Search Results (30)

This study conducts a diagnostic analysis of an extremely heavy rainfall event and its causative factors that occurred in Libya, North Africa on 10 September 2023. The Weather Research and Forecasting (WRF) model was also employed to perform some sensitivity experiments for this heavy rainfall event and further reveal its causes. Results indicate that the primary synoptic system responsible for this extreme precipitation event was an extratropical cyclone (storm) named “Daniel”. During the formation and development of this cyclone, the circulation at the 500 hPa level from the eastern Atlantic to western Asia exhibited a stable “two troughs and one ridge” pattern, with a upper-level cold vortex over the eastern Atlantic, a high-pressure ridge over central Europe, and a cut-off low over western Asia, collectively facilitating the formation and development of this cyclone. As this cyclone moved southward, it absorbed substantial energy from the Mediterranean Sea; following landfall, the intrusion of weak cold air enabled the cyclone to continue intensifying. Meanwhile, the northwest low-level jet stream to the west of the extratropical cyclone moved alongside the cyclone to the coastal regions of northeastern Libya, where it converged with water vapor transport belts originating from the Ionian Sea, the Aegean Sea, and the coastal waters of northeastern Libya. This convergence provided abundant water vapor for the rainstorm event, and under the combined effects of convergence and orographic lifting on the windward slopes of the coastal mountains, extreme precipitation was generated. In addition, the atmosphere over the coastal regions of northeastern Libya exhibited strong stratification instability, which was conducive to the occurrence of extreme heavy precipitation. Although WRF successfully reproduced the precipitation process, the precipitation amount was underestimated. Sensitivity experiments revealed that both the topography in the precipitation area and the sea surface temperature (SST) of the Mediterranean Sea contributed to this extreme heavy precipitation event. Full article

Extreme rainfall events are frequently associated with regions of complex topography, where terrain-induced convergence and uplift enhance storm development. Understanding the interaction between surface relief and atmospheric dynamics is essential for improving severe weather forecasting and hazard mitigation. Storm “Daniel”, which affected Greece from 4–7 September 2023, produced extreme rainfall and widespread flooding in the Thessaly region—a landscape characterized by significant elevation gradients. This study investigates the spatial relationship between lightning activity and terrain elevation, aiming to assess whether deep convection was preferentially triggered over mountainous regions or followed specific orographic patterns. High-resolution elevation data (SRTM 1 Arc-Second Global DEM) were used to calculate the mean elevation around each lightning strike across four spatial scales (2 km, 5 km, 10 km, and 20 km). Statistical analysis, including correlation coefficients and third-degree polynomial regression, revealed a non-linear relationship, with a distinct peak in lightning frequency at mid-elevations (~200–400 m). These findings suggest that topographic features at local scales can significantly modulate convective initiation, likely due to a combination of mechanical uplift and favorable thermodynamic conditions. The study integrates geospatial techniques and statistical modeling to provide quantitative insights into how terrain influences the formation, location, and intensity of thunderstorms during high-impact weather events. Full article

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application has not yet reached full maturity. We focus on applying machine learning models to create flood susceptibility maps (FSMs) for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. This study utilizes 13 explanatory variables derived from topographical, hydrological, hydraulic, environmental and infrastructure data to train the models, using Storm Daniel—one of the most severe recent events in the region—as the primary reference for model training. The most significant of these variables were obtained from satellite data of the affected areas. Four machine learning algorithms were employed in the analysis, i.e., Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Accuracy evaluation revealed that tree-based models (RF, XGBoost) outperformed other classifiers. Specifically, the RF model achieved Area Under the Curve (AUC) values of 96.9%, followed by XGBoost, SVM and LR, with 96.8%, 94.0% and 90.7%, respectively. A flood susceptibility map corresponding to a 1000-year return period rainfall scenario at 24 h scale was developed, aiming to support long-term flood risk assessment and planning. The analysis revealed that approximately 20% of the basin is highly prone to flooding. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision making for disaster preparedness in the region. Full article

Storm Daniel is arguably one of the most severe Mediterranean tropical-like cyclones (medicanes) ever recorded. Greece was one of the most affected areas, especially the central part of the country. The extreme precipitation that was observed along with the subsequent extensive flooding was considered a critical challenge to validate the regional version of the ICON (Icosahedral Non-Hydrostatic) numerical weather prediction (NWP) model. From a methodological standpoint, the short-range nature of the model was realized with 48 h runs over a sequence of cases that covered the storm period. The development of the medicane was highlighted via the tracking of the minimum mean sea level pressure (MSLP) in reference to the corresponding analysis of the European Center for Medium-Range Weather Forecasts (ECMWF). In a similar fashion, snapshots regarding the 500 hPa geopotential associated with the 850 hPa temperature were addressed at the 24th forecast hour of the model runs. Although the model’s performance over the four most affected synoptic stations of the Hellenic National Meteorological Service (HNMS) was mixed, the overall accumulated forecasted precipitation was in very good agreement with the corresponding total value of the observations over all the available synoptic stations. Full article

