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Among the 135 or more estuaries on the southeast Australian coastline, 45% are intermittently open. A number of others, which would normally have an intermittently open entrance, are kept permanently open to improve the flushing and water quality that are functions of the horizontal dispersion of the estuarine system. Field studies that investigate the dynamics and horizontal dispersion processes of open and closed states of Intermittenly Closed and Open Lakes and Lagoons (ICOLL) are conducted. Clusters of Lagrangian drifters were used to characterise the surface flow. Horizontal dispersion coefficients (K) under study varied significantly for different tidal phases; the mean ebb tide K (~4.7±3.8 m2 s-1) is an order of magnitude higher than that of the flood tide. During the closed state, K values were up to two orders of magnitude lower than those observed in the open state. These results highlight the contribution of tidal dispersion to transport and mixing processes within an ICOLL. The results showed that tidal pumping effects and tidal-induced horizontal velocity gradients are responsible for the horizontal surface transport and dispersion occurring during the open inlet state. The horizontal surface dispersion processes identified at different inlets and environmental conditions within this site are valuable for managing transport of particles, freshwater mixing, larvae transport, waste, and pest control.

Intermittently Closed and Open Lakes or Lagoons (ICOLLs) are a subcategory of estuaries that exhibit limited exchange with the ocean and river flows. [1] This study focuses on improving hydrodynamic modelling in shallow water estuaries using GPS tracked Lagrangian drifter data. The inclusion of drifter data will improve hydrodynamic models, which are commonly used to assess and manage risks of flooding, sediment transport, and pollutants. Traditionally, Eulerian hydrodynamic models used in shallow water systems are calibrated using observations from fixed instruments. With the recent availability of inexpensive GPS tracked drifters that can be deployed in shallow estuaries [2], a higher density of data can be collected to improve the confidence in the hydrodynamic model outputs. The numerical model [3] is set up for an ICOLL in South East Queensland (Currimundi Lake).This model will be calibrated and validated using both Lagrangian and Eulerian data. The effectiveness of assimilated Lagrangian drifter data on improving the prediction of velocity and water heights in the ICOLL is investigated. The calibrated numerical model will further be used for modelling advection-dispersion of potential contamination, sediment transport, and flood modelling within the ICOLL.

The Sacramento-San Joaquin River Delta in California becomes inadequate in fresh water resources, while the water demand in California keeps increasing. Large-scale numerical flow models, for example Delta Simulation Model II (DSM2) and River, Estuary, And Land Model (REALM), used as crucial water resources management tools, are capable of providing critical information about tidal forcing and salinity transport in the bays and channels of the Delta. Reliable flow estimation and prediction of these models, however, largely depend on an accurate representation of open boundary conditions and initial conditions, which are usually calibrated against historical data sets acquired from Eulerian measurements near the boundaries. In large watershed, unfortunately, these measurements have demonstrated many intrinsic limitations, for example small spatial coverage and sparse temporal sampling. Also, existing Eulerian sensors have many recorded failures, such as broken gauges, sensor drifts, process leaks, improper measuring devices, and many other random sources. More importantly, if the hydraulic system is radically altered, as in the case of extensive levee failures, the historical data sets can be of limited usage.In this dissertation, a sensing-modeling system featuring rapidly deployed Lagrangian drifters is developed. The system is capable of predicting regional flows and transport in the Delta in a real-time mode, without dependence on historical data. Lagrangian data is obtained when floating drifters move along with the flow and report their locations. The data provides instant information about the flow, including flow advections and eddy dispersions, and is further assimilated into underlying shallow water equation (SWE) models to characterize the flow state. Different approaches to facilitate the flow estimation have been investigated in this dissertation. First, a variational assimilation method (Quadratic Programming) is applied to a 1D SWE model (Linearized Sa

The Sacramento-San Joaquin River Delta in California becomes inadequate in fresh water resources, while the water demand in California keeps increasing. Large-scale numerical flow models, for example Delta Simulation Model II (DSM2) and River, Estuary, And Land Model (REALM), used as crucial water resources management tools, are capable of providing critical information about tidal forcing and salinity transport in the bays and channels of the Delta. Reliable flow estimation and prediction of these models, however, largely depend on an accurate representation of open boundary conditions and initial conditions, which are usually calibrated against historical data sets acquired from Eulerian measurements near the boundaries. In large watershed, unfortunately, these measurements have demonstrated many intrinsic limitations, for example small spatial coverage and sparse temporal sampling. Also, existing Eulerian sensors have many recorded failures, such as broken gauges, sensor drifts, process leaks, improper measuring devices, and many other random sources. More importantly, if the hydraulic system is radically altered, as in the case of extensive levee failures, the historical data sets can be of limited usage.In this dissertation, a sensing-modeling system featuring rapidly deployed Lagrangian drifters is developed. The system is capable of predicting regional flows and transport in the Delta in a real-time mode, without dependence on historical data. Lagrangian data is obtained when floating drifters move along with the flow and report their locations. The data provides instant information about the flow, including flow advections and eddy dispersions, and is further assimilated into underlying shallow water equation (SWE) models to characterize the flow state. Different approaches to facilitate the flow estimation have been investigated in this dissertation. First, a variational assimilation method (Quadratic Programming) is applied to a 1D SWE model (Linearized Sa

