Measurement Technologies

2.16

Measurement Technologies: Measure What, Where, Why, and How?

AJ Souza, R Bolaños, and J Wolf, National Oceanography Centre, Liverpool, UK D Prandle, Bangor University, Menai Bridge, UK © 2011 Elsevier Inc. All rights reserved.

2.16.1 2.16.1.1 2.16.1.2 2.16.2 2.16.2.1 2.16.2.1.1 2.16.2.1.2 2.16.2.2 2.16.2.2.1 2.16.2.2.2 2.16.2.2.3 2.16.2.2.4 2.16.2.2.5 2.16.2.2.6 2.16.2.3 2.16.2.3.1 2.16.2.3.2 2.16.2.3.3 2.16.2.4 2.16.2.4.1 2.16.2.4.2 2.16.2.4.3 2.16.3 2.16.3.1 2.16.3.1.1 2.16.3.1.2 2.16.3.1.3 2.16.3.1.4 2.16.3.1.5 2.16.3.1.6 2.16.3.2 2.16.3.2.1 2.16.3.2.2 2.16.4 2.16.4.1 2.16.4.2 2.16.4.2.1 2.16.4.2.2 2.16.5 References

Introduction Integrating Modeling, Theory, and Measurements Observational Data In Situ Measurements Sea Level: Tides and Surges Stilling well Pressure gauges Waves Wave staffs Wave buoys Bottom pressure sensors and PUV method Acoustic Doppler current profilers Multisensor arrays Data assimilation Mixing and Circulation Temperature and salinity Currents (in situ measurement) Turbulence measurements Sedimentation Optical Backscatter Point Sensor Acoustic backscatter profiling sensors (ABS and ADCP) Laser in situ scattering transmissometer Remote Sensing Satellite and Aircraft Sea-surface elevation Surface currents Waves Meteorology Temperature and salinity Ocean color Land-Based Radar High-frequency radar X-band radar Real-Time Monitoring Operational Oceanography Coastal Observatories The ISO The Western English Channel Observatory Developing a Monitoring Strategy

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Abstract Grand challenges in the coastal zone include addressing the threats from global climate change and sustainable environ­ mental management to maintain a balance between exploitation and conservation. Both require models that are able to differentiate and predict the impact of man’s activities from natural variability. Continued development of scientific under­ standing is necessary to formulate and interpret modeling simulations and to reconcile disparate findings from the diverse range of coastal environments. Systematic marine monitoring programs are vital for such development, involving combina­ tions of remote sensing, moorings, and coastal stations. This chapter aims to provide a background for designing relevant measurement programs, together with an assessment of the capabilities and limitations of associated measurement technol­ ogies. The scope is generally limited to dynamics, mixing, and sedimentation with limited coverage of biological and chemical parameters.

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2.16.1 Introduction The schematic Figure 1 summarizes processes involved in maintaining the coastal environment. Coastal dynamics are determined by a combination of tides, waves, winds, storms, density variations, river flows, and ‘hard’ and ‘soft’ geology/ sediment distributions. In shallow waters, coastal bathymetry (with characteristic sediment distributions and related bed fea­ tures) transforms open-sea forcing into distinctive small-scale features. As an example, away from the immediate vicinity of the bed, suspended concentrations of fine sediments in shelf seas are invariably less than 10 mg l−1, whereas in some tidal estuaries and in the bottom boundary concentration can increase to upward of 1000 mg l−1.

2.16.1.1

Integrating Modeling, Theory, and Measurements

Most coastal studies need to integrate modeling, theory, and observations – maximizing the use of existing observational data and optimizing planning for new measurements. Both historical and ‘proxy’ data must be exploited, for example, wave data constructed from wind records, flood statistics from adjacent locations, sedimentary records of flora and fauna as indicators of saline intrusion, and anomalous fossi­ lized bed features as evidence of extreme events. Technologies involved in providing observational data range from the development of sensors and platforms, the design of optimal monitoring strategies to the analysis, main­ tenance, and assimilation of data. Coastal gauges can directly measure tides, surges, waves, (relative) mean sea level, tem­ perature, salinity, turbidity, and river flows. Remote sensing by satellites, aircraft, and coastal radars provide expanded spatial patterns – including coastal bathymetry. Offshore moorings, ships’ (ferries) surveys, and autonomous underwater vehicles (AUVs) provide direct in situ data. However, monitoring

Tides, surges, waves, sediment exchange

parameters invariably involves some compromises in accuracy, resolution, representativeness, etc. A comprehensive monitoring strategy is likely to embed all of the above and include duplication and synergy to address quality assurance issues. Models can be used to identify spatial and temporal modes and scales of coherence to establish sam­ pling resolution and to optimize the selection of sensors, instruments, platforms, and locations. In addition to the immediate, localized requirements, infor­ mation may be needed about possible spatial and temporal changes in ocean circulation, which may influence regional climates and the supplies and sinks for nutrients, contami­ nants, thermal energy, etc. Associated data are provided by meteorological, hydrological, and shelf–sea models (Figure 2). Ultimately, fully coupled, real-time (operational) global models will emerge incorporating the total water cycle. The large depths of the oceans introduce long inertial lags in impacts from global climate change. By contrast, systematic regional measurements in shallower, more confined coastal areas may provide early warning of impending impacts (e.g., Schluter et al., 2008; Garcia-Reyes and Largier, 2010). Setup of coastal models requires accurate fine-resolution bathymetry, and ideally, corresponding descriptions of surficial sediments/bed roughness. Subsequent forcing requires tide, surge, and wave data at the open-sea boundary together with coastal and riverine inflows, alongside their associated tem­ perature, sediment, and ecological signatures. Rigorous model evaluation and effective assimilation of observational data into models require broad compatibility in their respective resolution and accuracy – temporally and spa­ tially across the complete parameter range. The latest models can accurately predict the immediate impact on tidal elevations and currents of specified changes in bathymetry (following dredging or reclamation river flow or bed roughness (linked to surficial sediments or flora and fauna)). Such models can also provide estimates of the

Anthropaogenic impacts: reclamation, dredging, dumping, coastal protection/retreat Waves Eddy viscosity

Flocculationaccelerated settling Turbulence dampingTidal hindered settling currents

Eddy diffusivity

Consolidation Effective bed Bioturbation roughness f(grain size, bed from)

Longer-term changes: mean sea level, storminess

Fluvial supply: river flow, sediment load (regulation)

Small scale, localized, instantaneous processes

Upscaling/parametrization to whole estuary, medium term

Figure 1 Schematic of internal and external factors determining the coastal environment.

Longer-term: local subsidence/ rebound, glacial overdeepening, geology

Measurement Technologies: Measure What, Where, Why, and How?

Coupled models Atmosphere Ocean Coast

Setup data Bathymetry Initial conditions Monitoring network Satellite Ships Buoys, etc.

A s s i m i l a t i o n

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Application

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Figure 2 Components of an operational modeling system.

variations in salinity distributions (ebb to flood, spring to neap tides, and flood to drought river flows), though with a reduced level of accuracy. The further step of predicting longer-term sediment redistributions remains problematic. Broadly, first-order hydrodynamics are now well under­ stood and can be accurately modeled. Hence, research focuses on ‘second-order’ effects, namely higher-order (and residual) tides, vertical, lateral, and high-frequency variability in currents and salinity. Problems concerning the net exchange of contami­ nants and sediments are much less tractable. Nonlinear interactions are important in the mixing and transport of such tracers, and accurate time-averaging requires second-order accuracy in the temporal and spatial distributions of currents, elevations, and density. Numerical simulation of these higher-order effects requires increasingly fine resolution. Higher resolution can provide immediate improvements in the accuracy of simulations. Similarly, adaptable and flexible grids alongside more sophisticated numerical methods can reduce problems of ‘numerical dispersion’. In the horizontal, rectangular grids are widely used, often employing polar coor­ dinates of latitude and longitude (Figure 3). Irregular grids, generally triangular or curvilinear, are used for variable resolu­ tion (Figure 4). The vertical resolution may be adjusted for detailed descriptions – near the bed, near the surface, or at the thermocline. The widely used Sigma coordinate system accommodates bottom following by making the vertical grid size proportional to depth. In computational fluid dynamics, continuously adaptive grids (Figure 5) provide a wide spec­ trum of temporal and spatial resolution especially useful in multiphase processes. In a validation exercise, differences between observations and model output will arise from errors in either. Some of the questions that need to be addressed before a model passes from development to operational status include: • How close must the model output and the test observations be to be useful? • What are the sensitivities to initial conditions at this level of model accuracy? • Do the assimilated data reduce output errors? • How rapidly do the errors build up as forecast period increases? • Do the spatial–temporal patterns of errors give clues to inadequacies in the processes that are simulated?

• What is the optimal set of observations for testing the model? • If the optimal set cannot be afforded, what is the tradeoff in accuracy with the set that can be afforded?

2.16.1.2

Observational Data

Comprehensive observational networks are needed, closely linked to modeling requirements, exploiting synergistic aspects of the complete range of instruments and platforms. Figure 6 shows the spatial and temporal resolution of various monitor­ ing systems. It is convenient to regard observational data in three categories: measurements, observations, and monitoring as described below. Process measurements aim to understand specific detailed mechanics, often with a localized focus over a short period, for example, derivation of an erosion formula for extreme combi­ nations of tides and waves. Upscaling of knowledge from process measurements provides algorithms required in coastal-scale models. Test-bed observations, involving intensive focused measure­ ments in a selected area, aim to describe a wide range of parameters over a wide area over a prolonged period (spanning the major cycles of variability). Thus, year-long measurements of tides, salinity, and sediment distributions throughout an estuary provide an excellent basis for calibrating, assessing, and devel­ oping a numerical modeling program. Test-bed observational programs are needed to assess model developments. Such obser­ vational data sets should be made widely available in complete, consistent, documented, and accessible formats. Monitoring implies permanent recording, such as tide gauges. Careful site selection, continuous maintenance, and sampling frequencies sufficient to resolve significant cycles of variability are essential. Permanent in situ monitoring is likely to be the most expensive component of any observational network, and it is important to optimize such networks in relation to the model­ ing objectives. Observer systems sensitivity experiments can be used to determine the value of the existence or omission of specific components in a new or existing monitoring system. Development of observational technologies has lagged behind model development and, despite recent advances, the range of marine variables that can be accurately measured is severely restricted. A new generation of instrumentation is needed for the validation of species-resolving ecosystem models.

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Figure 3 Example of a structured grid for the Irish Sea.

Modeling is relatively cheap and continues to advance rapidly, while observations are expensive, and technology developments often take decades. Thus, successful applications of models are generally limited by the paucity of resolution in observational data (especially bathymetry) used for setting up, initializing, forcing (meteorological and along model boundaries), assimila­ tion, and validation. This paucity of data is a critical constraint in environmental applications. More and better observational data, extending over longer periods, are essential if modeling accuracy and capabilities are to be enhanced. To define coastal boundary conditions, there is a related requirement for accurate (model) descriptions of the state of adjacent shelf seas. Permanent coastal monitoring networks have been established in coastal seas and estuaries measuring water levels, current profiles, surface winds, waves, tempera­ ture, suspended particulate matter (SPM), salinity, nutrients, etc., using tide gauges, mooring and drifting buoys, gliders, fixed platforms, ferries alongside remote sensing from satel­ lites, radar, and aircraft. Regional monitoring networks are being established via The Global Oceanographic Observing System (GOOS) networks, UNESCO (2003).

Figure 7 provides an historical perspective of how measur­ ing technologies have been developed and used for evolving practical requirements. Tide-gauge recordings constitute the longest time series of rigorous coastal observations. Ironically, while such gauges were generally installed to aid navigation and flood control, the outstanding present-day interest in these recordings concerns relative mean-sea-level rise. Presently, the most urgent challenge is to design monitoring systems to address global climate change issues – both to determine long-term trends and to provide warnings of extreme events. The following sections describe the range of measurement technologies that can be used for such purposes. Variables of interest include water levels, currents, and turbulence associated with tides, surges, waves, temperature and salinity alongside turbidity, sediment transport, and an ever-expanding range of biological and chemical components. The full scope of processes spans across atmosphere–seas– coasts–estuaries, between physics–chemistry–biology–geology– hydrology and extends over seconds to centuries and even millennia. Ecological monitoring presents particular difficulties, for example, in extracting ecological data such as chlorophyll a

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Figure 6 Spatial and temporal resolution of observations and models.

