A simulation study on the uncertainty of environmental contours due to sampling variability for different estimation methods

Environmental contours are often used in design of marine structures to identify extreme environmental conditions that may give rise to extreme loads and responses. Recently, attention has been given to the fact that different methods exist for establishing such contours, and that in some cases significant differences may be obtained from the various methods.In this study, another aspect of the uncertainty related to the calculation of environmental contours is addressed, namely the uncertainty due to sampling variability when environmental contours are constructed based on metocean data of finite sample size. The uncertainty of environmental contours for the joint distribution of significant wave height and wave period for different sample sizes (10, 25 and 100 years of data) are investigated considering different underlying datasets and for different estimation methods for the joint distribution. Both cases where samples are drawn from a known joint distribution of wave height and periods and cases where samples are drawn from a real hindcast dataset and fitted to the joint distribution are considered. The uncertainty of the estimated contours is quantified and discussed in light of differences that can be anticipated from the different methods used to calculate the contours. Moreover, the potential bias from assuming different estimation methods is illustrated.     

The effect of bathymetric filtering on nearshore process model results

Nearshore wave and flow model results are shown to exhibit a strong sensitivity to the resolution of the input bathymetry. In this analysis, bathymetric resolution was varied by applying smoothing filters to high-resolution survey data to produce a number of bathymetric grid surfaces. We demonstrate that the sensitivity of model-predicted wave height and flow to variations in bathymetric resolution had different characteristics. Wave height predictions were most sensitive to resolution of cross-shore variability associated with the structure of nearshore sandbars. Flow predictions were most sensitive to the resolution of intermediate scale alongshore variability associated with the prominent sandbar rhythmicity. Flow sensitivity increased in cases where a sandbar was closer to shore and shallower. Perhaps the most surprising implication of these results is that the interpolation and smoothing of bathymetric data could be optimized differently for the wave and flow models. We show that errors between observed and modeled flow and wave heights are well predicted by comparing model simulation results using progressively filtered bathymetry to results from the highest resolution simulation. The damage done by over smoothing or inadequate sampling can therefore be estimated using model simulations. We conclude that the ability to quantify prediction errors will be useful for supporting future data assimilation efforts that require this information.     

Geoacoustic Characterisation of Fine-grained Sediments using Single and Multiple Reflection Data

Fine-grained sediments commonly occur in areas of the continental shelf where wave and current energy are weak. Bulk density, compressional wave speed and attenuation are fundamental physical properties of these sediments required for predicting the response of the seabed for diverse branches of marine science. The traditional coring approach is time and labor-intensive, with large uncertainties associated with sediment disturbance in the sampling phase. Acoustic methods offer the advantages of remote sensing, i.e., sampling the sediment structure without mechanical disturbance and a significantly larger seabed coverage rate per unit time. Two different acoustic methods are described: one using short-range single-bounce interactions with the seabed, and the second using long-range modal propagation to infer the sediment properties. The relative strengths and sensitivities of each approach are explored through simulations guided by experience with measured data.     

Optimisation of underwater visual census and controlled angling methods for monitoring subtidal temperate reef fish communities

Standardised sampling protocols for monitoring fish stocks are essential to assess changes in stock status and provide a means to evaluate the effectiveness of fisheries management measures, such as marine protected areas (MPAs). This study aimed to optimise two standard sampling methods, namely underwater visual census (UVC) and controlled angling, for assessing subtidal reef fish communities. In terms of efficiency, variability and bias, UVC transects were found to be superior to point counts. For controlled angling, an effort of two angler-hours per fishing station provided low catch variability, high catch per unit effort and a representative catch. Whereas UVC provided less variable estimates of relative density, controlled angling provided greater sampling efficiency. It is thus recommended that the two methods be used in conjunction. The optimal sampling protocols identified are suitable for rapid assessments or long-term monitoring of subtidal, temperate reef fish communities.     

