Majid Niazkar

Climate hazards impose inevitable pressures on natural and managed ecosystems, human infrastructures, natural resources, socio-economic functioning, and security within the water-energy-food-ecosystem nexus. In this context, Climate Risk Assessment (CRA) plays a vital role in providing a better perspective on hazard, vulnerability, exposure, and essential responses for developing adaptation strategies. Considering different modelling assumptions, parameterization and initialization, climate projections entail specific geographical biases and uncertainties. Although bias-adjusting of climate projections before conducting CRA is recommended, a critical question emerges to delineate which climate models/scenarios should be chosen not only to provide the least bias/uncertainty but also to improve reliability for such regional CRA.The CLIMAAX project provides an open-access toolbox for conducting CRA for a variety of climate hazards (e.g., river and coastal flooding, heavy precipitation, drought, heatwaves, wind, snow, and wildfire). Among them a specific workflow is designed to address how to select climate model projections/scenarios for each NUTS2 region throughout Europe. The workflow is hosted on an interactive platform characterizing (i) bias and (ii) uncertainty currently for precipitation and surface temperature. It relies on a consolidated precalculated dataset and provides rapid calculation and graphical display of bias and uncertainty of climate projections with an easy-to-use GUI to facilitate understanding of climate model skills, their optimal use and exploitation to conduct CRA at regional scales for a wide community of users and practitioners. The first part of the workflow identifies which climate model has the lowest biases in precipitation and temperature. It starts by selecting the region of interest and EURO-CORDEX models. The biases of precipitation and temperature are calculated in percentages and degrees Celsius, respectively, for historical records simulated by climate models in comparison to either Eobs or ERA5 reanalysis datasets. The second part of the workflow provides an absolute or a relative range of precipitation and temperature accumulated in five 20-year windows in the period of 1986 - 2100. As an example, this study demonstrates the application of the workflow to a specific region to showcase its usefulness in the CRA process. Finally, this workflow plays a key role in evaluating biases, understanding uncertainties, reducing uncertainty ranges, which can result in more valuable and credible CRAs.Link to the current version of the workflow:https://handbook.climaax.eu/dashboards/bias-uncertainty/Acknowledgments: This research work was carried out as part of the CLIMAAX project with funding received from the European Union’s Horizon Europe – the Framework Programme for Research and Innovation (2021-2027) under grant agreement No. 101093864.

Coastal aquifers are more exposed to pollution and salinity than other hydrogeological systems due to their proximity to the sea, increasing groundwater withdrawals, and climate change. The aim of this study is not only to evaluate and compare the vulnerability of coastal aquifers using the DRASTIC and GALDIT models but also to investigate effects of different weighting methods on the results of vulnerability zoning. The spatio-temporal vulnerability assessment was conducted for coastal aquifers in Hormozgan Province in Iran over a 15-year period (2010–2024). After collecting information layers required for both models, vulnerability maps were calculated for three consecutive five-year periods using three weighting methods: (a) normal weighting, (b) Shannon entropy, and (c) particle swarm optimization (PSO) algorithm. The results indicate that the coastal areas of the western part of the province have the highest vulnerability in both models, and the intensity and extent of high-risk zones have increased in recent periods. Comparison of weighting methods revealed that normal weighting provided a conservative and uniform distribution, while the entropy method, due to its reliance on statistical dispersion of data in some areas, led to a hyperbole of the risk. In contrast, the PSO algorithm provided the most accurate and realistic results compared to classical fixed-weight and entropy-based vulnerability maps, as it was able to identify critical areas with higher spatial concentration and hydrogeological coherence. The combined results of DRASTIC and GALDIT demonstrated that parts of the coastal aquifers of Hormozgan are simultaneously in a critical state in terms of inherent vulnerability and salinity potential. The findings of this study can be used as a scientific basis for sustainable management of groundwater resources, withdrawal control, and protection climate adaptation planning in coastal areas.

Climate Risk Assessment (CRA) consists of four components based on the IPCC framework: (a) hazard, (b) exposure, (c) vulnerability, and (d) response/adaptation. Such assessment is essential not only to understand how each hydroclimatic hazard can have an impact on urban areas, but also to develop climate adaptation strategies. Although a wide range of tools for CRA have been developed, several challenges limit their consistent application in the urban environment. First, a typical urban area can be exposed to multiple natural hazards, which requires a framework to assess multi-hazard multi-risk impacts. Furthermore, characteristics of hydroclimatic hazards (e.g., magnitude and spatio-temporal variations) can alter due to climate changes. Finally, future projections are scenario-based, which inevitably introduces uncertainty in CRA. In this context, CLIMAAX presents a comprehensive CRA toolbox including a series of practical workflows in terms of Python scripts, each focusing on a specific climate hazard. Together, the workflows enable consistent multi-hazard assessments. The hazards considered in the CLIMAAX toolbox include river and coastal flooding, heavy rainfall, urban heatwaves, relative and agriculture droughts, wildfire, heavy snowfall and blizzards, and windstorm. The toolbox is available for implementation to any European region, but it can be extended to other regions with minor modifications to input data. To select climate projections, the CLIMAAX toolbox provides a workflow assessing bias and uncertainty of climate models/scenarios for any region in Europe. Using the CRA outputs, the toolbox supports key risk assessments that account for severity, urgency and capacity, enabling the integration of risk responses as the final step of CRA.This study has a twofold aim. First, it attempts to showcase applications of three CLIMAAX workflows, including river flooding, heavy rainfall, and coastal flooding, to the city of Genoa, Italy. Genoa’s river networks have experienced multiple flood events, like the ones in November 2011 and October 2014. Furthermore, the city is also affected by intense rainfall, as shown by the heavy precipitation event in November 2025, which caused flash flooding and street inundation. Moreover, coastal flooding represents an additional hazard, with the October 2018 event impacting beaches and coastal infrastructure. Based on the CRA results, river flooding was identified with the highest risk priority in Genova. Second, flooding hazard-specific risk outputs were translated into a spatially-explicit data-driven assessment of public-private adaptation infrastructure options at the city scale. Building on the CLIMAAX workflows, neighborhood-scale risk layers were mapped for pluvial flooding from heavy rainfall, fluvial flooding along Genoa’s river network, and coastal flooding to study adaptation options targeting different risk components. For a set of these options, alternative deployment strategies were explored, and avoided impacts alongside capital expenditures and operation and maintenance costs were quantified. This enables sub-city cost-benefit comparison of individual and combined infrastructures that best align with severity, urgency and capacity constraints, producing a basis for prioritizing adaptation pathways across Genoa’s neighborhoods. Acknowledgement: This research work was carried out as part of the CLIMAAX project with funding received from the European Union’s Horizon Europe – the Framework Programme for Research and Innovation (2021-2027) under grant agreement No. 101093864.

