Extreme Meteorological Events Research Articles

Employing automated electrical resistivity tomography for detecting short- and long-term changes in permafrost and active-layer dynamics in the maritime Antarctic

Abstract. Repeated electrical resistivity tomography (ERT) surveys can substantially advance the understanding of spatial and temporal freeze–thaw dynamics in remote regions, such as Antarctica, where the evolution of permafrost has been poorly investigated. To enable time-lapse ERT surveys in Antarctica, an automated ERT (A-ERT) system is required, as regular site visits are not feasible. In this context, we developed a robust A-ERT prototype and installed it at the Crater Lake CALM-S site on Deception Island, Antarctica, to collect quasi-continuous ERT measurements. We developed an automated data processing workflow to efficiently filter and invert the A-ERT datasets and extract the key information required for a detailed investigation of permafrost and active-layer dynamics. In this paper, we report on the results of two complete year-round A-ERT datasets collected in 2010 and 2019 at the Crater Lake CALM-S site and compare them with available climate and borehole data. The A-ERT profile has a length of 9.5 m with an electrode spacing of 0.5 m, enabling a maximum investigation depth of approximately 2 m. Our detailed investigation of the A-ERT data and inverted results shows that the A-ERT system can detect the active-layer freezing and thawing events with high temporal resolution. The resistivity of the permafrost zone in 2019 is very similar to the values found in 2010, suggesting the stability of the permafrost over almost 1 decade at this site. The evolution of thaw depth exhibits a similar pattern in both years, with the active-layer thickness fluctuating between 0.20–0.35 m. However, a slight thinning of the active layer is evident in early 2019, compared to the equivalent period in 2010. These findings show that A-ERT datasets, combined with the new processing workflow that we developed, are an effective tool for studying permafrost and active-layer dynamics with very high resolution and minimal environmental disturbance. The ability of the A-ERT setup to monitor the spatiotemporal progression of thaw depth in two dimensions, and potentially in three dimensions, and to detect brief surficial refreezing and thawing of the active layer reveals the significance of the automatic ERT monitoring system to record continuous resistivity changes. An A-ERT monitoring setup with a longer profile length can investigate greater depths, offering effective monitoring at sites where boreholes are costly and invasive techniques are unsuitable. This shows that the A-ERT setup described in this paper can be a significant addition to the Global Terrestrial Network for Permafrost (GTN-P) and the Circumpolar Active Layer Monitoring (CALM) networks to further investigate the impact of fast-changing climate and extreme meteorological events on the upper soil horizons and to work towards establishing an early warning system for the consequences of climate change.

A First Extension of the Robust Satellite Technique RST-FLOOD to Sentinel-2 Data for the Mapping of Flooded Areas: The Case of the Emilia Romagna (Italy) 2023 Event

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time.

Vulnerability to extreme weather events: mapping future hazards in Wielkopolska region, Poland

The aim of this study is to assess future hazards due to extreme meteorological events in the Wielkopolska region, Poland, based on five climate model projections and three scenarios: SSP126, 370, and 585. The paper analyzes the changes of mean and extreme precipitation, mean and extreme temperatures, and humidity index, as well as changes in difference between maximum temperatures observed from day to day and changes in difference between mean atmospheric pressure at the sea level observed from day to day. Additionally, we look at possible future occurrence of wildfires due to changes in fire weather conditions. Based on climate model projections, future hazard due to extreme meteorological events in Wielkopolska region is to be more serious and will be most noticeable in the end of twenty-first century and for two higher scenarios: SSP370 and SSP585. For near future, 2021–2050, projected conditions of meteorological extremes for analyzed scenarios are quite consistent. Therefore, there is a strong need for implementing adaptation actions. Nevertheless, such activities are so far lacking, and several adaptation options are not present in local and national legislation, even though they are recognized as effective.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Mean sea level, tidal components and surges in Guanabara Bay (Rio de Janeiro) from 1990 to 2021

