Search Results (47)

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

Climate change is projected to increase the intensity and frequency of natural hazards such as heat waves, extreme rainfall, heavy snowfall, typhoons, droughts, floods, and cold waves, potentially impacting the operational safety of critical infrastructure, including nuclear power plants (NPPs). Although quantitative indicators exist to screen-out natural hazards at NPPs, comprehensive methodologies for assessing climate-related hazards remain underdeveloped. Furthermore, given the variability and uncertainty of climate change, it is realistically and resource-wise difficult to evaluate all potential risks quantitatively. Using a structured expert elicitation approach, this study systematically identifies and prioritizes climate-related natural hazards for Korean NPPs. An iterative Delphi survey involving 42 experts with extensive experience in nuclear safety and systems was conducted and also evaluated using the best–worst scaling (BWS) method for cross-validation to enhance the robustness of the Delphi priorities. Both methodologies identified extreme rainfall, typhoons, marine organisms, forest fires, and lightning as the top five hazards. The findings provide critical insights for climate resilience planning, inform vulnerability assessments, and support regulatory policy development to mitigate climate-induced risks to Korean nuclear power plants. 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

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

Climate warming has led to changes in floods in snow-packed mountain areas, but how snowmelt contributes to floods in the high-altitude Tibetan Plateau remains to be studied. To solve this problem, we propose a more reasonable method for evaluating snowmelt’s contributions to floods. We use a distributed hydrological model with the capability to track snowmelt paths in different media, such as snowpack, soil, and groundwater, to assess snowmelt’s contribution to peak discharge. The study area, the Xiying River basin, is located northeast of the Tibetan Plateau. Our results show that in the past 40 years, the average annual air temperature in the basin has increased significantly at a rate of 0.76 °C/10a. The annual precipitation (precipitation is the sum of rainfall and snowfall) decreased at a rate of 5.59 mm/10a, while the annual rainfall increased at a rate of 11.01 mm/10a. These trends were not obvious. The annual snowfall showed a significant decrease, at a rate of 14.41 mm/10a. The contribution of snowmelt to snowmelt-driven floods is 85.78%, and that of snowmelt to rainfall-driven floods is 10.70%. Under the influence of climate change, the frequency of snowmelt-driven floods decreased significantly, and flood time advanced notably, while the intensity and frequency of rainfall-driven floods slowly decreased in the basin. The causes of the change in snowmelt-driven floods are the significant increase in air temperature and the noticeable decrease in snowfall and snowmelt runoff depth. The contribution of snowmelt to rainfall-driven floods slowly weakened, resulting in a slight decrease in the intensity and frequency of rainfall-driven floods. The results also indicate that rising air temperature could decrease snowmelt-driven floods. In snow-packed mountain areas, rainfall and snowmelt together promote the formation of and change in floods. While rainfall dominates peak discharge, snowpack and snowmelt play a significant role in the formation and variability of rainfall-driven floods. The contributions of snowmelt and rainfall to floods have changed under the influence of climate change, which is the main cause of flood variability. The changed snowmelt adds to the uncertainties and could even decrease the size and frequency of floods in snow-packed high mountain areas. This study can help us understand the contributions of snowmelt to floods and assess the flood risk in the Tibetan Plateau under the influence of climate change. Full article

Wind turbines are predominantly situated in remote, high-altitude regions, where they face a myriad of harsh environmental conditions. Factors such as high humidity, strong gusts, lightning strikes, and heavy snowfall significantly increase the vulnerability of turbine blades to fatigue damage. This susceptibility poses serious risks to the normal operation and longevity of the turbines, necessitating effective monitoring and maintenance strategies. In response to these challenges, this paper proposes a novel fault detection method specifically designed for analyzing wind turbine blade noise signals. This method integrates the Tyrannosaurus Optimization Algorithm (TROA) with a support vector machine (SVM), aiming to enhance the accuracy and reliability of fault detection. The process begins with the careful preprocessing of raw noise signals collected from wind turbines during actual operational conditions. The method extracts vital features from three key perspectives: the time domain, frequency domain, and cepstral domain. By constructing a comprehensive feature matrix that encapsulates multi-dimensional characteristics, the approach ensures that all relevant information is captured. Rigorous analysis and feature selection are subsequently conducted to eliminate redundant data, thereby focusing on retaining the most significant features for classification. A TROA-SVM classification model is then developed to effectively identify the faults of the turbine blades. The performance of this method is validated through extensive experiments, which indicate that the recognition accuracy rate is 98.7%. This accuracy is higher than that of the traditional methods, such as SVM, K-Nearest Neighbors (KNN), and random forest, demonstrating the proposed method’s superiority and effectiveness. 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

