Climate change-driven wildfires, especially in the Mediterranean, are not only becoming more frequent and severe but also amplifying flood risks by altering catchment hydrology. Yet, post-fire flood risk management remains inadequately addressed. In response, we develop an integrated simulation framework that combines meteorological, hydrological, hydraulic-hydrodynamic models and remote sensing techniques to represent post-wildfire flood hazards and support the design of Post-wildfire Flood Protection Treatments (PFPTs). We utilize the framework to accurately represent a post-wildfire flash flood event in a Mediterranean catchment in Greece. The flood event is simulated under three scenarios: pre-wildfire, post-wildfire without any PFPTs in place (reality), and post-wildfire with PFPTs. The results show that the wildfire's impact on flood extent was around a 24.1 % increase, but the PFPTs could have counterbalanced this impact. Moreover, we present an economic model for estimating the cost of the recommended PFPTs and the flood damage direct costs, combining an accounting and a semi-automated AI-based approach. The cost comparison reveals that the protection would have cost around EUR 5.05 million (just the 20 % of the flood damage costs, EUR 25.2 million) potentially saving EUR 6.37 million in flood damage. By filling critical knowledge gaps, our study offers insights into the dynamics of post-wildfire flood events and provides policymakers with valuable insights for timely risk mitigation amidst escalating fire-related disasters.

Urban rivers receiving multiple wastewater inputs require reliable diagnostic tools to disentangle overlapping nitrogen sources. Here, we investigate wastewater source attribution in a densely urbanized basin using isotopic tracers together with diagnostic indicators based on contaminants of emerging concern (CECs), rather than CEC occurrence alone. Monthly water sampling over 2 years at five stations in an urban river system included nitrate isotopes, conventional hydrochemistry, and a broad suite of CECs. Nitrate concentrations reached up to 18.3 mg L-1 as NO3--N, and δ15N-NO3- values ranged from +6.1 ‰ to +24.2 ‰, consistent with variations in total nitrogen loads along the river continuum. Although isotope signals indicated increasing wastewater influence downstream, isotopic information alone was insufficient to distinguish between human and animal derived wastewater sources. The integration of nitrate isotopes with CEC-derived diagnostic indices, including the Human Wastewater Index (HWI) and the Hospital Index (HI), within a Bayesian mixing framework based on characterized source signatures revealed that human wastewater contributions exceeded 60 % at all monitoring stations. Elevated HI values further indicated hospital wastewater inputs that were not detectable using isotopic variables alone. An ecological risk assessment indicated that, in 70 % of the samples, the combined risk from detected CECs was high, with over half of the individual compounds present at concentrations exceeding their respective acute toxicity thresholds. These results demonstrate that CEC-based diagnostic indicators provide complementary information to isotopic tracers and substantially improve wastewater source attribution in complex urban river systems, with direct implications for monitoring and management of urban water quality.

Stable isotope techniques (delta(18)Omicron, delta(2)Eta of H2O) were applied in the transboundary Prespa Lakes (Great and Little Prespa) to assess the water dynamics and the evolution of evaporation losses over the past several decades. The lakes currently experience high evaporation losses to inflows (E/I > 60%), which are significantly higher than in the 1980s. The results showed that the Great Prespa Lake (GPL) water level decline could be due to a drastic decrease in lake inflows over evaporation over the years due to climate change and water abstractions. River runoff contributed more (similar to 57%) to the recharge of the GPL in the wet period, whereas in the dry period direct precipitation was more significant. Our work highlights the advantage of using stable isotope techniques to address hydrological problems in comparison to conventional methods, and the need for collaborative efforts between countries to ensure sustainable usage of transboundary water resources.

