Search Results (82)

Background/Objectives: Snow can disrupt emergency medical services (EMSs); however, previous studies have mainly measured snowfall or prefecture-level exposure. These measures may not capture snow remaining on the ground or conditions within ambulance operating areas. We examined whether the daily snow depth assigned at the fire department level was associated with EMS time intervals and 1-month survival after out-of-hospital cardiac arrest (OHCA). Methods: This retrospective cohort study included 7395 adults with OHCA from the Akita Prefecture Utstein-style emergency transport registry between 2019 and 2023. Daily snow depth from the nearest Automated Meteorological Data Acquisition System (AMeDAS) station was assigned to each case by the fire department. Snow exposure was analyzed as >0 cm versus 0 cm, as five depth categories, and as a continuous variable using natural splines. Multivariable models were adjusted for age, sex, cardiac origin, initial rhythm, fire department area, witnessed status, bystander cardiopulmonary resuscitation, and year. Results: Call-to-scene time and total EMS time were longer with snow cover than without snow cover (median, 9 vs. 8 min and 34 vs. 31 min, respectively; both p < 0.001). Snow cover was associated with lower 1-month survival after adjustment (odds ratio [OR], 0.73; 95% confidence interval [CI], 0.54–0.98), but this association was attenuated after additional adjustment for call-to-scene time (OR, 0.77; 95% CI, 0.57–1.03). Category-based and spline analyses showed no clear dose–response relationship. Conclusions: Daily snow depth is consistently associated with longer EMS response and transport times. However, its association with 1-month survival remains unclear. This survival association may reflect broader winter conditions rather than snow cover itself. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

Snow depth is a critical parameter for characterizing snow dynamics and water resources, and its accurate inversion is essential for hydrological processes, climate studies, and disaster prevention in cold regions. Based on long-term daily ground meteorological observation data from the hydrological years 1961 to 2015 at two meteorological stations in Mohe and Mishan, Heilongjiang Province, China, this study integrates physical parameters of snow density and snow albedo from the ERA5-Land reanalysis data to systematically compare the performance of three machine learning and three deep learning models in retrieving daily snow depth. Four feature combination schemes were designed to evaluate the contributions of meteorological factors, lagged snow depth terms, and snow physical parameters. The results indicate that, for both machine learning and deep learning models, the first-order lagged value of snow depth is the most important variable determining prediction accuracy. In terms of model performance, machine learning methods excelled, with XGBoost performing particularly outstandingly, achieving optimal prediction accuracy and stability under the best feature combination (coefficient of determination, R², reaching 0.989; root mean square error, RMSE, of 1.19 cm). Among deep learning methods, 1D CNN demonstrated strong local feature extraction capabilities, achieving prediction accuracy comparable to the best-performing machine learning model (R² of 0.9878, RMSE of 1.26 cm). Notably, models specifically designed for time-series data, such as LSTM (R² of 0.9848, RMSE of 1.41 cm), and the more complex 1D CNN-LSTM hybrid model (R² of 0.9803, RMSE of 1.60 cm) did not show significant advantages in this study. This indicates that model complexity and predictive performance are not simply positively correlated. Through comprehensive analysis of data from both stations, this study demonstrates that a prediction framework centered on historical snow depth as the core driving factor, combined with key meteorological elements, is highly robust. Although the inclusion of ERA5-Land snow physical parameters did not significantly improve model accuracy, it provides important insights for the future development of hybrid models that integrate physical mechanisms with data-driven approaches. The findings offer an effective solution for reconstructing long-term snow depth time series and hold significant application value for simulating cryospheric hydrological processes and studying climate change. Full article