The accurate identification of precipitation types is very important for understanding the hydrological processes in cold regions. Existing identification methods have been established based on daily precipitation and meteorological data, which cannot match the high temporal resolution (such as hourly) simulations of hydrological processes. Based on the minutely surface meteorological data in the QTP from 2012 to 2021, we established three sub-models of the dynamic threshold method with wet-bulb temperature (T_w) and three sub-models of the frequency threshold method with air temperature (T_a) for distinguishing among precipitation types. The results revealed that the mean accuracy (ACC) of the three precipitation types was 0.86, and that these models provided a refined and accurate precipitation identification performance for the Qinghai–Tibet Plateau (QTP). However, these models performed well in the identification of rain and snowfall but performed poorly in the identification of sleet. In addition, the smaller the time scale and regional scales, the better the identification rate. In particular, snowfall is overestimated when daily precipitation-type separation thresholds are input into hourly or minute hydrological models. Therefore, to improve simulation performance, it is important to develop multi-temporal scale precipitation-type partitioning models, take regional variations into account when setting temperature thresholds, and conduct analyses at the finest possible time resolutions to minimize scale-related uncertainties. Full article

Climate change has increased the risk of snow drought, which is associated with a deficit in snowfall and snowpack. The objectives of this research are to improve drought identification in a warming climate by developing a new snow-based hydroclimatic aggregate drought index (SHADI) and to assess the impacts of snowpack and snowmelt in drought analyses. To derive the SHADI, an R-mode principal component analysis is performed on precipitation, snowpack, surface runoff, and soil water storage. Then, a joint probability distribution function of drought frequencies and drought classes, conditional expectation, and k-means clustering are used to categorize droughts. The SHADI was applied to the Red River of the North Basin (RRB), a typical cold climate region, to characterize droughts in a mostly dry period from 2003 to 2007. The SHADI was compared with the hydroclimatic aggregate drought index (HADI) and U.S. drought monitor (USDM) data. Cluster analysis was also utilized as a benchmark to compare the results of the HADI and SHADI. The SHADI showed better alignment with cluster analysis results than the HADI, closely matching the identified dry/wet conditions in the RRB. The major differences between the SHADI and HADI were observed in cold seasons and in transition periods (dry to wet or wet to dry). The derived variable threshold levels for different categories of drought based on the SHADI were close to, but different from, those of the HADI. The SHADI can be used for short-term lead prediction of droughts in cold climate regions and, in particular, can provide an early warning for drought in the warming climate. Full article

The aim of this study is to highlight the issue of missed supercooled liquid water (SLW) detection in the current radar/lidar derived products and to investigate the potential of the combined use of the EarthCARE mission and the Arctic Weather Satellite (AWS)—Microwave Radiometer (MWR) observations to fill this observational gap and to improve snowfall retrieval capabilities. The presence of SLW layers, which is typical of snowing clouds at high latitudes, represents a significant challenge for snowfall retrieval based on passive microwave (PMW) observations. The strong emission effect of SLW has the potential to mask the snowflake scattering signal in the high-frequency channels (>90 GHz) exploited for snowfall retrieval, while the detection capability of the combined radar/lidar SLW product—which is currently used as reference for the PMW-based snowfall retrieval algorithm—is limited to the cloud top due to SLW signal attenuation. In this context, EarthCARE, which is equipped with both a radar and a lidar, and the AWS-MWR, whose channels cover a range from 50 GHz to 325.15 GHz, offer a unique opportunity to improve both SLW detection and snowfall retrieval. In the current study, a case study is analyzed by comparing available PMW observations with AWS-MWR simulated signals for different scenarios of SLW layers, and an extensive comparison of the CloudSat brightness temperature (TB) product with the corresponding simulated signal is carried out. Simulated TBs are obtained from a radiative transfer model applied to cloud and precipitation profiles derived from the algorithm developed for the EarthCARE mission (CAPTIVATE). Different single scattering models are considered. This analysis highlights the missed detection of SLW layers embedded by the radar/lidar product and the sensitivity of AWS-MWR channels to SLW. Moreover, the new AWS 325.15 GHz channels are very sensitive to snowflakes in the atmosphere, and unaffected by SLW. Therefore, their combination with EarthCARE radar/lidar measurements can be exploited to both improve snowfall retrieval capabilities and to constrain snowfall microphysical properties. Full article