The Evrotas River Basin (ERB) is an agriculturally dominated catchment that receives waste inputs from scattered agro-industrial activities, such as food processing and dairies, municipal wastewater and septic systems. Monthly water sampling for ∼3 years for nitrate isotopes and conventional hydrochemical species at five monitoring sites in the ERB revealed the dominance of nitrate pollution of organic origin, related to human and animal wastes, spanning from upstream to downstream and obscuring the signal from nitrate synthetic fertilizer application. Nitrate concentrations reached up to 1.5 mg/L as NO3--N. The δ15Ν-ΝΟ3- and δ18O-NO3- values ranged from +2.0 ‰ to +16.0 ‰ and from +0.5 ‰ to +11.8 ‰, respectively. The relationship of δ15Ν-ΝΟ3- with δ18O-NO3-, δ18O-H2O, N species, dissolved oxygen (DO), land-use and environmental indicators, such as Water Pollution Level (WPL), revealed N-cycling and mixing processes, primarily linked to the uptake of nutrients by phytoplankton and metabolic activities. The development of a modelling framework for the application of a Bayesian mixing model and the incorporation of the N-cycling processes along the river revealed that the proportional contribution of organic wastes related to urban wastes and manure exceeded 50 % at all sites apart from the most downstream site where agro-industrial wastewaters from olive oil mills, fruit juice and dairy industries dominated. A comprehensive approach that integrates isotopic techniques, regulatory frameworks, and water footprint assessment is essential for preserving riverine health and advancing sustainable environmental management.

The water resource management of transboundary mountainous river basins under climate change is expected to be challenging. In order to contribute to the better understanding of climate change effects on the water resources of the mountainous and transboundary Prespa Lakes basin, a hydrological model of the Agios Germanos River, one of the main rivers discharging to Great Prespa Lake, was developed, and two water management plans under two different climate scenarios were examined. Based on the results, the impact of climate change on surface water resources was evident in all climate change scenarios examined, even under the most favorable water abstraction practices. Nevertheless, sensible water management can moderate the impact of climate change by up to 10% in an optimistic scenario in both the near- and long-term, and by up to 6% and 1% for the near- and long-term, respectively, in a pessimistic scenario. Integrated water management practices that moderate the impact of climate change on the water ecosystem services should be prioritized. Nature-based approaches could provide solutions regarding climate change adaptation and mitigation. Transboundary cooperation, data exchange mechanisms, common policy frameworks, and monitoring, reporting, and evaluation systems, could reduce human and ecosystems’ vulnerabilities and improve the water security of the area.

Ensemble weather forecasting involves the integration of multiple simulations to improve the accuracy of predictions by introducing a probabilistic approach. It is difficult to accurately predict heavy rainfall events that cause flash floods and, thus, ensemble forecasting could be useful to reduce uncertainty in the forecast, thus improving emergency response. In this framework, this study presents the efforts to develop and assess a flash flood forecasting system that combines meteorological, hydrological, and hydraulic modeling, adopting an ensemble approach. The integration of ensemble weather forecasting and, subsequently, ensemble hydrological-hydraulic modeling can improve the accuracy of flash flood predictions, providing useful probabilistic information. The flash flood that occurred on 26 January 2023 in the Evrotas river basin (Greece) is used as a case study. The meteorological model, using 33 different initial and boundary condition datasets, simulated heavy rainfall, the hydrological model, using weather inputs, simulated discharge, and the hydraulic model, using discharge data, estimated water level at a bridge. The results show that the ensemble modeling system results in timely forecasts, while also providing valuable flooding probability information for 1 to 5 days prior, thus facilitating bridge flood warning. The continued refinement of such ensemble multi-model systems will further enhance the effectiveness of flash flood predictions and ultimately save lives and property.

Selection of the appropriate sampling sites is a fundamental task in the design of a surface water quality monitoring network (WQMN). However, there is still no generally accepted approach for site selection and oftentimes authorities rely on empirical insights instead of systematic design. In this study, we integrated geoprocessing tools with a Fuzzy Analytic Hierarchy Process (F-AHP), coupled with a thorough sensitivity analysis (SA) to determine the optimal sites for a WQMN automatic stations. The proposed model is based on the combination of seven criteria, representing anthropogenic stressors and environmental status to identify high-priority sub-basins (“hot-spots”), in geographic information system (GIS). The results propose that “hot-spots” appear in clusters and are unevenly distributed over the study area. They encompass the biggest cities and their surroundings as well as the estuaries of the three main rivers. Moreover, the model identified monitoring “hot-spots” that occupy only small extents of the study area and could otherwise be undetected. Complementing the model, a SA was performed over the -50

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.