River water quality status is increasingly challenged by the combined effects of climate extremes and human activities. Furthermore, monitoring water quality parameters remains sparse and irregular across many river networks to conduct timely and effective assessments. To address these challenges, a new framework was developed to support river network-wide estimation of ecological water quality status. The framework combines a multi-risk approach with deep learning to investigate the relationship between climate hazards, anthropogenic pressures, exposure, and vulnerabilities under hot-dry and wet-dry conditions. It was implemented for the Veneto Region (Italy) to predict annual LIMeco, a nutrient-oxygen physico-chemical index used in regional assessments, for 865 river segments over 2010-2023, with data missingness reaching 66.8%. Hazard conditions were represented by annual hydroclimatic indicators capturing hot and wet/dry conditions and extremes, while anthropogenic pressures were described through land use composition and nutrient load proxies. Exposure and vulnerability were represented through basin characteristics (e.g., soil properties and topography), together with the presence of riparian and wetland areas as proxies of buffering capacity and management levels. To translate these drivers into spatially coherent predictions while acknowledging missing observations due to incomplete datasets, a hybrid spatio-temporal Graph Neural Network (GNN) was implemented in which (i) recent hydroclimatic variability was summarized over a short input window using the Gated Recurrent Units, (ii) information was propagated along upstream-downstream connectivity using GNN, and (iii) eco-hydrological clusters of basins were represented through a data-driven regime label with an unsupervised Machine Learning (ML), derived from the multi-risk indicators, enabling the information transfer between well-monitored and poorly-monitored river segments that have similar climate and land-based regimes. The hybrid spatio-temporal GNN was tested over multiple configurations and against other ML approaches (i.e., Multilayer perceptron and eXtreme Gradient Boosting). The comparison demonstrates that the hybrid GNN achieved the best performances with the highest test accuracies (RMSE = 0.028; NSE = 0.98) when using the embedded river basin clusters, providing the most stable performance across basin types and missingness levels. This highlighted how the inclusion of physically-based river network dynamics and auxiliary information can help to address missing data compared to other tested methodologies. The proposed framework can support scenario-oriented analysis for decision making and planning, given the representation of management-relevant indicators (e.g., riparian condition, wetland presence, land-use pressure), allowing for exploring future scenarios and responses under climate and socio-economic changes to support adaptation strategies.

Machine learning (ML) and deep learning (DL) models can play an important role when it comes to modelling complicated processes. Such capability is necessary for hydrological and climate-related applications. Generally, ML models utilize precipitation and temperature time series of a basin as input to develop a lumped rainfall-runoff model to simulate streamflow at the basin outlet. However, when it is divided into several sub-basins, Graph Neural Networks (GNN) can consider each sub-basin as a node and link them together using a connectivity matrix to account for spatial variations of hydroclimatic variables. In this study, GNN and various ML models with different types of architecture, ranging from neural networks, tree-based structure, and gradient boosting, were exploited for daily streamflow simulation over different case studies. For each case study, the basin was divided into a few sub-basins for which daily precipitation and temperature data were aggregated and used as input. For training GNN, the connection matrix of sub-basins was also used as input. Basically, 75% of historical records were utilized to train GNN and different ML models, e.g., artificial neural networks, support vector machine, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), and Category Boosting (CatBoost), while the rest was used for testing. Streamflow simulation was conducted with/without considering seasonality impact and lag times. The obtained results clearly demonstrate that considering seasonality and time lags can enhance accuracy of streamflow predictions based on Kling–Gupta efficiency (KGE). Furthermore, GNN with seasonality impact and time lags achieved promising results across different case studies with KGE>0.85 for training and KGE>0.59 for testing data, respectively. Among ML models, boosting models, e.g., LightGBM and XGBoost, performed slightly better than other ML models. for Finally, this comparative analysis provides valuable insights for ML/DL applications in climate change impact assessments.Acknowledgements: This research work was carried out as part of the TRANSCEND project with funding received from the European Union Horizon Europe Research and Innovation Programme under Grant Agreement No. 10108411.

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.