Several oil spill simulation models exist in the literature, which are used worldwide to simulate the evolution of an oil slick created from marine traffic, petroleum production, or other sources. These models may range from simple parametric calculations to advanced, new-generation, operational, three-dimensional numerical models, coupled to meteorological, hydrodynamic, and wave models, forecasting in high-resolution and with high precision the transport and fate of oil. This study presents a review of the transport and oil weathering processes and their parameterization and critically examines eighteen state-of-the-art oil spill models in terms of their capacity (a) to simulate these processes, (b) to consider oil released from surface or submerged sources, (c) to assimilate real-time field data for model initiation and forcing, and (d) to assess uncertainty in the produced predictions. Based on our review, the most common oil weathering processes involved are spreading, advection, diffusion, evaporation, emulsification, and dispersion. The majority of existing oil spill models do not consider significant physical processes, such as oil dissolution, photo-oxidation, biodegradation, and vertical mixing. Moreover, timely response to oil spills is lacking in the new generation of oil spill models. Further improvements in oil spill modeling should emphasize more comprehensive parametrization of oil dissolution, biodegradation, entrainment, and prediction of oil particles size distribution following wave action and well blow outs.

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of the climate crisis are being increasingly felt, the need for accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on meteorological variables using statistical techniques as well as artificial intelligence (AI) for a specific area of interest utilising data from an in situ meteorological station, and to produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location; in our case, in a hotel in the northern area of Crete, Greece. The temporal resolution of the measurements used was 3 h and the forecast horizon considered here was up to 2 days. As prediction techniques, seasonal autoregressive integrated moving average (SARIMA), AI techniques like the long short-term memory (LSTM) neural network and hybrid combinations of the two are used. Multiple meteorological variables are considered as input for the LSTM and hybrid methodologies, like temperature, relative humidity, atmospheric pressure and wind speed, unlike the SARIMA that has a single variable. Variables of interest are divided into those that present seasonality and patterns, such as temperature and humidity, and those that are more stochastic with no known seasonality and patterns, such as wind speed and direction. Two benchmark techniques are used for comparison and quantification of the added predictive ability, namely the climatological forecast and the persistence model, which shows a considerable amount of improvement over the naive prediction methods, especially in the 1-day forecasts. The results indicate that the examined hybrid methodology performs best at temperature and wind speed forecasts, closely followed by the SARIMA, whereas LSTM performs better overall at the humidity forecast, even after the correction of the hybrid to the SARIMA model. Lastly, different hybrid methodologies are discussed and introduced for further improvement of meteorological predictions.

Oil spills in the ocean are a matter of concern due to the damaging effect they can have on coastal and offshore resources. This work presents a review of present-day modeling techniques used in the mitigation of oil spills by booms, skimmers, chemical dispersants, and other equipment and the importance of the controlling parameters of these techniques. Three basic questions need to be addressed by oil spill models: (1) where the spill will move, (2) when will the spill get to the modeled endpoints, and (3) what will be its state when it arrives. The first two questions are relatively urgent, as far as response measures are concerned, and depend closely on the use of accurate data on winds, sea currents, and wave action as oil spill accidents evolve. Obtaining a reasonable answer to the third question lies in the use of reliable fate algorithms. Oil spill models can be divided in two types: Euleurian and Langragian. Adding to information regarding the oil type and its initial location, all oil spill models require data for the wind fields, sea state, sea-surface temperature, and currents, as well as other environmental parameters, if available. Such reliable data suit the needs of oil spill modeling predictions and are available daily at global, regional, and coastal scales within the broader scope of operational oceanography. Advanced oil spill models available at present use satellite synthetic aperture radar (SAR) images/data to detect possible oil slicks and assimilate slick and drifter observations to correct slick predictions. The emphasis of research and governmental institutions has been on improving 4D predictions obtained through simulation of oil spills backward in time to track the slicks back to their source. Such backward simulations, when integrated with ships' Automatic Identification Systems (AIS), will be used to locate the sources of oil slicks around the world's oceans and seas.

This study focuses on the quantification of climate change (CC) effects on agricultural water availability in Cyprus. Projections of climatic variables, based on Regional Climate Models (RCMs) forced by the Representative Concentration Pathways (RCPs) 4.5 and 8.5, were used as CC driving forces affecting water availability. Groundwater flow models were developed for specific high-interest agricultural areas in Larnaca and Paphos to assess the CC impacts on these groundwater systems, while the Standardized Precipitation–Evapotranspiration Index (SPEI) analysis was also adopted, for the first time in Cyprus, to assess future trends of water reservoir storage under the projected climatic conditions. Considering the current cultivation and irrigation practices, a decrease in groundwater level close to 1 m and further inland seawater intrusion in Larnaca aquifers are expected, while in Paphos’ aquifers, the predicted water table fluctuations are not significant. Additionally, SPEI values at the Asprokemos and Kouris dams are correlated with water storage measurements, showing that a SPEI downward trend observed in these reservoirs could set off an alarm to the water authorities with respect to water availability as more severe drought events are expected in the future. The expected pressure on surface waters imposes the need for an improved water management plan that will not depend on the further exploitation of groundwater.