Identifying and managing hotspots to extreme events : application to NW Mediterranean conditions

The impact of coastal storms is one of the costliest forms of natural disaster, as it affects a particularly valuable fringe both from the socioeconomic and ecologic perspectives. In addition, storm-induced erosion and inundation risks are expected to rise in the near future due to changing conditions related to climate change combined with current trends on urbanization and population growths and beach losses associated with long-term erosion. The assessment of the hazard component is complex, due to the multidimensionality of the processes involved, the inherent uncertainties of the analysis and the multiple scales in which the hazard characterization must be performed. In this context, there is a need for providing risk assessment methodologies allowing coastal managers integrated decision making based on the analysis of present and future conditions. The present PhD thesis focuses on the development of such methodologies, both at regional scale with the purpose of hotspot identification and at local scale with the aim of providing detailed hazard or risk assessments. The work starts with the study of two main sources of uncertainty involved in hazard assessment: the assignment of probabilities of occurrence to given hazard magnitudes and the definition of the shape of the storm to assess induced hazards. Applying lessons learned from these uncertainty studies, different hazard and risk assessment approaches are proposed based on the use of Bayesian Networks (BNs). They have been selected due to their efficiency in combining multiple variables and characterizing their dependency relations to predict system behaviour while explicitly including uncertainties. Obtained results highlight the importance of characterizing the hazard probabilities focusing the statistical assessment at coastal response (the so called response approach), especially if detailed hazard estimation (e.g. inundation maps) are to be produced. The use of the alternative approach (event approach, assigning probabilities to event characteristics to later estimate hazards from that event) produces only similar results when assessing simple variables such as run-up or total water levels at the coast, and only in locations with high correlations between involved storm variables. Results also indicate that detailed hazard estimation, as pursued with process-based modelling, may be affected by significant errors when using synthetic triangular events, i.e. design storms with assumed triangular evolution over their duration. These errors are observed to be significant for a wide range of tested conditions, involving different morphologies (dissipative to intermediate-reflective), wave climates (NW Mediterranean and N Adriatic) and storm energy contents. The proposed methodology at regional scale (~100 km) consists of simple hazard and exposure indicators calculated at ~1 km coastal sectors and has been successfully applied for hotstpot detection at the Maresme coast. The Tordera Delta is identified as a significant hotspot to both erosion and inundation risks. This framework has been improved with the use of BNs to account for intra-sector morphological variability and model uncertainties. Results from the BN regional assessment highlight the importance of a fully stochastic hazard characterization with the inclusion of model errors to avoid under-predictions. This BN approach also allowed a detailed assessment of the conditional dependencies between hazards and both storm and morphological characteristics giving deeper insight on system response and useful information for the development of coastal adaptation plans. The proposed methodology at the hotspot local scale (~1-10 km) consists of different BN set-ups following the Source-Pathway-Receptor-Consequences model and trained with large datasets of simulated hazards. They are applied to the Tordera Delta to analyse risk reduction measures and to stochastically obtain the risk profile under present and future scenarios. Hazard simulations are performed though detailed process-based modelling. A XBeach set-up is specifically developed for conditions at the Tordera Delta and validated with an historical extreme event (the Sant Esteve 2008 storm) obtaining a BSS of 0.68 for the morphological response at the subaerial beach. Local scale results show a high sensitivity of the Tordera Delta to incoming wave direction, with associated changes on induced hazards comparable to those of worst-case SLR scenarios (e.g. RCP 8.5, 2100). When assessing the efficiency of risk reduction measures, it is obtained that increasing beach height through an artificial dune is the most efficient action against inundation while managed receptors retreat is the only efficient option against erosion, as beach nourishment is very ephimeral even under mild storm conditions due to background erosion. The method also permits to define probabilistic setbacks for