Search Results (103)

This study analyzes the evolution of hydrometeorological risks and severe weather events in Catalonia through an extensive review of 21,312 news reports aired by Televisió de Catalunya (TVC) between 1984 and 2019, 10,686 (50.1%) of which focused on events within Catalonia. The reports are categorized by the type of phenomenon, geographic location, and reported impact, enabling the identification of temporal trends. The results indicate a general increase in the frequency of news coverage of hydrometeorological and severe weather events—particularly floods and heavy rainfall—both in Catalonia and the broader Mediterranean region. This rise is attributed not only to a potential increase in such events, but also to the expansion and evolution of media coverage over time. In the Catalan context, the most frequently reported hazards are snowfalls and cold waves (3203 reports), followed by rainfall and flooding (3065), agrometeorological risks (2589), and wind or sea storms (1456). The study highlights that rainfall and flooding pose the most significant risks in Catalonia, as they account for the majority of the reports involving serious impacts—1273 cases of material damage and 150 involving fatalities. The normalized data reveal a growing proportion of reports on violent weather and floods, and a relative decline in snow-related events. Full article

Tropical glaciers are highly sensitive to climate change, with their mass balance influenced by temperature and precipitation, which affects the accumulation area. In this study, we developed an open-source tool to map the accumulation area of glaciers in the Cordillera Blanca, Peru (1988–2023), using Landsat images, spectral indices, and the Otsu method. We analyzed trends and correlations between snow accumulation area, meteorological patterns from ERA5 data, and oscillation modes. The results were validated using field data and manual mapping. Greater discrepancies were observed in glaciers with debris cover or small clean glaciers (<1 km²). The Amazonian and Pacific sectors showed a significant trend in decreasing accumulation areas, with reductions of 8.99% and 10.24%, respectively, from 1988–1999 to 2010–2023. El Niño events showed higher correlations with snow accumulation, snowfall, and temperature during the wet season, indicating a stronger influence on the Pacific sector. The accumulation area was strongly anti-correlated with temperature and correlated with snowfall in both sectors at a 95% confidence level (α = 0.05). The highest correlations with meteorological parameters were observed during the dry season, suggesting that even minor changes in temperature or precipitation could significantly impact the accumulation area. Full article

Snow cover constitutes a medium that can be used as a way of assessing air pollution. The chemical composition of snow layers from the same snowfall event reflects the composition of atmospheric aerosols and dry precipitates, depending on the properties of the adsorbing surface and prevailing weather conditions. Analyzing snow samples provides reliable insights into anthropogenic pollution accumulated in soil and groundwater of different land use type areas, as well as allows the evaluation of the degree and sources of environmental pollution. The aim of the research was to determine the distribution of polycyclic aromatic hydrocarbons in various sites of Zawoja village and identify their possible sources and factors influencing their differentiation. A total of 15 surface snow samples of the same thickness and snowfall origin were collected from different locations in the village in the winter of 2024. The samples were pre-concentrated by solid phase extraction and analyzed by gas chromatography—tandem mass spectrometry. The sampling set was invented, and the extraction procedure and analysis parameters were optimized. A spatial distribution map of PAHs was created. The contamination of ∑16PAHs varied from 710 to 2310 ng/L in melted snow with the highest concentrations detected in Zawoja Markowa by the border of the Babia Góra National Park, which is interpreted mainly as a result of the topographical setting. Medium molecular weight PAHs were the dominant fraction, which, combined with specific PAH ratios, indicate the combustion of biomass and coal as the main source of contamination. Full article

An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in a change in a given situation; thus, their prediction would be very beneficial, both in terms of timely response to them and for their prevention, for example, the prevention of traffic accidents. However, this is currently challenging for researchers, who are called upon to manage and analyze a huge volume of data in order to design applications for predicting events using artificial intelligence and high computing power. Although significant progress has been made in this area, the heterogeneity in the input data that a forecasting application needs to process—in terms of their nature (spatial, temporal, and semantic)—and the corresponding complex dependencies between them constitute the greatest challenge for researchers. For this reason, the initial forecasting applications process data for specific situations, in terms of number and characteristics, while, at the same time, having the possibility to respond to different situations, e.g., an application that predicts a pandemic can also predict a central phenomenon, simply by using different data types. In this work, we present the forecasting applications that have been designed to date. We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. We study and analyze all possible scenarios that arise under different conditions. Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL. Full article

