such as landslides, floods, and droughts. After a comprehensive overview of snow avalanche hazards in Tibet area, the spatial distribution and main driving factors of snow avalanche hazards in the high mountain region in Tibet were presented in the study first. Snow avalanche-prone areas in Tibet were then mapped based on the snow cover distribution and DEM data and were validated against in situ observations. Results show that there are the highest frequencies of avalanche occurrences in the southeastern Nyainqentanglha Mountains and the southern slope of the Himalayas. In the interior of plateau, avalanche development is constrained due to less precipitation and much flatter terrain. The perennially snow avalanche-prone areas in Tibet account for 1.6% of the total area of the plateau, while it reaches 2.9% and 4.9% of the total area of Tibet in winter and spring, respectively. Snow avalanche hazards and fatalities appear to be increasing trends under global climate warming due to more human activities at higher altitudes. In addition to the continuous implementation of engineering prevention and control measures in pivotal regions in southeastern Tibet, such as in the Sichuan–Tibet highway and railway sections, enhancing monitoring, early warning, and forecasting services are crucial to prevent and mitigate avalanche hazards in the Tibetan high mountain regions, which has significant implications for other global high mountain areas. Full article

Avalanches present substantial hazard risk in mountainous regions, particularly when avalanches obstruct roads, either hitting vehicles directly or leaving traffic exposed to subsequent avalanches during cycles. Traditional detection methods often are designed to cover only a limited section of a road stretch, hampering effective risk management. This research introduces a novel approach using Distributed Acoustic Sensing (DAS) for avalanche detection. The monitoring site in Northern Norway is known to be frequently impacted by avalanches. Between 2022–2024, we continuously monitored the road for avalanches blocking the traffic. The automated alert system identifies avalanches affecting the road and estimates accumulated snow. The system provides continuous, real-time monitoring with competitive sensitivity and accuracy over large areas (up to 170 km) and for multiple sites on parallel. DAS powered alert system can work unaffected by visual barriers or adverse weather conditions. The system successfully identified 10 road-impacting avalanches (100% detection rate). Our results via DAS align with the previous works and indicate that low frequency part of the signal (<20 Hz) is crucial for detection and size estimation of avalanche events. Alternative fiber installation methods are evaluated for optimal sensitivity to avalanches. Consequently, this study demonstrates its durability and lower maintenance requirements, especially when compared to the high setup costs and coverage limitations of radar systems, or the weather and lighting vulnerabilities of cameras. Furthermore the system can detect vehicles on the road as important supplemental information for search and rescue operations, and thus the authorities can be alerted, thereby playing a vital role in urgent rescue efforts. Full article

The simulator for hydrological unstructured domains (SHUD) is a cutting-edge, distributed hydrological model based on the finite volume method, representing the next generation of coupled surface–subsurface hydrological simulations. Its applicability in high-altitude, cold regions covered by snow and permafrost, such as the Yellow River source region, necessitates rigorous validation. This study employed the China Meteorological Forcing Dataset (CMFD) to simulate streamflow in the Yellow River source region from 2006 to 2018, comprehensively assessing the suitability of the SHUD model in this area. The SHUD model excels in simulating monthly streamflow in the Yellow River source region, while its performance at the daily scale is comparable to existing models. It demonstrated significantly better performance in the warm season compared to the cold season, particularly in the middle and lower reaches of the region. Distinct seasonal and regional differences were observed in simulation performance across sub-basins. However, the model encounters limitations when simulating the extensively distributed permafrost areas in the upstream region, primarily due to oversimplification of the permafrost thawing and freezing processes, which points the direction for future model improvements. Additionally, the model’s shortcomings in accurately simulating peak streamflow are closely related to uncertainties in calibration strategies and meteorological data inputs. Despite these limitations, the calibrated SHUD model meets the hydrological simulation needs of the Yellow River Source Region across various temporal scales, providing significant scientific reference for hydrological simulation and streamflow prediction in cold regions with snow and permafrost. Full article