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bathymetry – with some modulation by bed roughness. The spectrum of tidal energy input is effectively constrained within a few tidal constituents and, in mid-latitudes, the lunar M2 constituent is often greater than the sum of all others – provid­ ing a convenient basis for linearization of the dynamical equations in deriving analytical solutions. In this subsection, we will concentrate on sea-level measure­ ments, which, generally, are not concerned with the measurement of surface gravity waves (discussed in the follow­ ing subsection) and should be filtered out of the system. Waves may be appreciable, thereby causing problems for most forms of tide gauge technology. Another factor that needs to be con­ sidered is the effects that the properties of sea water (salinity, temperature, and, hence, density) may have, especially on pressure and acoustic sensors. A full description and discussion can be found in IOC (2006). There are mainly four types of measuring technology in common use:

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Figure 4 Example of a triangular unstructured grid for the Irish Sea.

Figure 5 Adaptive grid for an ICOM simulation of a density current. From NERC Oceans 2025 program http://www.oceans2025.org.

from satellite data in view of dissolved oxygen matter (DOM), yellow substance, and suspended sediments, and is further com­ plicated by reflection from the bed in shallow water. Platforms range from in situ, coastal, vessel-mounted to remote sensing, for example, satellites, aircraft, radar, buoys, floats, moorings, gliders, AUVs, instrumented ferries, and shore-based tide gauges. Sensors use mechanical, electromag­ netic, optical, and acoustic media.

2.16.2 In Situ Measurements 2.16.2.1

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Spatial coverage (points per 100 km2)

Measurement Technologies: Measure What, Where, Why, and How?

Sea Level: Tides and Surges

Tides in shelf seas are almost entirely determined by a combi­ nation of tides along the adjacent ocean boundaries and

• a stilling well and float, in which the filtering of the waves is done through the mechanical design of the well; • pressure systems, in which subsurface pressure is monitored and converted to height based on knowledge of the water density and local acceleration due to gravity – such systems have additional specific application to ocean circulation studies in which pressure differences are more relevant than height differences; • acoustic systems, in which the transit time of a sonic pulse is used to compute distance to the sea surface; and • radar systems, which are similar to acoustic transmission, but use radar frequencies – most systems for measuring sea level have a precision of the order of 1 cm.

2.16.2.1.1

Stilling well

A stilling well gauge is probably the most common of all sea-level recording systems on a worldwide basis. These gauges were at one time employed at every port installation and were the primary technology by which sea-level records were com­ piled, although they are becoming less common. The idea of having a well is that it will still the water, and hence remove the effect of gravity waves and leave only long waves, such as tides and surges (Figure 8). The well usually has a float gauge, which

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Tide gauges

Meteorology

Tides

Storm Surges

In situ telemetry

Temperature Salinity

Waves

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Navigation

Coastal defense

Aircraft Radar Ferries

Satellite

Sediments Algal blooms Primary productivity

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Fish stocks Ecological communities

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Agriculture Defense (marine and terrestrial) Tourism

Sustainable exploitation

Figure 7 Historical development in key processes, end users, and observational technologies.

Float wheel Pen

Clock

Recording drum Pen wire tensioners

Counterweight

Float

Stilling well

Conical inlet Orifice Figure 8 Still well diagram.

in the past used to drive a pen and chart recorder and, more recently, some sort of digital recorder. The well tends to be a vertical concrete tube of 1 m in diameter coated in steel or plastic, with a connection to the sea; the ratio between the size of this connection to the size of the well determines the

characteristics of the physical wave filter (for more information see Noye, 1974a, 1974b, 1974c; IOC, 1985). Data from tradi­ tional pen-recording wells could be digitized to a resolution of typically 1 cm. All sea-level measurements can suffer local ‘dis­ tortion’ by density gradients, wave setup, etc.

Measurement Technologies: Measure What, Where, Why, and How?

2.16.2.1.2

Pressure gauges

Instruments that measure subsurface pressure instead of sea level directly have found widespread use. Knowledge of sea­ water density and gravitational acceleration is required to make the conversion from pressure to sea level. The two most com­ monly used types are the pneumatic bubbler gauges and pressure sensor gauges in which sensors are mounted directly in the sea.

2.16.2.1.2(i) Pneumatic bubbler gauges The pneumatic bubbler tide gauge has been successfully used worldwide for several decades. It replaced many of the float-operated harbor gauges as the primary standard for sea-level measurement in countries, such as the United States and the United Kingdom. Figure 9 shows a typical bubbler system. Air is passed at a metered rate along a small-bore tube to a pressure point fixed underwater well below the lowest expected sea level. The pressure point normally takes the form of a short vertical cylinder with a closed top face and open at the bottom. A small ‘bleed hole’ is drilled about half way down its length and metered air is entered through a connection on the top surface. As air from the tube enters the pressure point, it becomes compressed and pushes the water down inside the chamber until the level of the bleed hole is reached, at which time the air bubbles out through the hole and back to the surface. Provided that the rate of air flow is low and the air supply tube is not unduly long, the pressure of air in the system will equal that of the pressure due to the depth of the seawater above the bleed hole coupled with atmospheric pressure. A pressure-recording instrument connected into this supply tube at the landward end records the changes in water level as changing pressures, according to the law:

High pressure

h¼

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p − pa ρg

where h is the height of sea level above the bleed hole, p is the measured pressure, pa is the atmospheric pressure, and ρ is the seawater density, with g as the gravitational acceleration. It is common to use a sensor operating in the differential mode, sensors being so constructed that the system pressure is opposed by atmospheric pressure. Hence, the resultant pres­ sure experienced by the sensor becomes (p – pa), making the measured pressure directly proportional to the required sea level. It should be noted that accurate knowledge of the density is needed. Pugh (1987) provided estimates of the achievable accuracy (O (1 cm)) for bubbler gauges alongside indications of possible sources of malfunctioning, such as silting. 2.16.2.1.2(ii) Pressure sensor gauges Pressure sensors can be fixed directly in the sea to monitor subsurface pressure in a similar fashion to the bubbler gauge. The sensor is connected by a cable that carries power and signal lines to an onshore control and logging unit. In the sea, the active sensor is usually contained within a copper or titanium housing, with the cable entering through a watertight gland. Material used for the housing is chosen to limit marine growth. The assembly is contained in an outer protective tube to pro­ vide a stable fixture to a sea wall or rock outcrop. Where this is not possible, the pressure sensor may be placed securely on the seabed, but this method has disadvantages, as deployment and maintenance usually require a diving team. Any bed-mounted instrument risks problems with both erosion and siltation. Pressure-based instruments can be operated from batteries for periods of a year or more, as they consume a very small amount of power. This can be advantageous, even where elec­ trical supplies are available. Therefore, they have been used

Low pressure

Pressure reduction valve

Differential pressure transducer Instantaneous pressure Pm

Needle valve for flow rare control

Connecting tube: Length: I Radius: a Volume Vt = πa 2l

Air compressor

Atmospheric pressure PA(ζA)

Gauge elevation H Wave amplitude S

Instantaneous water

Total system volume = Vt + Vb = V Pressure of gas = Pm = atmospheric pressure + pgζi

Gas buffer volume Vb

Horizontal cross-section area A

Figure 9 Bubbler pressure tide gauge.

level ζi Water density P

Gas outlet level gauge datum

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Measurement Technologies: Measure What, Where, Why, and How?

extensively in remote areas, such as oceanic islands and Polar regions. Pressure sensors are available in two varieties that provide either an absolute or a differential signal. If an absolute transducer is employed, the sensor provides a measurement of the total pressure, including sea level and atmosphere. Therefore, a separate barometer is required usually in the form of an identical transducer open to the atmosphere. Both sensors are synchronized to the same clock, so they can readily be subtracted to yield sea level (with subsequent correction for density and acceleration due to gravity). Differential pressure transducers have a vented cable in which the reference side of the transducer is open to the atmosphere. Vented systems are known to suffer from occasional blockage and are used less frequently in hazardous environments. In addition, a record of barometric pressure is valuable for oceanographic studies; hence, the two-transducer option is most frequently employed. Relatively inexpensive pressure sensors use strain gauge or ceramic technology in which changes in water pressure cause changes in resistance or capacitance in the pressure element. The most accurate, but expensive, sensors use a quartz element, the resonant frequency of which varies with the strain applied to it. The resulting signal, which is normally a frequency pro­ portional to the applied pressure, is carried down the signal cable to the control electronics where it is converted into phy­ sical units and can be displayed and stored by a data logger.

2.16.2.1.2(iii) Acoustic gauges A number of acoustic tide gauges have been developed, which depend on measuring the travel time of acoustic pulses reflected vertically from the sea surface. This type of measure­ ment can theoretically be made in the open with the acoustic transducer mounted vertically above the sea surface, but in certain conditions the reflected signals may be lost. To ensure continuous and reliable operation, the sensor may be located

inside a tube that provides some degree of surface stilling and protects the equipment; some sensors even constrain the acous­ tic pulses within a narrow vertical tube, which is contained inside the previous one. This outer tube does not completely filter out wave action but, by averaging a number of measure­ ments, the desired filtering is achieved. The velocity of sound in air varies significantly with temperature and humidity (~0.17%per °C), and some form of compensation is necessary to obtain sufficient accuracy. The simplest method is to measure the air temperature continuously at a point in the air column and use this to calculate the sound velocity. To account for temperature gradients in the air column, temperature sensors may be required at a number of different levels. A more accurate method is by use of an acoustic reflector at a fixed level in the air column. By relating the reflection from the sea surface to that from the fixed reflector, direct compensa­ tion for variation in sound velocity between the acoustic transducer and the fixed reflector can be achieved. Figure 10 shows an example of a tubed acoustic gauge.

2.16.2.1.2(iv) Radar gauges Radar tide gauges have become available during the past few years from several manufacturers. Although this technology is relatively new, radar gauges are being purchased and installed by a number of agencies as replacements for older instruments or for completely new networks. The reason is that they are as easy to operate and maintain as acoustic sensors, without their main disadvantage: their high dependence on the air tempera­ ture. Radar gauges have a relatively low cost and the engineering work necessary to install them is relatively simple compared to other systems. The instruments are supplied with the necessary hardware and software to convert the radar mea­ surements into a sea-level height. In addition, the output signals are often compatible with existing data loggers or can

Tide house Backup data collection platform

Satellite antenna Primary data collection platform Acoustic sensor

Sensor backing board assembly with staff Benchmark

Protective well

Pier

Calibration/sounding tube Instantaneous water level inside well Waves Ambient mean water level Piling

Figure 10 Tubed acoustic tide gauge.

Density stratification Pressure sensor

Current

Measurement Technologies: Measure What, Where, Why, and How?

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Radar gauge

Power and signal cable

Data logger Bubbler gauge Half the radar travel distance

Air pipe

Air compressor and central panel pressure transducer and data logger

Height of water above bubbler outlet Bubbler outlet Figure 11 Diagram of a combined radar and bubbler installation.

be interfaced to a communication network. Like many modern systems, they can be set up using a portable computer. The active part of the gauge is located above the water sur­ face and measures the distance from this point to the air–sea interface (Figure 11). The gauge has to be mounted in such a way that there are no restrictions or reflectors in the path of the radar beam, between the gauge mounting and the sea surface. It has to be positioned above the highest expected sea level and preferably above the highest expected wave height, so as to prevent physical damage. It has many advantages over traditional systems in that it makes a direct measurement of sea level. The effects of density and temperature variations, even in the atmosphere, are unim­ portant. The main constraint is that the power consumption may be relatively large in radar systems, if used on a continuous basis in a rapid sampling mode. Averages are typically taken over periods of minutes. This may limit its use in some applications (e.g., tsunami warning), where observations are required on a continuous high-frequency (e.g., 15 s) basis. In such areas, pres­ sure gauges may be more appropriate, although work and research is still being done concerning this particular application. Radar gauges fall into two categories: those that transmit a continuous frequency and use the phase shift between trans­ mitted and received signal to determine sea-level height (frequency-modulated continuous waves (FMCW)). The OTT Kalesto, Miros, and Radac instruments use this method. The VEGA and SEBA systems use pulsed transmissions and time­ of-flight measurement. All these gauges have undergone initial tests and intercomparisons by various agencies in different countries. Details of these tests can be found in IOC Workshop Report No. 193.