Global climate warming has exacerbated extreme snowfall events. The Southeastern Tibet (ST) region has become a high-incidence area for avalanches due to its unique topographical and climatic conditions. However, current research has paid insufficient attention to the thresholds for avalanches triggered by extreme snowfall. Therefore, the aim of this study is to construct the I-D (intensity-duration) thresholds for avalanche events triggered by extreme snowfall in southeastern Tibet, providing a scientific basis for disaster prevention and mitigation work in this region. Based on the snowfall data from 1951 to 2020, this study calculated four extreme snowfall indices, namely SF1d, SF90p, SF95p, and SF99p, to determine extreme snowfall events. And 33 avalanche events during this period were verified through the confusion matrix. This study found that the intensity of extreme snowfall events in southeastern Tibet has increased while the frequency has decreased. The I-D threshold parameters α (from 5.79 to 14.88) and β (from −2.81 to −0.66) within the study area were determined, and the overall threshold is I = 9.29 × D^−2.27 (D represents the duration of snowfall, with the unit being days.). It was also found that extreme snowfall in the study area has a significant positive correlation in with the ST. The terrain has a greater impact on the snowfall intensity, but its regulation on the duration of events is limited. Overall, in southeastern Tibet, if the single-day snowfall exceeds 12.38 mm (the regional average value of the SF1d index) or the cumulative snowfall within the previous 30 days exceeds 64.85 mm (the regional average value of the three indices of SF90p, SF95p, and SF99p), it can be considered that an extreme snowfall event has occurred. At the same time, the threshold of I = 9.29 × D^−2.27 can be used to forecast avalanches triggered by extreme snowfall events in the entire region. Full article

As precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding extreme weather events, which is becoming increasingly important as precipitation patterns change over time. With precipitation analysis, multiple valuable information can be determined, such as storm intensity, duration, and frequency. To enhance understanding of precipitation data and analysis results, researchers often use graphical representation methods to show the data in visual formats. Although existing precipitation analysis and basic visual representations are helpful, it is critical to have a comprehensive analysis and visualization system to detect significant patterns and anomalies in high-resolution temporal precipitation data more effectively. This study presents a visual analytics system enabling interactive analysis of hourly precipitation data across all U.S. states. Multiple coordinated visualizations are designed to support both single and multiple-station analysis. These visualizations allow users to examine temporal patterns, spatial distributions, and statistical characteristics of precipitation events directly within visualizations. Case studies demonstrate the usefulness of the designed system by evaluating various historical storm events. Full article

Marine Cold Air Outbreaks (MCAOs) are critical drivers of high-latitude climates because they regulate the exchange of heat, moisture, and momentum between cold continental or polar air masses and relatively warmer ocean surfaces. In this study, we combined CloudSat–CALIPSO observations (2007–2017) with ERA5 reanalysis data to investigate the microphysical properties and vertical structure of snowfall during MCAOs. By classifying events using a low-level instability parameter, we provide a detailed comparison of the vertical and spatial characteristics of different snowfall regimes, focusing on key cloud properties such as the effective radius, particle concentration, and ice water content. Our analysis identified two distinct snowfall regimes: shallow stratocumulus-dominated snowfall, prevalent during typical MCAOs and characterized by cloud top heights below 3 km and a comparatively lower ice water content (IWC), and deeper snowfall occurring during non-CAO conditions. We demonstrate that, despite their lower instantaneous snowfall rates, CAO-related snowfall events cumulatively contribute significantly to the total ice mass production in the subpolar North Atlantic. Additionally, CAO events are characterized by a greater number of ice particles near the surface, which are also smaller ($r_{\mathrm{eff}}$ of 59 μm versus 62 μm) than those associated with non-CAO events. These microphysical differences impact cloud optical properties, influencing the surface radiative balance. Full article

Regular meteorological observations in Padua started in 1725 and have continued unbroken up to the present, making the series one of the longest in the world. Daily mean temperatures and precipitation amounts have recently been homogenized for the entire 1725–2024 period, making it possible to add new measurements without further work. Starting from the temperature series, the trends of cold and warm spells are investigated in this paper. The ongoing warming that started in the 1970s is extensively analyzed on the basis of the variability of the mean values and a magnitude index that captures both the duration and intensity of a spell and by investigating the frequency of extreme events by means of Intensity–Duration–Frequency curves. The periods with the greatest deviation from the climatological average are analyzed in detail: February 1740 and April 1755, the months with the largest negative and positive temperature anomalies, respectively, in the 300-year-long series. Moreover, the analysis of snow occurrences extracted from the original logs, together with the pressure observations from the long series of London and Uppsala, made it possible to evaluate the most typical synoptic situations leading to snow events in Padua for the whole period. Full article

