This paper presents a new approach to statistically characterize and simulate the wave climate under storm conditions. The methodology includes the joint selection of the parameters that identify storm events (significant wave height threshold, minimum storm duration and minimum interarrival time between consecutive storms) by means of hypothesis testing on the distribution functions of the number of storm events and the elapsing time between storms, providing an improved characterization of the parameters that define storm events. The main wave variables and their temporal dependence are characterized by non-stationary mixture distribution functions and a vector autoregressive model. This allows to adequately reproduce the random temporal evolution of storm events, crucial for the study of damage progression in maritime structures without the use of predefined geometries. The long-term time series of storm events and calm periods is obtained using copula functions which analyze the joint dependence of storm duration and interarrival time for separate climate intervals. The model is applied to hindcast data at a location of the Mediterranean sea close to the Granada coast in Spain to show its ability to reproduce wave storm conditions accounting for the time variability of the storminess. An example of application, using a large number of simulations and a damage progression model in a maritime structure, is presented.
This work proposes a new general procedure to stochastically analyze multi-model multivariate wave climate time series projections at different temporal scales. For every projection, it characterizes significant wave height, peak period and mean direction by means of univariate non-stationary distributions capable of capturing cyclic climate behavior over a reference time interval duration. The temporal dependence between the values at a given sea state and previous short-term wave climate is described with a vector autoregressive model (VAR). The multi-model ensemble wave climate characterization is based on a compound distribution of the individual non-stationary distributions and a weighted averaged VAR model. The methodology is applied to bias-adjusted wave climate projections derived using WaveWatch III forced by wind field data from EURO-CORDEX models at a location close to the Mediterranean Spanish coast. Results are compared to hindcast data which shows a clear bi-seasonal behavior. Different temporal references were considered, starting with a 1-year reference period to analyze overall changes in wave climate at scales ranging from days, months and seasons with respect to historic conditions. The results show that the projected wave climate has a very different temporal behavior than hindcast data, delaying and widening/shortening the start and duration of the two main seasons and including shorter term variations. Regarding the energetic content of the sea states, the compound variable highest percentiles of the significant wave height present lower values than the hindcast (&3-10%) during the traditionally more severe period (November-March) but higher values (&10-35%) during the calmer months. The projected peak period presents a similar temporal pattern to the hindcast data, while the mean wave direction shows a significant change from the historical bi-modal behavior towards more likely easterly waves throughout the year. Additionally, a 10-year analysis is done to find larger temporal variabilities such as decadal variations associated with the North Atlantic Oscillation. The observed temporal variability in the yearly seasonal pattern throughout the century is addressed by analysing 20-year rolling windows in all the model projections and in the compound variable. The compound distribution shows significant temporal variabilities throughout the century with the most severe periods and more likely severe waves during summer at the end of the century.
The objective of the present study is to demonstrate the informative capacity of the longitudinal anomaly of potential energy (LAPE) in the analysis of the magnitude and spatiotemporal variability of estuarine processes. For this purpose, a LAPE balance equation is formulated. The LAPE integrates and varies with the vertical and longitudinal density distribution. The formulation is applied on a subtidal scale to each box or stretch of the Guadalquivir River estuary, a narrow, highly turbid, weakly stratified, and strongly anthropized estuary. Data recorded by a large network of monitoring stations in 2008 and 2009 are used to quantify advective transports as well as the transports associated with longitudinal dispersion and vertical turbulent mixing in different hydraulic regimes. In low-river flow conditions, (river flows Q < 40 m 3 s −1), the magnitude of LAPE transports decreases upstream and varies locally, depending on neap-spring tidal cycles. The direction of the net LAPE transport creates convergence zones that are particularly consistent with maximum levels of estuarine turbidity. During high-river flows (Q > 400 m 3 s −1), this convergence disappears and the maximum longitudinal density gradient moves towards the mouth. More specifically, tidal pumping-induced LAPE governs during these conditions and manages to compensate the sum of the mean nontidal and dispersive and differential advective LAPE transports. However, during the post-riverflood period, the mechanisms controlling recovery downstream from the mouth are the longitudinal dispersive and differential advective LAPE transports. Furthermore, the convergence zone reappears with a longitudinal gradient of the net LAPE transport that is even greater than in low-river flow conditions.
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