2.16.2.2

Waves

Wave observations are required as a fundamental part of under­ standing wave growth, transformation, dissipation, and interactions with currents, offshore and coastal structures, and the coast (see Chapter 2.10). Observations of waves can be raw time series, or one-dimensional (1D) or two-dimensional (2D)

spectra derived from high-frequency observations. Wave obser­ vations are also critically important for validation of wave models, which can then provide a larger spatial and temporal coverage of wave information as hindcasts and forecasts. Directional wave measurements usually describe basic wave parameters in terms of the wave amplitude, period, and direc­ tion: the wave amplitude measurement most commonly employed is known as the significant wave height, and the peak period provides the dominant frequency of the wave energy. The mean or peak wave direction indicates which way the waves are propagating. There are many techniques for col­ lecting spectral information, which have evolved from relatively simple methods to more sophisticated ones, each of which has advantages and disadvantages. These include deployment of in situ instruments, such as directional wave buoys, arrays of current meters, and pressure sensors. Remote-sensing techniques involving microwave radar systems, aircraft, or satellites are also used to obtain wave parameters. In this section, a brief descrip­ tion of different measuring systems is presented. For further details on the instrumentation and analysis, the reader is referred to Tucker and Pitt (2001), COST Office (2005), Marshall and Bishop (1984), and Gower (1981). A long time series of wave observations is required to reliably describe the wave climate (Wolf et al., 2011). There are only a few long-term wave data sets in UK waters, for example, the Met Office Marine Automatic Weather Station (MAWS) system con­ sisting of various met-ocean recording systems, some of which have been maintained for several decades (Hawkes et al., 2001). Data from the Seven Stones Light Vessel since 1962 led to the earliest observation of an increase in wave height in the North Atlantic in recent years (Bacon and Carter, 1991). This observa­ tion has since been validated and extended using altimeter wave data and models and attributed largely to changes observed in the North Atlantic atmospheric circulation patterns, principally the North Atlantic oscillation (NAO) (Woolf et al., 2002, 2003).

2.16.2.2.1

Wave staffs

Various methods are described by Tucker and Pitt (2001), including resistance-wire and capacitance-wire gauges.

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Measurement Technologies: Measure What, Where, Why, and How?

2.16.2.2.2

Wave buoys

A wave buoy is a surface-following instrument, normally anchored to the seabed by a mooring, which can measure the vertical acceleration or the water surface elevation. An acceler­ ometer mounted within the buoy registers the rate at which the buoy is rising or falling with the waves. The most commonly used instrument for measuring waves in deep water is the ‘heave, pitch, and roll buoy’ that measures the surface height and slope in two orthogonal directions; for example, the Datawell Directional Waverider measures three-component accelerations of the buoy, which are integrated to yield the horizontal and vertical displacements of the buoy. The hull and mooring of this buoy were designed to give it good wave-following characteris­ tics, thereby allowing the buoy displacements to approximate the displacements of an actual water particle at the sea surface. The procedure to convert the raw measurements into wave parameters presently employed by many buoys is based on Fourier analysis, in which the sea surface is described as a sum of sine and cosine waves with different amplitudes, frequencies, and directions. Buoys can also contain an electronic compass and additional accelerometers in order to measure the direc­ tional components of the wave field. Different buoys are currently available with a variety of sizes and instrumentations (e.g., anemometers and current meters). An alternative type of buoy is the spar buoy, which com­ bines the surface-following properties described above with wave staffs, as in the previous section, for example, the air–sea interaction spar (ASIS) buoy. The ASIS buoy is specially designed to produce little surface disturbance, and to be a partial wave follower at low frequencies. The relative location of the interface is measured by an array of capacitance wave staffs, while the motion of the buoy is monitored using linear accelerometers and rate gyros. This combination permits high-resolution measurement of wave directional properties from 10 cm to 600 m.

2.16.2.2.3

Bottom pressure sensors and PUV method

The pressure induced by a wave is in phase with the water surface elevation; therefore, a measurement of pressure pro­ vides information on wave conditions. However, the pressure decays with depth and, thus, pressure sensors can be used to measure waves but only in relatively shallow areas. Pressure measurements combined with current measurement can pro­ vide extra wave information by using the PUV (pressure and orthogonal current components) method, for example, Krogstad (1991) and Wolf (1997). The method uses linear wave theory to compute the depth attenuation and convert velocity and pressure spectra recorded at the depth of the instrument to surface elevation spectra (see Chapter 2.10 for details of linear wave theory and indications of the likely accuracy of the above conversion). The wave direction is esti­ mated by comparing the magnitude of the cross spectra at each spectral frequency and, thus, a wave direction for each fre­ quency is obtained.

2.16.2.2.4

sound is moving relative to the receiver, the frequency of the sound at the receiver is shifted from the transmitted frequency. The measurements are performed into a series of bins along three or more beams and then combined to infer the velocity profile encompassed by these beams. This technology has been used for several years and ADCPs are now routinely deployed around the world. ADCPs are generally applied over long ranges of 10–100 m, for mean velocity measurements where large bin widths are used. The resulting tradeoff between reso­ lution and velocity accuracy statistically requires velocity measurements to be averaged over several minutes. Three methods are available for obtaining the wave spectra from the ADCP: a PUV method using bottom pressure, a sur­ face tracking method, and a wave array method using the near-surface orbital velocities obtained from the separate beams. The latter method provides the most reliable data. The ADCP can profile the water volume all the way to the surface; thus, it can be mounted in much deeper water than a tradi­ tional pressure instrument. From the time series of velocities, the power spectra are calculated. To get a surface height spectrum, the velocity spectrum is translated to surface displa­ cement using linear wave theory. To calculate directional spectra, phase information must be preserved. Each bin beam is considered an independent sensor in an array that makes a measurement of one component of the wave field velocity. The cross spectrum is then calculated between each sensor and every other sensor in the array. The result is a cross-spectral matrix that contains phase information along the path between each sensor and every other sensor at each frequency band, and is linearly related to the directional spectrum at each frequency, thus, by inverting this, the directional spectrum is obtained. ADCPs can also be used to measure the sediment bed-load velocity (see later), although there are problems with measur­ ing suspended sediment concentrations because the signal depends not only on sediment particle density but also on individual particle size and aggregated particle floc size.

Acoustic Doppler current profilers

The cross spectra of a tri-directional current meter can provide the same wave information obtained from buoy measure­ ments. The acoustic Doppler current profilers (ADCPs) measure the velocity of water using the principle of Doppler shift of frequency due to the ambient current. If a source of

2.16.2.2.5

Multisensor arrays

Since 2002, the WaveNet system of nearshore wave buoys has been deployed and maintained for the Environment Agency (EA) and Department for Environment Food and Rural Affairs (DEFRA) by the Centre for Environment, Fisheries & Aquaculture Science (CEFAS) around the coast of England and Wales. One of these wave buoys is in Liverpool Bay, adjacent to the National Oceanography Centre (NOC) Irish Sea Observatory (ISO) Site A mooring near the Liverpool Bar Light in 22-m water depth. This has now provided over seven complete years of directional wave observations. Also as part of the ISO (Howarth et al., 2006), a coastal high-frequency radar system provides a grid of current and wave data over Liverpool Bay, and a wave buoy has been deployed in the mouth of the Dee in the Hilbre Channel. Other sources of wave observations are high-frequency current and pressure data from in situ acous­ tic instruments, such as the Sontek ADV or wave-enabled ADCP measurements at Site A, since 2002, and Site B, since 2005, and other remote sensing techniques, such as X-band radar and satellite altimetry. Figure 12 shows the setup of the UK NOC ISO), from where we have taken examples of wave measure­ ments from wave buoys, ADCPs, and high-frequency radar (Figure 13). For a more extensive description of these available data, see Chapter 2.12.

Measurement Technologies: Measure What, Where, Why, and How?

371

m Ferry ts en em easur

Glider

Afbi mooring me asure

nts

Sate

llite s

ensin

g

me Ferr y

Irish Sea Observatory measurements and location POL Tide gauge CTD stations Moorings HF radar station HF radar coverage

0

30

60 km

Hibre Island marine radar coverage

UK areas draining to Irish Sea Hydrometric area boundaries

Elevation (m)

−5−41 41−64 64−110 110−199 199−370 370−701 701−1342

Figure 12 Schematic of the NOC Irish Sea Observatory.

2.16.2.2.6

Data assimilation

Data assimilation forms the interface between models, obser­ vation, and theory and, thus, is an essential component in simulation systems (see also Chapter 2.17). Section 2.16.4.1 provides a fuller description of the principles of assimilation. Practically, data assimilation usually refers to the use of

available measurements to correct a model’s first prediction in space and time. It allows the optimal combination of model and observations to give improved forecasts and max­ imize the benefit from the observations. Assimilation techniques have been used for atmospheric and ocean model­ ing, including wave properties. Wave data assimilation started

372

Measurement Technologies: Measure What, Where, Why, and How?

(a)

(b)

6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

(c) Hs (m)

4

buoy ADV ADCP

3 2 1 0

(d)

10

buoy ADV ADCP

Tp (s)

8 6 4 2

Wave dir (deg)

(e)

buoy ADV ADCP

150 100 50 0

24 Jan 2003

25

26

27

28

29

30

31

1 Feb

2

3

4

5

6

7

8

9

10

11

Figure 13 Example of wave measurements within the NOC Irish Sea Observatory: (a) and (b) are significant wave height, peak period and direction from the HF radar respectively, and (c), (d), and (e) are comparisons of ADCP, ADV, and wave buoy significant wave height, period, and direction, respectively.

in the 1980s, first with assimilation of integrated wave para­ meters and later for the full wave spectrum. Different research has proved the improvement of model forecast through data assimilation of swell, for example, Komen (1985). Bidlot et al. (2002) showed that assimilation of satellite radar altimetry wave heights reduces the model wave height errors nearly 10–20%. Because of the availability of wave height data from a large number of altimeter satellites, most of early data assim­ ilation focused on wave height data in open ocean wave models. More recently, with the advances of spectral wave observations, full wave spectra have also been used for assim­ ilation. To assimilate data, statistical procedures are used to interpolate and extrapolate data and the satellite data and merge it with the model’s first prediction fields (Lionello et al., 1992). Some common approaches are by means of statistical interpolation, Kalman filters, and Bayesian techni­ ques. Further details on data assimilation can be found in

Lahoz et al. (2010). An attempt to apply data assimilation for high-frequency radar wave data into the SWAN wave model was carried out by Siddons et al. (2009).

2.16.2.3 2.16.2.3.1

Mixing and Circulation Temperature and salinity

Temperature and salinity are usually measured using conductivity–temperature–depth sensors, although tempera­ ture is also measured by itself using thermistors. These are relatively cheap, depending on accuracy and response time. Conductivity, hence salinity, is a more expensive measurement. Salinity is an important core measurement in oceanography and climate science (Schmidt, 2008). With the establishment of the international network of almost 3000 Argo floats and the launch of the Aquarius and Soil Moisture and Ocean Salinity (SMOS), the salinity and temperature of the upper ocean will

Measurement Technologies: Measure What, Where, Why, and How?

be monitored at a spatial and temporal scale as never before (ACT, 2007a). In the coastal ocean and estuaries, measurements of variability in salinity over short time and space scales are fundamental for understanding the dynamics and fluxes of freshwater, sediment, nutrients, and contaminants. Salinity is one of the most important variables to measure in any Integrated Ocean Observing System (IOOS) (Ocean US, 2002). Present commercial instruments measure salinity by measuring conductivity, and the basic difference in these systems is whether they utilize an inductive or a conductive sensor and whether the sensors measure in an internal or an external configuration. For more information on individual commercial instruments, refer to ACT (2007a).

2.16.2.3.2

Doppler shift when a train passes Train approaches– higher pitch

Train recedes– lower pitch

Figure 14 Schematic of the Doppler effect as a train approaches and recedes. From Teledyne RDI broadband primer.

