Growing concerns about elevated nitrate levels in natural springs on the southern slope of the Irazu Volcano, Cartago province in Costa Rica, were the driving force for a multi-disciplinary study on the problem. The region is characterized by its high agricultural output and steep slopes. More than sixty springs located in the area are managed by a large number of local water authorities, ASADAS. The study focused on determining the main sources of nitrate pollution. Anthropological activities such as agricultural practices and domestic actions are found to contribute the most. A multivariate polynomial regression model was used with a large set of parameters. From the results it can be seen that human activities within the by law determined 200 meter radius protection zone around the springs, are most influential on high nitrate concentrations in the springs. Furthermore a stakeholder analysis, fieldwork, financial analysis of alternatives and farmer interviews were performed to produce a comprehensive list of recommendations for the various stakeholders. The recommendations are compiled to ensure a future with clean drinking water for all the citizens in the region.

Understanding the propagation of a flood is crucial for effective emergency response measures. While traditional numerical models provide reliable flood simulations, their high computational costs pose significant limitations during emergencies. Deep learning models have recently demonstrated potential in accelerating hydrological calculations while preserving high accuracy. Although various deep learning flood models have been developed, many are limited to specific case studies or neglect the dynamic propagation of flood waves, constraining their application during emergencies. To address this, Bentivoglio et al., 2023 proposes the use of a physics-based surrogate model for spatio-temporal flood modelling; the shallow water equation graph neural network (SWE-GNN). The model demonstrates promising results on small virtual landscapes, showcasing strong generalizability to unseen breach locations and domains, while achieving computational speed ups. In this research, the real-world applicability of the SWE-GNN for time-sensitive situations is analyzed. Two dike rings in the Netherlands are selected as our case study areas. The model is trained and tested within the same domain to evaluate its application during a crisis. Performance is assessed using statistical metrics and practical evaluations, including direct and indirect damage models. The SWE-GNN model is able to correctly predict the spatio-temporal evolution of floods for unseen breach locations. The mean average errors in time are of 0.027 m and 0.029 m for water depth and of 0.007 m^2/s and 0.006 m^2/s on units discharge. The resulting flood maps prove viable for practical applicability, presenting good results for both direct as indirect damage assessment. Additionally, the SWE-GNN demonstrates a speedup of roughly 5 to 6 times for the test case areas compared to a traditional numerical model. In this project, we affirm that the SWE-GNN represents a promising innovation for a new approach to time-sensitive flood modeling, providing a reliable alternative to numerical models in situations with time constraints.