The response of the Gulf of Mexico to wind and heat flux forcing: What has been learned in recent years?

The Loop Current and its shed eddies dominate the circulation and dynamics of the Gulf of Mexico (GoM) basin. Those eddies are strongly energetic and are the cause of intense currents that may penetrate several hundred meters deep. However, there are regions in the GoM and periods of time in which the local atmospheric forcing plays an important role in its dynamics and thermodynamics. The circulation on the shelves, and particularly on the inner shelf, is mainly wind-driven with seasonality, changing direction during the year with periods of favorable upwelling/downwelling conditions. The wind-driven circulation is associated with the transport of waters with different temperature and salinity characteristics from one region to another. The interannual variability of the circulation on the shelves is linked to the atmospheric variability. Intraseasonal variability of the wind patterns considerably affects the likelihood and magnitude of upwelling and downwelling. The geometry of the GoM is such that large-scale winds may drive opposing upcoast/downcoast currents along different parts of the curving coast, resulting in convergence or divergence zones. The width of the shelves in the GoM is variable;while the West Florida Shelf, the Texas-Louisiana shelf and the Campeche Bank are more than 200 km wide, they are narrower near Veracruz and Tabasco. Another consequence of the GoM physiography and the wind forcing is the development of cross-shelf transports in the southern Bay of Campeche, the southern Texas shelf and southeast of the Mississippi river, which in turn vary during the year. During autumn-winter (from September to April), the GoM is affected by cold fronts coming from the northwest United States, which are associated with strong, dry, and cold winds that mix its waters and generate large sensible and latent heat fluxes from the ocean to the atmosphere. These frontal passages also cool the GoM surface waters due to mixing with lower temperature subsurface waters. During summer, tropical cyclones crossing the GoM can dramatically affect circulation and coastal upwelling.     

Sublethal Effects of Crude Oil on the Community Structure of Estuarine Phytoplankton

While the ecological impacts of crude oil exposure have been widely studied, its sublethal effects on phytoplankton community structure in salt marsh estuaries have not been well documented. The purpose of this study was to simulate oil spill conditions using a microcosm design to examine short-term (2 day) changes in phytoplankton community composition and total biomass following exposure to crude oil obtained from the Deepwater Horizon oil spill and a mixture of Texas crude oils. Microcosm experiments were performed in situ in North Inlet Estuary near Georgetown, SC. A control and six replicated experimental treatments of crude oil additions at final concentrations of 10, 50, or 100 μl l⁻¹ of either Deepwater Horizon spill oil or the Texas crude mixture were incubated under in situ conditions. Photopigments were analyzed using high-performance liquid chromatography and community composition was determined using ChemTax. Total phytoplankton biomass (as chl a) declined with increasing crude oil concentrations. Prasinophytes, the most abundant microalga in both experiments, showed no response to oil exposure in one experiment and a significant negative response in the other. Diatoms euglenophytes and chlorophytes appeared relatively resistant to oil contamination at the exposure levels used in this study, maintaining or increasing their relative abundance with increasing oil concentrations. Chlorophytes and cyanobacteria increased in relative abundance while cryptophyte abundance decreased with increasing oil concentrations. The results of these experiments suggest that low levels of crude oil exposure may reduce total biomass and alter phytoplankton community composition with possible cascade effects at higher trophic levels in salt marsh estuaries.     

Global sensitivity analysis in an ocean general circulation model: a sparse spectral projection approach

Polynomial chaos (PC) expansions are used to propagate parametric uncertainties in ocean global circulation model. The computations focus on short-time, high-resolution simulations of the Gulf of Mexico, using the hybrid coordinate ocean model, with wind stresses corresponding to hurricane Ivan. A sparse quadrature approach is used to determine the PC coefficients which provides a detailed representation of the stochastic model response. The quality of the PC representation is first examined through a systematic refinement of the number of resolution levels. The PC representation of the stochastic model response is then utilized to compute distributions of quantities of interest (QoIs) and to analyze the local and global sensitivity of these QoIs to uncertain parameters. Conclusions are finally drawn regarding limitations of local perturbations and variance-based assessment and concerning potential application of the present methodology to inverse problems and to uncertainty management.     