Wind-wave variability in a shallow tidal sea—Spectral modelling combined with neural network methods

In this paper the wind-wave variability in the tidal basins of the German Wadden Sea is modelled with combined numerical and neural-network (NN) methods. First, the wave propagation and transformation in the study area are modelled with the state-of-the-art third-generation spectral wave model SWAN. The ability of SWAN to accurately reproduce the phenomena of interest in nonstationary conditions governed by highly variable winds, water levels and currents is shown by comparisons of the modelled and measured mean wave parameters at four stations. The principal component analysis of the SWAN results is then used to reveal the dominating spatial patterns in the data and to reduce their dimensionality, thus enabling an efficient and relatively straightforward NN modelling of mean wave parameters in the whole study area. It is shown that the data produced with the approach developed in this work have statistical properties (discrete probability distributions of the mean wave parameters etc.) very close to the properties of the data obtained with SWAN, thus proving that this approach can be used as a reliable tool for wind wave simulation in coastal areas, complementary to (often computationally demanding) spectral wave models.     

Forecasting ocean wave energy: Tests of time-series models

This paper evaluates the ability of time-series models to predict the energy from ocean waves. Data sets from four Pacific Ocean sites are analyzed. The energy flux is found to exhibit nonlinear variability. The probability distribution has heavy tails, while the fractal dimension is non-integer. This argues for using nonlinear models. The primary technique used here is a time-varying parameter regression in logs. The time-varying regression is estimated using both a Kalman filter and a sliding window, with various window widths. The sliding window method is found to be preferable. A second approach is to combine neural networks with time-varying regressions, in a hybrid model. Both of these methods are tested on the flux itself. Time-varying regressions are also used to forecast the wave height and wave period separately, and combine the forecasts to predict the flux. Forecasting experiments are run at an hourly frequency over horizons of 1-4 h, and at a daily frequency over 1-3 days. All the models are found to improve relative to a random walk. In the hourly data sets, forecasting the components separately achieves the best results in three out of four cases. In daily data sets, the hybrid and regression models yield similar outcomes. Because of the intrinsic variability of the data, the forecast error is fairly high, comparable to the errors found in other forms of alternative energy, such as wind and solar.     

Estimating the potential of ocean wave power resources

The realistic assessment of an ocean wave energy resource that can be converted to an electrical power at various offshore sites depends upon many factors, and these include estimating the resource recognizing the random nature of the site-specific wave field, and optimizing the power conversion from particular wave energy conversion devices. In order to better account for the uncertainty in wave power resource estimates, conditional probability distribution functions of wave power in a given sea-state are derived. Theoretical expressions for the deep and shallow water limits are derived and the role of spectral width is studied. The theoretical model estimates were compared with the statistics obtained from the wave-by-wave analysis of JONSWAP based ocean wave time-series. It was shown that the narrow-band approximation is appropriate when the variability due to wave period is negligible. The application of the short-term models in evaluating the long-term wave power resource at a site was illustrated using wave data measured off the California coast. The final example illustrates the procedure for incorporating the local wave data and the sea-state model together with a wave energy device to obtain an estimate of the potential wave energy that could be converted into a usable energy resource.     

Analytical intercomparison results from the 1990 Intergovernmental Oceanographic Commission open-ocean baseline survey for trace metals: Atlantic Ocean

“Dissolved” (< 0.4 μm filtered) and “total dissolvable” (unfiltered) trace element samples were collected using “clean” sampling techniques from four vertical profiles in the eastern Atlantic Ocean on the first IOC Trace Metals Baseline expedition. The analytical results obtained by 9 participating laboratories for Mn, Fe, Co, Ni, Cu, Zn, Cd, Pb, and Se on samples from station 4 in the northeast Atlantic have been evaluated with respect to accuracy and precision (intercomparability). The data variability among the reporting laboratories was expressed as 2 × SD for a given element and depth, and was comparable to the 95% confidence interval reported for the NASS seawater reference standards (representing analytical variability only). The discrepancies between reporting laboratories appear to be due to inaccuracies in standardization (analytical calibration), blank correction, and/or extraction efficiency corrections.Several of the sampling bottles used at this station were not adequately pre-cleaned (anomalous Pb results). The sample filtration process did not appear to have been a source of contamination for either dissolved or particulate trace elements. The trace metal profiles agree in general with previously reported profiles from the Atlantic Ocean. We conclude that the sampling and analytical methods we have employed for this effort, while still in need of improvement, are sufficient for obtaining accurate concentration data on most trace metals in the major water masses of the oceans, and to enable some evaluation of the biogeochemical cycling of the metals.     

