Geographically distributed predictions of future climate, obtained through climate models, are widely used in hydrology and many other disciplines, typically without assessing their reliability. Here we compare the output of various models to temperature and precipitation observations from eight stations with long (over 100 years) records from around the globe. The results show that models perform poorly, even at a climatic (30-year) scale. Thus local model projections cannot be credible, whereas a common argument that models can perform better at larger spatial scales is unsupported. Résumé Des prévisions distribuées dans l'espace du climat futur, obtenues à l'aide de modèles climatiques, sont largement utilisées en hydrologie et dans de nombreuses autres disciplines, en général sans évaluation de leur confiance. Nous comparons ici les sorties de plusieurs modèles aux observations de température et de précipitation de huit stations réparties sur la planète qui disposent de longues chroniques (plus de 100 ans). Les résultats montrent que les modèles ont de faibles performances, y compris à une échelle climatique (30 ans). Les projections locales des modélisations ne peuvent donc pas être crédibles, alors que l'argument courant selon lequel les modèles ont de meilleures performances à des échelles spatiales plus larges n'est pas vérifié.

We compare the output of various climate models to temperature and precipitation observations at 55 points around the globe. We also spatially aggregate model output and observations over the contiguous USA using data from 70 stations, and we perform comparison at several temporal scales, including a climatic (30-year) scale. Besides confirming the findings of a previous assessment study that model projections at point scale are poor, results show that the spatially integrated projections are also poor. Citation Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. & Mamassis, N. (2010) A comparison of local and aggregated climate model outputs with observed data. Hydrol. Sci. J. 55(7), 1094–1110.

We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catchments over the contiguous United States. Point hydrological predictions are obtained using the Génie Rural à 4 paramètres Journalier (GR4J) hydrological model and exploited as predictor variables within quantile regression settings. Six machine-learning quantile regression algorithms and their equal-weight combiner are applied to predict conditional quantiles of the hydrological model errors. The individual algorithms are quantile regression, generalized random forests for quantile regression, generalized random forests for quantile regression emulating quantile regression forests, gradient boosting machine, model-based boosting with linear models as base learners and quantile regression neural networks. The conditional quantiles of the hydrological model errors are transformed to conditional quantiles of daily streamflow, which are finally assessed using proper performance scores and benchmarking. The assessment concerns various levels of predictive quantiles and central prediction intervals, while it is made both independently of the flow magnitude and conditional upon this magnitude. Key aspects of the developed methodological framework are highlighted, and practical recommendations are formulated. In technical hydro-meteorological applications, the algorithms should be applied preferably in a way that maximizes the benefits and reduces the risks from their use. This can be achieved by (i) combining algorithms (e.g., by averaging their predictions) and (ii) integrating algorithms within systematic frameworks (i.e., by using the algorithms according to their identified skills), as our large-scale results point out.

Beyond doubt, desalination is growing rapidly worldwide. However, there are still obstacles to its wider implementation and acceptance such as: (a) high costs and energy use for fresh water production; (b) environmental impacts from concentrate disposal; (c) a complex, convoluted and time-consuming project permitting process; and (d) limited public understanding of the role, importance, benefits and environmental challenges of desalination. In this paper, a short review of desalination in Greece is being made. Data on the cost of desalination shows a decrease in the future and the potential of water desalination in Greece. The paper summarizes the current status in southeastern Greece (e.g., Aegean islands and Crete), and investigates the possibility of production of desalinated water from brackish water.

Despite the great scientific and technological advances in flood hydrology, everyday engineering practices still follow simplistic approaches that are easy to formally implement in ungauged areas. In general, these "recipes" have been developed many decades ago, based on field data from typically few experimental catchments. However, many of them have been neither updated nor validated across all hydroclimatic and geomorphological conditions. This has an obvious impact on the quality and reliability of hydrological studies, and, consequently, on the safety and cost of the related flood protection works. Preliminary results, based on historical flood data from Cyprus and Greece, indicate that a substantial revision of many aspects of flood engineering procedures is required, including the regionalization formulas as well as the modelling concepts themselves. In order to provide a consistent design framework and to ensure realistic predictions of the flood risk (a key issue of the 2007/60/EU Directive) in ungauged basins, it is necessary to rethink the current engineering practices. In this vein, the collection of reliable hydrological data would be essential for re-evaluating the existing "recipes", taking into account local peculiarities, and for updating the modelling methodologies as needed.

Storm Daniel initiated on 3 September 2023, over the Northeastern Aegean Sea, causing extreme rainfall levels for the following four days, reaching an average of about 360 mm over the Peneus basin, in Thessaly, Central Greece. This event led to extensive floods, with 17 human lives lost and devastating environmental and economic impacts. The automatic water-monitoring network of the HIMIOFoTS National Research Infrastructure captured the evolution of the phenomenon and the relevant hydrometeorological (rainfall, water stage, and discharge) measurements were used to analyse the event’s characteristics. The results indicate that the average rainfall’s return period was up to 150 years, the peak flow close to the river mouth reached approximately 1950 m³/s, and the outflow volume of water to the sea was 1670 hm³. The analysis of the observed hydrographs across Peneus also provided useful lessons from the flood-engineering perspective regarding key modelling assumptions and the role of upstream retentions. Therefore, extending and supporting the operation of the HIMIOFoTS infrastructure is crucial to assist responsible authorities and local communities in reducing potential damages and increasing the socioeconomic resilience to natural disasters, as well as to improve the existing knowledge with respect to extreme flood-simulation approaches.