Wildfires are an escalating global threat, jeopardizing ecosystems and human activities. Among the repercussions in the ecosystem services of burnt areas, there are altered hydrological processes, which increase the risks of flash floods. There is limited research addressing this issue in a comprehensive way, considering pre- and post-fire conditions to accurately represent flood events. To address this gap, we present a novel approach combining multiple methods and tools for an accurate representation of post-fire floods. The 2019 post-fire flood in Kineta, Central Greece is used as a study example to present our framework. We simulated the meteorological conditions that caused this flood using the atmospheric model WRF-ARW. The burn extent and severity and the flood extent were assessed through remote sensing techniques. The 2D HEC-RAS hydraulic–hydrodynamic model was then applied to represent the flood event, using the rain-on-grid technique. The findings underscore the influence of wildfires on flooding dynamics, highlighting the need for proactive measures to address the increasing risks. The integrated multidisciplinary approach used offers an improved understanding on post-fire flood responses, and also establishes a robust framework, transferable to other similar cases, contributing thus to enhanced flood protection actions in the face of escalating fire-related disasters.

Monitoring the ecological status of rivers is essential for protecting freshwater biodiversity and ecosystem health. The main objective of this work was to predict the ecological quality of Greek rivers using a machine learning approach based on the Extreme Gradient Boosting (XGBoost) classifier. We used a dataset that comprises ecological, physicochemical, geomorphological, and sample-related parameters collected from the national monitoring network of Greek rivers as well as climate parameters from the ERA5-Land dataset. More specifically, we developed multiple models that predicted the ecological quality class derived by four quality elements (QEs) that are benthic macroinvertebrates, benthic diatoms, fish, and physicochemical quality. The Shapley Additive exPlanations (SHAP) approach was implemented for quantifying the contributions of the predictors on the quality class. We finally developed a web interface tool that can simulate what-if scenarios to predict the quality class under altered environmental conditions. Our findings showed that total phosphorus, nitrate, and ammonium were important predictors for benthic macroinvertebrates, benthic diatoms, and physicochemical quality, whereas for fish, predictors related with the geomorphology (e.g., altitude and slope) had a higher influence. The SHAP plots revealed the synergistic effect of predictors on the quality classes, highlighting a negative effect of increased nutrients on achieving the good quality class based on macroinvertebrates and diatoms and a positive relationship between altitude and slope with the good ecological quality class based on fish. Furthermore, the web interface could provide a useful tool for water managers to predict quality classes for water bodies under what-if scenarios.

Wildfires pose a growing global danger for ecosystems and human activities. The degraded ecosystem functions of burnt sites, include, among others, shifts in hydrological processes, land cover, vegetation, and soil erosion, that make them more vulnerable to flood and extreme sediment transport risks. Several post-fire erosion and flood protection treatments (PFPs) have been developed to avoid and mitigate such consequences and risks. The Mediterranean region faces severe climate change challenges that are projected to escalate the wildfire and post-fire flood risks. However, there is limited research on the dynamics of post-fire flood risks and their mitigation through the design of the appropriate PFPs. This paper aims to cover this gap by simulating a real post-fire flash-flood event in Central Greece, and design the PFPs for this case study, considering their suitability and costs. An integrated framework was used to represent the flood under the baseline scenario: the storm conditions that caused the flood were simulated using the atmospheric model WRF-ARW; the burn extent, severity, and the flood extent were retrieved through remote sensing analyses; and a HEC-RAS hydraulic-hydrodynamic model was developed to simulate the flood event, applying the rain-on-grid technique. Several PFPs were assessed, and certain channel- and barrier-based PFPs were selected as the most suitable for the study area. The recommended PFPs were spatially represented within a geographic information system (GIS). Moreover, we present a detailed analysis of their expected costs. This study provides an interdisciplinary and transferable framework for understanding and enhancing the flood resilience of burnt sites.