44-year wave hindcast for the Eastern Mediterranean

Within the EU-funded project HIPOCAS (Hindcast of Dynamic Processes of the Ocean and Coastal Area of Europe), high-resolution wave hindcasts were performed for the period 1958–2001 over the Eastern Mediterranean. The state-of-the-art WAM model was used for producing the 44-year wave data set. The wave model was driven by wind data generated from the regional atmospheric model REMO. The WAM model outputs of significant wave height were validated against in-situ measurements and satellite data. The model results show a good agreement with observations. The homogeneous wave data set produced by the model was then used for a study of long-term variability and climatic trends in the region.     

High-frequency variability of vertical distribution of suspended sediment concentration under irregular waves

Based on the data of a field experiment in a storm environment, we examined the specific features of the variability of the vertical profiles of the concentration of suspended sand sediments at a discreteness shorter than the wave period. Physical mechanisms that determine the processes of suspension and shape the types of vertical distributions of sediments are considered. A mathematical model for the prediction of vertical distributions of the suspended sediments governed by irregular wave forcing is developed. The model is verified from data of field and laboratory experiments.     

Stochastic bivariate time series models of waves in the North Sea and their application in simulation-based design

In this paper, we present and evaluate three long-term wave models for application in simulation-based design of ships and marine structures. Designers and researchers often rely on historical weather data as a source for ocean area characteristics based on hindcast datasets or in-situ measurements. The limited access and size of historical datasets reduces repeatability of simulations and analyses, making it difficult to assess the sampling variability of performance and loads on marine vessels and structures. Markov, VAR and VARMA wave models, producing independent long-term time series of significant wave height (H_s) and spectral peak period (T_p), is presented as possible solutions to this problem. The models are tested and compared by addressing how the models affect interpretation of design concepts and the ability to replicate statistical and physical characteristics of the wave process. Our results show that the VAR and VARMA models perform sufficiently in describing design performance, but does not capture the physical process fully. The Markov model is found to perform worst of the tested models in the applied tests, especially for measures covering several consecutive sea states.     

Optical moorings-of-opportunity for validation of ocean color satellites

Buoy-mooring platforms are advantageous for time-series validation and vicarious calibration of ocean color satellites because of their high temporal resolution and ability to perform under adverse weather conditions. Bio-optical data collected on the Bermuda Testbed Mooring (BTM) were used for comparison with satellite ocean color data in an effort to further standardize sampling and data processing methods for high quality satellite-mooring comparisons. Average percentage differences between satellite-measured and mooring-derived water leaving radiances were about 20% at the blue wavelengths, decreasing to as low as 11% in the blue-green to green wavebands. Based on a series of data processing methods and analyses, recommendations concerning rigor of quality control for collected data, optimal averaging of high-frequency data, sensor self-shading wind corrections, and instrumentation placement requirements are given for the design and application of optical moorings for ocean color satellite validation. Although buoy-mooring platforms are considered to be among the very best methods to validate ocean color satellite measurements, match-up discrepancies due to water column variability and atmospheric corrections remain important issues.     