AbstractGuanabara Bay, located in the Metropolitan Region of Rio de Janeiro, one of the largest urban coastal areas in the Southern Hemisphere, is subject to intense maritime traffic due to the presence of several ports. These facilities are affected by sea level fluctuations, influenced by atmospheric and astronomical forces, which motivates synoptic and climatological analysis, including meteorological and astronomical tidal phenomena. This study aimed to assess the evolution of tidal components and the relative mean sea level (MSL) between 1990 and 2021, as well as the atmospheric influence on extreme meteorological tide events, in which the MSL exceeded ±2 and ±3 (±29.98 and ±44.97 cm) standard deviations. The results have shown that, albeit small, the main tidal components (, , , , , , and ) indicated positive trends in the increase of amplitude and phase, despite the small quantities. For the relative MSL, an increase of 0.30 cm·year−1 was observed in agreement with estimates from global series from satellite altimeters and climate model predictions. Pressure and wind field analyses led to the identification of seasonal variability and the pattern of evolution of atmospheric systems associated with extreme events. In the case of positive surges, very intense winds from the SW, extending along the S/SE Brazilian coast, are caused by the presence of a high‐pressure centre over the continent and low‐pressure centres over the ocean, leading to water piling up. In turn, negative surges are caused by the presence of an anticyclone over the ocean, generating NE winds parallel to the coast of Rio de Janeiro, inducing Ekman transport and lowering the sea level. Therefore, the extreme sea level events in the Guanabara Bay are not triggered by local forces, but rather depend on the temporal persistence and direction of winds along the S/SE Brazilian coast.

Changes in South American surface ozone trends: exploring the influences of precursors and extreme events

Abstract. In this study, trends of 21st-century ground-level ozone and ozone precursors were examined across South America, a less-studied region where trend estimates have rarely been comprehensively addressed. Therefore, we provided an updated regional analysis based on validated surface observations. We tested the hypothesis that the recent increasing ozone trends, mostly in urban environments, resulted from intense wildfires driven by extreme meteorological events impacting cities where preexisting volatile organic compound (VOC)-limited regimes dominate. We applied the quantile regression method based on monthly anomalies to estimate trends, quantify their uncertainties and detect trend change points. Additionally, the maximum daily 8 h average (MDA8) and peak-season metrics were used to assess short- and long-term exposure levels, respectively, for the present day (2017–2021). Our results showed lower levels in tropical cities (Bogotá and Quito), varying between 39 and 43 nmol mol−1 for short-term exposure and between 26 and 27 nmol mol−1 for long-term exposure. In contrast, ozone mixing ratios were higher in extratropical cities (Santiago and São Paulo), with a short-term exposure level of 61 nmol mol−1 and long-term exposure levels varying between 40 and 41 nmol mol−1. Santiago (since 2017) and São Paulo (since 2008) exhibited positive trends of 0.6 and 0.3 nmol mol−1 yr−1, respectively, with very high certainty. We attributed these upward trends, or no evidence of variation, such as in Bogotá and Quito, to a well-established VOC-limited regime. However, we attributed the greater increase in the extreme percentile trends (≥ 90th) to heat waves and, in the case of southwestern South America, to wildfires associated with extreme meteorological events.

Is the number of extreme meteorological events increasing due to global climate change? (On the example of hurricanes in North America)

Is the number of extreme meteorological events increasing due to global climate change? (On the example of hurricanes in North America)

Comparative analysis of the variability and impacts of tropical cyclones in flood-prone areas of Zimbabwe

Tropical cyclones (TCs) are extreme meteorological events that cause significant deaths, infrastructure damage, and financial losses around the world. In recent years, the Eastern Highlands of Zimbabwe's have become increasingly vulnerable to TCs caused by Indian Ocean tropical cyclones making landfall more frequently. There is still a limited understanding of the phenomenon and the quantification of its impacts. The aim of this research is to conduct a comparative analysis of the variability in the severity of tropical cyclones by analysing historical storm tracks and mapping the environmental impacts in Zimbabwe's Chimanimani and Chipinge districts. Results indicate that, between 1945 and 2022, the Eastern Highlands of Zimbabwe experienced 5 of the total 865 cyclones in the Southwest Indian Ocean. The maximum sustained winds from the Cyclone Idai in the Eastern Highlands were recorded as 195 km/h. Some of the remote sensing-based indices used to extract spatial information about the condition of vegetation, wetlands, built-up area, and bar land during pre and post cyclonic events included the Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI). Analysis of NDVI in the Eastern Highlands revealed that there was a significant decrease in vegetated area because of the cyclone impact, with a decrease of 2.1% and 16.68% for cyclone Japhet and Idai respectively. The MNDWI shows a 10.74% increase in water content after cyclone Eline. Field validation in 2019 confirms the research findings. An Operations Dashboard Disaster Management System was developed in order to disseminate information to the affected stakeholders about the potential risk that the face due to the occurrence of the natural phenomena.

