RESUMEN.El temporal del 8 de Agosto de 2015 se caracterizó por la ocurrencia conjunta de condiciones meteo-oceanográficas que causaron la destrucción de infraestructura costera y edificaciones en varias localidades del litoral central de Chile. El oleaje extremo se sumó a rachas de vientos provenientes del noroeste y una mínima histórica de la presión atmosférica asociada al sistema frontal. La marea de origen astronómico -aun cuando no extrema-hizo su contribución para peraltar el nivel del mar sobre el cual las olas rompieron con violencia en el borde costero. Para estudiar el evento se procesaron: a) registros de 64 estaciones meteorológicas distribuidas en el sector costero de las regiones de Valparaíso y Coquimbo, b) registros de 6 estaciones mareográficas en Chile Central, c) pronósticos y registro de oleaje en aguas profundas, y d) un modelo de propagación de oleaje desde aguas profundas hacia los sectores más afectados de la región. A partir del análisis de estas variables se explican los daños observados en la infraestructura costera. Palabras clave: registros de viento, oleaje, presión atmosférica, marea astronómica. The storm of August 8, 2015 in the regions of Valparaíso and Coquimbo, Central ChileABSTRACT. A storm of August 8, 2015 was characterized by the joint occurrence of meteorological and oceanographic conditions that caused the destruction of coastal infrastructure and buildings in several localities of the central coast of Chile. The extreme swell was added to wind gusts coming from the northwest winds, and a historical minimum of atmospheric pressure. The tide of astronomical origin -although not extreme-, made its contribution to the sea level banking on which the waves broke violent on the coastal edge. In order to study the event, records of: a) 64 weather stations distributed in the coastal zone of Valparaiso and Coquimbo regions, b) six tide gauge stations, c) forecast and wave records in deep-waters, and d) a model of deep-water wave propagation towards the most affected sectors of the region, were processed. From the analysis of these variables, the observed damages in the coastal infrastructure are explained.

En este trabajo se expone el proceso de calibración del modelo numérico IH2VOF y un análisis de sensibilidad del caudal de sobrepaso en una defensa costera. Para efectuar estos trabajos, se replican los ensayos de un modelo físico a escala reducida de 1:50 en el rompeolas principal del puerto de Pòvoa de Varzim, Portugal (Neves et al., 2008). Los parámetros analizados en el proceso de calibración corresponden a la porosidad y los coeficientes de fricción lineal y no-lineal , que caracterizan a los medios porosos. Para ensayos con oleaje irregular y generación de oleaje de primer orden, que incorpora sólo la banda espectral asociada al oleaje como condición de borde, los parámetros de fricción no tienen una gran incidencia en el cálculo del sobrepaso, pues actúan sobre un volumen relativamente pequeño de defensa costera. La porosidad, en contraste, juega un rol no despreciable en los resultados. Una vez calibrados estos parámetros, se repiten los ensayos usando un forzamiento de segundo orden, que incorpora tanto la banda del oleaje como las ondas infragravitatorias asociadas a los grupos de olas. Los resultados muestran que la generación de primer orden sobrestima el caudal de sobrepaso, lo que se hace más evidente para condiciones de oleaje de mayor intensidad. Para el forzamiento de segundo orden, en contraste, se obtienen resultados cercanos a los reportados por Neves et al. (2008). Finalmente, se recomienda utilizar el forzamiento de segundo orden tanto en la modelación numérica como física del sobrepaso de oleaje para evitar el sobredimensionamiento injustificado de defensas costeras.

The coast of Chile has been exposed to marine submersion events from storm surges, tsunamis and flooding due to heavy rains. We present evidence of these events using sedimentary records that cover the last 1000 years in the Pachingo wetland. Two sediment cores were analyzed for granulometry, XRF, pollen, diatoms and TOC. Three extreme events produced by marine submersion and three by pluvial flooding during El Niño episodes were identified. Geochronology was determined using a conventional dating method using 14C, 210Pbxs and 137Cs). The older marine event (E1) was heavier, identified by a coarser grain size, high content of seashells, greater amount of gravel and the presence of two rip-up clasts, which seems to fit with the tsunami of 1420 Cal AD. The other two events (E3 and E5) may correspond to the 1922 (E3) tsunami and the 1984 (E5) storm waves, corroborated with a nearshore wave simulation model for this period (SWAM). On the other hand, the three flood events (E2, E4, E6) all occurred during episodes of El Niño in 1997 (E6), 1957 (E4) and 1600 (E6), represented by layers of fine-grain sands and wood charcoal remains.

<p>Urban settlements near to coastal environments are exposed to ocean and cryosphere change, such as sea level rise and extreme sea levels. High-resolution sea level prediction systems have become fundamental tools for taking preventive measures in the face of extreme events, mainly in the most vulnerable coastal locations. Techniques such as Machine Learning (ML) are at the forefront of the development in this sector, as they can reduce the computational time needed to reproduce the results of costly high resolution dynamic models. In this line, different authors have reported results for the prediction of oceanographic variables using ML approaches (Camus et al., 2019; Costa et al., 2020; Zust et al., 2021), mainly for significant wave height, sea level and surge component of sea level. Generally, these works use global and/or regional databases as training data for ML tools.</p> <p>With the aim of developing a data-driven system for sea level downscaling, by means of very high-resolution circulation model output used as a training data for a ML framework, in this work the results of a long-term numerical modeling of sea level are presented, carried out in the Northern Adriatic. The numerical model implemented correspond to SURF-SHYFEM, a 3-D finite element hydrodynamic model that solves the primitive equations under hydrostatic and Boussinesq approximations. As atmospheric forcing, mean sea level pressure, and meridional and zonal components of wind speed have been included, both from the ERA5 database. For the boundary conditions, sea level has been considered from two databases, the Copernicus Mediterranean Forecasting System (available from November 2020 to present, with tides included in sea level) and the Copernicus Mediterranean Sea Physics Reanalysis (available from 1987 to June 2021, without tides in sea level). Both databases were used on initial analysis in the representation of surge component of sea level when tides are or not included in the boundary condition. The validation of the results has been carried out by comparison with tide gauges located near the Venice Lagoon, from ISPRA[1] and PSMSL[2].</p> <p>The results show that the model reproduces accurately the sea level (correlation 94% and RMSE 0.09 [m]) and the surge component of sea level (correlation 91% and RMSE 0.06 [m]) measured at the location of the tide gauge. The next step will consist of using such output as a training set for ML-based techniques, with the aim of developing an accurate and cost-effective downscaling tool.</p> <div><br /> <div> <p>[1] Istituto Superiore per la Protezione e la Ricerca Ambientale. Available at: https://www.mareografico.it/</p> </div> <div> <p>[2] Permanent Service for Mean Sea Level. Available at: https://psmsl.org/</p> <p> </p> <p><strong> REFERENCES</strong></p> <p>Camus, P., Herrera, S., Guitiérrez, J.M. and Losada, I.J. (2019). Statistical downscaling of seasonal wave forecast. Ocean Modelling 138, 1-12.</p> <p>Costa, W., Idier, D., Rohmer, J., Menendez, M. and Camus, P. (2020). Statistical prediction of extreme storm surges based on a fully supervised weather-type downscaling model. J. Mar. Sci. Eng. 8, 1028.</p> <p>Zust, L., Fettich, A., Kristan, M. and Licer, M. (2021). HIDRA 1.0: Deep-Learning-Based ensemble sea level forecasting in the Northern Adriatic. Geosci. Model. Dev. 14, 2057-2074.</p> </div> </div>