Multivariate analysis of nonlinearity in sandbar behavior

International audienceAlongshore sandbars are often present in the nearshore zones of storm-dominated micro- to mesotidal coasts. Sandbar migration is the result of a large number of small-scale physical processes that are generated by the incoming waves and the interaction between the wave-generated processes and the morphology. The presence of nonlinearity in a sandbar system is an important factor determining its predictability. However, not all nonlinearities in the underlying system are equally expressed in the time-series of sandbar observations. Detecting the presence of nonlinearity in sandbar data is complicated by the dependence of sandbar migration on the external wave forcings. Here, a method for detecting nonlinearity in multivariate time-series data is introduced that can reveal the nonlinear nature of the dependencies between system state and forcing variables. First, this method is applied to four synthetic datasets to demonstrate its ability to qualify nonlinearity for all possible combinations of linear and nonlinear relations between two variables. Next, the method is applied to three sandbar datasets consisting of daily-observed cross-shore sandbar positions and hydrodynamic forcings, spanning between 5 and 9 years. Our analysis reveals the presence of nonlinearity in the time-series of sandbar and wave data, and the relative importance of nonlinearity for each variable. The relation between the results of each sandbar case and patterns in bar behavior are discussed, together with the effects of noise. The small effect of nonlinearity implies that long-term prediction of sandbar positions based on wave forcings might not require sophisticated nonlinear models

Non-linear complex principal component analysis of nearshore bathymetry

International audienceComplex principal component analysis (CPCA) is a useful linear method for dimensionality reduction of data sets characterized by propagating patterns, where the CPCA modes are linear functions of the complex principal component (CPC), consisting of an amplitude and a phase. The use of non-linear methods, such as the neural-network based circular non-linear principal component analysis (NLPCA.cir) and the recently developed non-linear complex principal component analysis (NLCPCA), may provide a more accurate description of data in case the lower-dimensional structure is non-linear. NLPCA.cir extracts non-linear phase information without amplitude variability, while NLCPCA is capable of extracting both. NLCPCA can thus be viewed as a non-linear generalization of CPCA. In this article, NLCPCA is applied to bathymetry data from the sandy barred beaches at Egmond aan Zee (Netherlands), the Hasaki coast (Japan) and Duck (North Carolina, USA) to examine how effective this new method is in comparison to CPCA and NLPCA.cir in representing propagating phenomena. At Duck, the underlying low-dimensional data structure is found to have linear phase and amplitude variability only and, accordingly, CPCA performs as well as NLCPCA. At Egmond, the reduced data structure contains non-linear spatial patterns (asymmetric bar/trough shapes) without much temporal amplitude variability and, consequently, is about equally well modelled by NLCPCA and NLPCA.cir. Finally, at Hasaki, the data structure displays not only non-linear spatial variability but also considerably temporal amplitude variability, and NLCPCA outperforms both CPCA and NLPCA.cir. Because it is difficult to know the structure of data in advance as to which one of the three models should be used, the generalized NLCPCA model can be used in each situation

Numerical Assessment of Infragravity Swash Response to Offshore Wave Frequency Spread Variability

We use a numerical model, already validated for this purpose, to simulate the effect of wave frequency spread on wave transformation and swash amplitudes. Simulations are performed for planar beach slope cases and for offshore wave spectra whose frequency spread changes over realistic values. Results indicate that frequency spread, under normally approaching waves, affects swash amplitudes. For moderately dissipative conditions, the significant infragravity swash increases for increasing values of the offshore frequency spread. The opposite occurs under extremely dissipative conditions. The numerical analysis suggests that this inverted pattern is driven by the effect that different distributions of incoming long?wave energy have on low?frequency wave propagation and dissipation. In fact, with large frequency spreads, wave groups force relatively short subharmonic waves that are strongly enhanced in the shoaling zone. This process leads to an infragravity swash increase for increasing frequency spread under moderately dissipative conditions in which low?frequency energy dissipation in shallow water is negligible or small. However, under extremely dissipative conditions, the significant low?frequency energy dissipation associated with large frequency spreads overturns the strong energy growth in the shoaling zone eventually yielding an infragravity swash decrease for increasing frequency spread.This work has been funded under (1) the RETOS INVESTIGACION 2014 (Grant BIA2014-59718-R) program of the Spanish Ministry of Economy and Competitiveness and (2) the NEPTUNE 2 project, L. R. 7/2007 by Regione Autonoma della Sardegna

Observations of velocities, sand concentrations, and fluxes under velocity-asymmetric oscillatory flows

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MODEX: Laboratory experiment exploring sediment spreading of a mound under waves and currents

