Predictive models of minimum temperatures for the south of Buenos Aires province.

Depending on the time of development of a crop temperature below 0 °C can cause damage to the plant, altering its development and subsequent yield. Since frosts are identified from the minimum air temperature, the objective of this research paper is to generate forecast -(predictive) models at 1, 3 and 5 days of the minimum daily temperature (Tmin) for Bahía Blanca city. Non-linear numerical models are generated using artificial neural networks and geometric models of finite elements. Six independent variables are used: temperature and dew point temperature at meteorological shelter level, relative humidity, cloudiness observed above the station, wind speed and direction measured at 10 m altitude. Data have been obtained between May and September from 1956 to 2015. Once the available data had been analyzed, this period was reduced to 2007-2015. For the selection of the most suitable model, the correlation coefficient of Pearson (R), the determination coefficient (R2) and the Mean Absolute Error (MAE) are evaluated. The results of the study determine that the geometric model of finite elements with 4 variables, over 9 years (2007-2015) and separated by the season of the year is the one that presents better adjustment in the forecast of Tmin with up to 5 days of anticipation.

Galerkin's formulation of the finite elements method to obtain the depth of closure.

Coastal erosion and lack of sediment supply are a serious global problem. It is therefore necessary to determine the depth of closure (DoC) of a beach-key parameter in the calculation of the sand volume and the location of the beach protection elements-in a precise way. For this reason, this work generates a numerical model based on Galerkin's formulation of finite elements that provides sufficient precision for the determination of DoC with a minimum investment. Thus, after the generation of three models in which the difference was the dependent variables, the least complex has been chosen. It is composed of the variables: median sediment size, wave height and period associated with the mean flow, as well as the angle that the mean flow forms with respect to the studied profile in absolute value (α). The selected model has been compared with the most commonly used models currently in use, having an average absolute error of 0.36 m and an average MAPE of <7.5%, which represents an improvement of >70% over current models. In addition, it presents a high stability, since after the random disturbance of all the input variables (up to 5%), the model error remains stable, increasing the MAPE by a maximum of 7.4% and the average absolute error by 0.15 m. Therefore, it is possible to use the model to infer the DoC in other study areas where the values of the variables are similar to those studied here, although the selected method can be extrapolated to other parts of the world.

Gravel beaches nourishment: Modelling the equilibrium beach profile.

The erosion of the world's coasts and the shortage of sand to mitigate beach erosion are leading to the increasingly common use of gravel for coastal protection and beach nourishment. Therefore, in order to determine the amount of gravel required for such actions, it is important to know perfectly the equilibrium profile of gravel beaches. However, at present, this profile is obtained from formulas obtained mainly after channel tests, and therefore most of them do not adapt to the real profiles formed by gravel beaches in nature. In this article, 31 variables related to sedimentology, waves, morphology and marine vegetation present on the beaches are studied to determine which are the most influential in the profile. From the study carried out, it is obtained that these variables are the steepness and probability of occurrence of the wave perpendicular to the coast, the profile starting slope (between MWL and -2m), the energy reduction coefficient due to Posidonia oceanica as well as the width of the meadow. Using these variables, different numerical models were generated to predict accurately the gravel beach profile, which will lead to a saving in the volume of material used in the order of 1300m3/ml of beach with respect to current formulations, and a greater certainty that the beach nourishment carried out will have the desired effect.

Modelling of Escherichia coli concentrations in bathing water at microtidal coasts.

Monitoring of the quality of bathing water in line with the European Commission bathing water directive (Directive 2006/7/EC) is a significant economic expense for those countries with great lengths of coastline. In this study a numerical model based on finite elements is generated whose objective is partially substituting the microbiological analysis of the quality of coastal bathing waters. According to a study of the concentration of Escherichia coli in 299 Spanish Mediterranean beaches, it was established that the most important variables that influence the concentration are: monthly sunshine hours, mean monthly precipitation, number of goat cattle heads, population density, presence of Posidonia oceanica, UV, urbanization level, type of sediment, wastewater treatment ratio, salinity, distance to the nearest discharge, and wave height perpendicular to the coast. Using these variables, a model with an absolute error of 10.6±1.5CFU/100ml is achieved. With this model, if there are no significant changes in the beach environment and the variables remain more or less stable, the concentration of E. coli in bathing water can be determined, performing only specific microbiological analyses to verify the water quality.