Currents (in situ measurement)

Circulation (currents) in many coastal oceans are dominated by periodic tidal currents, which can reach values of 0(1) m s−1; nevertheless, the residual circulation with slower currents, dri­ ven by either baroclinic processes or frictional and nonlinear processes, can be more important when looking at net fluxes. Therefore, these currents are important when studying the transport of nutrients, contaminants, and other ecologically important variables. Residual currents tend to be of the order of a few mm s−1, with occasional maxima upward of 10–20 mm s−1; hence, there is the need of high accuracy when measuring them. Currents can be measured in Lagrangian mode, utilizing drifters, bottles, etc., and following their paths over time intervals, or in an Eulerian manner – measuring the flow at a fixed point over time intervals. Lagrangian observations are relatively simple to carry out and evolved from the use of drifter buoys to radar tracking to the current satellite-tracking devices used nowadays. Eulerian measurements progressed from the use of rotor and vane instruments to electromagnetic instruments and then to acoustical instruments. In this chap­ ter, we will focus on the use of acoustic Doppler instruments because they are the most common current-measuring devices used at present. 2.16.2.3.2(i) Acoustic Doppler current profilers Acoustic Doppler current meters (velocimeters and profilers) are used to measure currents in the ocean. The idea, as its name indicates, is to use the Doppler shift to calculate velocity. The Doppler shift is the observed change of sound pitch that results from relative motion. An example of the Doppler effect is the changing sound made by a vehicle as it approaches, that is, a car or a train has a higher pitch as it approaches and a lower pitch as it recedes (Figure 14). The change in pitch is proportional to how fast the vehicle is moving (this is the same technique used in speed cameras and speed guns by police agencies). Hence, if we can measure how much the pitch changes, we can also measure how fast the vehicle moves (RDI, 1996). The speed of sound is C ¼F λ

½1

where F the sound frequency and λ the sound wavelength. The Doppler shift, FD, is the difference between the frequencies when there is no relative movement and when either the target or the source, or both, are moving relative to each other:

373

� FD ¼ F

V C

� ½2

where V is the relative velocity between source and receiver. In the case of all the acoustic Doppler profilers and current meters (but not velocimeters), the transducer works as both a trans­ mitter and a receiver, so that the equation changes to � � V ½3 FD ¼ 2F C Therefore, if we know the original frequency and the velocity of sound, we can measure the frequency change and infer the along-sound (beam) velocity. Because we can only measure the along-beam velocity, instruments have three or four trans­ ducers, so that the three velocity components can be measured. The problem of this technique is that each along-beam velocity is measured in a different part of the water; hence, we have to assume that the velocity field is the same within that area (more information can be found in RDI, 1996). 2.16.2.3.2(ii) Broadband and coherent techniques Until now, we have been working with the Doppler efect in terms of frequency, but to understand how broadband (patented by RDI) and the phase-coherent techniques work, we have to discuss signal changes in the time domain – this is termed time dilation. If we send a pulse of sound to a stationary particle, the echo from this pulse of sound will have the same phase difference between it and the transmitted signal as all subsequent pulses and echoes (Figure 15(a)). If the particle moves away from the transmitter, it will take longer for the sound to go back and forth (Figure 15(b)). This change in travel time caused by the extra distance traveled is called propagation delay, or time dilation. Echoes from a particle always look the same when the particle does not move, that is, there is no propagation delay (Figure 15(a)). Echoes have the same relative phase, which means zero phase change. In the case of superimposing a second echo on a particle that is moving away from the transducer, then the second echo will be delayed with respect to the first echo. In the case of the example shown in Figure 15(a), the delayed echo, shown as a dashed line, has a phase delay of 40°. A propagation delay corresponds to a change in distance; therefore, if we measure the delay, and we know the speed of sound, we can estimate how

374

Measurement Technologies: Measure What, Where, Why, and How?

(a)

Time dilation 1 0.5 0

–0.5 –1 (b)

0

200

400

600

800

1000

1200

200

400

600

800

1000

1200

200

400

600

800

1000

1200

1 0.5

0

–0.5

–1

0 (c)

1 0.5 0

–0.5 –1

0

Phase (°) Figure 15 Time dilation diagram: (a) echo of a nonmoving particle; (b) echo of a moving particle with a 40° phase; and (c) echo for a moving particle with a 400° degree phase.

much the particle has moved. Because we know the time lag between pulses and echoes, we can calculate the particle velocities. The main problem of measuring the phase is that phase can only be measured between 0° and 360°; once it reaches 360°, it starts again at 0°; hence, in the case of the example in Figure 15, a change of 40° (Figure 15(b)) or a change of 400° (Figure 15(c)) would be the same; this is called phase ambi­ guity. To solve this problem, we need to determine how many times the phase has passed through 360°; this is called ambi­ guity resolution. Broadband and pulse-coherent instruments use the autocorrelation method to process complicated real-world echoes to obtain velocity. In pulse-coherent processing, two pulses are transmitted into the water. As explained before, the change in phase between the pulse pair is measured so that each pulse pair produces a single velocity estimate and, thus, the pair is defined as a ping. The time between the two pulses determines the maximum velocity detectable and also determines the maximum range of the system. There is no other acoustic method that can produce such high-precision (with accuracies of the order of mm s−1) velocity data with such small cells and rapid sampling (Lohrmann et al., 1995); this technique is used by acoustic Doppler velocimeters (ADVs) and is available in certain Doppler profilers from Teledyne RDI, Nortek, and Sontek. Broadband works by transmitting a series of coded pulses, all in sequence inside a single long pulse; we obtain many echoes from many scatterers, all combined into a single echo. We extract the propagation delay by computing the autocorrelation at the time lag separating the coded pulses.

The success of this computation requires that the different echoes from the coded pulses (all buried inside the same echo) be correlated with one another (RDI, 1996). The advan­ tage of broadband and pulse-coherent modes is that the accuracy of the data is improved enormously in comparison with narrowband (Figure 16; Wilson et al., 1997). 2.16.2.3.2(iii) 3D velocities As mentioned above, each transducer can only measure an along-transducer velocity; therefore, to measure the three com­ ponents of velocity, we will need a minimum of three beams; here, we will use four beams for simplicity, in order to calculate both Earth-coordinate velocities and Reynolds stresses (Figure 17). Also, because the beams will be measuring differ­ ent water columns, we will have to assume that there is horizontal homogeneity, which is reasonable over small spatial scales for most of the time in shelf seas and estuaries. We will explain next how to solve the 3D velocities in a four-beam array. This configuration was chosen for simplicity and because it is the only configuration that can be used to calculate Reynolds stresses using the variance method (Lu and Lueck, 1999; Stacey et al., 1999). The method is based on the fact that an ADCP has two pairs of opposing acoustic beams, and that each beam measures a velocity that is actually a weighted sum of the local horizontal and vertical velocities (Figure 17). The velocities for each beam are given by u1 ¼ v sin θ þ w cos θ; u2 ¼ −v sin θ þ w cos θ

½4

Measurement Technologies: Measure What, Where, Why, and How?

coastal zone (e.g., Joordens et al., 2001). Further discussion on the commercially available undulators can be found in ACT (2007b). Although the above is an efficient use of ship time, it is still an expensive exercise because the ship and full crew have to be present while carrying out the measurements. In recent years, the development of communications, battery technology, and electronics has helped to evolve autonomous vehicles that can make measurements independently. There are primarily two kinds of autonomous vehicles: the AUVs, which are propelled by electric motors and have an endurance of a few days and possess independence between their horizontal and vertical movements, and the glider, which is driven by buoyancy changes. For these vehicles to move horizontally, they have to dive and rise in a saw-tooth manner. The advantage that these vehicles have is that their endurance is of several weeks (Figure 18).

40 30

Mode 7: East cpt

20

cm s–1

10 0

–10 –20 –30 –40 13

14 15 16 17 Time in hours on 21 May 1997

18

Figure 16 Comparison of broadband (dashed) and narrowband (contin­ uous) velocity estimates. From Wilson, T.C., Lwiza, K.M.M., Allen, G.L., 1997. Performance comparison of RDI ADCPS: broadband versus narrow­ band. Oceans ′97 MTS/IEEE Conference Proceedings, vol. 1, pp. 120–125.

u3 ¼ u sin θ þ w cos θ; u4 ¼ −u sin θ þ w cos θ

½5

where θ is the angle of the acoustic beam from the vertical (20° in this case) and u, v, and w are the horizontal and vertical velocity components. So that u¼

u 3 − u4 ; 2 sin θ

v¼

u 1 − u2 ; 2 sin θ

w¼

u 1 þ u2 u3 þ u4 ¼ 2 cos θ 2 cos θ

½6

One of the advantages of using ADCPs is that these instruments can be mounted on moving platforms, and the tidal compo­ nents can be removed by using either simple tidal analysis (Lwiza et al., 1991) or more complex polynomial functions (Candela et al., 1992; Carrillo et al., 2001). As discussed above, to achieve a quasi-3D picture of the water-column structure and stability, the CTDs, ADCPs, and a suite of ancillary instruments have been mounted in roving instruments that can profile the water column as they move horizontally. The most primitive examples of this are the undulators pioneered by the Guildline SeaBat. These instru­ ments are connected to the ship via a conductive cable, which transmits instructions, data, and power. The water column is profiled as the ship moves and data are transmitted in real time, achieving a horizontal resolution of nearly 200 m in the

2.16.2.3.3

Turbulence measurements

The influence of turbulence on the dynamics of currents and waves and their interaction with near-bed processes has been acknowledged to be of great importance, although turbulence itself remains poorly understood. Development of turbulence models is supported by the development of measuring techniques, such as the microstructure profiler and acoustic instruments that provide a direct comparison with simulated energy dissipation rates. 2.16.2.3.3(i) Microstructure shear probes The development of velocity microstructure profilers began in the 1970s (e.g., Osborn, 1974); this development followed through to the 1980s and shear probes became a more routine instrument of observation in the 1990s. At present, there are several commer­ cial brands of shear profilers; for a historical review see Lueck et al. (2002). Microstructure shear probes have a radially symmetric airfoil faced in the profiling direction. The idea is that the probe experiences the transverse component of velocity as a lifting force at the airfoil, while being insensitive to the axial forces. A piezoelectric beam is connected to the airfoil, which senses the lift force and produces a current voltage that is proportional to the instantaneous cross-stream component of velocity (Figure 19) (Prandke et al., 2000). Shear profilers are able to profile between 5 m below the sur­ face (this is to exclude the ship’s turbulent wake) and nearly 15 cm from the seabed. The profilers usually free fall at a speed of around 0.65–0.8 m s−1 and are tethered to the ship, although some rising

y

z

ϕ3

ϕ1

v3 w3

3 1 4 Figure 17 ADCP geometry.

b3

ϕ2

2

375

x

y 4

θ

3

376

Measurement Technologies: Measure What, Where, Why, and How?

To

To

2.15.2.3.3(ii) Estimation of turbulence using ADCPs In the quest to measure and calculate turbulence parameters, the use of ADCPs has become common practice in recent years (Stacey et al., 1999; Rippeth et al., 2002; Souza et al., 2004). The main turbulence parameters are

e llit te

e llit te

sa

sa

• • • •

Figure 18 The University of Washington sea glider diagram.

versions exist. The rate of dissipation of turbulent kinetic energy (TKE), ε, is estimated from the measured vertical shear � ε ¼ 7:5 μ

∂u ∂z

�2 ½7

The mean shear square is calculated by deriving the power spectrum for each section of the record (1 m), which allows the elimination of high-frequency noise and the application of spectral correction for rolloff in the shear probe response and correction for the Nasmyth spectrum (Simpson et al., 1996). This method is based on the assumption that the turbulence is stationary within each interval (Dewey et al., 1987).

Reynolds stresses; TKE production (P); eddy viscosity (Nz); and TKE dissipation (ε).

Great effort has been put into studying the theoretical errors of the ADCP method (Williams and Simpson, 2004) and to achieve a clear validation by comparing ADCP Reynolds stres­ ses with estimates from other instrumentation (Howarth and Souza, 2005; Souza and Howarth, 2005). We can now have some confidence in the TKE production estimates derived from ADCPs. More recently, Wiles et al. (2006) have been using the structure function method to estimate TKE dissipation, with promising results, although the dissipation values appear to be slightly underestimated. The advantages of the ADCP are its simplicity and versati­ lity. The instrument is simple to set and use and will allow the user to have full water-column estimates of P, ε, and SPM; if it is set up properly, it will also provide directional wave spectra. Estimates of SPM concentrations are dependent on a user’s knowledge of particle sizes or floc sizes, or on calibrations derived from gravimetric samples. The ADCP can also measure the velocity of the sediment bedload (see later). To calculate the Reynolds stresses, we need to separate the velocities into mean and fluctuating quantities. Writing eqn [6] for the fluctuating quantities, then the ensemble means of the velocity products are

〈u0 w0 〉¼

〈u0 23 〉−〈u0 24 〉 4 sin θ cos θ

and〈v0 w0 〉¼

〈u0 21 〉−〈u0 22 〉 4 sin θ cos θ

½8

where the angle brackets represent the temporal means and the primes indicate temporal fluctuations. 〈u′w′〉 and 〈v′w′〉 are the Reynolds stress per unit density. The rate of production of TKE (P), in W m−3 is estimated from the product of the Reynolds stresses and the mean velocity shear according to � � ∂u ∂v þ〈v0 w0 〉 P ¼ −ρ 〈u0 w0 〉 ∂z ∂z

½9

where ρ is the water density and z is the vertical coordinate. Using this method, we can also estimate the value of Nz as τx τy ∂u ∂v ¼ Nz ¼ Nz ¼〈u0 w0 〉and ¼〈v0 w0 〉 ρ ∂z ρ ∂z

Figure 19 Shear probes.

