DEVELOPMENT OF MODELS AND TIME SERIES ANALYSIS TOOLS FOR OCEANOGRAPHIC PARAMETERS ANALYSIS AND FORECASTING

Project leaders

Luciano Telesca, Jorge Omar Pierini

Agreement

ARGENTINA - CONICET - Consejo Nacional de Investigaciones Científicas y Técnicas

Call

CNR/CONICET 2011-2012

Department

ICT

Thematic area

Engineering, ICT and technologies for energy and transportation

Status of the project

New

Research proposal

Sea level height in nearshore environment is of great importance for the monitoring and prediction of changes in complex marine ecosystems, as well as for planning and constructing coastal and offshore structures. The instantaneous measurements are not stationary spatially and temporally. They vary under the synergetic influence of changing tides, atmospheric forcing, and currents.
Recently the availability of accurate ocean tide models has become increasingly important, as tides are the main contributor to disposal and movement of sediments, tracers and pollutants, and also due to a wide range of offshore applications in engineering, environmental observations, exploration and oceanography. A tidal level record is a determinant factor in constructions or activities in coastal areas. Tides can be conventionally predicted by harmonic analysis, which is the superposition of many sinusoidal constituents with amplitudes and frequencies determined by a local analysis of the measured tide. Doodson (1957) employed the least-squares method in determining harmonic parameters and it has been widely used to predict the sea level. However, accurate predictions of tide levels could not be obtained without a large number of tide measurements by the harmonic method, which is used only in the prediction of periodic tidal components. Furthermore their nonstationary behaviour does not allow to get very reliable results by using only Fourier-based methods, due to their sensitiveness to nonstationarities; thus, Fourier-based methods could not be suitable in investigating and characterizing the dynamics of the ocean tide level.
Recent methods, based on the concept of fractal have revealed their good  potential in getting into insight the time dynamics of nonstationary signals. The detrended fluctuation analysis (DFA) and the multifractal-DFA (MF-DFA)(Kantelhardt et al. 2001), for instance, allow to estimate dynamical parameters of a time series, like scaling exponents, dynamical crossovers, persistence degrees, important for a deep understanding of the dynamical mechanisms that govern such time series. Singular spectrum analysis (SSA) (Vautard et al. 1992), Wavelet-based multiresolution analysis allow to describe as best as possible nonstationary, noisy and also short time series. Information-based measures, like the Fisher Information Measure (FIM) (Vignat and Berher 2003), are also further tools for getting information about the type of dynamics in signals. Such methods have been already applied by both teams in several scientific fields, and their application to sea water level and other oceanographic data could lead to their better dynamical characterization.
Moreover, in contrast to traditional harmonic analysis, the artificial neural network (ANN) model could be able to recognize and predict nonlinear and nonperiodic signals. Neural networks have been recently showed their abilities to solve different problems in oceanography. ANNs have been applied to provide reliable predictions of sea currents (Babovic 1999), to solve problems related with the estimate of the wave parameters (Makarynskyy 2004), to characterize sea level data (Makarynskyy et al. 2004), wind wave data (Makarynskyy 2006), to perform tidal prediction (Lee et al. 2007), wind wave forecasts with field observation (Makarynskyy 2007), as well as predicting sea level variations (Makarynska and Makarynskyy 2008). These and many other scientific contributions exploited the ANN capability to determine interrelations among the elements within a complex estuary system.
Therefore, the project intends to perform the following activities: 1) the development and application of time series analysis tools for the investigation of the nonstationary time dynamics of oceanographic data; and 2) the development and application of ANN models for  forecasting of oceanographic parameters (tides, sea surfece temperature, waves, etc).
The tidal level data at Mareographic Tower, Puerto Belgrano and Ingeniero White harbors in Bahia Blanca estuary (Argentina) will be used to test the performance of the ANN models and time series tools developed in the framework of the present project. Bahia Blanca is a mesotidal coastal - plain estuary located in the south of the Buenos Aires Province, Argentina. It consists of a series of NW-SE channels separated by islands and wide tidal flats, which are the remanents of a late Pleistocene - early Holocene delta, and has an area of 1150km2 (Melo 2004). The estuary has an elongated shape with a total length of 68km, being 200m and 4km wide near its head and mouth, respectively. Its mean depth is 10m, although values of the order of 22m are found at the mouth of the estuary (Pierini 2007). The circulation in the estuary is dominated by semidiurnal and stationary tides. The mean tidal amplitude is 2.4m and the tidal range and tidal current amplitude increase headward. Prevailing winds are NW-N and SE-S for over 40% and 10% of the time, respectively (Piccolo et al. 1989). These wind directions are important because they blow parallel to the main channels. Wind is a major factor in the Bahia Blanca Estuary dynamics since it produces strong delays or advances of the tidal wave and large differences between the real and the predicted astronomical tides. The amplitude of the tidal wave increases with a decrease in depth of the channel. Bahia Blanca is a hyper-synchronous type estuary, where the amplitude increases steadily from the mouth to the head, implying that the convergence effect on the tidal wave is larger than the friction effect (Pierini 2007). The complex bathymetry and coastal configuration of the estuary, as well as the continental discharges may affect harmonic analysis results, making ANNs an alternative tool for short term based estimates of sea level.

Research goals

The specific objectives of the project are: 1) to investigate the time dynamics of ocean tidal signal, by means of advanced statistical time series tools and get information about the dynamics of the tidal signal in terms of correlation structures, persistence, scaling, crossover phenomena, trends, periodicity; 2) to investigate the relationship between the sea water level and wind, and other available coastal parameters; 3) to develop a ANN back propagation network for tidal forecasting; 4) to perform short-term and long-term tidal forecasting; 5) to develop hydrodynamic and atmospheric models for the tide generation, by means of all the data collected and the results obtained from the applied methods; 6) to disseminate preliminary and final results of the project in international conferences, publications, and in a project-oriented internet website.

Last update: 14/06/2025