Using satellite altimetry to correct mean temperature and salinity fields derived from Argo floats in the ocean regions around Australia

We present results from a suite of methods using in situ temperature and salinity data, and satellite altimetric observations to obtain an enhanced set of mean fields of temperature, salinity (down to 2000-m depth) and steric height (0/2000 m) for a time-specific period (1992–2007). Firstly, the improved global sampling resulting from the introduction of the Argo program, enables a representative determination of the large-scale mean oceanic structure. However, shortcomings in the coverage remain. High variability western boundary current eddy fields, continental slope and shelf boundaries may all be below their optimal sampling requirements. We describe a simple method to supplement and improve standard spatial interpolation schemes and apply them to the available data within the waters surrounding Australia (100°E–180°W; 50°S–10°N). This region includes a major current system, the East Australian Current (EAC), complex topography, unique boundary currents such as the Leeuwin Current, and large ENSO related interannual variability in the southwest Pacific. We use satellite altimetry sea level anomalies (SLA) to directly correct sampling errors in in situ derived mean surface steric height and subsurface temperature and salinity fields. The surface correction is projected through the water column (using an empirical model) to modify the mean subsurface temperature and salinity fields. The errors inherent in all these calculations are examined. The spatial distribution of the barotropic–baroclinic balance is obtained for the region and a ‘baroclinic factor’ to convert the altimetry SLA into an equivalent in situ height is determined. The mean fields in the EAC region are compared with independent estimates on repeated XBT sections, a mooring array and full-depth CTD transects.     

OSCR wave measurements-some preliminary results

OSCR is an HF radar system that has been developed for high spatial resolution coastal surface current measurement. This paper describes preliminary results that demonstrate that wave measurement can be successfully obtained from suitably processed OSCR data. Comparisons with data from a WAVEC directional buoy are presented and show encouraging agreement. Some of the limitations to the measurement process are discussed and indicate a maximum range of about 20 km. Surface current variability on short time scales presents the most serious obstacle to wave measurement. This appears to be more of a problem when the mean currents are large, in that in these circumstances the data fail initial quality control criteria. However, in lower mean currents, the effect is often still present and leads to errors in long wave measurement     

The impact of various methods of wave transfers from deep water to nearshore when determining extreme beach erosion

Several levels of increasing complexity of transferring wave information from offshore to nearshore have been studied to quantify their influence on extreme beach erosion estimates. Beach profiles which have been monitored since 1976 were used to estimate extreme beach erosion and compared to predictions. Examination of the wave propagation assumptions revolves around two types of offshore to nearshore transfer: excluding or including wave breaking and bottom friction. A second complication is whether still water level variations (ocean tide plus storm surge) are included.The inclusion of various combinations of wave propagation processes other than shoaling and refraction in the wave transfer function changes on the extreme erosion distribution tail through lowering estimates above one year return period. This brings the predicted tails closer to the observations, but does not capture the upper limit of storm demand implied by the extensive beach profile data set. Including wave breaking has a marked effect on probabilistic estimates of beach erosion. The inclusion of bottom friction is less significant. The inclusion of still water level variability in the wave transfer calculation had minimal impact on results for the case study site, where waves were transferred from offshore to water at 20 m depth. These changes were put into perspective by comparing them to changes resulting from limiting beach erosion by adjusting the statistical distributions of peak wave height and storm duration to have maximum limits. We conclude that the proposed improvements on wave transformation methods are as significant as limiting wave erosion potential and worth including.     

电子罗盘校准方法的设计与实现

电子罗盘在波浪浮标中用于实时提供波浪浮标所处的方位.为解决嵌入了Matlab的电子罗盘校准系统可移植性差、过程繁杂、运行速度慢的问题,设计了新的校准系统.将由14位串行A/D转换器Max194从波高数据采集系统中获得的模拟信号转换成的数字信号接入到微控制器中,微控制器负责控制方位数据的采样频率和A/D转换芯片,再利用串口转换芯片将输出的电平信号转换成RS232信号,由计算机对此进行处理.该系统采用了绘图功能完备、处理速度出众、可移植性好的ProEssentials结合VC++来处理多功能方位测试台采集到的方位数据,并绘制成采样数据比较图和误差图,来直观地展现电子罗盘的误差情况.实验测试表明,该电子罗盘校准系统具有可移植性好、功耗低、稳定性强、运行速度快的特点.该校准系统已经广泛应用于波浪浮标、3m浮标等的生产中.     