Winter mixed-phase precipitation (P) impacts transportation, electric power grids, and homes. Forecasting winter precipitation such as freezing precipitation (ZP), freezing rain (ZR), freezing drizzle (ZL), ice pellets (IPs), and the snow (S) and rain (R) boundary remains challenging due to the complex cloud microphysical and dynamical processes involved, which are difficult to predict with the current numerical weather prediction (NWP) models. Understanding these processes based on observations is crucial for improving NWP models. To aid this effort, Environment and Climate Change Canada deployed specialized instruments such as the Vaisala FD71P and OTT PARSIVEL disdrometers, which measure P type (PT), particle size distributions, and fall velocity (V). The liquid water content (LWC) and mean mass-weighted diameter (${\mathrm{D}}_{\mathrm{m}}$) were derived based on the PARSIVEL data during ZP events. Additionally, a Micro Rain Radar (MRR) and an OTT Pluvio2 P gauge were used as part of the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX) field campaign at Sorel, Quebec. The dataset included manual measurements of the snow water equivalent (SWE), PT, and radiosonde profiles. The analysis revealed that the FD71P and PARSIVEL instruments generally agreed in detecting P and snow events. However, FD71P tended to overestimate ZR and underestimate IPs, while PARSIVEL showed superior detection of R, ZR, and S. Conversely, the FD71P performed better in identifying ZL. These discrepancies may stem from uncertainties in the velocity–diameter (V-D) relationship used to diagnose ZR and IPs. Observations from the MRR, radiosondes, and surface data linked ZR and IP events to melting layers (MLs). IP events were associated with colder surface temperatures (Ts) compared to ZP events. Most ZR and ZL occurrences were characterized by light P with low LWC and specific intensity and ${\mathrm{D}}_{\mathrm{m}}$ thresholds. Additionally, snow events were more common at warmer T compared to liquid P under low surface relative humidity conditions. The Pluvio2 gauge significantly underestimated snowfall compared to the optical probes and manual measurements. However, snowfall estimates derived from PARSIVEL data, adjusted for snow density to account for riming effects, closely matched measurements from the FD71P and manual observations. Full article

Highways play a critical role in global energy transitions and climate change mitigation, making the accurate forecasting of operational energy demand essential for improving energy efficiency and promoting green energy applications. This study develops a multi-scenario energy demand forecasting model focused on five key operational contexts: service areas, tunnels, toll stations, management centers, and roadside facilities. The model integrates user characteristics, behavioral patterns, and meteorological data, employing agent-based modeling (ABM) and the fuzzy C-means (FCM) clustering algorithm to simulate and analyze energy demand. Results indicate that during major holidays, total daily electricity consumption and peak demand increase by 143.2% and 43.8%, respectively, compared to baseline conditions. Conversely, during snowfall events, total electricity consumption and peak demand decrease by 8.8% and 11.7%, respectively. These findings provide valuable data support and a scientific basis for sustainable energy management in highway operations, contributing to the broader application of green energy solutions. Full article

Background: The increasing frequency and severity of extreme weather events (EWEs) as a consequence of climate change pose critical challenges on the transport and logistics sector, hence requiring systematic evaluation and strategic adaptation. Methods: This study conducts a comprehensive systematic literature review (SLR) of 147 peer-reviewed articles and reports through a PRISMA framework to comprehensively identify key weather-induced challenges, quantify their operational, infrastructural and economic impacts, and explore alternative mitigation strategies. Results: With a greater focus on rainfall, flooding and snowfall, this study highlights their notable impacts causing reductions in transport efficiency, increased maintenance costs and substantial financial losses. Also, it emphasizes the role of advanced technologies, resilient infrastructure, and adaptive policy frameworks as critical enablers for enhancing sector resilience while simultaneously formulating a robust roadmap for cities and companies with actions ranging from direct operational adjustments to long-term transformational changes in policy and infrastructure. Conclusions: This work underscores the importance of using a data-driven approach to safeguard transport and logistics systems against evolving climate risks contributing to the broader goal of sustainable urban resilience and operational continuity. Full article