Water isotopes (& delta;O-18, & delta;H-2) were systematically monitored in two river catchments to investigate the isotopic spatiotemporal variation and the differences between them. The Pinios River Basin (PRB) exhibited lower average & delta;O-18 and & delta;H-2 values (-7.9 & PTSTHOUSND; and -50.8 & PTSTHOUSND;, respectively) compared to the Evrotas River Basin (ERB) (-6.5 & PTSTHOUSND; and -38.2 & PTSTHOUSND;, respectively) but higher in range (3.3 & PTSTHOUSND; vs 1.2 & PTSTHOUSND; for & delta;O-18, respectively). The Bayesian modelling results showed higher groundwater contribution in the PRB (25-50%) than in the ERB (15-35%) relative to precipitation during the wet period. The isotopic spatial variability was attributed to the influence of local precipitation, evaporation and additional flow pathways (e.g. soil water). The correlation analysis showed that the isotopic composition is controlled by the catchment altitude, slope and discharge. This study highlights the catchment physiographic control on the isotopic composition of rivers, which can support strategies for better water resources management.

The Pinios River Basin (PRB) is the most intensively cultivated area in Greece, which hosts numerous industries and other anthropogenic activities. The analysis of water samples collected monthly for-1 1/2 years in eight monitoring sites in the PRB revealed nitrate pollution of organic origin extending from upstream to downstream and occurring throughout the year, masking the signal from the application of synthetic fertilizers. Nitrate concentrations reached up to 3.6 mg/l as NO3 �-N, without exceeding the drinking water threshold of-11.0 mg/l (as NO3 �-N). However, the water quality status was "poor" or "bad" in-50 % of the samples based on a local index, which considers the potential impact of nitrate on aquatic biological communities. The & delta;15N-N & Omicron;3 � and & delta;18O-NO3 � values ranged from +4.4 %o to +20.3 %o and from-0.5 %o to +14.4 %o, respectively. The application of a Bayesian model showed that the proportional contribution of organic pollution from industries, animal breeding facilities and manure fertilizers exceeded 70 % in most river sites with an overall uncertainty of-0.3 (UI90 index). The & delta;18O-NO3 � and its relationship with & delta;18O-H2O revealed N-cycling and mixing processes, which were difficult to identify apart from the uptake of nutrients by phytoplankton during the growing season and metabolic activities. The strong correlation of & delta;15N-N & Omicron;3 � values with a Land Use Index (LUI) and a Point Source Index (PSI) highlighted not only the role of non-point nitrate sources but also of point sources of nitrate pollution on water quality degradation, which are usually overlooked. The nitrification of organic wastes is the dominant nitrate source in most rivers in Europe. The systematic monitoring of rivers for nitrate isotopes will help improve the understanding of N-cycling and the impact of these pollutants on ecosystems and better inform policies for protection measures so to achieve good ecological status.

Water turbidity is one of the more important water quality parameters that is strictly linked with the productivity of the lake and is commonly used as an indicator of the trophic state. However, limited field data availability across wide geographic gradients may hinder the conduction of large scale longitudinal studies. In this study, time series of lake turbidity and trophic state index (TSI) between 2002 and 2012 were obtained from the Copernicus Lake Water products to create a large longitudinal dataset of lake variables for 22 European lakes. The dataset was combined with estimates of nutrient concentrations and surface water temperature obtained from the Hydrological Predictions for the Environment (HYPE) and ERA5-Land data repositories, that were used as environmental predictors. Hence, the validity of the lake water quality parameters was tested by a) exploring their spatial and temporal variability and b) identifying associations with the environmental predictors. For this purpose, seasonal Mann-Kendall tests were applied to find significant inter-annual trends of turbidity and TSI for each lake, and generalized additive models (GAMs) were employed to identify the main parameters that shape their temporal dynamics. Although we did not find significant inter-annual changes, our findings highlighted the strong influence of seasonality and surface water temperature in defining the temporal variability patterns in most of the lakes. In addition, the importance of nutrients varied among lakes as several lakes exhibited narrow nutrient gradients reflecting relatively stable nutrient conditions during the examined period. Other lake intrinsic factors, such as local climate and biotic interactions, are important drivers of shaping turbidity and nutrient dynamics. This study highlighted the usefulness of combining lake data from large repositories in conducting large scale spatial studies as a valuable asset for future lake research and management purposes.