Floods are considered one of the most devastating natural hazards, frequently resulting in substantial loss of lives and widespread damage to infrastructure. In the period of 4–7 September 2023, the region of Thessaly experienced unprecedented rainfall rates due to Storm Daniel, which caused significant flooding and many damages and fatalities. The southeastern areas of Trikala were among the many areas of Thessaly that suffered the effects of these rainfalls. In this research, a flood susceptibility assessment (FSA) of the broader area surrounding the settlement of Megala Kalyvia is carried out through the analytical hierarchy process (AHP) as a multicriteria analysis method, using Geographic Information Systems (GIS). The purpose of this study is to evaluate the prolonged flood susceptibility indicated within the area due to the past floods of 2018, 2020, and 2023. To determine the flood-prone areas, seven factors were used to determine the influence of flood susceptibility, namely distance from rivers and channels, drainage density, distance from confluences of rivers or channels, distance from intersections between channels and roads, land use–land cover, slope, and elevation. The flood susceptibility was classified as very high and high across most parts of the study area. Finally, a comparison was made between the modeled flood susceptibility and the maximum extent of past flood events, focusing on that of 2023. The results confirmed the effectiveness of the flood susceptibility assessment map and highlighted the need to adapt to the changing climate patterns observed in September 2023. Full article

Air–sea interactions play a pivotal role in shaping cyclone development and evolution. In this context, this study investigates the role of ocean optical properties and solar radiation penetration in modulating subsurface heat content and their subsequent influence on the intensity of Mediterranean cyclones. Using a regional coupled ocean–wave–atmosphere model, we conducted sensitivity experiments for Storm Daniel (2023) comparing two solar radiation penetration schemes in the ocean model component: one with a constant light attenuation depth and another with chlorophyll-dependent attenuation based on satellite estimates. Results show that the chlorophyll-driven radiative heating scheme consistently produces warmer sea surface temperatures (SSTs) prior to cyclone onset, leading to stronger cyclones characterized by deeper minimum mean sea-level pressure, intensified convective activity, and increased rainfall. However, post-storm SST cooling is also amplified due to stronger wind stress and vertical mixing, potentially influencing subsequent local atmospheric conditions. Overall, this work demonstrates that ocean bio-optical processes can meaningfully impact Mediterranean cyclone behavior, highlighting the importance of using appropriate underwater light attenuation schemes and ocean color remote sensing data in coupled models. Full article

Background: In September 2023, Storm Daniel triggered catastrophic flooding across Thessaly, in central Greece, leading to the deaths of approximately 483,476 animals and heightening concerns about zoonotic diseases, particularly Q fever caused by Coxiella burnetii. Sofades, a municipality in the Karditsa region that is severely impacted by the floods, emerged as a critical area for evaluating the risk of zoonotic disease transmission. This study aimed to determine the seroprevalence status of Coxiella burnetii Phase 1 IgA antibodies among residents in the rural area of Sofades after the Daniel floods. Methods: Serum samples were obtained from a convenient sample of residents with livestock exposure between 1 March and 31 March 2024. Enzyme-linked immunosorbent assay (ELISA) was used to detect Coxiella burnetii Phase 1 IgA antibodies. Descriptive analyses summarized demographic data, and logistic regression was employed to examine the association between gender, age, and positive ELISA results. Results: The overall seroprevalence was 16.66%. Males had a significantly higher positivity rate (28.57%) than females (6.25%). Seropositivity was more frequent among individuals aged 41–80 years, with peak prevalence observed in the 61–80 age group. Conclusions: This cross-sectional study offers a snapshot of Coxiella burnetii exposure in a high-risk rural population post-flood. The slightly higher seroprevalence in Sofades (16.66%) compared to Karditsa (16.1%) suggests limited influence of environmental factors on transmission. Despite limitations in causal inference, the findings highlight the need for enhanced surveillance and targeted public health measures. Longitudinal studies are needed to assess the long-term impact of environmental disasters on Q fever dynamics. Full article

