The southwestern Portuguese coast, particularly the Algarve region, is at significant risk of an earthquake exceeding magnitude 8, which could generate a destructive tsunami. Coastal areas such as Quarteira and Vilamoura in the Loulé municipality are especially vulnerable due to their dense population of permanent residents and seasonal tourists, compounded by the long distances required to reach safety zones. Despite the known seismic and tsunami hazards, there have been limited studies that develop tailored mitigation strategies specific to tsunami evacuation. This study contributes to the tsunami science by evaluating potential building damages due to shaking and tsunami impact and providing decision support for evacuation strategies. Using Geographic Information Systems (GIS), tsunami scenarios are modeled based on historical seismic events, such as the 1755 Lisbon earthquake, to identify high-risk coastal zones and optimize evacuation routes, considering local topography and population density. Additionally, wave propagation and arrival times are analyzed to ensure timely access to population escape and to mitigate damage to critical infrastructures. Our findings contribute to the developing a comprehensive tsunami risk mitigation strategy for the Loulé municipality, focusing on prevention, public safety, and emergency preparedness. This research supports land use planning, disaster risk reduction, and emergency planning decisions while also advancing the understanding of urban risk by addressing hazards, exposure, and vulnerability in coastal areas. Key objectives of this study include acquiring and implementing historical tsunami data, assessing recommending evaluating the region´s vulnerability, and recommendations for good practices and mitigation measures. The study emphasizes the importance of raising public awareness, enhancing governance, and strengthening capacity to effectively reduce disaster risks.

This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations incur penalties. This research introduces the novel application of the TFT, a deep–learning model tailored for time series forecasting and uncovering complex patterns, to predict hydropower production based on meteorological data, historical production records, and plant capacity. Key challenges such as filtering observed hydropower outputs (to remove strong, and unpredictable human influence) and adapting the historical series to installed capacity increases are discussed. An analysis of meteorological information from several sources, including ground information, reanalysis, and forecasting models, was also undertaken. Regarding the latter, precipitation forecasts from the European Centre for Medium–Range Weather Forecasts (ECMWF) proved to be more accurate than those of the Global Forecast System (GFS). When combined with ECMWF data, the TFT model achieved significantly higher accuracy in potential hydropower production predictions. This work provides a framework for integrating advanced machine learning models into operational hydropower scheduling, aiming to reduce classical modeling efforts while maximizing energy production efficiency, reliability, and market performance.

The transformative potential of deep learning models is felt in many research fields, including hydrology and water resources. This study investigates the effectiveness of the Temporal Fusion Transformer (TFT), a deep neural network architecture for predicting daily streamflow in Portugal, and benchmarks it against the popular Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model. Additionally, it evaluates the performance of TFTs through selected forecasting examples. Information is provided about key input variables, including precipitation, temperature, and geomorphological characteristics. The study involved extensive hyperparameter tuning, with over 600 simulations conducted to fine–tune performances and ensure reliable predictions across diverse hydrological conditions. The results showed that TFTs outperformed the HBV model, successfully predicting streamflow in several catchments of distinct characteristics throughout the country. TFTs not only provide trustworthy predictions with associated probabilities of occurrence but also offer considerable advantages over classical forecasting frameworks, i.e., the ability to model complex temporal dependencies and interactions across different inputs or weight features based on their relevance to the target variable. Multiple practical applications can rely on streamflow predictions made with TFT models, such as flood risk management, water resources allocation, and support climate change adaptation measures.

Accurate prediction of streamflow is essential for sound water resources management but remains a complex task due to the dynamic nature of hydrological processes, imprecision in meteorological data, and measurement challenges. Recent advancements in deep learning have demonstrated the potential of data-driven models to extract and identify complex temporal dependencies in large hydro-meteorological datasets (e.g. [1-2]).This work evaluates the ability of Temporal Fusion Transformers (TFTs) to predict daily streamflow across catchments in Mainland Portugal, using meteorological input data derived from ERA5-Land (reanalysis dataset) and geomorphological descriptors. TFTs are a relatively novel deep-learning architecture that is being explored in hydrology (e.g., [3-4]). It incorporates the well-tested Long Short-Term Memory (LSTM) architecture with transformers, potentially offering possibilities of improved performance and partial explainability of predictions.The methodology for the application to ungauged catchments relies on straightforward cross-validation with holdout samples. Although all considered catchments are monitored by gauging stations, streamflow observations at the various locations are selectively hidden from the model during calibration and validation, allowing a full controlled emulation of ungauged conditions on the test subsets.Model performance is benchmarked against calibrated Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological models. Results show that TFTs achieve comparable predictive skill in ungauged settings when compared to locally calibrated HBV counterparts, while providing probabilistic predictions with limited explainability.The capability for specialization is also investigated. Indeed, it is shown that retraining a general-purpose “ungauged” TFT on a previously unknown time series, even with only a limited number of observations, can lead to substantial improvements in predictive skill.The proposed framework offers a practical and scalable solution for streamflow estimation in data-scarce and ungauged catchments. By relying on globally available data and static catchment characteristics, the approach can be transferred to regions with limited measurement networks, reducing dependence on long-term observations. The probabilistic outputs further enhance decision-making by explicitly quantifying predictive uncertainty, a critical factor for risk-informed planning, supporting operational water resources management and early warning systems.[1] Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.[2] Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., and Nearing, G. S.: Deep learning rainfall–runoff predictions of extreme events, Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, 2022. [3] Koya, S. R., Roy, T.: Temporal Fusion Transformers for streamflow prediction: Value of combining attention with recurrence. J. Hydrol., 637, 131301. https://doi.org/10.1016/j.jhydrol.2024.131301, 2024.[4] He, M., Jiang, S., Ren, L., Cui, H., Qin, T., Du, S., Zhu, Y., Fang, X., Xu, C.: Streamflow prediction in ungauged catchments through use of catchment classification and deep learning. J. Hydrol., 639, 131638. https://doi.org/10.1016/j.jhydrol.2024.131638, 2024.