This paper discusses the main achievements of DISARM (Drought and fIre ObServatory and eArly waRning system) project, which developed an early warning system for wildfires in the Eastern Mediterranean. The four pillars of this system include (i) forecasting wildfire danger, (ii) detecting wildfires with remote sensing techniques, (iii) forecasting wildfire spread with a coupled weather-fire modeling system, and (iv) assessing the wildfire risk in the frame of climate change. Special emphasis is given to the innovative and replicable parts of the system. It is shown that for the effective use of fire weather forecasting in different geographical areas and in order to account for the local climate conditions, a proper adjustment of the wildfire danger classification is necessary. Additionally, the consideration of vegetation dryness may provide better estimates of wildfire danger. Our study also highlights some deficiencies of both EUMETSAT (Exploitation of Meteorological Satellites) and LSA-SAF (Satellite Application Facility on Land Surface Analysis) algorithms in their skill to detect wildfires over islands and near the coastline. To tackle this issue, a relevant modification is proposed. Furthermore, it is shown that IRIS, the coupled atmosphere-fire modeling system developed in the frame of DISARM, has proven to be a valuable supporting tool in fire suppression actions. Finally, assessment of the wildfire danger in the future climate provides the necessary context for the development of regional adaptation strategies to climate change.

Climate change is a major challenge for the global tourism sector, affecting destinations worldwide. Greece, known for its scenery and abundant cultural history, is particularly vulnerable to these impacts. Tourism is a key driver of Greece’s economy, yet climate change threatens both natural environments and cultural sites. To evaluate the impacts of climate change on different types of Greek tourism (beach, sightseeing, winter tourism in mountainous areas), the widely used Holiday Climate Index (beach and urban versions) alongside three additional climatic indices customized for Greek climatic conditions, namely the Urban Climate Comfort Index, the Beach Utility Index, and the Mountainous Winter Climate Index were utilized for top tourist destinations of Greece. The results indicate that urban tourism may face challenges during peak summer months due to rising temperatures, but the shoulder seasons (April–May and September–October) will offer improved conditions, potentially extending the tourist season. For beach tourism, favorable conditions are expected to increase from April to October, with significant gains in June and September. Winter tourism in mountainous areas, especially snow-dependent activities like skiing, is at risk due to the declining snow availability. Overall, the study highlights both the challenges and opportunities posed by climate change for Greece’s tourism sector. It emphasizes the importance of adaptation strategies, including infrastructural improvements and promoting alternative activities, to minimize negative impacts and enhance the future attractiveness of Greek tourism.

In this paper, the results of a climate change impact and vulnerability assessment conducted for the agricultural sector of Cyprus are presented. The assessment is based on the outputs of specialized climatic and crop models, while it incorporates quantified socio-economic vulnerability indicators of the Cypriot agriculture. The results are aggregated at municipal level in order to support regional and local adaptation planning. The assessment was performed for two representative concentration pathways (RCP4.5, RCP8.5), as well as for extreme climatic scenarios. Following, an economic assessment was made on the expected change in revenues of the agricultural sector. The results of climatic simulations indicated that future increases in temperature will be characterized by a strong seasonal trend, with the highest increases occurring in summer. Precipitation is expected to decrease throughout the island, where the highest decreases (50%) are expected during summer (RCP8.5). This trend will affect mainly tomato, grapevine, and olive tree, whose growing cycle takes place during summer. By contrast, crops covering autumn-winter season, such as potato, barley, and wheat, are expected to partially avoid harsh summer conditions. The results of the economic assessment show that the changes in total revenues are insignificant, because, under all scenarios, a loss in one crop is compensated by a gain in another crop. However, the farmers as well as the government should take action to increase the resilience of the agricultural sector, with a special focus on those crops and areas that are expected to be adversely affected by climate change impacts.