The dispersion of microplastics in the sea is an emerging and crucial environmental problem. In this preliminary study, the hydrodynamics of microplastics transferred from flooded agricultural areas to the sea was assessed. The Daniel storm in 2023 in region of Thessaly, Greece, initiated the transfer of plastic debris via the Pinios River, which subsequently discharged to the coastal basin at the south area of Thermaikos Gulf (NW Aegean Sea). Field sampling and laboratory measurements of microplastics collected at the mouth of the Pinios were conducted. The dispersion of microplastics discharged by the Pinios River is subject to the dominant wind conditions over the area, which in turn determines the water circulation in the NW Aegean Sea. Thus, a hydrodynamic model was initially applied, followed by a transport model for the study of the dispersion of the microplastics. The models were applied for SW and NE winds and indicated that the majority of microplastics with a settling velocity 0.1 m/s accumulate in areas close to the river’s mouth or lateral coastal zones; however, under the influence of SW winds, minor quantities tend to reach the east coasts of the Thermaikos Gulf, while massive quantities are transported away from the river’s mouth in case of microplastics floating on the sea’s surface. Full article

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. Full article

The anticipated rise in extreme flood events in the Eastern Mediterranean region indicates an increase in significant societal impacts that have the potential to extend beyond the flooded areas and affect multiple sectors. Despite the criticality of understanding storm and flood risk and how they propagate in modern interconnected societies, the scope and complexity of storm- and flood-triggered cascading effects are still poorly comprehended. This study explores the effects created by the extreme Storm Daniel, occurring in Thessaly, Greece in 2023, aiming to gather new evidence on the types and scale of these cascading effects by analyzing its impacts in the region through fieldwork and official data collection. The results, as a contribution to existing knowledge on cascade effects, provide insights into the nature, the extent, the propagation mechanisms, and the consequences of these triggering events leading to diverse cascade effects. The study identifies the interactions between different phenomena following this extreme storm event to offer a better understanding of how impacts propagate, and therefore a better understanding of future challenges connected with this type of cascading hazards framework, ultimately contributing to predicting and mitigating associated risks. Full article

The 2023 storm Daniel hit areas of Greece, Bulgaria, Turkey and Libya, leading to severe flooding phenomena. One of the severely affected areas was the Thessaly Region in central Greece, which was subjected to extreme precipitation, with historic record rainfalls. This paper presents an overview of the observed damage to the built environment (buildings, bridges, slopes, etc.) and the resulting soil response or soil–structure interaction phenomena associated with the severe flooding caused by storm Daniel. To assist readers, reported cases of damage and supporting evidence (such as photos, rainfall level, etc.) are introduced in an interactive map of the affected area, illustrating the spatial effects of this severe storm on the built environment. Full article

The rapid detection of landslide phenomena that may be triggered by extreme rainfall events is a critical point concerning timely response and the implementation of mitigation measures. The main goal of the present study is to identify susceptible areas by estimating changes in the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), and Synthetic Aperture Radar (SAR) amplitude ratio before and after extreme rainfall events. The developed methodology was utilized in a case study of Storm Daniel, which struck central Greece in September 2023, with a focus on the Mount Pelion region on the Pelion Peninsula. Using Google Earth Engine, we processed satellite imagery to calculate these indices, enabling the assessment of vegetation health, soil moisture, and exposed soil areas, which are key indicators of landslide activity. The methodology integrates these indices with a Weight of Evidence (WofE) model, previously developed to identify regions of high and very high landslide susceptibility based on morphological parameters like slope, aspect, plan and profile curvature, and stream power index. Pre- and post-event imagery was analyzed to detect changes in the indices, and the results were then masked to focus only on high and very high susceptibility areas characterized by the WofE model. The outcomes of the study indicate significant changes in NDVI, NDMI, BSI values, and SAR amplitude ratio within the masked areas, suggesting locations where landslides were likely to have occurred due to the extreme rainfall event. This rapid detection technique provides essential data for emergency services and disaster management teams, enabling them to prioritize areas for immediate response and recovery efforts. Full article

This study investigates the performance of published bio-optical remote sensing indices/algorithms for monitoring water quality changes in Lake Karla, Greece, caused by Storm Daniel after the September 2023 flooding event. Commonly applied indices were utilised to estimate chlorophyll-a (Chl-a) and total suspended solids (TSS) using Sentinel-2 high-resolution optical imagery. In situ measurements were undertaken and water samples were collected during the pre-flooding period, post-flooding, and one-year post-flood, providing a basis for validating the remote sensing models. Monitoring results showed that most physicochemical parameters changed considerably. Chl-a and TSS were estimated by testing five and seven indices, respectively. Regarding the Chl-a estimation, the NDCI and 2-BDA indices outperformed other models, having high correlations with in situ Chl-a measurements and effectively following the in situ Chl-a temporal trends. Among the TSS indices, NDWI and TUR-IND demonstrated better performances, effectively capturing the variations in suspended solids. Overall, this study highlights the potential of Sentinel-2 imagery in assessing water quality changes, particularly in response to flooding events. It is an exploratory approach to assess the feasibility of utilising optical satellite data for evaluating the environmental impacts of natural disasters on lake water quality and supports decision-making in environmental management. Additionally, it identifies potential challenges and considerations that must be addressed to ensure effective application. Full article

Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events. Full article