Timely identification of problems in electrical distribution networks is crucial to preventing major failures, reducing costs, and ensuring a reliable power supply. This paper presents a monitoring, inspection, and early warning system designed specifically for electrical networks, utilizing satellite imagery to complement traditional inspections. The system uses spectral indices derived from satellite images to monitor environmental factors such as humidity, vegetation, snow cover, and burned areas, offering a comprehensive view of the grid’s surroundings. Collected daily, this information detects changes that may pose risks to power lines and infrastructure. The system also allows users to include custom indices, ensuring flexibility in various environmental and network contexts. An integrated AI model estimates vegetation height from Sentinel-2 images, identifying potential risk areas where vegetation could threaten power lines. One key advantage of the system is the reduced reliance on costly, frequent manual inspections, lowering operational expenses compared to other methods like aerial photography or LiDAR scanners. Additionally, it provides early alerts to grid operators when potential issues are detected, enabling timely intervention and proactive maintenance. This improves network efficiency and reliability by enhancing the response to critical situations and facilitating preventive risk management. Full article

Climate change is resulting in significant transformations in mountain areas all over the world, causing the melting of glacier ice, reduction in snow accumulation, and permafrost loss. Changes in the mountain cryosphere are not only modifying flora and fauna distributions but also affecting the stability of slopes in those regions. For all these reasons, and because of the risks these phenomena pose to the population, the dentification of dangerous areas is a crucial step in the development of risk reduction strategies. While several methods and examples exist that cover the assessment and computation of single sub-components, there is still a lack of application of risk assessment due to glacier melting over large areas in which the final result can be directly employed in the design of risk mitigation policies at regional and municipal levels. This research is focused on landslides and gravitational movements on slopes resulting from rapid glacier melting phenomena in the Valle d’Aosta region in Italy, with the aim of providing a tool that can support spatial planning in response to climate change in Alpine environments. Through the conceptualization and development of a GIS-based and multi-criteria approach, risk is then estimated by defining hazard indices that consider different aspects, combining the experience acquired from studies carried out in various disciplinary fields, to obtain a framework at the regional level. This first assessment is then deepened for the Lys River Valley, where the mapping of hazardous areas was implemented, obtaining a classification of buildings according to their hazard score to estimate the potential damage and total risk relating to possible slope instability events due to ice melt at the local scale. Full article

Optical remote sensing images have a wide range of applications but are often affected by cloud cover, which interferes with subsequent analysis. Therefore, cloud removal has become indispensable in remote sensing data processing. The Tibetan Plateau, as a sensitive region to climate change, plays a crucial role in the East Asian water cycle and regional climate due to its snow cover. However, the rich ice and snow resources, rapid snow condition changes, and active atmospheric convection in the plateau as well as its surrounding mountainous areas, make optical remote sensing prone to cloud interference. This is particularly significant when monitoring snow cover changes, where cloud removal becomes essential considering the complex terrain and unique snow characteristics of the Tibetan Plateau. This paper proposes a novel Multi-Scale Attention-based Cloud Removal Model (MATT). The model integrates global and local information by incorporating multi-scale attention mechanisms and local interaction modules, enhancing the contextual semantic relationships and improving the robustness of feature representation. To improve the segmentation accuracy of cloud- and snow-covered regions, a cloud mask is introduced in the local-attention module, combined with the local interaction module to modulate and reconstruct fine-grained details. This enables the simultaneous representation of both fine-grained and coarse-grained features at the same level. With the help of multi-scale fusion modules and selective attention modules, MATT demonstrates excellent performance on both the Sen2_MTC_New and XZ_Sen2_Dataset datasets. Particularly on the XZ_Sen2_Dataset, it achieves outstanding results: PSNR = 29.095, SSIM = 0.897, FID = 125.328, and LPIPS = 0.356. The model shows strong cloud removal capabilities in cloud- and snow-covered areas in mountainous regions while effectively preserving snow information, and providing significant support for snow cover change studies. Full article

This study analyzes the forest flammability hazard in the south of Tyumen Oblast (Western Siberia, Russia) and identifies variation patterns in fire areas depending on weather and climate characteristics in 2008–2023. Using correlation analysis, we proved that the area of forest fires is primarily affected by maximum temperature, relative air humidity, and the amount of precipitation, as well as by global climate change associated with an increase in carbon dioxide in the atmosphere and the maximum height of snow cover. As a rule, a year before the period of severe forest fires in the south of Tyumen Oblast, the height of snow cover is insignificant, which leads to insufficient soil moisture in the following spring, less or no time for the vegetation to enter the vegetative phase, and the forest leaf floor remaining dry and easily flammable, which contributes to an increase in the fire area. According to the estimates of the CMIP6 project climate models under the SSP2-4.5 scenario, by the end of the 21st century, a gradual increase in the number of summer temperatures above 35 °C is expected, whereas the extreme SSP5-8.5 scenario forecasts the tripling in the number of such hot days. The forecast shows an increase of fire hazardous conditions in the south of Tyumen Oblast by the late 21st century, which should be taken into account in the territory’s economic development. Full article