½10

where τx and τy are the eastward and northward components of stress. The method above is also referred to as the ‘variance method’. For a more detailed explanation of the variance method, see Stacey et al. (1999) and Rippeth et al. (2002). To estimate the TKE dissipation rate, we use the structure function method. The method was first developed by meteor­ ologists to estimate ε from radar measurements (Lhermitte, 1968) and has been applied to the marine environment, with promising results, by Wiles et al. (2006). The method is based on the second-order structure function D(z, r), defined as

Measurement Technologies: Measure What, Where, Why, and How? Dðz; rÞ ¼〈½v0 ðzÞ− v0 ðz þ rÞ

2

〉

½11

D(z, r) is the mean square of the along-beam velocity fluctuation (v′) difference between two points separated by a distance r. If we use the Taylor cascade theory to relate length scales and velocity scales to isotropic eddies, then (Wiles et al., 2006) Dðz; rÞ ¼ Cv2 ε2=3 r2=3

½12

Cv2

is a constant with a value between 2 and 2.2 for atmo­ where spheric studies; we will use this assumption for marine studies, although it appears to underestimate the dissipation values. 2.16.2.3.3(iii) Example of estimating turbulence The variance method has been clearly validated by Howarth and Souza (2005) and Souza and Howarth (2005) and has become a very popular technique to estimate turbulence characteristics in shelf seas and estuaries. There are now numerous

377

examples of the use of ADCPs to estimate Reynolds stresses and turbulence production. We will show here an example from the Gulf of California, Mexico, which is arguably one of the best examples available. The data from the Gulf of California are plotted in Figure 20 with ε (a), P (b), and SPM (c). As expected, the highest rate of TKE production is found near the bed (Figure 20(b)) with values decreasing about an order of magnitude between the bottom and 12 mab (metres above bed). The near-bed (∼1.5 mab) maximum P is of the order of 10−2 W m−3 during both ebb and flood with minimum values at slack water of the order of 10−4 W m−3. The bottom production P shows a quarter-diurnal periodicity because it is dependent on the current speed, that is, there are two peaks of current speed per semi-diurnal tidal cycle. There is an apparent asymmetry between flood and ebb, with higher values of P and greater extension up into the water column during ebb.

(a) 20

0

18 16

−1

14 12 10 8

6

4

2

147 147.2 147.4 147.6 147.8 148 148.2 148.4 148.6 148.8 149

−2 −3 −4 −5

(b) 20

0

18 16

−1

14 12 10 8

6

4

2

147 147.2 147.4 147.6 147.8 148 148.2 148.4 148.6 148.8 149

−2 −3 −4 −5

(c) 20

0

18 16

−1

14 12 10 8

6

4

2

147 147.2 147.4 147.6 147.8 148 148.2 148.4 148.6 148.8 149

−2 −3 −4 −5

Figure 20 Example ADCP estimates of (a) TKE dissipation; (b) TKE production (both in log (kWm−3); and (c) suspended sediment concentration (g m−3).

378

Measurement Technologies: Measure What, Where, Why, and How?

Estimates of Nz were calculated from hourly averages of Reynolds stresses and shear. These estimates of Nz show varia­ bility between 10−3 and 10−2 m2 s−1 in the bottom half of the water column, with maximum values around peak flow and low values around slack water. The mean Nz shows a typical profile, with values slightly increasing from the bottom (3  10−3 m2 s−1) to about 3.8  10−3 m2 s−1 at about 3 mab, followed by a continuous decrease to near zero at 12 mab (see Souza et al., 2004). The techniques used to estimate turbulence parameters explained above are becoming common practice with ADCPs. However, nowadays the standard way to carry out turbulence measurements is using an ADV (R). The ADV (R) follows a direct method of measuring the Reynolds stresses that involves rapid sampling of the three components of velocity in a small sampling volume, so that terms of the type 〈u‘w’〉 can be calculated directly from the covariance of u and w. This approach was pioneered by Bowden and Fairbairn (1956) using electromagnetic current meters. With this method, we can derive not only Reynolds stresses, but also TKE from direct estimates of the variances of u’, v’, and w’: K¼

� 1� 02 02 02 〈 u 〉 þ 〈 v 〉 þ 〈 w 〉 2

½13

In recent years, the use of electromagnetic current meters (ECMs) has been superseded by ADVs, which are simpler to use and can sample smaller water volumes, depending on frequency and manufacturer. The ADVs used in these measure­ ments were Sontek 5 MHz Ocean instruments, which have a volume sample of nearly 2 cm3 at sampling rates of about 25 Hz. These instruments are highly accurate (long-term error

of the order of 3  10−6 m2 s−2) and have become the standard for boundary studies in laboratory and field experiments. The advantage of having direct estimates of both TKE and Reynolds stresses is that it is possible to explore relationships between them. It has been considered that Reynolds stresses are proportional to TKE, with a proportionality constant of 0.19. Figure 21 shows the plot of Reynolds stresses against 0.19 TKE, with the continuous line being the 1 to 1 value; the data do not fall on this line, but are very close, with the actual value being about 0.21. The advantage of using ADVs is that we can use the inertial dissipation method to calculate TKE dissipation rate. This method was first used in meteorology and was originally described by Deacon (1959) and reviewed more recently by Huntley (1988). The inertial dissipation method involves the use of spectra of turbulent fluctuations and is based on the assumption that the wave numbers at which turbulence is produced are well separated from the wave numbers at which the TKE is dissipated by viscosity. This range of wave numbers between production and dissipation is known as the inertial subrange. In this range, the flux of energy from high to low wave numbers must be equal to the dissipation rate, if we assume that there are no local sources or sinks of TKE. Following Tennekes and Lumley (1972), the 3D inertial spec­ trum must be given by EðkÞ ¼ αε2=3 k − 5=3

where k is the wave number and α is the 3D Kolmogorov constant. In practice, we do not have estimates of the 3D wave number spectrum, but 1D spectra that are functions of the along-flow wave number, k1. These 1D spectra φii(k1),

Holyhead Jun–July 0.01 0.009 0.008

0.19TKE (m2 s–2)

0.007 R 2 = 82.7%

0.006 0.005 0.004 0.003 0.002 0.001 0

0

½14

0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 ⎮Reynolds stresses (m2 s–2)⎮

Figure 21 Comparison between Reynolds stresses and turbulent kinetic energy (TKE).

Measurement Technologies: Measure What, Where, Why, and How? where I = 1,2, and 3 are the longitudinal, transverse, and vertical turbulence fluctuations, so that: −5=3

iiðk1 Þ ¼ αi ε2=3 k 1

½15

which is similar to the previous equation, but with a different Kolmogorov constant. In practice, we will tend to use the vertical flow spectra, as we expect that it will be less contami­ nated by wave motion in the inertial subrange (Grant et al., 1984; Huntley, 1988). Because we usually collect data as a time-series of turbulence, we have to use Taylor’s frozen turbu­ lence concept to relate the frequency spectrum to the wave-number spectrum, as follows: iiðkÞ ¼ iiðf Þ2π= 〈u〉

½16

This technique has recently been extended by Lorke and Wüest (2005) for use with ADCPs.

2.16.2.4

Sedimentation

The predominant influences on sediment regimes in estu­ aries (Figure 22) are tidal and storm currents, enhanced in exposed shallow water by wave stirring and pumping (e.g., Lambrechts et al., 2010). Detailed accounts of the mechanics of sediment motion associated with tidal cur­ rents and waves can be found in Van Rijn (1993) and Soulsby (1997). Postma (1967) described the general fea­ tures of the erosion, deposition, and intervening transport of SPM in tidal regimes. For all but the coarsest grain sediment, several cycles of ebb and flood movement may occur between erosion and subsequent deposition. Hence, deposition can occur over a wide region beyond the source. Since time in suspension increases for finer, slowly settling material, such mechanisms may contribute to a residue of fine materials on tidal flats and to the trapping of coarser material in deeper channels. Understanding and predicting concentrations of SPM in estuaries are important because of their impact on • light attenuation and thereby primary production; • pathways for adsorbed contaminants; • rates of accretion and erosion and associated bathymetric evolution; and • smothering and even burying of the benthos.

Eddy

diffusivity

Eddy

viscosity

Tidal currents

Flocculationaccelerated settling Turbulence dampinghindered settling

Consolidation Effective bed Bioturbation roughness f(grain size, bed form) Figure 22 Influences on sediment transport processes.

2.16.2.4.1

379

Optical Backscatter Point Sensor

The optical backscatter point sensor (OBS) is an optical sensor for measuring turbidity and suspended solids concentrations by detecting IR light scattered from suspended matter (see Figure 23). The response of the OBS sensors strongly depends on the size, composition, and shape of the suspended particles and flocs (see Figure 23). Battisto et al. (1999) showed that the OBS response to clay of 2 mm is 50 times greater than to sand of 100 mm of the same concentration. Hence, each sensor has to be calibrated using sediment from the site of interest (see Figure 23). The measurement range for sand par­ ticles (in water free of silt and mud) is about 1–100 kg−3 . The sampling frequency generally is 2 Hz. The OBS sensors consist of a high-intensity infrared emit­ ting diode (IRED), a detector (four photodiodes), and a linear, solid-state temperature transducer (Downing et al., 1981). The (optical backscatter) sensor measures IR radiation scattered by particles in the water at angles ranging from 140° to 165°. IR radiation from the sensor is strongly attenuated in clear water – more than 98% after traveling just 0.2 m (D and A Instruments, 1989). Therefore, even bright sunlight does not interfere with measurements made in shallow water. The performance of the OBS sensor is claimed to be superior to most other in situ turbidity sensors because of its small size and sample volume, linear response and wide dynamic range, insensitivity to bubbles and phytoplankton, ambient light rejection, low temperature coefficient, and low cost. OBS sen­ sors are about the same size (or larger) than the length scales of gradients in the sand concentrations being measured. This may cause hydrodynamic noise in the output signal because of turbulent flow around the sensor that redistributes the particles in the water and increases the variation of sediment concentra­ tion above natural levels. Furthermore, the volume sampled by the OBS sensors depends on how far the IR beam penetrates into the water. This decreases as sediment concentration increases and, therefore, the sample volume is constantly vary­ ing with concentration, which may also cause random noise in the output signal. From limited tests performed by the manu­ facturer, it appears unlikely that random noise would exceed 30% of the mean signal in situations with high concentrations of coarse sediment. The manufacturer recommends post-processing the data with a low-pass filter to reduce ran­ dom noise in the output signal. Experiments have shown that the OBS sensor gain varies with particle size. From mud (< 4 μm) to coarse sand (>200 μm), the gain decreases by a factor of  10. Hatcher et al. (2000) have used OBS sensors measuring at wavelengths of 442, 470, 510, 589, 620, and 671 nm with source beams originating from color light emitting diodes (LEDs) (six-channel OBS; multispectral OBS), which can be used to measure concentrations of sediment mixtures (multiple grain sizes). This makes it possible to measure spectral responses of suspended particle concentrations across the opti­ cal range of wavelengths. Using the differential response of the backscatter coefficient of the suspended constituents at six wavelengths, an accurate estimation of concentration of mixtures can be obtained. This method is based on the simultaneous solution of linear equa­ tions that relate output of optical backscatter sensors to concentrations of various constituents of suspended sediments (see Green and Boon, 1993). The basic requirements are:

(a)

(b)

60,000

20 cm minimum

Beach sand (mg l–1)

48,000

Beach sand 36,000 24,000 12,000 0 0

20

40 60 Output (% of range)

80

100

80

100

80

100

Amazon River (mg l–1)

4,000

50°

2.7 cm minimum

3,200

Amazon River mud 2,400 1,600 800

30° 0

Sea bed

0

Beam axis

20

40 60 Output (% of range)

50% 1/2 power points

100%

Formazin River (NTU)

1,500 1,200

Formazin standard 900 600 300 0 0

20

40

60

Output (% of range)

Figure 23

(a) OBS schematic; (b) OBS calibration examples.

Measurement Technologies: Measure What, Where, Why, and How?

(1) linear sensor response to concentration of a particular sedi­ ment size, (2) different sensor response to different sediment sizes, and (3) negligible grain shielding and multiple scattering.

2.16.2.4.2 ADCP)

Acoustic backscatter profiling sensors (ABS and

Acoustic backscatter (ABS) measurement is a nonintrusive tech­ nique for the monitoring of suspended sediment particles in the water column and seabed characteristics. An ABS instru­ mentation package comprises acoustic sensors, data acquisition, storage and control electronics, and data extraction and reduction software. An overview of the ABS technique is given by Smerdon et al. (2004). Hereafter, a summary of this is given. The basic principle of the acoustic backscatter approach is as follows. A short pulse (10 μs) of acoustic energy is emitted by a sonar transducer (1–5 MHz). As the sound pulse spreads away from the transducer, it insonifies any suspended material in the water column. This scatters the sound energy, reflecting some of it back toward the sonar transducer, which also acts as a sound receptor (Figure 24). Knowing the speed of sound in water, the scattering strength of the suspended material, and the sound propagation characteristics, a relationship may be developed between the intensity of the received echoes and the characteristics of the suspended material. With typical acoustic ranges in excess of 1 m, the acoustic head remains outside the area of study and therefore makes the instrument nonintrusive. The magnitude of the backscattered signal can be related to the sediment concentration, particle size, and the time delay between transmission and reception. The ABS intensity from a uniform field of particles of constant concentration is assumed to be an inverse function of the distance from the source with corrections for attenuation due to water and particles.