Long-term evolution of Baltic Sea wave climate near a coastal segment in Poland; its drivers and impacts

The paper analyses long-term variability of wave climate near Poland for the 1958–2002 period. With spectral and cross-spectral analysis, linear regression and singular spectrum analysis the modes of long-term variability were quantified for the most energetic months (November–February). For monthly indices of North Atlantic Oscillation from 1950 until 2006, it was established that the long-term trends of NAO and significant wave height demonstrate a gentle coupling. For Januaries this relationship is strongest and dates back to 1960, for Februaries a certain consistency appears since 1975. For Novembers and Decembers no statistically discernible coupling was found. Thus, the Baltic Sea appears to be the easternmost NAO-affected region, despite its separation from the Atlantic. The hydrodynamic variability also includes a non-trivial oscillation in the January wave energy records with T=8 years. The same periodicity was identified with the multi-channel SSA technique in the long-term shoreline data of a neighboring beach. The study shows that even almost entirely isolated water bodies are becoming exposed to global climatic phenomena and accelerated erosion of sandy beaches, typical for the South Baltic region. On the other hand, the 8-year hydrodynamic cycle can be viewed as the driver of long-term shoreline evolution.     

Variability of Coulter Counter Data and their Relationship with Other Oceano-graphical Parameters

Abstract. Coulter counter data (from the upper 100 m of water) gathered during a cruise in October, 1976 in the Spanish waters of the Western Mediterranean are analysed in this paper. The ANOVA shows that the main sources of variability are the geographical situation and the depth level, while the sampling method especially affects the results for the larger particles. Repeating the counting in the same sample also introduces some changes which have less importance in the overall sense. Correlations of the particle counts with chlorophyll a, total POC and PON, POC and PON above 200u.m, estimated POC based on the particle size spectra, and SECCHI disc visibility were calculated and are discussed in order to assess the kind of information given by seston counts obtained from two different sampling methods and expressed both in absolute values (ppm by volume) and as a percentage of volume for the different channels.
Some striking results arise which suggest that the use of a Coulter counter together with some kind of sample treatment might give an insight into the qualitative and quantitative characterization of the pelagic ecosystem.     

Forecasting seasonal to multi-year shoreline change

This contribution details a simple empirical model for forecasting shoreline positions at seasonal to interannual time-scales. The one-dimensional (1-D) model is a simplification of a 2-D behavioural-template model proposed by Davidson and Turner (2009). The new model is calibrated and tested using five-years of weekly video-derived shoreline data from the Gold Coast, Australia. The modelling approach first utilises a least-squares methodology to calibrate the empirical model coefficients using the first half of the dataset of observed shoreline movement in response to known forcing by waves. The model is then verified by comparison of hindcast shoreline positions to the second half of the observed shoreline dataset. One thousand synthetic time-series of wave height and period are generated that encapsulate the statistical characteristics of the modelled wave field, retaining the observed seasonal variability and sequencing characteristics. The calibrated model is used in conjunction with the simulated wave time-series to perform Monte Carlo forecasting of the resulting shoreline positions. The ensemble-mean of the 1000 individual five-year shoreline simulations is compared to the unseen shoreline time-series. A simple linear trend forecast of the shoreline position was used as a baseline for assessing the performance of the model. The model performance relative to this baseline prediction was quantified by several objective methods, including cross-correlation (r), root mean square (RMS) error analysis and Brier Skill tests. Importantly, these tests involved no prior knowledge of either the wave forcing or shoreline response. The new forecast model was found to significantly improve shoreline predictions relative to the simple linear trend model, capturing well both the trend and seasonal shoreline variabilities observed at this site. Brier Skill Scores (BSS) indicate that the model forecasts based on unseen data were rated as ‘excellent’ (BSS = 0.83), and root mean square errors were less than 7 m (≈ 14% of the observed variability). The standard deviations of the 1000 individual simulations from ensemble-averaged ‘mean’ forecast were found to provide a useful means of predicting the higher-frequency (individual storm) shoreline variability, with 98% of the observed shoreline data falling within two standard deviations of the forecast position.