In recent decades, large-scale urbanisation has developed rapidly, resulting in significant changes in the local and regional environment and climate. Large metropolitan areas worldwide induce significant changes in local atmospheric circulation and boundary layer meteorology by modifying the underlying surface characteristics and through the emission of anthropogenic heat and pollutants into the atmosphere. We investigate the urban heat island (UHI) characteristics in the city of Athens, Greece, which is one of Europe’s largest metropolitan complexes with a population of approximately 3.7 million inhabitants. The UHI effect is intense due to the city’s size, dense construction, high incident solar radiation, and almost complete lack of natural vegetation, with previous studies suggesting a temperature rise of 4 °C on average in the city centre compared to summer background conditions. We used high-resolution WRF simulations (1-km horizontal grid) driven with ERA5 reanalysis data to produce surface temperature maps in the city of Athens and the surrounding areas (Region of Attiki) during the summer period of 1 July–20 August 2021. Different model parameterizations were tested, both with respect to urban characteristics and physical parameters. The daily minimum and maximum temperatures (T_min and T_max) derived from the model were validated against observational data from a dense network of weather stations covering metropolitan Athens and surrounding locations. We further investigate the influence of different meteorological conditions on the UHI gradients as produced by the model and the observational dataset, including the extreme heat wave of 28 July–5 August 2021, during which persistent maximum temperatures of >40 °C were recorded for nine consecutive days. The results indicate a strong correlation between WRF output and recorded minimum and maximum temperatures throughout the test period (R ranges from 0.80 to 0.93), with an average mean absolute bias (MAB) of 1.5 °C, and reveal the intensity and spatiotemporal variability of the UHI phenomenon in the city of Athens, with UHI magnitude reaching 8–9 °C at times. Our work aims to maximise the potential of using high-resolution WRF modelling for simulating extreme heat events and mapping the UHI effect in large metropolitan complexes.

The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, utilizing high-resolution satellite imagery from the WorldView-3 system. By combining these state-of-the-art technologies, this study demonstrates the algorithm’s effectiveness at recognizing and segmenting the ancient structures within the archaeological site of Delos, Greece. Despite the computational constraints, the outcomes are promising, with around 25.91% of the initial vector data (434 out of 1675 polygons) successfully identified. The algorithm achieved an impressive F1 Score of 0.93% at a threshold of 0.9, indicating its high precision in differentiating specific features from their environments. This research highlights AI’s crucial role in archaeology, enabling the remote analysis of vast areas through automated or semi-automated techniques. Although these technologies cannot supplant essential on-site investigations, they can significantly enhance traditional methodologies by minimizing costs and fieldwork duration. This study also points out obstacles, such as the complexity of and variability in archaeological remains, which complicate the creation of standardized data libraries. Nevertheless, as AI technologies progress, their applications in archaeology are anticipated to broaden, fostering further innovation within the discipline.

This study presents an analysis of 40-years (1981–2020) precipitation climatology of ERA5-Land and E-OBS gridded datasets over Greece. The analysis focused on trends in total annual, low, moderate, and extreme precipitation—as well as number of rainy days—corresponding to the climatically diverse geographical areas of Greece. Substantial differences were found in the results between the two datasets that could be attributed to the nature of data sources. In general, ERA5-Land revealed slightly negative trends in total precipitation amount, an increase in low and moderate precipitation intensity, and an overall positive trend in extreme precipitation intensity. E-OBS demonstrated a rather negative trend in total precipitation amount, a clear decline in Crete in low precipitation intensity, a positive trend in northern Greece, and a negative trend in Southern Greece and Crete for moderate intensity, while no clear trend was revealed for most parts of Greece for extreme precipitation intensity. Regarding the corresponding results for the number of rainy days (NRD), a significant reduction was evident for E-OBS data and a slight increase for ERA5-Land data for total precipitation amount. Analysis of low precipitation intensity indicated a slight increase for ERA5-Land and an overall reduction for E-OBS. The differences are less pronounced for moderate precipitation intensity, while for extreme precipitation intensity, differences were considered as rather localized. Finally, both datasets showed positive trends in northern or mountainous geographical areas while exhibiting controversial results mainly over southern and coastal zones.