different hazards and risk levels, and characterizes hazardous storm characteristics under different scenarios. Finally, the BN approach is tested as storm-induced retreat predictive model based on simple storm parameters. Once fed with a large number of storm simulations, results show a great potential to perform as surrogate of simple parametric models at complex study sites where their applicability is limited, e.g. curvilinear coasts with high alongshore morphological variability and beach-structure interactions. Overall, the use of BNs to characterize hazards and risks associated to coastal storms at different scales has been proved robust, as it can include many natural variabilities and problem uncertainties by efficiently assimilating large datasets. It is a flexible and communication-friendly approach that can be adapted to other specific problems related to coastal evolution, or other natural hazards. The method can be potentially imporved with larger datasets and/or used in combination with other machine learning techniques. El impacto de temporales en la costa es uno de los fenómenos naturales más costosos, dado que afecta a un litoral de gran valor tanto des del punto de vista socioeconómico como ecológico. Además, se espera un aumento de los riesgos de erosión e inundación durante los episodios de tormenta, debido a las condiciones climáticas cambiantes, así como a las tendencias actuales de urbanización, aumento de población en las costas y a la pérdida progresiva de playas asociada a la erosión a largo plazo. La multidimensionalidad de los procesos involucrados, las incertidumbres inherentes en los análisis y las múltiples escalas para abordar el problema hacen que la erosión e inundación costera sean complejas de evaluar. Por ello, se requieren metodologías de evaluación de riesgo que permitan una gestión integrada basada en el análisis de condiciones presentes y futuras. Esta Tesis Doctoral se centra en el desarrollo de dichas metodologías, tanto a escala regional, con el objetivo de identificar “hotspots” (zonas sensibles), como a escala local con el propósito de proporcionar evaluaciones detalladas de riesgo o amenaza. El trabajo empieza por el estudio de dos de las principales fuentes de incertidumbre en el análisis de amenazas: la asignación de probabilidades a las magnitudes de erosión e inundación y la adopción de formas de tormenta sintéticas para la estimación de las mismas. De los resultados de dichos estudios se desprenden las metodologías propuestas, que se basan en el uso de Redes Bayesianas (RBs). Éstas se han seleccionado por su eficiencia para predecir el comportamiento de sistemas, combinando múltiples variables y evaluando sus inter-dependencias, permitiendo al mismo tiempo la inclusión de incertidumbres de forma explícita. Los resultados obtenidos destacan la importancia de basar el análisis estadístico en la respuesta costera a la hora de de asignar probabilidades a las amenazas (el llamado método de la respuesta), en especial si se requiere la obtención de resultados detallados como mapas de inundación. El uso del método alternativo (el método del evento, que asigna probabilidades a características de tormenta para luego estimar su impacto) lleva a resultados parecidos solo cuando se analizan variables simples como el remonte o el nivel total de mar en la costa, y únicamente en lugares con una alta correlación entre las variables de tormenta implicadas. Los resultados también indican que la adopción de tormentas sintéticas triangulares, i.e., tormentas diseñadas con forma triangular a lo largo de su duración, puede llevar a errores significativos si se pretende una estimación detallada de la respuesta costera frente a las tormentas, como por ejemplo la que se busca con el uso de modelos numéricos basados en la física de los procesos. Dichos errores se han observado para un amplio rango de condiciones analizadas, incluyendo diferentes morfologías (disipativas y reflejantes), climas de oleaje (Noroeste Mediterráneo o Adriático Norte) y energía de las tormentas. La metodología propuesta a escala regional (~100 km) consiste en el uso de indicadores simples de amenaza y de exposición, calculados para sectores de ~1 km. Se ha aplicado con éxito en la costa del Maresme, donde el delta de la Tordera ha sido identificado como principal hotspot para el riesgo tanto de erosión como de inundación. La metodología ha sido posteriormente mejorada con el uso de las RBs, permitiendo tener en cuenta tanto la variabilidad morfológica intra-sector como la incertidumbre asociada a los modelos usados. Los resultados obtenidos con la RB regional destacan la importancia de caracterizar estocásticamente las amenazas junto con la consideración de los errores de los modelos para evitar sub-predicciones. La RB también ha permitido la caracterización de las correlaciones entre magnitudes de amenaza y características de tormenta o de morfología, dando mayor información sobre la respuesta del sistema, de gran utilidad para el desarrollo de plan