Calibration in uniform suspensions is required to find this relationship (e.g., Figure 20). The theoretical background of the acoustics method is described in detail by Thorne and Hanes (2002). The sensor comprises acoustic transducers that emit pulses of sound, which are incident on the seabed. They receive sound reflected by the seabed and suspended sediment in the inter­ vening water mass. The instrument records the amplitude of reflected sound at gated intervals, thus building a reflected sound profile. With low angles of incidence, the technique may be used to monitor the formation and progress of seabed ripples. Perpendicular incidence angles will yield information on sediment suspension between the sensor head and the seabed, and on the erosion or accretion of the bed level. The vertical resolution is limited by the length of the acoustic pulse and by the speed at which the signal is digitized and recorded. A vertical resolution of about 1 cm is feasible. Temporal resolu­ tion depends on the pulse repetition rate and on the number of pulses, which must be averaged to produce statistically meaningful backscatter profiles. To resolve particle size distri­ butions from sediment concentration would require multiple-frequency devices with impractically high upper fre­ quency limits. The acoustic method is most appropriate for particle size distributions of the order of tens to hundreds of microns (say 10–500 µm). Usually, a multifrequency acoustic instrument (ABS) is used to determine the sand concentrations in the near-bed region (lowest 1 m of the water column). Experimental and theoretical work by Thorne et al. (1995) has addressed the problem of sound attenuation due to sus­ pended sediment by measuring the signal strength of the bottom echo. Hay and Sheng (1992) carried out an analysis of three-frequency ABS signals in which they developed a pro­ cedure to extract not only suspended sediment concentration,

F1 F2 F3

1−2 m above bed

T/R

Scattering and attenuation by randomly distributed irregularly shaped particles

Pulse of sound

Bedforms Monitor bed levels

Figure 24 ABS schematic.

381

382

Measurement Technologies: Measure What, Where, Why, and How?

but also particle size distribution with encouraging results. By simultaneously measuring both parameters, it is now possible, in principle, to estimate directly the vertical mass flux by settling.

2.16.2.4.3

obtained by multiplying the area in each size bin by its mean diameter. A full summary on the sediment transport techniques can be found in Tables 1–3.

Laser in situ scattering transmissometer

The laser in situ scattering transmissometer (LISST) is used to measure the concentration and, in the case of the LISST-100, also the size distribution of SPM in estuaries and the coastal ocean. Agrawal and Pottsmith (2000) first presented the LISST-100, which has since been used in numerous studies of noncohesive and cohesive SPM (Agrawal and Traykovski, 2001; Mikkelsen and Pejrup. 2001; Fugate and Friedrichs, 2003; Ellis et al., 2004; Mikkelsen et al., 2005; Jago et al., 2006; Winter et al., 2007). A schematic diagram of the instrument’s optics, which is based on the principle of diffraction of a light source due to scatterers, is presented in Figure 25. A collimated 670-nm laser beam, 6 mm in diameter, illu­ minates suspended particles in the sample volume of path length 5 cm. Light, which is forward-scattered over small angles, is collected by the receiving lens and focused onto the circular ring detector. The detector consists of 32 logarithmi­ cally spaced concentric rings, which measure the diffracted energy over a range of angles from 0.05° to 5°. At the center of the ring detector, a hole allows a photodiode to measure the transmitted power of the laser, hence attenuation over the beam path. Light scattered due to small particles is measured by the outer rings, while larger particles have a smaller scatter­ ing angle. The exact relationship between the intensity, scattering angle, and particle size is computed using the Mie theory for spherical particles, which is used to calculate a kernel matrix, K. This matrix relates the 32-element scattering power, E, to the area distribution of the size class, NA, via E ¼ τC½KNA þ zscat

½17

where τ is the measured beam attenuation relative to particle-free water, C is an instrument calibration constant, and zscat is the background light E observed in particle-free water. The required matrix inversion is performed by the instru­ ment manufacturer and the volumetric concentration is

2.16.3 Remote Sensing Remote sensing techniques have matured to provide useful descriptions of ocean winds, waves, temperature, ice condi­ tions, suspended sediments, chlorophyll, and eddy and frontal locations. Unfortunately, these techniques provide only sea-surface values and in situ observations are necessary both for acquisition of vertical profiles and to correct for atmo­ spheric distortions in calibration (Table 4).

2.16.3.1

Satellite and Aircraft

2.16.3.1.1

Sea-surface elevation

Information on sea-surface elevation is important for predict­ ing tides and storm surges. This can be obtained from radar altimetry, but since the instrument measures distance from sea level to the satellite, detailed information on the satellite orbit is required to convert sea level to fixed co-ordinates. Such information can be obtained for the purposes of tidal analysis, but is not generally available soon enough in order for nearreal-time assimilation into storm-surge prediction models. In the absence of a sufficiently accurate geoid model, altimetry can, so far, only provide information of the variable part of the topography due to ocean dynamics, but this variability can be related to the eddy kinetic energy of the surface circulation (Samuel et al., 1994). In order to resolve mesoscale features at high latitudes, the altimeter ground track should have a cross-track spacing of the order of a few tens of kilometers and a repeat period of a few days. This should be possible using data from two radar altimeter satellites flying simulta­ neously, such as European remote sensing satellite (ERS)-l/2 and TOPEX/Poseidon. In addition, for coastal applications, improvements in the antenna-tracking mechanism are neces­ sary to prevent loss of data when the ground track crosses over from land to sea.

Ring detector Laser and collimating optics

Transmitted power sensor Sample volume

Figure 25 LISST system schematic.

Receive lens

Measurement Technologies: Measure What, Where, Why, and How?

Table 1

Table 2

383

Characteristics of bed-load sampling methods in coastal seas

Type of sediment

Type of method

Type of sampling

Accuracy

Sand

Bed-form tracking of ripples using acoustic profilers (side scan sonar)

St5 and alone

Factor 2 (in deep water with nonbreaking waves); factor 2–3 in surf zone

Characteristics of suspended load sampling methods in coastal seas

Type of sediment

Type of method

Type of sampling

Accuracy

Mud/Silt

Optical (OBS)

Factor 2–3 (with no in situ calibration Samples available)

Sand

Mechanical trap-type sampler (streamer traps) Pump sampler Acoustic (ASTM, ABS)

Online Stand-alone Online Online Online Stand-alone

50% (only usable for longshore transport from pier or platform) Factor 2–3 (with no in situ calibration samples available)

Factor 2–3 (only usable in shallow surf Zone < 1 m)

Lueck, R.G., Wolk, F., Yamazaki, H., 2002. Oceanic velocity microstructure measurements in the 20th century. Journal of Oceanography 58, 153–174. Williams, E., Simpson, J.H., 2004. Uncertainties in estimates of Reynolds stress and TKE production rate using the variance method. Journal of Atmospheric and Oceanic Technology 21, 347–357.

Table 3 Type of sediment Mud/Silt

Sand

Characteristics of sampling methods for particle size and fall velocity in coastal seas

Type of method

Type of sampling

Accuracy

In situ video camera (size and settling velocity; mainly flocs) In situ laser-diffraction (particle size incl. flocs) In situ laser-reflectance (particle size; not for flocs) In situl Laser-diffraction (particle size)

Online

50% (under-sampling of very fine sediments; lower limit of nearly 20 μm) VIS, INSSEV (outside surf zone) 20–30% (lower limit of about 5 μm and upper limit of nearly 500 μm) LISST-100and LISST-25 (outside surf zone) 20–30% (lower limit of nearly 5 μm and upper limit of nearly 500 μm) PARTEC (outside surf zone) 20–30% (lower limit of nearly 5 μm and upper limit of nearly 500 μm) LISST-100, LISST-25 (outside surf zone) 20–30% (lower limit of nearly 5 μm and upper limit of nearly 500 μm) PARTEC (outside surf zone)

In situ laser-reflectance (particle size)

2.16.3.1.2

Online stand-alone Online stand-alone Online stand-alone Online stand-alone

Surface currents

Currents can be identified in thermal IR images through gradi­ ents in sea-surface temperature (SST) and ocean pigment distributions due to differences between water masses of differ­ ent origin (Figure 26). In synthetic aperture radar (SAR) images, current features are mapped because of changes in surface rough­ ness across fronts (Figure 27). In waters near the coast, land-based Doppler high-frequency radar has proven to be a suitable system that can provide quantitative measurements of surface currents, for example, Figure 28 (Crombie, 1955; Lipa and Barrick, 1986; Andersen and Smith 1989; Prandle, 1991; Shay et al., 1993; see also Chapter 2.12). Large-scale monitoring studies have suggested that it is possible to observe currents using over-the-horizon radar, which utilizes ionospheric reflec­ tions; an example of this is shown in Figure 29, where surface

currents from Hurricane Hortense are mapped using this tech­ nique (Harlan and Georges, 1997). SAR (Figure 27) is able to image the surface expressions of features such as eddies, meanders, fronts, and jets, thereby providing qualitative information on their structure and evolu­ tion (Johannessen et al., 1991, 1994; Lyzenga, 1991). Products that may be considered for operational use include manually interpreted images and geographical coordinates of the relevant observed features. Even though SAR is capable of ‘seeing’ both through clouds and the absence of daylight, it is unable to image circulation features at very low or high wind speeds, and its capability can also be degraded in the presence of heavy rain. Reflecting water masses of various origins, these circulation features frequently also have an expression in the surface

384

Measurement Technologies: Measure What, Where, Why, and How?

Geophysical and oceanographic features and processes observed by remote sensing techniques

Table 4

Satellite surface remote sensing monitoring by Visible near IR

Thermal IR

Passive MW

SAR

RA

Scatt.

I. Geophysical Variables and features Temperature fronts Current fronts Mesoscale eddies Upwelling Wind fronts Wind speed Wind direction Surface waves Internal waves II. Water quality Algae blooms Surfactants Oil spills Turbidity & sediments III. Sea ice paraments Ice concentration Ice types Ice motion Ice edge

Modified from Johannessen, J.A., Røed, L.P., Johannessen, O.M., Evensen, G., Hackett, B., Petterson, L.H., Haugan, P.M., Sandven, S., Shuchman, R., 1993. Monitoring and modeling of the marine coastal environment. Photogrammetric Engineering and Remote Sensing 59 (3), 351–361.

(a)

(b)

(c)

Figure 26 Satellite images from the Irish Sea: (a) temperature; (b) chlorophyll; and (c) visible band 551 nm. Courtesy of NEODAAS via the NOC Coastal observatory.

Measurement Technologies: Measure What, Where, Why, and How?

385

Figure 27 SAR image provided by NASA Visible Earth (http://visibleearth.nasa.gov/).

400 W

336 W

312 W

5400 N

m/s 5336 N

400 W 5400 N

5400 N

5336N

5336N

400 W

336 W

312 W

400 W

336 W

312 W 5400 N

5400 N

5336 N

5336 N

5336 N

5336 N

current speed and direction: 18:00 06/01/2011

336 W

400 W

336 W

312 W

400 W

336 W

312 W 5400 N

5400 N

m/s

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

400 W

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

current speed and direction: 15:00 06/01/2011

current speed and direction: 12:00 06/01/2011

m/s

312 W 5400 N

m/s

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

336 W

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

5336 N

current speed and direction: 21:00 06/01/2011

312 W

Figure 28 HF 3-h currents at the NOC Irish observatory derived from HF radar.

400 W

336 W

312 W

386

Measurement Technologies: Measure What, Where, Why, and How?