Dendrochronology and extreme climate signals recorded in seven Icelandic shrubs: A multi-species approach in the sub-Arctic

Iceland, located in the climatologically sensitive subarctic zone, is one of the key areas for studying climate change and the current Arctic warming. Poor and rich heathland covers approximately 30% of Iceland, with heath vegetation being the area’s most important vegetation type. Prostrate and erect dwarf shrub and shrub species are the primary sources of dendrochronological information on the changes and characteristics of Iceland’s climate. Here, we investigate the dendrochronological potential of seven common Icelandic heathland species (Dryas octopetala, Calluna vulgaris, Salix arctica, Salix herbacea, Empetrum nigrum, Juniperus communis nana, and Betula nana) and explore the impact of climate conditions on their growth, particularly with regard to extreme meteorological events. We conducted comparative analyses among species and observed their climatic responses in a climatically, morphologically, and geologically homogeneous setting in north-eastern Iceland. After sampling, measurements, and cross-dating, we constructed local chronologies for the seven species. Of approximately 200 samples, only 113 were included in the final shrub chronologies. All chronologies covered at least 50 years, with few of them exceeding 100 years. Dendroclimatological analysis indicated that above-average temperatures in June and summer positively influence the growth of D. octopetala (June–September), J. communis (July–August), B. nana (July–August), C. vulgaris (June–August), and S. arctica (August–September). The opposite is observed for S. herbacea and E. nigrum. Climate–growth correlations indicate that the growth of these species is negatively correlated with temperature and positively correlated with winter and summer precipitation. Furthermore, among the long-lived woody species growing in Iceland, seven common species were used to assess the impact of extreme meteorological conditions on their growth. The most pronounced extreme year in the wood anatomy of Icelandic shrubs was 1979, depicted as very narrow or missing rings and as the blue ring in J. communis.

Rainfall Potential and Consequences on Structural Soil Degradation of the Most Important Agricultural Region of Mexico

This study investigates the historical variability in annual average precipitation in the northwest region of Mexico, aiming to evaluate the cumulative impact of precipitation on soil degradation and associated risks posed by rainfall. Despite being known as “The Agricultural Heart of Mexico”, the region’s soil has experienced significant damage to its granulometric structure due to unpredictable rainfall patterns attributed to climate change. Sixteen historical series of average annual rainfall were analyzed as stationary stochastic processes for spectral analysis. The results revealed exponential decay curves in each radial spectrum, indicating a linear relationship between frequency and amplitude. These curves identified initial impulses correlated with moments of severity for structural damages caused by rainfall-induced degradation. The degradation process, exacerbated by water stress, accelerates, as evidenced by maps illustrating approximately 75% soil damage. In the context of climate change and the uncertainty surrounding soil responses to extreme meteorological events, understanding this phenomenon becomes crucial. Recognizing the dynamic nature of soil responses to environmental stressors is essential for effective soil management. Emphasizing the need to employ numerical processes tailored to new environmental considerations related to observed soil damages is crucial for sustainable soil management practices in any region.

Property risk assessment and dynamic premium pricing based on ARIMA

This study aims to improve the efficiency of insurance underwriting decisions and assess community risks, and constructs a dynamic premium pricing model through the ARIMA model. In-depth analysis of historical data provides quantitative and optimal support for insurers to address premium setting and underwriting decision challenges. The results show that the model can predict future risks and losses, facilitate risk classification and premium determination. The classified statistical method analyzes the frequency and time correlation of extreme meteorological events, and provides basis for insurance policy adjustment. Research provides more accurate and dynamic premium pricing and underwriting decision-making tools, simplifies the risk assessment process, and is conducive to insurance market stability and resource optimization, service optimization and long-term market stability.