Observations of wind and waves in the central Bay of Bengal during BOBMEX-99 and their effect on mixed layer depth variability due to forced mixing

Time-series wind and wave measurements were carried out onboard INS Sagardhwani in the central Bay of Bengal during BOBMEX-99. Various other marine meteorological and oceanographic measurements relevant to monsoon studies were also collected simultaneously. The observed variations of wind and waves and the associated mixed layer depth (MLD) variability based on both temperature and density criteria at 3 hourly intervals are presented in this paper as a case study. At the time-series location (13‡N, 87‡E) wind varied between 6 and 16m/s and the predominant direction was southwesterly. The significant wave height and period varied from 1.9 to 3.7m and 8 to 13 s respectively. Some of the available statistical predictive methods for the determination of MLD by forced mixing are utilized to test the extent of mechanical mixing within the top layer of water by the local wind and wave activity. The same is extended to formulate a new empirical relation for gross estimation of effective depth within which the sound energy is generally trapped during its transmission in the surface duct. The present analysis aiming for estimation of observed MLD variability (35 to 75 m) using the suggested simple empirical relation reveals that, the mixed layer variability observed during the experiment depends on both local ocean variability as well as remote forcing as reported earlier.     

Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter

Ensemble methods present a practical framework for parameter estimation, performance prediction, and uncertainty quantification in subsurface flow and transport modeling. In particular, the ensemble Kalman filter (EnKF) has received significant attention for its promising performance in calibrating heterogeneous subsurface flow models. Since an ensemble of model realizations is used to compute the statistical moments needed to perform the EnKF updates, large ensemble sizes are needed to provide accurate updates and uncertainty assessment. However, for realistic problems that involve large-scale models with computationally demanding flow simulation runs, the EnKF implementation is limited to small-sized ensembles. As a result, spurious numerical correlations can develop and lead to inaccurate EnKF updates, which tend to underestimate or even eliminate the ensemble spread. Ad hoc practical remedies, such as localization, local analysis, and covariance inflation schemes, have been developed and applied to reduce the effect of sampling errors due to small ensemble sizes. In this paper, a fast linear approximate forecast method is proposed as an alternative approach to enable the use of large ensemble sizes in operational settings to obtain more improved sample statistics and EnKF updates. The proposed method first clusters a large number of initial geologic model realizations into a small number of groups. A representative member from each group is used to run a full forward flow simulation. The flow predictions for the remaining realizations in each group are approximated by a linearization around the full simulation results of the representative model (centroid) of the respective cluster. The linearization can be performed using either adjoint-based or ensemble-based gradients. Results from several numerical experiments with two-phase and three-phase flow systems in this paper suggest that the proposed method can be applied to improve the EnKF performance in large-scale problems where the number of full simulation is constrained.     

Improved initial sampling for the ensemble Kalman filter

In this paper, we discuss several possible approaches to improving the performance of the ensemble Kalman filter (EnKF) through improved sampling of the initial ensemble. Each of the approaches addresses a different limitation of the standard method. All methods, however, attempt to make the results from a small ensemble as reliable as possible. The validity and usefulness of each method for creating the initial ensemble is based on three criteria: (1) does the sampling result in unbiased Monte Carlo estimates for nonlinear flow problems, (2) does the sampling reduce the variability of estimates compared to ensembles of realizations from the prior, and (3) does the sampling improve the performance of the EnKF? In general, we conclude that the use of dominant eigenvectors ensures the orthogonality of the generated realizations, but results in biased forecasts of the fractional flow of water. We show that the addition of high frequencies from remaining eigenvectors can be used to remove the bias without affecting the orthogonality of the realizations, but the method did not perform significantly better than standard Monte Carlo sampling. It was possible to identify an appropriate importance weighting to reduce the variance in estimates of the fractional flow of water, but it does not appear to be possible to use the importance weighted realizations in standard EnKF when the data relationship is nonlinear. The biggest improvement came from use of the pseudo-data with corrections to the variance of the actual observations.     

The Exxon and BP oil spills: a comparison of psychosocial impacts

We address the research question: ‘Did the 2010 BP Deepwater Horizon oil spill have similar psychosocial impacts as the 1989 Exxon Valdez oil spill?’ We answer this question by comparing survey results from a random sample of Cordova, Alaska, residents collected 18 months after the Exxon spill with a random sample of residents in the Alabama coastal counties of Baldwin and south Mobile 1 year after the BP disaster. Analysis revealed similarly high levels of psychological stress for survivors of both disasters. For residents of coastal Alabama, the strongest predictors of psychosocial stress were exposure to oil, ties to renewable resources, concerns about their economic future, worries about air quality, and safety issues regarding seafood harvests in oiled areas. Differences between south Mobile and Baldwin counties were related to the former community’s economic ties to renewable resources and Baldwin County’s dependence on tourism for economic sustainability.     

Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

Accidental release of crude oil into the sea due to human activity causes water pollution and heavy damages to natural ecosystems killing birds, fish, mammals and other organisms. A number of monitoring systems are used for tracking the spills and their effects on the marine environment, as well as for collecting data for feeding models. Among them, Earth observation technologies play a crucial role and moderate spatial resolution satellite systems are able to collect images with a very short revisit time or even daily. This paper describes the use of Moderate-Resolution Imaging Spectroradiometer data for monitoring large oil slicks with the fluorescence/emissivity index and object-based image analysis. Two case studies are presented: the Deepwater Horizon (2010) and the Campos Basin (2011) oil spill accidents. Results show that it is possible to track the dynamics of the slick both for massive and long-lasting accidents and for smaller and very quick accidents. The main advantages of the method proposed are a straightforward implementation, a fast and semi-automated data processing and the capability of integration of daytime and nighttime acquisitions, as well as its adaptability to different sensors.     

Disaster experience,social capitals,and behavioral health

On April 20, 2010, the Deepwater Horizon oil rig exploded, and oil spilled from the breached well-head for months, leading to an unprecedented environmental disaster with implications for behavioral health. Disasters are thought to affect behavioral health, and social capital is thought to ameliorate behavioral health impacts after disasters, though empirical evidence is mixed. One possible explanation for the discrepancy in findings relates to the activation of social capital in different contexts. In a disaster context, certain types of social capital may be more beneficial than others, and these relationships could differ between those directly affected by the disaster and those who are unaffected. The goal of this study is to assess the relationships between different forms of social capital (community engagement, trust, and social support) on different behavioral health indicators (depression, anxiety, and alcohol misuse) using data from the first wave of the Survey of Trauma, Resilience, and Opportunity among Neighborhoods in the Gulf (STRONG), a probabilistic household telephone survey fielded 6 years after the onset of the Deepwater Horizon oil spill (DHOS). We employ a structural equation modeling approach where multiple social capital and behavioral health variables can be included and their pathways tested in the same model, comparing the results between those who reported experiencing disruptions related to the DHOS and those who did not. Among those who experienced the DHOS, social support was negatively associated with both depression (β?=???0.085; p?=?0.011) and anxiety (β?=???0.097; p?=?0.003), and among those who did not experience the DHOS, social support was positively associated with alcohol misuse (β?=?0.067; p?=?0.035). When controlling for the other social capital variables, social support was the only form of social capital with a significant relationship to behavioral health, and these relationships differ based on whether or not a person experienced the disaster. This suggests that social capital does not have a uniformly ameliorative relationship with behavioral health in the aftermath of disasters.

     

Google Earth and Google Fusion Tables in support of time-critical collaboration: Mapping the deepwater horizon oil spill with the AVIRIS airborne spectrometer

Web interfaces have made remote sensing image resources more accessible and interactive. However, many web-based and Digital Earth opportunities for remote sensing have not yet been fully explored and could greatly facilitate scientific collaboration. In many cases, these resources can augment traditional proprietary software packages, which can have limited flexibility, spatiotemporal controls, and data synthesis abilities. In this paper, we discuss how web services and Google Earth were used for time-critical geovisualizations of the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Deepwater Horizon oil spill imaging campaign. In particular, we describe how (1) AVIRIS Google Earth products were used to visualize the spatial and temporal characteristics of the campaign’s image acquisitions, critically needed for flight planning, (2) the Google Fusion Table cloud-based service was applied to create a highly-interactive image archive and mapping display, and (3) the Google Fusion Table API was utilized to create a flexible PHP-based interface for metadata creation and as the basis for an interactive data catalog. Although there are other possible software and programming approaches to these activities, we highlight freely-accessible and flexible solutions and bring attention to the newly introduced Google Fusion Tables as a collaborative scientific platform.     