26° N 13/0000 25° N

24° N

23° N

22° N

21° N

11/1200 −1

1.0 m s

75° W

74° W

73° W

72° W

71° W

70° W

69° W

20° N 68° W

Figure 29 Radar-derived surface current vectors superimposed onto the track of hurricane Hortense. The black squares are the eye of the storm locations, 6 h apart; the white square is the position of the eye of the storm at the time of the image.From Harlan, J.A., Georges, T.M., 1997. Observations of Hurricane Hortense with two over the horizon radars. Geophysical Research Letters 24 (24), 3241–3244.

temperature and ocean color field. Hence, they may be detect­ able by visible and IR radiometers under cloud-free and/or daylight conditions. Thus, products combining information from these different types of sensors will be useful under varied environmental conditions and, therefore, will be more suited for operational use. Although circulation features can be imaged by SAR, it is not generally possible to make a good quantitative estimate of the magnitudes of the currents involved, although there are a number of numerical models that aim to predict the radar backscatter variations produced in association with various types of surface current pattern, oceanic fronts, internal waves, and so on. Interferometric SAR analysis is able, under suitable circumstances, to give direct quantitative measurements of sur­ face currents (Shemer et al., 1993; Moller et al., 1998), but is, at present, only deployed on aircraft for marine applications (Figure 30). The use of satellite radar altimetry to determine geostrophic currents by means of measuring sea-surface slope is possible for the determination of large-scale, time-variant current fields (but breaks down near the equator). The variance in the eleva­ tion gradients can be used to obtain an estimate of the eddy

kinetic energy of the circulation, which is a useful parameter with which to quantify the mesoscale eddies.

2.16.3.1.3

Waves

The significant wave height (Hs) can be determined using satellite altimeter measurements (Rufenach and Alpers, 1978; Bauer et al., 1992; Guillaume and Mognard, 1992), and such measurements have been used to validate numerical wave forecasting models (Wu et al., 1994). The use of ERS and TOPEX/Poseidon altimeter data for Hs in conjunction with ERS scatterometer data for wind produces encouraging improvements in wave forecast model predictions (Le Meur et al., 1996). Present operational products include assimilation of satellite altimeter-derived Hs into an operational regional wave forecasting model (Breivik et al., 1996); significant improvements in the wave analysis and short-term forecasts for the North Sea were found. Another significant remote-sensing data type for wave observation is the radar altimeter (ERS-1/2, TOPEX/Poseidon, Seasat, and GEOS-3), which can provide climatological wave­ height- as well as wind-speed information (Lasnier et al., 1996; Paci and Campbell, 1996). The along-track resolution for the

36°42′ N

1

3 4 5 2

6 7 8 9 10

25 cm s 36°41.6′ N 121°53′ W

11

15

−1

121°48′ W

Figure 30 Comparison between drifter-derived currents (solid) and interferometric SAR (dashed). From Shemer, L., Marom, M., Markman, D., 1993. Estimates of currents in the nearshore ocean region using interferometric synthetic aperture Radar. Journal of Geophysical Research 98 (C4), 7001–7010.

Measurement Technologies: Measure What, Where, Why, and How?

radar altimeters is typically 7 km. These data can be used for offshore oil industry design and operational planning pur­ poses, as well as, for example, coastal engineering design, naval architecture, and ship routing. Wave direction and wavelength can be determined using ERS-SAR, both in image mode and globally in ‘wave mode’, and also in the high-resolution modes of RADARSAT. The typical resolution of satellite SAR, about 30 m, means that only waves with periods of about 5 s or more can be resolved. The wave pattern visible on a SAR image may be very different from the in situ wave field; as well as having a 180° directional ambiguity, a complex post-processing of the image is usually necessary to extract the directional wave spectrum. It is usually necessary to start from an initial ‘first guess’ spectrum, derived from, say, a numerical model simulation (Hasselmann and Hasselmann, 1991; Krogstad et al., 1994). Progress in reducing the directional ambiguity and in improving the signal-to-noise ratio has recently been made by employing ‘Single Look Complex’ data from ERS-SAR (Engen and Johnsen, 1995). SAR does appear to give convincing images of swell waves propagating onto coasts, including the effects of depth refrac­ tion, shadowing, and diffraction. The ability of SAR in image mode to provide rather detailed pictures of wave fields near shorelines, at least for the longer swell waves, should be useful in monitoring the coastal environment and its changes, includ­ ing the locations of rip currents, long-shore drift, and oilier currents, which impact the transport of sediments. Wave refraction by bottom topography, and the resulting change in surface roughness monitored by SAR, may be used to monitor the evolution of sandbanks in shallow-water areas, as well as to chart bathymetry in poorly surveyed regions (Calkoen et al., 1991; Calkoen and Wensink, 1993; Calkoen, 1996; Hesselmans, 1996). SAR wave mode data are now used to provide corrections to forecast wave directions in operational wave-forecasting mod­ els, although assimilation of these data is still at a preliminary stage (Breivik et al., 1996; Paci and Campbell, 1996).

2.16.3.1.4

Meteorology

Mesoscale weather and ocean features can be monitored by polar orbiting satellites with sensors operating in a wide part of the electromagnetic wave spectrum. Microwave sensors acquire data independent of sunlight and clouds, are used to monitor wind, waves, ocean currents, oil spills, and sea ice. Visible and IR sensors (e.g., National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA)/AVHRR, ERS-ATSR (European Remote Sensing Satellite-Along Track Scanning Radiometer), IRS-P3-MOS, Sea-viewing Wide Field-of-view Sensor (SeaWiFS)) monitor SST, fronts, currents, eddies, and ocean color. Small-scale fea­ tures such as oil slicks, nearshore circulation, and wave fields, can, under favorable meteorological conditions (normally, the wind speed must be in the range of 3–11 m s−1), be monitored with high-resolution polar orbiting radar sensors, even when clouds are present. Both active (radar) and passive (radiometer) microwave sensors have been shown capable of retrieving the ocean surface wind speed, with active microwave instruments being used to also retrieve the wind direction. With the US Navy’s WindSat mission, a space-based radiometer system has also been shown capable of determining the wind

387

direction using polarimetric and multilook observations. However, the presence of significant cloud liquid water pre­ sents significant challenges for the passive polarimetric technique and thus limits its utility in supporting opera­ tional marine weather forecasting and warning. The development and refinement of instrumentation and algo­ rithms for ocean surface wind retrieval is an ongoing process that is being conducted in both the active and passive remote-sensing areas. Wind speed and direction over the global ocean can be determined from the radar scatterometers on Quickscat, NASA Scatterometer (NSCAT), and ADvanced Earth Observing Satellite (ADEOS)-2, as well as on ERS-1/2 satellites (Stoffelen and Anderson, 1997a, 1997b, 1997c), and with the Advanced Scatterometer (ASCAT) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), which is used in operational marine weather forecasting. It is also possible to determine detailed patterns of wind speed, and sometimes direction, from SAR images, and, at a lower resolution, from real-aperture satellite side-looking radar data. Satellite altimeter data can also be used to determine wind speed along the orbit (Monaldo, 1988; Witter and Chelton, 1991; Carter et al., 1992). Scatterometer observations over the ocean provide direct estimates of the global wind-vector field at spatial resolution of generally 25 km, although Quickscat can achieve resolutions of twice that, with an accuracy of 2 m s−1 in speed, 15° in direction, but usually with a directional ambiguity of 180°. For some applications, such as those in semi-enclosed seas, straits, coastal regions, and estuaries, this resolution is, how­ ever, too coarse. In these regions, wind-field estimates retrieved from high-resolution SAR images can be very useful. Today, SAR is the only space-borne instrument that can provide high spatial resolution images (30 m ground resolution) for quanti­ tative measurements of the mesoscale wind field at a spatial scale of typically 10  10 km. The spatial and temporal cover­ age of ERS data is limited, and not suitable for operational monitoring. However, wide-swath SAR data from RADARSAT and Environmental Satellite (Envisat) offer better data coverage.

2.16.3.1.5

Temperature and salinity

The distribution of SST provides significant information related to a wide range of marine processes and phenomena such as ocean currents, fronts, mesoscale eddies, and upwel­ ling phenomena. This allows use of satellite-derived SST information in the mapping of ocean circulation (Johannessen et al., 1991, 1993, 1996, 1997), fisheries (Pettersson, 1990), and algal blooms (Johannessen et al., 1989), and in assimilation of SST data in physical circulation models (Stanev, 1994). SST is observed from space by thermal IR imagery during cloud-free conditions, using the thermal IR channels of the NOAA/AVHRR and from the ERS ATSR sensor systems. These instruments measure the SST distribution at a spatial resolution of 1 km and an accuracy of 0.5 °C or better (NOAA, 1995). The structure of mesoscale ocean circulation features in the North Sea tidal front and the Norwegian coastal current were documented through the early applications of this type of Earth observation data (Johannessen, 1986; Johannessen et al., 1989). An example of an AVHRR scene is shown in Figure 26(a).

388

Measurement Technologies: Measure What, Where, Why, and How?

Measuring salinity from space is still challenging, but tech­ niques using passive microwave radiometry have been under development for some time (Lagerloef et al., 1995). The first measurements from space were carried out by the Skylab S-194 radiometer (Lerner and Hollinger, 1977). So far, successful results have been obtained by airborne L-band radiometry in the coastal zone (Miller et al., 1998) and satellite systems using this method have been proposed (Kerr, 1998). There are cur­ rently two missions trying to measure salinity from space: the European Space Agency (ESA)’s SMOS satellite (launched in 2009) and the National Aeronautics and Space Administration (NASA) Aquarius, which will be launched in June 2011; both instruments expect to be able to measure salinity with a resolu­ tion of 0.2 PSU (Practical Salinity Unit). These systems use L-band scatterometers. Other attempts have been carried out using a combination of X- and C-band radiometers (Reul et al., 2009). In coastal areas, the measurement of colored dissolved organic matter (CDOM) has been undertaken using visible band radiation at 440 nm wavelength (Binding and Bowers, 2003).

2.16.3.1.6

Ocean color

Ocean color observations started with the launch of the Coastal Zone Color Scanner on board the Nimbus 7 (October 1978 to June 1986). No other serious attempts to measure ocean color from space were made until 1996, when the National Space Development Agency of Japan launched the Ocean Color and Temperature Scanner (OCTS). Probably the most successful mission has been that of the NASA SeaWiFS, which was launched in 1997 and is still working long after its expected life span. Nowadays, there is the Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution between 250 and 1000 m and the ESA Medium Resolution Imaging Spectrometer (MERIS), with an approximately 250 m resolu­ tion near the coast. Ocean color data have revolutionized the understanding of marine primary productivity, producing a global picture of the oceans’ chlorophyll. Nevertheless, the estimation of chloro­ phyll in near-coastal waters has been difficult, due to the high concentrations of suspended sediments and CDOM in these so-called Case II waters, although recent advances with MERIS and MODIS have been successful in producing correct orderof-magnitude estimates of chlorophyll. As mentioned, esti­ mates of CDOM have been achieved with empirical, local formulations using 440-nm radiation (Binding and Bowers, 2003), while estimates of suspended sediments have been achieved using the 667-nm band (e.g., Binding et al., 2003). Attempts to obtain particle size spectra have been carried out by van der Lee et al. (2009). Examples of MODIS composite images for sea-surface chlorophyll and wavelength 551 nm (another sediment proxy) for the Irish Sea are shown in Figure 26. Unfortunately, these techniques provide only sea-surface values and in situ observations are necessary both for acquisi­ tion of vertical profiles and for calibration. The improved spatial resolution provided from aircraft surveillance is espe­ cially valuable in coastal regions. Both LIDAR (LIght Detection and Ranging) and SAR can be used to determine sequences of bathymetric evolution. High-frequency- and X-band radars can provide synoptic surface fields of currents, waves, and winds in the coastal zone.

2.16.3.2

Land-Based Radar

In this section, we will discuss land-based radar systems, which are statically deployed on coastal areas overlooking the sea instead of on air-borne or space-borne platforms. The most commonly used radars are the high-frequency radar and the standard marine radar, which works in the X band.

2.16.3.2.1

High-frequency radar

High-frequency radars, developed during the cold war (e.g., Crombie, 1955; Barrick, 1972), operate in the 3–30 MHz frequency range of the electromagnetic spectrum and are able to obtain information about the ocean at ranges of up to 150 km. The change in frequency between the trans­ mitted and the backscattered signal, due to the Doppler effect, allows the determination of surface currents, wind direction, and the ocean wave spectrum over a large area of the ocean surface (Barrick, 1972). Therefore, the large coverage obtained by radar is a major advantage over in situ wave measurement, for example, wave buoys. The radar information is contained within the Doppler spectrum, which is characterized by two large peaks known as the first-order Bragg peaks that corre­ spond to ocean waves of half the radar wavelength, moving directly toward and away from the radar. The Bragg peaks are used to measure radial surface currents and wind direction. Ocean wave information is determined from the continuum that surrounds the Bragg peaks – known as the second-order effects. Barrick (1977) calculated the second-order radar cross section for deep waters, which gives the relationship between the Doppler spectrum and the ocean wave-number spectrum. The mathematical analysis of second-order ocean wave inter­ actions is presented in Lipa and Barrick (1986), Holden and Wyatt (1992), and Weber and Barrick (1977) (see Figure 28 for an example of high-frequency-radar-derived currents).

