Spectral quantification of nonlinear behaviour of the nearshore seabed and correlations with potential forcings at Duck, N.C., U.S.A

Local bathymetric quasi-periodic patterns of oscillation are identified from monthly profile surveys taken at two shore-perpendicular transects at the USACE field research facility in Duck, North Carolina, USA, spanning 24.5 years and covering the swash and surf zones. The chosen transects are the two furthest (north and south) from the pier located at the study site. Research at Duck has traditionally focused on one or more of these transects as the effects of the pier are least at these locations. The patterns are identified using singular spectrum analysis (SSA). Possible correlations with potential forcing mechanisms are discussed by 1) doing an SSA with same parameter settings to independently identify the quasi-periodic cycles embedded within three potentially linked sequences: monthly wave heights (MWH), monthly mean water levels (MWL) and the large scale atmospheric index known as the North Atlantic Oscillation (NAO) and 2) comparing the patterns within MWH, MWL and NAO to the local bathymetric patterns. The results agree well with previous patterns identified using wavelets and confirm the highly nonstationary behaviour of beach levels at Duck; the discussion of potential correlations with hydrodynamic and atmospheric phenomena is a new contribution. The study is then extended to all measured bathymetric profiles, covering an area of 1100m (alongshore) by 440m (cross-shore), to 1) analyse linear correlations between the bathymetry and the potential forcings using multivariate empirical orthogonal functions (MEOF) and linear correlation analysis and 2) identify which collective quasi-periodic bathymetric patterns are correlated with those within MWH, MWL or NAO, based on a (nonlinear) multichannel singular spectrum analysis (MSSA). (...continued in submitted paper)Comment: 50 pages, 3 tables, 8 figure

Observations of bedforms on a dissipative macrotidal beach

NERC NE/H004262/1 and NE/H02543X/1 DRIB

Recent insights into inter-annual sandbar dynamics

Based on model hindcasts of the bar cycle at two locations along the Dutch coast, the dominant processes and mechanisms that govern the bar amplitude growth and decay during net inter-annual offshore migration, the occurrence of bar switches and the inter-site differences in the bar cycle return period (Tr) are identified. Bar growth and decay are closely related to the wave-induced longshore current as it affects the distribution of the cross-shore sediment transport. The modelling results suggest that cross-shore processes may trigger a bar switch in the case of specific antecedent morphological configurations combined with storm conditions. The deceleration of the offshore migration rate as the bar moves to deeper water (the morphodynamic feedback loop) contrasts with the initial enhanced offshore migration behavior of the bar for steeper slopes. The bed slope in the barred zone is the most important parameter governing Tr.Coastal Engineerin

Measuring high spatiotemporal variability in saltation intensity using a low-cost Saltation Detection System : wind tunnel and field experiments

The commonly observed over prediction of aeolian saltation transport on sandy beaches is, at least in part, caused by saltation intermittency. To study small-scale saltation processes, high frequency saltation sensors are required on a high spatial resolution. Therefore, we developed a low-cost Saltation Detection System (SalDecS) with the aim to measure saltation intensity at a frequency of 10 Hz and with a spatial resolution of 0.10 m in wind-normal direction. Linearity and equal sensitivity of the saltation sensors were investigated during wind tunnel and field experiments. Wind tunnel experiments with a set of 7 SalDec sensors revealed that the variability of sensor sensitivity is at maximum 9% during relatively low saltation intensities. During more intense saltation the variability of sensor sensitivity decreases. A sigmoidal fit describes the relation between mass flux and sensor output measured during 5 different wind conditions. This indicates an increasing importance of sensor saturation with increasing mass flux. We developed a theoretical model to simulate and describe the effect of grain size, grain velocity and saltation intensity on sensor saturation. Time-averaged field measurements revealed sensitivity equality for 85 out of a set of 89 horizontally deployed SalDec sensors. On these larger timescales (hours) saltation variability imposed by morphological features, such as sand strips, can be recognized. We conclude that the SalDecS can be used to measure small-scale spatiotemporal variabilities of saltation intensity to investigate saltation characteristics related to wind turbulence

A predictive data-driven approach based on reduced order models for the morphodynamic study of a coastal water intake

For many environmental applications, field measurement techniques are increasingly evolving, resulting in more complex and complete datasets. The statistical analysis of these datasets is challenging, and requires the use of relevant mathematical tools. Furthermore, the access to a richer collection of data offers a new optimistic perspective on data-driven modeling, to complement, or even replace, process-based modeling. The presented work is within the context of a power plant water intake monitoring. The intake channel is subject to massive sediment arrivals, which represents a clogging risk. One of the challenges is therefore to better understand the sediment dynamics observed in the channel, and to characterize their correlation to environmental forcing. The final goal is to proceed to the forecasting of the dynamics using the knowledge of forcing parameters. Luckily, due to monitoring needs, bathymetric measurements of the channel are realized on a regular basis, along with meteorological and hydrodynamic survey. A statistical study is hereby proposed on the basis of this data. Firstly, a Proper Orthogonal Decomposition (POD) is applied to the two-dimensional bathymetric data set, in order to reduce it to a low-dimensional set of time dependent scalar coefficients. The latter are linked to the physical forcings via an adapted statistical model. In this study, a Polynomial Chaos Expansion (PCE) is used for this purpose. Consequently, a data-driven model is proposed, on the basis of a POD-PCE coupling. The proposed step-by-step methodology could also be transposed to other applications