The dispersal of sand from submerged mounds in the nearshore is driven by the interplay of processes such as converging and recirculating flows, changing roughness, bed slope effects and wave focusing/refraction. This morphological diffusivity is key to understanding sand bars in shallow seas, tidal inlets, estuaries, and the nearshore response to human interventions such as nourishments and dredging. Most of the work on the evolution of submerged mounds has been based on fluvial studies, focusing on flow without waves. In these cases, circular mounds tend to deform to crescentic (barchan) shapes. In contrast, observations of sandbars and berms in the nearshore subjected to waves show much more complex translation and deformation behavior. This contribution introduces the laboratory MOrphological Diffusivity Experiment (MODEX) aimed at examining morphological diffusivity under different forcing conditions. The experiment particularly addresses the linkages between small scale (local) effects (e.g. bed slope, bedforms) on the adjustment of sandy mounds.Peer ReviewedPostprint (published version

Multi‐decadal coastline dynamics in Suriname controlled by migrating subtidal mudbanks

This is the final version. Available from Wiley via the DOI in this record. DATA AVAILABILITY STATEMENT The pre-processing scripts that are used to define outliers for coastline position estimates and annual change metrics are available through Github: https://github.com/jobbo90/offshore_boundary/ releases/tag/v0.2 The reported coastline position estimates and indications of mudbank presence can be found in the online GEE repository (v02), which also includes the scripts used to derive these indicators: https://code.earthengine.google.com/?accept_repo=users/jobdevries90/ MangroMud The UAV drone datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.For the development of climate-resilient coastal management strategies, which focus on challenges in the decades to come, it is critical to incorporate spatial and temporal variability of coastline changes. This is particularly true for the mud-dominated coastline of Suriname, part of the Guianas, where migrating subtidal mudbanks cause a cyclic instability of erosion and accretion of the coast that can be directly related to interbank and bank phases. The coastline hosts extensive mangrove forests, providing valuable ecosystem services to local communities. Recent studies on mudbank dynamics in Suriname predominantly focused on large-scale trends without accounting for local variability, or on local changes considering the dynamics of a single mudbank over relatively short time scales. Here we use a remote sensing approach, with sufficient spatial and temporal resolution and full spatial and temporal coverage, to quantify the influence of mudbank migration on spatiotemporal coastline dynamics along the entire coast of Suriname. We show that migration of six to eight subtidal mudbanks in front of the Suriname coast has a strong imprint on local coastline dynamics between 1986 and 2020, with an average 32 m/yr accretion during mudbank presence and 4 m/yr retreat of the coastline during mudbank absence. Yet, coastal erosion can still occur when mudbanks are present and coastal aggregation may happen in the absence of mudbanks, exemplifying local variability and thus suggesting the importance of other drivers of coastline changes. The novel remote sensing workflow allowed us to analyse local spatial and temporal variations in the magnitude and timing of expanding and retreating trajectories. Our results demonstrate that it is essential that all coastal behaviours, including changes that cannot be explained by the migration of mudbanks, are included in multi-decadal management frameworks that try to explain current variability, and predict future coastline changes in Suriname.NWO WOTRO Joint Sustainability Development Goal Research Progra

Unmixing water and mud: Characterizing diffuse boundaries of subtidal mud banks from individual satellite observations

This is the final version. Available from Elsevier via the DOI in this record. Mapping of subtidal banks in mud-dominated coastal systems is crucial as they influence not only shoreline and ecosystem dynamics but also economic activities and livelihoods of local communities. Due to associated spatiotemporal variations in suspended particulate matter concentrations, subtidal mudbanks are often confined by diffuse and rapidly changing boundaries. To avoid inaccurate representations of these mudbanks in remote sensing images, it is necessary to unmix distinctive reflectance signals into representative landcover fractions. Yet, extracting mud fractions, in order to characterize such diffuse boundaries, is challenging because of the spectral similarity between subtidal- and intertidal features. Here we show that an unsupervised decision tree, used to derive spatially explicit and spectrally coherent image endmembers, facilitates robust linear spectral unmixing on an image-to-image basis, enabling the separation of these coastal features. We found that resulting abundance maps represent cross-shore gradients of vegetation, water and mud fractions present at the coast of Suriname. Furthermore, we confirmed that it is possible to separate land, water and an initial estimate of intertidal zones on individual images. Thus, spectral signatures of end-member candidates, determined from relevant index histograms within these initial estimates, are consistent. These results demonstrate that spectral information from well-defined spatial neighbourhoods facilitates the detection of diffuse boundaries of mudbanks with a spectral unmixing approach.NWO WOTR

Sediment transport in non-linear skewed oscillatory flows : the TRANSKEW experiments

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