Proposed life cycle assessment approach to estimate the environmental impact due to high altitude platform systems

The anthropic activities, starting from the early industrial development phases, are pointed-out as concurrent causes affecting the environmental conditions of planet Earth as well as people life quality. Evidences of these influences have been observed even in the remote territories like North and South poles. Every day news report about extreme meteorological events affecting vast geographical areas with heavy loss in terms of victims and damages to infrastructures. In these potential risk scenarios, it is extremely important to support emergency management with timely and accurate geo-spatial information and also to restore minimal communication services. In these severe conditions a relevant role may be played by High-Altitude Platform Systems (HAPS) operating at high altitudes, ranging from 17 to 22 kilometres, typically in the stratosphere, extending coverage for large areas and improving connectivity in remote or underserved areas, bridging the digital divide and providing connectivity to rural or isolated regions, carrying remote sensing instruments, such as imaging sensors or atmospheric sensors, to gather data about the Earth’s surface, weather patterns, or atmospheric conditions. To analyse their sustainability, the proposed work presents the definition of a Life Cycle Assessment (LCA) for two distinct “lighter than air” configurations: an “innovative blimp airship” (IBA) and a classical blimp airship (CBA) as baseline. While IBA configuration is considered as an unmanned, the CBA airship is a manned configuration. The goal of this activity is to support the characterization and validation of the environmental contribution in terms of CO2 emissions due to innovative technologies, concepts and industrial processes adopted for the development of such HAPS configurations. For this assessment SimaPro tool has been used. The applied methodology has adopted the ISO standards: ISO-14040:2006 and ISO-14044:2018 as guidelines to estimate the collectable benefits due to the adoption of innovative eco-design solutions. Specifically, this paper is focused on the identification of materials and on inventory development in order to support the LCA process.

Evaluation of Surface Water Quality in the Betwa River Basin through the Water Quality Index Model and Multivariate Statistical Techniques.

The Betwa River Basin (BRB), a sub-basin of the River Yamuna, is the oldest flowing water system in Central India. The water quality of the rivers are under stress, hence regular monitoring and appraisal is required to know the health of the rivers. Factor analysis and principal component analysis (FA/PCA) multivariate statistical techniques were used to extract three and four varimax factors that explained 96.408 and 100.00 percent of the total variance in water quality, respectively. Cluster analysis (CA) categorizes observed items into distinct quality categories based on correlations between stations and years. Point industrial/sewage effluents, diffuse pollution as runoff from arable land, erosion, and natural source pollution contribute to the pollution of the BRB. As a result, water quality is threatened or impaired, and conditions often departed from natural or desirable levels at Rajghat, Garrauli, Mohana, and Shahijina stations. According to the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), the surface water quality at the Rajghat and Mohana stations corresponds to fair ecological status. However, the surface water quality of the Garrauli and Shahijina stations has a marginal water quality as per CCME-WQI. From 1985 to 2018, the Shahijina had the most considerable load of nutrients and organic matter, as determined by the CCME-WQI and by comparing the water quality data. A thorough examination had revealed a fluctuating trend in the BRB pollution, particularly at all stations. Results indicate that between 1985 and 2018, the only defense mechanism of the river was the auto purification mechanism, which is strongly influenced by the drought, point pollution source, and extreme meteorological events that probably cause these fluctuations.

Mortality from extreme meteorological and hydrogeological events in Italy: a rising health threat connected to climate change

The aim of the present work is to assess mortality associated to extreme meteorological and hydrological events (storms, floods, landslides, avalanches) for the period 2003–2020 in Italy. These extreme events are particularly worrying phenomena due to their increasing frequency and intensity connected to climate change. The considerable rise of extreme meteorological events in Italy has been having a dramatic impact on the environment and territories, particularly on intrinsically fragile ones, and on resident populations. More than 90% of Italian municipalities are at risk for extreme events, with more than 8 million inhabitants exposed. Number of deaths and SMRates due to such extreme events (X International Classification of Diseases: X36, X37 and X38) in Italy were calculated from regional to municipal level by ENEA mortality database (data source ISTAT). Geographic maps were elaborated by QGIS software (QGIS, RRID:SCR_018507) version 3.28. In the selected period, 378 overall deaths were detected: 321 deaths due to landslides and avalanches, 28 to cataclysmic storm, and 29 to floods. The regions with the highest mortality levels and/or number of municipalities involved and the municipalities at highest risk were identified. In consideration of the forecasted increase of such extreme events in Italy, the knowledge of Italian areas at highest risk can be used in the decision-making processes to assess priorities, allocate financial resources, define warning measures, and undertake preventive or mitigation actions. Moreover, the attributable mortality levels can be a useful basis for further risk assessment research aimed at estimating the cost in terms of human lives’ loss associated to such events in future climatic scenarios.