Inflatable structures have attracted increasing attention in recent years due to their light weight, translucency, rapid assembly or disassembly, mobility, and self-cleaning performance. Meanwhile, their flexible characteristics and low-damping behavior render the structures prone to significant deformation and vibration under wind and snow loads and may even lead to structural failure. Therefore, numerous researchers have conducted in-depth investigations into the mechanical response of such structures under wind and snow loads. However, existing studies on inflatable structures subjected to wind and snow loads have mainly focused on an air-supported form, and the mechanical behavior of inflatable ribbed arch structures has not yet been sufficiently investigated. To investigate the mechanical behavior and deformation patterns of inflatable ribbed arch structures subjected to wind and snow loads, static tests were conducted on three specimens with varying spans, heights, and cable arrangements. Following inflation to an internal pressure of 250 kPa and preloading with the tarpaulin weight, the wind load and snow load were converted to the equivalent concentrated loads and applied in five incremental stages. Displacement monitoring points (DMPs) were tracked using a total station. Under the wind load, a consistent wind-induced deformation pattern was observed across specimens characterized by inward displacement in Region I, upward displacement in Region II, and negligible change in Region III. The maximum horizontal displacements of Specimens A, B, and C were 76 mm, 140 mm, and 249 mm, respectively. Under snow load, the upper sections of all three specimens experienced significant downward displacement, while both sides demonstrated a slight tendency for outward expansion and upward lift. The maximum vertical displacements of Specimens A, B, and C were −27 mm, −233 mm, and −255 mm, respectively. The findings of this study provide deeper insights into the mechanical behavior of inflatable arch structures under wind and snow loads and can serve as a valuable reference for their design and optimization. Full article

Snow is an essential meteorological variable and an indicator of the changing climate. Its variations, particularly in snow depth and snow water equivalent, result mainly from changes in winter precipitation and air temperature. Recently, these conditions have been thoroughly investigated worldwide, revealing a general prevailing decline in precipitation and increasing tendencies in air temperatures. However, no systematic or up-to-date studies for Bulgaria exist. The main goal of the current project is to fill this national knowledge gap in the snow conditions in our mountains. For that purpose, we used 31 stations with altitudes ranging from 527 to 2925 m a.s.l. for the period between 1961 and 2020, covering two significant reference climatic periods. We extracted data about snow cover maximums, mean air temperatures, and precipitation amounts for the whole winter season in mountainous regions from October to April; however, we mainly present the results for the three winter months: December, January, and February. Most of the stations do not demonstrate any significant trends for snow depth maximums, except for the three lower stations in central west Bulgaria, which show significant increases. On the opposite end of the scale, two of the highest stations demonstrated notable decreases. The time series for the precipitation amounts are also predominantly indefinite. Significant decreasing trends can be found at the highest three alpine stations. The change in the mean seasonal air temperature is predominantly positive—17 of the stations show positive trends, and for 12, the increases are significant. The altitude of the strongest seasonal temperature rise lies between 1000 and 1700 m. Finally, due to the obvious nonlinearity of some of the time series, we decided to check for change points and a nonlinear approach to fit the data. This analysis demonstrates general changes in the investigated characteristics from the beginning of the 1970s to the middle of the 1980s. Full article

Climate change in Antarctica has profound global implications, influencing sea level rise, atmospheric circulation, and the Earth’s energy balance. This study presents a data-driven baseline analysis of meteorological observations from a British Antarctic Survey automatic weather station (2020–2024). Temporal and seasonal analyses reveal strong insolation-driven variability in temperature, snow depth, and solar radiation, reflecting the extreme polar day–night cycle. Correlation analysis highlights solar radiation, upwelling longwave flux, and snow depth as the most reliable predictors of near-surface temperature, while humidity, pressure, and wind speed contribute minimally. A linear regression baseline and a Random Forest model are evaluated for temperature prediction, with the ensemble approach demonstrating superior accuracy. Although the short data span limits long-term trend attribution, the findings underscore the potential of lightweight, reproducible pipelines for site-specific climate monitoring. All analysis codes are openly available in github, enabling transparency and future methodological extensions to advanced, non-linear models and multi-site datasets. Full article

Avalanches occur frequently in mountainous areas and pose significant threats to roads and infrastructure. Clarifying how terrain conditions influence avalanche initiation and movement is critical to improving hazard assessment and response strategies. This study focused on a wet-snow slab avalanche that occurred on 26 March 2024, in the Simutas region of the northern Tianshan Mountains, Xinjiang, China. The authors combined remote sensing imagery, high-resolution meteorological station observations, field investigations, and numerical simulations (RAMMS::Avalanche) to analyze the avalanche initiation mechanism, dynamic behavior, and path recurrence characteristics. Results indicated that persistent heavy snowfall, rapid warming, and substantial daily temperature fluctuations triggered this avalanche. The predominant southeasterly (SE) winds and the northwest-facing (NW) shaded slopes created favorable leeward snow deposition conditions, increasing snowpack instability. High-resolution meteorological observations provided detailed wind, temperature, and precipitation data near the avalanche release zone, clearly capturing snowpack evolution and meteorological conditions before avalanche initiation. Numerical simulations showed a maximum avalanche flow velocity of 19.22 m/s, maximum flow depth of 12.42 m, and peak dynamic pressure of 129.3 kPa. The simulated avalanche deposition area and depth closely matched field observations. Multi-temporal remote sensing images indicated that avalanche paths in this area remained spatially consistent over time, with recurrence intervals of approximately 2–3 years. The findings highlight the combined role of local meteorological processes and terrain factors in controlling avalanche initiation and dynamics. This research confirmed the effectiveness of integrating remote sensing data, high-resolution meteorological observations, and dynamic modeling, providing scientific evidence for avalanche risk assessment and disaster mitigation in mountain regions. Full article

