Uncertainty Assesment of Long Term Shoreline Prediction

Payo, Andrés; Baquerizo, Asunción; Losada, Miguel Á.

doi:10.1142/9789812701916_0167

Cited by 5 publications

(4 citation statements)

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“…This type of analysis is usually aimed at simulating time series with the same probabilistic structure, so that they can be used to infer the random response of a given system. Some examples of applications are (i) the study of beach evolution (Payo et al 2004;Baquerizo and Losada 2008;Callaghan et al 2008;Fe ´lix et al 2012;Ranasinghe et al 2012), (ii) the optimal design and management of an oscillating water column system (Jalo ´n et al 2016;Lo ´pez-Ruiz et al 2018), (iii) the planning of maintenance strategies of coastal structures (Lira- Loarca et al 2020), and (iv) the assessment of water quality management strategies in an estuary according to density variations and recovery time (Cobos 2020). It has also been used for the analysis of observed wave climate variability in the preceding century and the expected changes in projections under a climate change scenario (Loarca et al 2021).…”

Section: Introductionmentioning

confidence: 99%

A method to characterize climate, Earth or environmental vector random processes

Cobos

Otiñar

Magaña

et al. 2022

Stoch Environ Res Risk Assess

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We propose a general methodology to characterize a non-stationary random process that can be used for simulating random realizations that keep the probabilistic behavior of the original time series. The probability distribution of the process is assumed to be a piecewise function defined by several weighted parametric probability models. The weights are obtained analytically by ensuring that the probability density function is well defined and that it is continuous at the common endpoints. Any number of subintervals and continuous probability models can be chosen. The distribution is assumed to vary periodically in time over a predefined time interval by defining the model parameters and the common endpoints as truncated generalized Fourier series. The coefficients of the expansions are obtained with the maximum likelihood method. Different sets of orthogonal basis functions are tested. The method is applied to three time series with different particularities. Firstly, it is shown its good behavior to capture the high variability of the precipitation projected at a semiarid location of Spain for the present century. Secondly, for the Wolf sunspot number time series, the Schwabe cycle and time variations close to the 7.5 and 17 years are analyzed along a 22-year cycle. Finally, the method is applied to a bivariate time series that contains (1) freshwater discharges at the last regulation point of a dam located in a semiarid zone in Andalucía (Spain) which is influenced not only by the climate variability but also by management decisions and (2) the salinity at the mouth of the river. For this case, the analysis, that was combined with a vectorial autoregressive model, focus on the assessment of the goodness of the methodology to replicate the statistical features of the original series. In particular, it is found that it reproduces the marginal and joint distributions and the duration of sojourns above/below given thresholds.

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Section: Introductionmentioning

confidence: 99%

A method to characterize climate, Earth or environmental vector random processes

Cobos

Otiñar

Magaña

et al. 2022

Stoch Environ Res Risk Assess

Self Cite

View full text Add to dashboard Cite

show abstract

“…This type of analysis is usually aimed at simulating time series with the same probabilistic structure, so that they can be used to infer the random response of a given system. Some examples of applications are (i) the study of beach evolution (Payo et al, 2004;Baquerizo and Losada, 2008;Callaghan et al, 2008;Félix et al, 2012;Ranasinghe et al, 2012), (ii) the optimal design and management of an oscillating water column system (Jalón et al, 2016;López-Ruiz et al, 2018), (iii) the planning of maintenance strategies of coastal structures (Lira-Loarca et al, 2020), and (iv) the assessment of water quality management strategies in an estuary according to density variations and recovery time (Cobos, 2020). It has also been used for the analysis of observed wave climate variability in the preceding century and the expected changes in projections under a climate change scenario (Loarca et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Method to Characterize Climate, Earth or Environmental Vector Random Processes

Budia

Otiñar

Redondo

et al. 2021

Preprint

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We propose a methodology to characterize a multivariate non-stationary vector random process that can be used for simulating random realizations that keep the probabilistic behavior of the original time series. The marginal probability distribution of each component process is assumed to be a piecewise function deﬁned by several weighted parametric probability models. The weights are obtained analytically by ensuring that the probability density function is well deﬁned and that it is continuous at the common endpoints. The probability model is assumed to vary periodically in time over a predeﬁned time period by deﬁning the model parameters and the common endpoints as truncated generalized Fourier series. The coeﬃcients of the expansions are obtained with the maximum likelihood method. Three diﬀerent types of sets of orthogonal functions are tested. The method is applied to three time series with diﬀerent particularities. Firstly, it is shown its good behavior to capture the highly variable freshwater discharges at a dam located in a semiarid zone in Andalucía (Spain) which is inﬂuenced not only by the climate variability but also by management decisions. Secondly, for the Wolf sunspot number time series, the Schwabe cycle and time variations close to the 7.5 and 17 years are analyzed along a 22-year cycle. Finally, the method is applied to a bivariate (velocity and direction) wind time series observed at a location of the Atlantic Ocean. For this case, the analysis, that was combined with a vectorial autoregresive model, focus on the assessment of the goodness of the methodology to replicate the statistical features of the original series. In particular, it is found that it reproduces the marginal and joint distributions, the wind rose, and the duration of sojourns above given thresholds.

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“…Then, a standard form for the evolution of each of the variables is assumed, typically a triangular or rectangular shape for Hs and a rectangular shape for the other variables, obtaining the time evolution of all the variables along the storm (see e.g. Payo et al 2004, Baquerizo and Losada 2008. This kind methodologies ensures the correct reproduction of the distribution of Hs,peak, as well as the reproduction of the joint distributions of Hs,peak and the concomitant values of the other variables; however, this methodology does not ensure that the distribution of Hs (all values above the threshold that defines the storm) or the joint distribution of Hs and the other variables (i.e.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Sea Storms Including Multivariate Storm Evolution

Solari¹,

Losada²

2018

Int. Conf. Coastal. Eng.

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A new method for the simulation of storms is proposed which takes into account the multivariate evolution of the storms, allowing to innovate in the form of each simulated storm, for all the variables involved. The method is based on two novel aspects: (a) measured storms are grouped using clusters techniques and a set of average evolution forms is defined for each cluster, one for each of the variables involved, and (b) a Vector Autoregressive model is fitted to the differences between the average evolution of each variable and the actual measured evolutions. The ability of the methodology to properly reproduce the joint probability distribution of all the variables involved is demonstrated for a case study at the mid Rio de la Plata northern coast.

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Uncertainty Assesment of Long Term Shoreline Prediction

Cited by 5 publications

References 0 publications

A method to characterize climate, Earth or environmental vector random processes

A method to characterize climate, Earth or environmental vector random processes

A Method to Characterize Climate, Earth or Environmental Vector Random Processes

Simulation of Sea Storms Including Multivariate Storm Evolution

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