Short‐term effects of Storm Daniel on Salmo farioides (Karaman, 1938) in a high‐gradient stream

AbstractClimate change is expected to increase the magnitude, frequency, and intensity of extreme natural phenomena such as droughts and floods. These events can have devastating impacts on aquatic biota, thus there is a dire need to assess species' natural dynamics and to incorporate them into species' conservation actions and flood management decisions. In September 2023, an extreme meteorological event (Storm Daniel) occurred over the eastern Mediterranean, causing unprecedented high flooding events in many streams and rivers of central Greece. Almost 2 months after the event, we investigated the effects of the storm on Salmo farioides in a high‐altitude stream (Kraniotiko stream, Acheloos river basin) by comparing pre‐flood (June and August 2023) with post‐flood (October 2023) data on fish population abundances and size structure. Our results provide direct evidence that the population of S. farioides was almost wipedout from the investigated stream, as post‐flood fish densities were 9.3% of the pre‐flood data (August) densities. Small‐sized individuals (6–10 cm) were extirpated, indicating a 99% decrease during the post‐flood period, totally reversing the increasing trend observed from June to August (pre‐flood period). In conclusion, the profound and rapid decline observed in the population of S. farioides following the extreme flooding event highlights the urgent need for proactive conservation measures and adaptive management strategies to safeguard vulnerable aquatic species in the face of escalating climate‐related challenges.

The impact of climate change on the crime scene and forensic sciences.

Climate change causes great impact on ambient temperatures. In forensic pathology, temperature has always been an important variable to determine numerous thanatodiagnostic and thanatochronological parameters, as crucial elements to make scientifically valid forensic conclusions. The aim of this work is to describe the impact that climate change is having on the forensic science and crime scene analysis. A literature review was carried out on the search engines Pubmed, Scopus and Google Scholar. The keywords "climate change" AND "forensic" were used. Climate change is having a significant impact on forensic science in several areas. Among these it is necessary to consider: 1) global warming and crime scenes: high temperature of the planet causes frequent extreme meteorological events in multiple periods of the year; 2) Relationship between climate and PMI (Post Mortem Interval): Temperature influences the rapidity of putrefaction; 3) Relationship between climate and fauna: global warming is influencing the geographical distribution of animal species, with real migrations of species. Fauna has a decisive impact in the forensic field (as in the case of entomology) creating new models of animal-cadaver interaction; 4) Relationship between climate and forensic botany: Climate change influences the flowering and fruiting times of plants, impacting knowledge on botany used in the forensic field. It is essential to adopt prevention measures to address the emerging challenges.

Flash flood prediction in St. Lucia island through a surrogate hydraulic model

ABSTRACT Recent flood disasters caused by extreme meteorological events highlight the need of fast and reliable tools for flooding forecast. For our purposes, the danger associated with floods is embodied in a single risk-level flag which considers both local water depth and velocity. The methodology here derived is applied and validated for the case study of the St. Lucia island in the eastern Caribbean Sea that experiences flash flooding as a result of combined intense rainfall and steep slopes, difficult to predict with traditional early-warning systems. A multi-layer perceptron neural network is trained on a high-fidelity dataset generated through full two-dimensional shallow water simulations of real and synthetic events. The dataset is validated against social markers obtained from real events. The predictive capabilities of the neural network model are tested on the out-of-box case of the Dean and Tomas hurricanes and compared with the solutions of the shallow water solver. The surrogate solver allows a significant speed-up in the prediction time with respect to traditional CFD (seconds vs hours), showing a high precision and accuracy, with accuracy, precision and F1-score above 0.99.