The spatiotemporal changes and driving mechanisms of rain-on-snow (ROS) events and their intensities are crucial for responding to disasters triggered by such events. However, there is currently a lack of detailed assessment of the seasonal variations and driving mechanisms of ROS events and their intensities in China. Therefore, this study utilized daily meteorological data and daily snow depth data from 513 stations in China during 1978–2020 to investigate spatiotemporal variations of ROS events and their intensities. Also, based on the detrend and partial correlation analysis model, the driving factors of ROS events and their intensity were explored. The results showed that ROS events primarily occurred in northern Xinjiang, the Qinghai–Tibet Plateau, Northeast China, and central and eastern China. ROS events frequently occurred in the middle and lower Yangtze River Plain in winter but were easily overlooked. The number and intensity of ROS events increased significantly (p < 0.05) in the Changbai Mountains in spring and the Altay Mountains and the southeast part of the Qinghai–Tibet Plateau in winter, leading to heightened ROS flood risks. However, the number and intensity of ROS events decreased significantly (p < 0.05) in the middle and lower Yangtze River Plain in winter. The driving mechanisms of the changes for ROS events and their intensities were different. Changes in the number of ROS events and their intensities in snow-rich regions were driven by rainfall days and quantity of rainfall, respectively. In regions with more rainfall, these changes were driven by snow cover days and snow water equivalent, respectively. Air temperature had no direct impact on ROS events and their intensities. These findings provide reliable evidence for responding to disasters and changes triggered by ROS events. Full article

Rapid climate warming and intensified human activities are causing profound alterations in terrestrial hydrological systems. Understanding shifts in hydrological regimes and the underlying mechanisms driving these changes is crucial for effective water resource management, watershed planning, and flood disaster mitigation. This study examines the hydrological regimes of the Heilongjiang-Amur River Basin, a transboundary river basin characterized by extensive permafrost distribution in northeastern Asia, by analyzing long-term daily meteorological (temperature, precipitation, evaporation) and hydrological data from the Komsomolsk, Khabarovsk, and Bogorodskoye stations. Missing daily runoff data were reconstructed using three machine learning methods: Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Convolutional Long Short-Term Memory Networks (CNN-LSTM). Trend analysis, abrupt change detection, and regression techniques revealed significant warming and increased actual evapotranspiration in the basin from 1950 to 2022, whereas precipitation and snow water equivalent showed no significant trends. Climate warming is significantly altering hydrological regimes by changing precipitation patterns and accelerating permafrost thaw. At the Komsomolsk station, an increase of 1 mm in annual precipitation resulted in a 0.48 mm rise in annual runoff depth, while a 1 °C rise in temperature led to an increase of 1.65 mm in annual runoff depth. Although annual runoff exhibited no significant long-term trend, low-flow runoff demonstrated substantial increases, primarily driven by temperature and precipitation. These findings provide critical insights into the hydrological responses of permafrost-dominated river basins to climate change, offering a scientific basis for sustainable water resource management and strategies to mitigate climate-induced hydrological risks. Full article