Much data in the ocean is Lagrangian in nature. Its full use in ocean prediction could advance significantly the Navy's ability both to predict ocean conditions and to assess the optimal strategies for deploying Lagrangian observational devices and their associated sensors. The development of a fully operational, integrated data assimilation scheme will afford such a naval predictive capacity in fixed ocean regions that can be comprehensively surveyed by Lagrangian measuring devices. The long-term objective of this project is the building of comprehensive and reliable data assimilation (DA) platform for incorporating Lagrangian data into ocean models. It will form the basis of an integrated prediction scheme for the ocean that can feed on both purely Lagrangian and mixed source data.

The long-term goal of this project is the development and application of new methods of investigation for the use of Lagrangian data. Special attention is given to the development of new techniques for the assimilation of Lagrangian data in Eulerian numerical models. Another objective is to improve previous results on statistical prediction of particle transport using data analysis and stochastic models. To develop methods in the framework of the ODDAS DRI objectives, namely to use information from drifting sensors to improve the prediction of spreading in the velocity field. The project has the following specific objectives: 1) To develop and apply new techniques for assimilation of Lagrangian data in regional ocean circulation models. 2) To investigate statistical prediction of particle transport using data analysis and stochastic models.

We present a method to use lagrangian data from remote sensing observation in a data assimilation process for parameters identification in a river hydraulics model based on the bidimensional shallow water equations. The trajectories of particles advected by the flow can be extracted from video images and are used in addition to classical eulerian observations. This lagrangian data bring information on the surface velocity thanks to an appropriate transport model. Numerical twin data assimilation experiments demonstrate that this method makes it possible to significantly improve the identification of bed elevation and initial conditions.

We present a method to use lagrangian data from remote sensing observation in a data assimilation process for parameters identification in a river hydraulics model based on the bidimensional shallow water equations. The trajectories of particles advected by the flow can be extracted from video images and are used in addition to classical eulerian observations. This lagrangian data bring information on the surface velocity thanks to an appropriate transport model. Numerical twin data assimilation experiments demonstrate that this method makes it possible to significantly improve the identification of bed elevation and initial conditions.

This report details the kinetic analysis of sea ice motion data collected during the Arctic Ice Dynamics Joint Experiment (AIDJEX) in the Beaufort Sea. In addition, relationships between the ice kinematic parameters and associated ambient noise are presented. These relationships were determined by an extensive correlation process between the noise and ice motion time histories. Time scales of the various modes of ice motion were calculated by season. Also, seasonal time and space scales were calculated for ambient noise at 10 Hz, 32 Hz, and 1000 Hz. Contents of this section include: Seasonal Arctic Ambient Noise Temporal Autocorrelations, Beaufort Sea, 1975-1976; Seasonal Arctic Ambient Noise Spatial Autocorrelations, Beaufort Sea, 1975-1976; Seasonal Arctic Ambient Noise Power Spectra, Beaufort Sea, 1975-1976. Keywords: Lagrangian data; Thermal cracking., See also Parts 1 and 2, AD-A165 303.

This report details the kinematic analysis of sea ice motion data collected during the Arctic Ice Dynamics Joint Experiment (AIDJEX) in the Beaufort Sea. In addition, relationships between the ice kinematic parameters and associated ambient noise are presented. These relationships were determined by an extensive correlation process between the noise and ice motion time histories. Time scales of the various modes of ice motion were calculated by season. Also, seasonal time and space scales were calculated for ambient noise at 10 Hz, 32 Hz, and 1000 Hz. Contents of this section include: Seasonal Arctic Ambient Noise Variations, Beaufort Sea, 1975-1976; Two-Dimensional Contour Maps of Arctic Ambient Noise Variations, 8-9 August 1975 (Summer); and Two-Dimensional Contour Maps of Arctic Ambient Noise Variations, 16-17 November 1975 (Fall). Keywords: Lagrangian data; Thermal cracking., See also Part 3, Section 3, AD-A165 305.