Although the impact of hydrology on the ecological status of surface water bodies has been highly recognised, the hydrological regime alteration assessment has proven to be a challenging task. In this context, an extensive structured review analysis was used as a research method to investigate the strength and limitations of the hydrological regime alteration assessment methods as adopted by each member of the European Environment Agency and the cooperating countries, according to the Water Framework Directive 2000/60, as well as to propose future directions. The review was also widened to include the methods currently used worldwide in the hydrological alteration studies and the supporting software tools developed. The implementation of a common methodology on a European scale is not applicable, since a single approach would not be able to cope with the regional needs and conditions. The main limitation in almost all the methods developed by European countries and worldwide is the need for a flow time series of high temporal resolution, so as to also capture the systems’ extreme high and low flows. Automatic monitoring systems for rivers can provide a solution. Additionally, hydrological modelling may provide the necessary data for the definition of the reference conditions. Nevertheless, the main limitations of the methodologies reviewed and the challenge for future development are the incorporation of the groundwater contribution to the hydrological regime and the development of quantitative relationships between flow alteration and ecological response.

This study aims to unravel and quantify the impact of sea surface temperature (SST) on the formation, intensity, structure and track of the Mediterranean tropical-like cyclone (medicane) Ianos occurred on 15-20 September 2020 at the central Mediterranean. This study, thus, demonstrates how Ianos would be in past and future climate conditions, assuming that SST changes over the years, but preserving the same atmospheric conditions. To investigate the SST impact, the medicane was simulated using the Advanced Weather and Research Forecasting (WRF-ARW) model. The numerical experiments were initialized either with SST analysis data (control experi-ment) or applying a uniform decrease and increase to SST analysis by 1 degrees C and 2 degrees C (four sensitivity experi-ments). In this way, the past and future climatic SSTs were concisely approximated. Analysis of various thermodynamic parameters in combination with phase space diagrams, revealing the thermal symmetry and the warm core structure of the cyclone, indicated that Ianos was very sensitive on SST. Thus, SST changes especially by & PLUSMN;2 degrees C had significant impact on its intensity, changing the period of tropical features while also determining the track and the landfall location. Overall, the average enthalpy flux (i.e., the sum of sensible and latent heat fluxes) in Ianos changed by approximately-39% and + 50% when SST changed by-2 degrees C and + 2 degrees C, respectively. This, in turn, affected the characteristics of Ianos causing changes for example in the average wind speed (approximately-15% and + 15%) and the average precipitation (approximately-56% and + 44%). This study quantifies the impacts of SST on Ianos medicane that have important research and socioeconomic impli-cations with a view to a changing future. Therefore, it could support scientists, decision-makers and civil pro-tection in the adaptation to extreme weather phenomena by building climate resilience and sustainability.

This study aims at assessing meteorological, hydrological, and hydraulic modeling to develop a flash flood forecasting tool.The flash flood that occurred in the Evrotas River Basin (ERB) on 26 January 2023 is used as a case study.Precipitation over 150 mm and water depths exceeding 2.5 m were recorded.The meteorological model initialized one day before flooding and simulated precipitation; the hydrological model, using meteorological input data, simulated discharge; and the hydraulic model, using discharge, estimated water depth at a bridge.The results indicate that the system can provide skillful and timely flash flood forecasts, thereby facilitating flood warnings.

A cost effective and easily applied methodological approach for the identification of the main factors involved in flood generation mechanisms and the development of rainfall threshold for incorporation in flood early warning systems at regional scale is proposed. The methodology was tested at the Pinios upstream flood-prone area in Greece. High frequency monitoring rainfall and water level/discharge time-series were investigated statistically. Based on the results, the study area is impacted by “long-rain floods” triggered by several days long and low-intensity precipitation events in the mountainous areas, that saturate the catchment and cause high flow conditions. Time lag between the peaks of rainfall and water level was 17–25 h. The relationship between cumulative rainfall R sum on the mountainous areas and maximum water level MaxWL of the river at the particular river site can be expressed as: MaxWL = 1.55ln( R sum ) − 3.70 and the rainfall threshold estimated for the mountainous stations can be expressed as: R sum = 20.4* D 0.3 , where D is the duration of the event. The effect of antecedent moisture conditions prior each event was limited to the decrease of the time lag between rainfall and water level response. The limitations of the specific methodological approach are related to the uncertainties that arise due to the other variables contributing to the complex flood generating mechanisms not considered (e.g., the effect of snowmelt and air temperature, soil characteristics, the contribution of tributaries, or the inadequate maintenance of river network that may cause debris accumulation and river bank failure).