Monitoring land use/land cover (LULC) dynamics facilitates effective management and mitigation measures by providing timely and accurate information on the landscape. This study investigates LULC dynamics in Sagarmatha National Park (SNP), one of the most popular destinations for mountain tourism, and Khaptad National Park (KNP), which are emerging destinations, though popular among domestic tourists. A random forest classification algorithm was employed to generate LULC dynamics using Landsat data. High-resolution Planet Scope images and Google Earth images were used for accuracy assessment. Archived tourist and climatic data were analyzed to explore the impacts on LULC change. Cellular automata–artificial neural network (CA-ANN)-based LULC predictions were employed to predict future LULC. LULC dynamics of SNP revealed an increase in bare land, grassland, shrubland, glacial lakes, agriculture, and water bodies; however, snow/glacier and forest cover experienced substantial decreases of 140.25 km² and 15.36 km², respectively, from 1989 to 2021. In KNP, LULC dynamics showed an increasing trend in grassland, agriculture, water bodies, and bare land; however, forest and shrubland experienced a decrease of 18.63 km² and 10.48 km². The forest loss (19.33 km²) in the buffer zone of KNP was greater compared to the buffer zone of SNP (13.45 km²). The increment in built-up area was 0.80 km² in SNP and 1.11 km² in KNP, indicating escalating tourist activities and population growth. For SNP, the mean annual precipitation and temperature data from 1994 to 2023 showed decreasing and increasing patterns, respectively. However, the mean annual precipitation and temperature trends in KNP demonstrated an increasing pattern. Under the business-as-usual scenario, the estimated forest loss will be 1.61 km² in SNP by 2032 and 23.8 km² in KNP by 2030. A significant decline in snow/glaciers is projected for the core zone of SNP, with a loss of 22.84 km² expected by 2032. This study provides a baseline information on LULC changes in SNP and KNP. Further, it showcases the necessity of diversified national park policies as per the requirement. Full article

In this study, we utilized Random Forest machine learning classification to assess the current state of glaciers in the western United States using Sentinel-2A satellite imagery. By analyzing Sentinel-2A imagery from September 2020 and comparing it to the RGI inventory, the study determined the current conditions of the glaciers. Our findings unveiled a significant reduction in both glacier area and volume in the western United States since the mid-20th century. Currently, the region hosts 2878 glaciers and perennial snowfield spanning eight states, covering a total area of 428.32 ± 7.8 km² with a corresponding volume of 9.00 ± 0.9 km³. During the study period, a loss of 244.31 km² in glacier area was observed, representing a 36.32% decrease when contrasted with the RGI boundaries. The volume lost during this period amounted to 4.96 km³, roughly equivalent to 4.7 gigatons of water. Among the states, Washington experienced the most significant glacier area reduction, with a loss of 133.16 km². Notably, glaciers in the North Cascade Range of Washington, such as those in Mt. Baker and Mt. Shuksan, now cover, on average, only 85% of their original glacier boundaries with ice and snow at the conclusion of the 2020 hydrological year. Major glaciers, including the White River Glacier, West Nooksack Glacier, and White Chuck Glacier, have lost more than 50 percent of their original area. Full article

The rivers of the Himalayas heavily rely on the abundance of snow, which serves as a vital source of water to South Asian countries. However, its impact on the hydrological system of the region is mainly felt during the spring season. The melting of snow and consequent base flow significantly contribute to the incoming streamflow. This article examines the evaluation of the proportionate contribution to the total streamflow of Beas River up to Pandoh Dam through the snow melt. To analyze the snow melt, the snowmelt runoff model (SRM) has been utilized via dividing the study area into seven different elevation zones within a range of 853–6582 m and computing the percentage of snow cover, ranging from 15% to 90% across the basin. To validate the accuracy of the model, several metrics, such as coefficient of determination (R²) and volume difference (VD), are utilized. The R² reveals that over the span of ten years, the daily discharge simulations exhibited efficiency levels ranging from 0.704 to 0.795, with VD falling within the range of 1.47% to 20.68%. This study has revealed that a significant amount of streamflow originates during the summer and monsoon periods, with snowmelt ranging from 10% to 45%. This research provides crucial understanding of the impact of snowmelt on streamflow, supplying essential knowledge on freshwater supply in the area. Full article

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R²). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R² (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation. Full article