Effect of Deepwater Horizon Oil on Growth Rates of Juvenile Penaeid Shrimps

Marsh shoreline, an important habitat for juvenile penaeid shrimps, was extensively oiled in coastal Louisiana by the Deepwater Horizon oil spill of 2010. The effect of this spill on growth was examined for brown shrimp Farfantepenaeus aztecus and white shrimp Litopenaeus setiferus held for 7 days in field mesocosms in Barataria Bay during May and August 2011, respectively. The experiments each had 10 treatment combinations, five apparent oil levels, each one with and without added food. Mesocosms were placed in northern Barataria Bay along shorelines that varied in oiling (designated as heavy, moderate, light, very light, or none based on NOAA surveys), and shrimp in half the mesocosms received additional food. Polycyclic aromatic hydrocarbon (PAH) concentrations determined from sediment cores collected at each mesocosm were significantly higher at heavy and moderate than very light shorelines and also higher at moderate than light and none shorelines. Brown shrimp grew more slowly at heavy than very light or none shorelines, and a statistically significant negative relationship was detected between brown shrimp growth rates and sediment PAH concentrations. In August, PAH sediment concentrations had decreased significantly from the values measured in May, no significant difference in white shrimp growth rates was detected among oiling levels, and no relationship was detected between white shrimp growth and sediment PAH concentrations. Both brown shrimp and white shrimp grew more rapidly in mesocosms where food was added. Our study shows that exposure to nonlethal concentrations of petroleum hydrocarbons can reduce growth rates of juvenile penaeid shrimps.     

Ensemble filter methods with perturbed observations applied to nonlinear problems

The ensemble Kalman filter (EnKF) has been shown repeatedly to be an effective method for data assimilation in large-scale problems, including those in petroleum engineering. Data assimilation for multiphase flow in porous media is particularly difficult, however, because the relationships between model variables (e.g., permeability and porosity) and observations (e.g., water cut and gas–oil ratio) are highly nonlinear. Because of the linear approximation in the update step and the use of a limited number of realizations in an ensemble, the EnKF has a tendency to systematically underestimate the variance of the model variables. Various approaches have been suggested to reduce the magnitude of this problem, including the application of ensemble filter methods that do not require perturbations to the observed data. On the other hand, iterative least-squares data assimilation methods with perturbations of the observations have been shown to be fairly robust to nonlinearity in the data relationship. In this paper, we present EnKF with perturbed observations as a square root filter in an enlarged state space. By imposing second-order-exact sampling of the observation errors and independence constraints to eliminate the cross-covariance with predicted observation perturbations, we show that it is possible in linear problems to obtain results from EnKF with observation perturbations that are equivalent to ensemble square-root filter results. Results from a standard EnKF, EnKF with second-order-exact sampling of measurement errors that satisfy independence constraints (EnKF (SIC)), and an ensemble square-root filter (ETKF) are compared on various test problems with varying degrees of nonlinearity and dimensions. The first test problem is a simple one-variable quadratic model in which the nonlinearity of the observation operator is varied over a wide range by adjusting the magnitude of the coefficient of the quadratic term. The second problem has increased observation and model dimensions to test the EnKF (SIC) algorithm. The third test problem is a two-dimensional, two-phase reservoir flow problem in which permeability and porosity of every grid cell (5,000 model parameters) are unknown. The EnKF (SIC) and the mean-preserving ETKF (SRF) give similar results when applied to linear problems, and both are better than the standard EnKF. Although the ensemble methods are expected to handle the forecast step well in nonlinear problems, the estimates of the mean and the variance from the analysis step for all variants of ensemble filters are also surprisingly good, with little difference between ensemble methods when applied to nonlinear problems.     

Noah-MP模型中积雪模拟对参数化方案的敏感性评估

针对Noah-MP模型多参数化方案、模拟结果不确定性范围难以确定的特点,选取北疆地区具有代表性的阿勒泰站气象资料作为模型驱动数据,探讨了积雪对多参数化方案的敏感性。在不考虑模型参数和驱动数据不确定性的条件下,设计了集合数为13824的多参数化方案集合模拟试验。选用Natural selection方法对物理过程的敏感性进行分析,并在敏感性分析结果的基础上进一步讨论了模拟结果的不确定性。结果表明:积雪对地表热交换、雨雪分离、土壤温度底层边界条件和第一层积雪或土壤时间方案4个物理过程敏感;在不考虑驱动数据和模型参数不确定性的条件下,多参数化方案集合模拟试验中的不确定性主要来源于敏感物理过程。去除敏感物理过程中能够明显降低模拟性能的参数化方案后,集合模拟结果的不确定性大幅减小。最后,根据分析结果构建了该站雪深和雪水当量模拟的最优参数化方案组合。     

Sensitivity Evaluation of Snow Simulation to Multi-parameterization Schemes in the Noah-MP Model