Supercooled liquid water (SLW) refers to droplets in clouds that remain unfrozen at temperatures below 0 °C. SLW is an important intermediate hydrometeor in the processes of snowfall and rainfall that can modulate the radiation budget. This study investigates the distribution of supercooled cloud water over mainland China using the East Asia–Pacific cloud macro- and microphysical properties dataset (2016), derived from Himawari-8 observations. The results show that the highest frequency of SLW in liquid-phase stratus clouds occur at the eastern slope of the Tibetan Plateau, the western side of the Sichuan Basin. Additional SLW is mostly found in liquid-phase clouds over the Sichuan Basin and its adjacent areas in southern China. In the region with the highest frequency of SLW, the mechanical forcing of the Tibetan Plateau causes the convergence of low-level airflow within the basin, which also carries moisture that is forced to ascend stably, creating a favorable condition for the formation of supercooled clouds. As the airflow continues to ascend, it encounters the mid-to-upper-level westerlies and temperature inversion. At the mid-to-upper level, the westerlies exhibit stronger wind speeds, directing flow towards the basin. Concurrently, the temperature inversion stabilizes the atmospheric stratification, limiting the further ascent of airflow. This inversion can also restrain convection and upward motion within the clouds, allowing for SLW to exist and persist for an extended period. Full article

The Global Precipitation Measurement Mission (GPM) improved spaceborne precipitation data. The GPM dual-frequency precipitation radar (DPR) provides information on total precipitation (TP), snowfall precipitation (SF) and snowfall flags (surface snowfall flag (SSF) and phase near surface (PNS)), among other variables. Especially snowfall data were hardly validated. This study compares GPM DPR TP, SF and snowfall flags on the Tibetan Plateau (TiP) against TP and SF from six well-known model-based data sets used as ground truth: ERA 5, ERA 5 land, ERA Interim, MERRA 2, JRA 55 and HAR V2. The reanalysis data were checked for consistency. The results show overall high agreement in the cross-correlation with each other. The reanalysis data were compared to the GPM DPR snowfall flags, TP and SF. The intercomparison performs poorly for the GPM DPR snowfall flags (HSS = 0.06 for TP, HSS = 0.23 for SF), TP (HSS = 0.13) and SF (HSS = 0.31). Some studies proved temporal or spatial mismatches between spaceborne measurements and other data. We tested whether increasing the time lag of the reanalysis data (+/−three hours) or including the GPM DPR neighbor pixels (3 × 3 pixel window) improves the results. The intercomparison with the GPM DPR snowfall flags using the temporal adjustment improved the results significantly (HSS = 0.21 for TP, HSS = 0.41 for SF), whereas the spatial adjustment resulted only in small improvements (HSS = 0.12 for TP, HSS = 0.29 for SF). The intercomparison of the GPM DPR TP and SF was improved by temporal (HSS = 0.3 for TP, HSS = 0.48 for SF) and spatial adjustment (HSS = 0.35 for TP, HSS = 0.59 for SF). Full article

The Padua temperature series is one of the longest in the world, as daily observations started in 1725 and have continued almost unbroken to the present. Previous works recovered readings from the original logs, and digitalized and corrected observations from errors due to instruments, calibrations, sampling times and exposure. However, the series underwent some changes (location, elevation, observing protocols, and different averaging methods) that affected the homogeneity between sub-series. The aim of this work is to produce a homogenized temperature series for Padua, starting from the results of previous works, and connecting all the periods available. The homogenization of the observations has been carried out with respect to the modern era. A newly released paleo-reanalysis dataset, ModE-RA, is exploited to connect the most ancient data to the recent ones. In particular, the following has been carried out: the 1774–2023 daily mean temperature has been homogenized to the modern data; for the first time, the daily values of 1765–1773 have been merged and homogenized; and the daily observations of the 1725–1764 period have been connected and homogenized to the rest of the series. Snowfall observations, extracted from the same logs from which the temperatures were retrieved, help to verify the robustness of the homogenization procedure by looking at the temperature frequency distribution on snowy days, before and after the correction. The possibility of adding new measurements with no need to apply transformations or homogenization procedures makes it very easy to update the time series and make it immediately available for climate change analysis. Full article

A thermal system in the very deep Lake Tazawa (maximum depth, 423 m) was investigated by estimating the heat budget. In the heat budget estimate, the net heat input at the lake’s surface and the heat input by river inflow and groundwater inflow were considered. Then, the heat loss by snowfall onto the lake’s surface was taken into account. Meanwhile, the lake water temperature was monitored at 0.2 m to the bottom by mooring temperature loggers for more than two years. The heat storage change of the lake from the loggers was calibrated by frequent vertical measurements of water temperature at every 0.1 m pitch by a profiler with high accuracy (±0.01 °C). The heat storage change (W/m²) obtained by the temperature loggers reasonably accorded to that from the heat budget estimate. In the heat budget, the net heat input at lake surface dominated the heat storage change, but significant heat loss by river inflow sporadically occurred, caused by the relatively large discharge from a reservoir in the upper region. How deeply the vertical water circulation in the lake occurs in winter was judged according to the differences between water temperatures at 0.2 m depth and at the bottom and between vertical profiles of dissolved oxygen over winter. It is strongly suggested that the whole water circulation process does not occur every winter, and if it does, it is very weak. A consistent increase in the water temperature at the bottom is probably due to the conservation of geothermal heat by high frequency of incomplete vertical water circulation. Full article