Surface albedo measurements of snow and ice on Lake Ulansu in the Central Asian arid climate zone were conducted during the winter of 2016–2017. Observations were categorized into three stages based on the ice growth and surface condition: bare ice, snow cover, and melting. During the bare ice stage, the mean surface albedo was 0.35 with a decreasing trend due to the accumulation of wind-blown sediment on the ice surface (range: 0.99–1.87 g m⁻²). Two snowfall events occurred during the snow cover stage, significantly increasing the surface albedo to 0.91. During the melting stage, the albedo decreased at a decay rate of 0.20–0.30/day. Four existing albedo schemes were evaluated but found unsuitable for Lake Ulansu. A new surface albedo scheme was proposed by incorporating the existing albedo schemes with the measured data. This scheme incorporated the effect of sediment content on bare ice albedo for the first time. It demonstrated a modelling efficiency of 0.933 over the entire 3-month period, which was used to evaluate the fit between the predicted and observed values. When validated with albedo observations from other winters, it achieved a modelling efficiency of 0.940. The closer the value is to 1, the better the model’s predictive accuracy, indicating a higher level of reliability in the model’s performance. This scheme has potential applicability to other lakes in the Central Asian arid climate zone, which is characterized by low precipitation, frequent sandstorms, and intense solar radiation. Full article

In desert ecosystems, litter decomposition is the primary source of soil nutrients and is strongly affected by extreme climate events, which may influence desert plant survival and species diversity. To date, the effects of snowfall changes on litter decomposition in desert species remain poorly understood. Here, a snowfall manipulation experiment was conducted in Northwest China that included snowfall addition and removal treatments, as well as a natural snowfall control. Compared to the control, snowfall addition increased the amount of litter mass lost for Salsola passerina and Reaumuria soongarica during the snow-covered period by 21.54% and 21.8%, respectively. In contrast, snowfall addition effects differed between species during the snow-free period. More carbon was released from the S. passerina litter in the snowfall addition treatment during the snow-free period. Similarly, during the snow-covered period, more carbon and nitrogen were released from the R. soongorica litter in the snowfall addition treatment. Overall, the proportion of litter mass lost (from the annual total) increased with snowfall addition in the snow-covered period but was reduced with snowfall addition in the snow-free period. In the snow-covered period, the snowfall addition treatment affected litter mass loss to the same extent in both species but impacted S. passerina more strongly than R. soongorica in the snow-free period due to differences in soil urease activity. Changes in snowfall, therefore, significantly influenced litter decomposition in both desert species, but these effects differed between the snow-covered and snow-free period, particularly for litter with a higher C:N ratio. 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

Monitoring autumn vegetation dynamics in alpine regions is crucial for managing local livestock, understanding regional productivity, and assessing the responses of alpine regions to climate change. However, remote sensing-based vegetation monitoring is significantly affected by snowfall. The impact of autumn snowfall, particularly when vegetation has not fully entered dormancy, has been largely overlooked. To demonstrate the uncertainties caused by autumn snowfall in remote sensing-based vegetation monitoring, we analyzed 16 short-term snowfall events in the Qinghai–Tibet Plateau. We employed a synthetic difference-in-differences estimation framework and conducted simulated experiments to isolate the impact of snowfall from other factors, revealing its effects on vegetation indices (VIs) and autumn phenology estimation. Our findings indicate that autumn snowfall notably affects commonly used VIs and their associated phenology estimates. Modified VIs (i.e., Normalized Difference Infrared Index (NDII), Phenology Index (PI), Normalized Difference Phenology Index (NDPI), and Normalized Difference Greenness Index (NDGI)) revealed greater resilience to snowfall compared to conventional VIs (i.e., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) in phenology estimation. Areas with remaining green vegetation in autumn showed more pronounced numerical changes in VIs due to snowfall. Furthermore, the impact of autumn snowfall closely correlated with underlying vegetation types. Forested areas experienced less impact from snowfall compared to grass- and shrub-dominated regions. Earlier snowfall onset and increased snowfall frequency further exacerbated deviations in estimated phenology caused by snowfall. This study highlights the significant impact of autumn snowfall on remote sensing-based vegetation monitoring and provides a scientific basis for accurate vegetation studies in high-altitude regions. Full article