On account of the latest community Noah land surface model with multi-parameterization (Noah-MP) schemes and its uncertainty breadth in simulation results being difficult to be determined, this study assessed the sensitivity of snow to physics options using meteorological data from the Altay Station in northern Xinjiang. The Noah-MP physics ensemble simulation with the total number of 13 824 was designed without the consideration of the uncertainties of forcing data and parameters. The natural selection approach was used to analyze the sensitivity of physical processes. Based on the results of sensitivity analysis, the uncertainty of ensemble simulation results was further discussed. The results showed that snow was sensitive to the physical processes of surface-layer exchange coefficient, partitioning precipitation into rainfall and snowfall, lower boundary condition of soil temperature, and first-layer snow or soil temperature time scheme; Uncertainties in multi-parameterization ensemble simulation experiments were mainly from sensitive physical processes under the condition of disregarding uncertainties of forcing data and parameters. After removing the parameterization schemes that notably reduced simulation performance in sensitive physical processes, the uncertainty breadth in ensemble simulations decreased significantly. Finally, an optimal combination group of parameterization schemes for this station was configured.     

<Emphasis Type="Italic">Deepwater Horizon</Emphasis> Oil Spill Impacts on Salt Marsh Fiddler Crabs (<Emphasis Type="Italic">Uca</Emphasis> spp.)

The Deepwater Horizon oil spill was the largest marine oil spill in US waters to date and one of the largest worldwide. Impacts of this spill on salt marsh vegetation have been well documented, although impacts on marsh macroinvertebrates have received less attention. To examine impacts of the oil spill on an important marsh invertebrate and ecosystem engineer, we conducted a meta-analysis on fiddler crabs (Uca spp.) using published sources and newly available Natural Resources Damage Assessment (NRDA) and Gulf of Mexico Research Initiative (GoMRI) data. Fiddler crabs influence marsh ecosystem structure and function through their burrowing and feeding activities and are key prey for a number of marsh and estuarine predators. We tested the hypothesis that the spill affected fiddler crab burrow density (crab abundance), burrow diameter (crab size), and crab species composition. Averaged across multiple studies, sites, and years, our synthesis revealed a negative effect of oiling on all three metrics. Burrow densities were reduced by 39 % in oiled sites, with impacts and incomplete recovery observed over 2010–2014. Burrow diameters were reduced from 2010 to 2011, but appeared to have recovered by 2012. Fiddler crab species composition was altered through at least 2013 and only returned to reference conditions where marsh vegetation recovered, via restoration planting in one case. Given the spatial and temporal extent of data analyzed, this synthesis provides compelling evidence that the Deepwater Horizon spill suppressed populations of fiddler crabs in oiled marshes, likely affecting other ecosystem attributes, including marsh productivity, marsh soil characteristics, and associated predators.     

Subtidal sea level variability in a shallow Mississippi River Deltaic estuary, Louisiana

The relative roles of river, atmospheric, and tidal forcings on estuarine sea level variability are examined in Breton Sound, a shallow (0.7 m) deltaic estuary situated in an interdistributary basin on the Mississippi River deltaic plain. The deltaic landscape contains vegetated marshes, tidal flats, circuitous channels, and other features that frictionally dissipate waves propagating through the system. Direct forcing by local wind stress over the surface of the estuary is minimal, owing to the lack of significant fetch due to landscape features of the estuary. Atmospheric forcing occurs almost entirely through remote forcing, where alongshore winds facilitate estuary-shelf exchange through coastal Ekman convergence. The highly frictional nature of the deltaic landscape causes the estuary to act as a low-pass filter to remote atmospheric forcing, where high-frequency, coastally-induced fluctuations are significantly damped, and the damping increases with distance from the estuary mouth. During spring, when substantial quantities of controlled Mississippi River inputs (?q = 62 m³ s^-1) are discharged into the estuary, upper estuary subtidal sea levels are forced by a combination of river and remote atmospheric forcings, while river effects are less clear downestuary. During autumn (?q = 7 m³ s^-1) sea level variability throughout the estuary is governed entirely by coastal variations at the marine boundary. A frequency-dependent analytical model, previously used to describe sea level dynamics forced by local wind stress and coastal forcing in deeper, less frictional systems, is applied in the shallow Breton Sound estuary. In contrast to deeper systems where coastally-induced fluctuations exhibit little or no frictional attenuation inside the estuary, these fluctuations in the shallow Breton Sound estuary show strong frequency dependent amplitude reductions that extend well into the subtidal frequency spectrum.     