Trends In Extreme Weather Events With Socio-Economic Damage Over The Period 1991-2019 In Russia And Its Regions

Increased number of extreme weather events is one of the most serious hazards of climate change over the territory of Russia. However, there is a lack of comprehensive analysis of the number of extreme weather events that caused social and economic damage in the country and its regions. This paper analyzes changes in the total number of events with damage (meteorological for the period 1991-2019 and agrometeorological – for 2004-2019), disaggregated by their types and by regions. The Mann-Kendall test is applied to detect statistical significance (0.05 level of significance, normal distribution). The results show an increase in the number of meteorological extreme events with damage for 1990-2019 in Russia from 130 to 257 events per year on average for the 1990s and 2010s, respectively, while the proportion of events with damage in relation to the total number of extreme events decreased over this period. We found statistically significant trends only for a few types of extreme events: hot and cold temperature, strong wind, heavy rain and droughts (increase by 0.9, 9.4, 11.4, 25.9 and 13.3 events/10 years, respectively). Number of heavy rain precipitation events is the only unidirectional stable growth trend. Unusual increasing trend in cold extreme events with damage in Russia can be attributed to the greater damage to the economy and population from cold extremes than hot ones. The regional distribution of trends across the territory of the Russian Federation is heterogeneous. However, significant changes in the number of extreme events of strong winds, heavy rains and soil drought by regions are statistically positive and observed mostly in some southern and central regions of European part and the Western Siberia. The development of adaptation plans to the negative effects of climate change is a first priority for these regions. A system for monitoring economic and non-economic damage from extreme events must be developed in Russia.

Spatial and Temporal Variability Characteristics and Driving Factors of Extreme Precipitation in the Wei River Basin

As global climate change intensifies, the global atmospheric circulation process is undergoing significant changes, and the local water vapor pattern has also changed. This study takes the Wei River Basin as the research area. Firstly, an evaluation index system for extreme precipitation was established, and the time-series characteristics of the magnitude, frequency, and duration of extreme precipitation were analyzed. Statistical methods were used to analyze the non-consistency in time-series changes in extreme precipitation indicators. Using spatial heterogeneity analysis methods, the spatial variation differences in extreme precipitation in the Wei River Basin were identified. This study selected the El Niño-Southern Oscillation (ENSO) index, global land-ocean temperature index (LOTI), and land surface temperature (LST) index to quantitatively evaluate the impact of climate change on regional extreme precipitation and analyzed the correlation between temperature and extreme precipitation, identifying the key driving factors of extreme precipitation changes. The conclusions of this study are as follows: (1) The southern region of the Wei River Basin experiences more frequent and intense precipitation events, while the northern region experiences relatively few. (2) From 1981 to 2021, the intensity, frequency, and duration of precipitation events in the Wei River Basin gradually increased, with the most significant increase in extreme precipitation in the Guanzhong Plain. (3) Global climate change has an important impact on precipitation events in the Wei River Basin. The increase in the ENSO, LOTI, and LST indices may indicate an increase in the probability of drought and flood events in the Wei River Basin. The relationships between extreme precipitation and temperature present a peak structure. This conclusion is helpful to better understand the impact of climate change on extreme precipitation in the Wei River Basin and provides some support for the response to extreme meteorological events under the background of future climate change.

Exploring the mesoscale connectivity of phytoplankton periodic assemblages' succession in northern Adriatic pelagic habitats

An appropriate model for phytoplankton distribution patterns is critical for understanding biogeochemical cycles and trophic interactions in the oceans and seas. Because phytoplankton dynamics in coastal waters are more complex due to shallow depth and proximity to land, more accurate models applied to the correct spatial and temporal scales are needed. Our study investigates the role of the atmosphere and hydrosphere in pelagic habitat by modelling phytoplankton assemblages at two Long Term Ecological Research sites in the northern Adriatic Sea using niche-forming environmental variables (wind, temperature, salinity, river discharge, rain, and water column stratification). To study the synchronization between the phytoplankton community and these environmental variables at the two LTER sites, we applied current linear and nonlinear numerical methods for ecological modelling. The aim was to use periodic and/or non-periodic properties of the environmental variables to classify the phytoplankton assemblages at one LTER site (Gulf of Trieste - Slovenia) and then predict them at another LTER site 100 km away (Gulf of Venice - Italy). We found that periodicity played a role in the explanatory and predictive power of the environmental variables and that it was more important than non-periodic events in defining the common structure of the two pelagic habitats. The non-linear classification functions of the neural networks further increased the predictive power of these variables. We observed partial synchronization of communities at the mesoscale and differences between the original and predicted assemblages under similar environmental conditions. We conclude that mesoscale connectivity plays an important role in phytoplankton communities in the northern Adriatic. However, the loss of periodicity of niche-forming variables due to more frequent extreme meteorological and hydrological events could loosen these connections and affect the temporal succession of phytoplankton assemblages.