The effectiveness of snow data assimilation is closely related to the satellite data quality control that affects snow cover data used for assimilation and meteorological forcings that drive land surface model to estimate snow depth, especially over headwater regions where in situ measurements are sparse and land surface simulations are challenging. This study proposes a joint quality control scheme based on precipitation constraints and cloud thresholds, uses the Ensemble Square Root Filter to assimilate the controlled data to improve snow depth estimation from the Conjunctive Surface-Subsurface Process model version 2 (CSSPv2), and explores the impacts of different forcing data on the assimilation. The correlation between the assimilated monthly snow depth data and the in situ measurements averaged over 21 stations during November–February of 2000–2015 is 0.93, and the root mean square error is 0.22 cm. Compared with CSSPv2 model simulation, the correlation increased by 5.6%, and the error decreased by 18.5%. The joint quality control scheme has led to an average accuracy improvement of 47%, while the high-quality forcing data have resulted in an average enhancement of 58%. This study suggests that satellite data quality control and meteorological forcings are important for increasing correlation and decreasing error for snow depth assimilation, respectively. Full article

This study evaluates the accuracy of the U.S. interactive multisensor snow and ice mapping system (IMS) 1 km product with recall, precision, false alarm rate, and overall accuracy by utilizing ground-observed snow depth data from meteorological stations in China over six snow seasons spanning from 2015 to 2020. Furthermore, it examines the correlation between recall and snow depth, as well as the correlation between overall accuracy and snow depth. The results reveal the following: The precision typically peaks during the middle of the snow season, predominantly exceeding 80% but rarely reaching 100%, with minimal spatial variation. The precision generally ranges from 75% to 80%, exhibiting slight spatial variations during this period. The overestimation rate remains below 5%, seldom exceeding 10%. The overall accuracy mostly exceeds 95%, showing no significant spatial variations. Across both China and its five regions, the recall demonstrates a significant logarithmic correlation with snow depth, while the overall accuracy exhibits an almost horizontal linear correlation with snow depth once it exceeds 3 cm. Full article

Investigating the changes in snow cover caused by climate change is extremely important and has attracted increasing attention in cryosphere and climate research. Optimal remote sensing-based snow datasets can provide long-term daily and global spatial-temporal snow-cover distribution at regional and global scales. However, the application of these snow-cover products is inevitably limited because of the space–time discontinuities caused by cloud obscuration, which poses a significant challenge in snowpack-related studies, especially in High Mountain Asia (HMA), an area that has high-elevation mountains, complex terrain, and harsh environments and has fewer observation stations. To address this issue, we developed an improved five-step hybrid cloud removal strategy by integrating the daily merged snow-cover probability (SCP) algorithm, eight-day merged SCP algorithm, decision tree algorithm, temporal downscaling algorithm, and optimal threshold segmentation algorithm to produce a 21-year, daily cloud-free snow-cover dataset using two daily MODIS snow-cover products over the HMA. The accuracy assessment demonstrated that the newly developed cloud-free snow-cover product achieved a mean overall accuracy of 93.80%, based on daily classified snow depth observations from 86 meteorological stations over 10 years. The time series of the daily percentage of binary snow-cover over HMA was analyzed during this period, indicating that the maximum snow cover tended to change more dramatically than the minimum snow cover. The annual snow-cover duration (SCD) experienced an insignificantly increasing trend over most of the northeastern and southwestern HMA (e.g., Qilian, eastern Kun Lun, the east of Inner Tibet, the western Himalayas, the central Himalayas, and the Hindu Kush) and an insignificant declining trend over most of the northwestern and southeastern HMA (e.g., the eastern Himalayas, Hengduan, the west of Inner Tibet, Pamir, Hissar Alay, and Tien). This new high-quality snow-cover dataset will promote studies on climate systems, hydrological modeling, and water resource management in this remote and cold region. Full article

Accurately quantifying the threat of collapse due to the weight of settled snow on the roof of a structure is crucial for ensuring structural safety. This quantification relies upon direct measurements of the snow water equivalent (SWE) of settled snow, though most weather stations in the United States only measure snow depth. The absence of direct load measurements necessitates the use of modeled estimates of SWE, which often results in the underestimation of the scale/variance parameter of the distribution of annual maximum SWE. This paper introduces a novel bias correction method that employs a bootstrap technique with regression-based models to calibrate the variance parameter of the distribution. The efficacy of this approach is demonstrated on real and simulated datasets. The findings reveal varied levels of success, with the efficacy of the proposed approach being inherently dependent on the quality of the selected regression-based model. These findings demonstrate that integrating our approach with a suitable regression-based model can produce unbiased or nearly unbiased annual maximum SWE distribution parameters in the absence of direct SWE measurements. Full article

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a⁻¹ over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones. Full article

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season’s backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former’s lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring. Full article