Gradual Deformation and Iterative Calibration of Gaussian-Related Stochastic Models

This paper describes a new method for gradually deforming realizations of Gaussian-related stochastic models while preserving their spatial variability. This method consists in building a stochastic process whose state space is the ensemble of the realizations of a spatial stochastic model. In particular, a stochastic process, built by combining independent Gaussian random functions, is proposed to perform the gradual deformation of realizations. Then, the gradual deformation algorithm is coupled with an optimization algorithm to calibrate realizations of stochastic models to nonlinear data. The method is applied to calibrate a continuous and a discrete synthetic permeability fields to well-test pressure data. The examples illustrate the efficiency of the proposed method. Furthermore, we present some extensions of this method (multidimensional gradual deformation, gradual deformation with respect to structural parameters, and local gradual deformation) that are useful in practice. Although the method described in this paper is operational only in the Gaussian framework (e.g., lognormal model, truncated Gaussian model, etc.), the idea of gradually deforming realizations through a stochastic process remains general and therefore promising even for calibrating non-Gaussian models.     

非线性滤波方法与陆面数据同化

陆面数据同化研究近几年成为地球科学研究的新兴领域,其中以非线性滤波为代表的数据同化方法发展迅速并得到了广泛应用。在贝叶斯理论框架内,从递推贝叶斯估计理论的角度系统地分析了扩展卡尔曼滤波、无迹卡尔曼滤波、集合卡尔曼滤波、SIR粒子滤波等非线性滤波方法的异同;针对应用比较广泛的集合卡尔曼滤波和SIR粒子滤波应用中存在的问题,论述了几种提高滤波性能的实用方法,如协方差矩阵的Localization方法、协方差矩阵的Inflation方法、双集合卡尔曼滤波方法、扰动集合、扰动大气驱动和模型参数、平方根集合卡尔曼滤波以及粒子滤波算法的改进等。最后总结讨论了各种非线性滤波方法应用中的特点、难点以及各种算法在陆面数据同化中的应用前景和发展方向。     

Tidal and Meteorological Forcing of Suspended Sediment Flux in a Muddy Mesotidal Estuary

Although the supply and fate of suspended sediment is of fundamental importance to the functioning and morphological evolution of muddy estuaries, reliable sediment budgets have been established in only a few cases. Especially for smaller estuaries, inadequate bathymetric surveys and a lack of intertidal sedimentation data often preclude estimation of the sediment budget from morphological change, while instrument-derived residual fluxes typically lie well within the errors associated with measurement of much larger gross tidal transports. Given suitably long-term records of continuously monitored suspended sediment concentration (SSC), however, analysis of the major scales of variation in sediment transport and their relation to hydrodynamic and meteorological forcing permits qualitative testing of hypotheses suggested by directly measured residual fluxes. This paper analyzes data from a 1-year acoustic Doppler profiler deployment in the Blyth estuary, a muddy mesotidal barrier-enclosed system on the UK east coast. Flux calculations indicate a small sediment import equivalent to just 1.5% of the gross flood tide transport. Little confidence can be assigned to either the magnitude or direction of such a small residual when considered in isolation. However, the inference that the sediment regime is finely balanced is qualitatively supported by the close similarity between flood-tide and ebb-tide SSC values. Singular spectrum analysis of the SSC time series shows the expectedly large contributions to the variance in SSC at intratidal and subtidal (semimonthly and monthly) scales but also picks out intermittent variability that is initially attributed to a combination of non-tidal surge and wind stress forcing. Closer examination of the data through cross-correlograms and event-scale analysis indicates that local meteorological forcing is the major factor. Acting through the resuspension of intertidal mudflat sediments at times of strong westerlies, meteorological forcing is directly implicated in episodic sediment export from the estuary. Thresholding of tide-averaged fluxes using a range of critical wind stress values further indicates that ‘tide-dominated’ (i.e., low wind stress) and ‘wave-dominated’ (high wind stress) conditions are associated with sediment import and export. Sediment balance is potentially sensitive to the frequency of high wind stress events, since the associated sediment exports are several times larger than the average import under calm conditions. Intermittent meteorological forcing may thus exert an important control on the sedimentary balance of otherwise tidally dominated muddy estuarine systems, and the role of wind climate should not be overlooked in studies of estuary response to environmental change.