2.16.3.2.2

X-band radar

The X-band radar operates on a different principle than the high-frequency radar. It is based around a standard, although high-specification, marine radar with a rotating antenna. A short pulse of 10 GHz microwave energy is transmitted in a narrow 1° beam and anything capable of reflecting that beam, including rocks, ships, and waves, bounces the energy back to the transceiver. The range of any particular target is determined by the time it takes the reflected energy to return to the radar. The result of this is that the radar produces a 360° plan view of the sea surface and anything on it, every time the antenna rotates. Sequences of these images, to a range of up to 8 km from the radar, can be recorded and are of interest when analyzing the behavior of the waves visible on the image sequences. Analysis of waves at Holderness was carried out by Wolf and Bell (2001) and Wolf (2005). Another interest for coastal applications is the derivation of depth inversions from these data, which involves determining the shoaling behavior of the waves (e.g., shortening the wavelength for a fixed wave period) and thereby inferring the water depth and possibly the current that affected that wave behavior (Bell, 1998, 1999). This allows the production of bathymetric maps covering large areas of complex sandbanks without the need for boat surveys, and hence permits the monitoring of any sandbank movement regularly over a period of years. It is also possible to

Measurement Technologies: Measure What, Where, Why, and How?

empirically calibrate the data in order to determine 2D wave spectra (Wolf and Bell, 2001; Ocean Waves, 2007).

2.16.4 Real-Time Monitoring 2.16.4.1

Operational Oceanography

Although present-day coastal issues may seem far removed from real-time ‘operational’ forecasting, recognition of global-scale developments in modeling and measurements of marine systems is important (GOOS, UNESCO, 2003). Ultimately, coastal management needs to be linked to devel­ opments in operational oceanography on both regional and global scales. Such links will utilize the advantages of systema­ tic modeling, observational procedures, and communication procedures developed over decades by meteorological agencies. A major objective of operational oceanography is to mini­ mize damage from future events by reducing uncertainties in forecasting, ranging from episodic storms to long-term rises in sea levels and temperatures. Operational oceanography is cen­ tral to sustainable exploitation and management of our marine resources. Existing operational forecasting systems provide real-time and near-real-time products describing wind fields, wave height spectra, temperature and salinity, floating sea ice, chlorophyll, tides, currents, and storm surges. Real-time operational forecasts include tidal predictions and hazard warning, for example, storm surges, oil or chemical spill movements, search and rescue, eutrophication, and toxic algal blooms. Pre-operational simulations often involve assessing and under­ standing the health of marine ecosystems and resources and their likely sensitivity to changing conditions. These are typi­ cally concerned with assessment of absorptive capacity for licensing of discharges, and evaluating environmental impacts of intervention (reclamation, dredging, etc.) and climate change. Exploratory applications extend from the formulation of environmental management policies to developing the underpinning science and technology to address both anthro­ pogenic influences and natural trends. Effective operation of real-time forecasts requires the resources of a meteorological agency for communications, processing, and dissemination of forcing data, alongside oceanographic data centers that are responsible for dissemination of quality-controlled marine data. Operational oceanography is defined as the activity of rou­ tinely making, disseminating, and interpreting measurements of coasts, seas, oceans, and the atmosphere to provide forecasts, nowcasts, and hindcasts. Forecasting includes real-time numer­ ical prediction of processes, such as storm surges, wave spectra, and sea-ice occurrence. Forecasts on a climatic or statistical basis may extend forward for hours, days, months, years, or even decades. In ‘nowcasting’, observations are assimilated in numerical models and the results are used to create the best estimates of fields at the present time, without forecasting. These observations may involve daily or monthly descriptions of sea ice, sea-surface temperature, toxic algal blooms, state of stratification, depth of the mixed layer, or wind-wave data. Observational data for ‘hindcasting’ are assimilated into mod­ els to compile sets of historic fields and distributions (typically monthly or annually) of variables such as sea-surface elevation,

389

water temperature and salinity, nutrients, radionuclides, metals, and fish stock assessments. Data assimilation forms the interface between models, observations, and theory, and thus is an essential component in simulation systems, Figure 2. Assimilation is used to transfer observed information to update the model state, the model forcing, and/or model coefficients. The challenge is to take advantage of the complementary char­ acter of models and observations and to combine the generic, dynamically continuous character of the process-knowledge that is embedded in models with the specific character of observed data. Progress in aircraft and satellite remote sensing (Johannessen et al., 2000) will dictate the rate of development of operational oceanography for many variables and para­ meters. Lead times of a decade or more are required for the development of new sensors, commercial production of proto­ type instruments, and international agreement on new satellite programs. Remotely sensed data must be processed in hours if it is to be useful in operational forecasting. The need for enhanced information from atmospheric models is a high-priority item in operational forecasting. As an example, accuracy and extent, in time ahead, of wind forecasts are the primary limiting factors for wave and surge forecasting. The ultimate goal is dynamical coupling of estuaries–seas–ocean marine models through to terrestrial and atmospheric mod­ ules, that is, the global integration of water, thermal, and chemical budgets (Prandle et al., 2005).

2.16.4.2

Coastal Observatories

A coastal observatory is an extensive monitoring system set up in coastal waters in order to provide comprehensive data on multiple variables and parameters that are necessary to under­ stand the important physical, chemical, and biological processes taking place in the coastal waters. Data are generally measured at high frequency (several times per day, even several times per hour), often at multiple depth levels. Most coastal observatories provide data in (near) real-time by using teleme­ try to transfer measurements back to land. Coastal observatories generally have one or more fixed platforms and/ or buoys with a number of instruments and sensors to measure a range of parameters, such as temperature and salinity, current profiles, waves, and meteorological conditions. The first coastal observatory was developed by Rutgers University for the New Jersey coast, USA. The Long-term Ecosystem Observatory (LEO) became operational in 1998 and has become the prototype for most of the US cabled coastal observatories (Figure 31), such as the Martha’s Vineyard and the Monterey Accelerated Research System and the base for the US-NSF Ocean Observatories Initiative (OOI). Because the cost of laying the nodes for cable observatories is very large, alternative ways of commu­ nications have been pursued, as in the case of the UK National Oceanography Centre ISO. Additional components of a coastal observatory can include: • sensors to measure turbidity, chlorophyll, and nutrients; • drifters, measuring surface currents and properties such as temperature and salinity; • tide gauges, with sensors for meteorology, waves, tempera­ ture, and salinity;

390

Measurement Technologies: Measure What, Where, Why, and How?

Sea WiFS NOAA Polar Orbiters

MODIS

OCEANSAT

FYI-C

AVIRIS PROTEUS Movie Filming

PHILLS-2

Tuckerion Control Center

PHILLS Rutgers Marine Field Station

Salinity Scanner

SPECTIR

NY New York Rutgers

New Jersey

LEO-15

DE

Figure 31 Rutgers University LEO-15 schematic (http://marine.rutgers.edu/cool/LEO/LEO15.html).

• shore-base high-frequency radar measuring waves and sur­ face currents; • satellite data – IR (for sea-surface temperature) and visible (for chlorophyll and suspended sediment); • instrumented ferries (ferry boxes or ships of opportunity), including data for near-surface temperature, salinity, turbid­ ity, chlorophyll, and/or nutrient concentrations; • meteorological data from local met stations; • research vessels, to service moorings and to conduct spatial surveys (in situ data); and • operational models that can integrate (near) real-time mea­ surements into a (pre-) operational coastal prediction system whose results are displayed on a web site.

2.16.4.2.1

The ISO

The objective of the ISO is to understand a coastal sea’s response both to natural forcing and to the consequences of human activity. The observatory integrates (near) real-time measurements with coupled models into a preoperational

coastal prediction system whose results are displayed on the ISO web site. The concept is founded on obtaining data in (near) real time, using telemetry, from underwater to the sea surface to land to NOC to the web site (‘armchair oceanography’) (see Figure 12). It will grow and evolve as resources and tech­ nology allow, all the while building up a long time series. The foci are the impacts of storms, variations in river discharge (especially the Mersey, Dee, and Ribble), and seasonality of stratification and nutrients and algal blooms in Liverpool Bay.

2.16.4.2.2

The Western English Channel Observatory

The Western Channel Observatory (WCO) is an oceanographic time-series and marine biodiversity reference site in the Western English Channel. It has been an area of UK marine monitoring for over a century. The modeled area lies within 1.2875° to 7.5875° W and 48.4752° to 50.8752° N (Figure 32), although the main focus of its observational field work lies within 30 km of Plymouth Marine Laboratory and the city of Plymouth (50.3714° N, 4.1424° W). By inte­ grating quantitative in situ measurements, modeling studies,

Measurement Technologies: Measure What, Where, Why, and How?

30

391

Plymouth L4 E1

Latitude

50 N

30

49 N

30 7 W

−120

6 W

−100

5 W

−80

4 W Longitude

−60

3 W

−40

2 W

−20

0

Figure 32 Plymouth Marine Laboratory Western Channel Observatory modeling area and monitoring stations L4 and E1.

and satellite remote sensing, the WCO elucidates processes and changes that are occurring within this component of the marine ecosystem. The WCO has two autonomous buoy platforms, designed to produce data for operational oceanography. Each buoy is equipped with an array of sensors to study both atmo­ spheric and marine variables and parameters.

2.16.5 Developing a Monitoring Strategy To understand and quantify the threat of global climate change, whole-system models are required, incorporating the impacts on marine biota and their potential biogeographic consequences. The introduction of Water Framework Directives for governance of regional seas and coasts empha­ sizes the need for development of well-validated, reliable models for simulating water quality–ecology–fisheries. A sys­ tems approach is needed, capable of integrating marine modules and linking these into holistic simulators (geological, socio-economic, etc.). Rationalization of modules to ensure consistency with the latter is an important goal, together with standardization of prescribed inputs, such as bathymetry, and tidal boundary conditions. Successful applications of models are generally limited by the paucity of resolution in observational data (especially bathymetry) used for setting up, initializing, forcing (meteor­ ological and along model boundaries), assimilation, and validation. This paucity of data is a critical constraint in envir­ onmental applications. More and better observational data, extending over longer periods, are essential if modeling accu­ racy and capabilities are to be enhanced. Comprehensive observational networks are needed, exploit­ ing synergistic aspects of the complete range of instruments and platforms and integrally linked to modeling requirements. Permanent in situ observations are likely to be the most expensive

component of any operational system. It is important to opti­ mize the observational network in relation to the modeling system for the requisite forecasts. There is a parallel requirement for accurate (model) descriptions of the state of adjacent shelf seas in order to define estuarine boundary conditions. Upscaling of knowledge from process studies is required to link small-scale (micro) measurements to larger-scale and longer-scale (macro) algorithms employed in numerical models. These studies involve measurement programs extending to water level, currents, temperature and salinity, waves, turbulence, bed features, sedi­ mentary, botanical, biological, and chemical constituents. The associated costs dictate that these would cover a limited but repre­ sentative number of estuaries. Specific ‘deliverables’ would be: (1) complete, consistent, documented, accessible, bench-test observational data sets for model validation; (2) development of monitoring strategies, elaborating synergistic values of the range of systems and sensors; and (3) assessment and development of sensors, instruments, and platforms. Maximum use should be made of the synergy between satellite, aircraft, ship, sea surface, seabed, and coastal (radar) instrumentation (Prandle and Flemming, 1998). Likewise, new assimilation techniques should be used for bridging gaps in monitoring capabilities. Observer systems sensitivity experi­ ments’ can be used to determine the value of the existence or omission of specific components in a new or existing monitor­ ing system. A basic monitoring strategy for studying bathymetric changes, capable of better resolving processes operating in estuaries, should include the following: 1. shore-based tide gauges throughout the length of an estuary, supplemented by water level recorders in the deeper channels; 2. regular bathymetric surveys, for example, 10-year intervals with more frequent re-surveying in regions of the estuary where low-water channels are mobile; however, in the case

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Measurement Technologies: Measure What, Where, Why, and How?

of for example, the Daly Estuary, Australia, bathymetric surveys have to occur after cyclones, because there is no fixed re-surveying periodicity; and 3. a network of moored platforms with instruments for mea­ suring currents, waves, sediment concentrations, temperature, and salinity.

Acknowledgments The authors would like to acknowledge the National Oceanography Centre Irish Sea Observatory Project; the NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS); and NASA Visible Earth for data and images used in this publication.

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Relevant Websites http://www.aquatecgroup.com – Aquatec Group: Homepage.

http://www.cefas.co.uk – Centre for Environment, Fisheries and Aquaculture Science.

http://cobs.pol.ac.uk – Irish Sea Observatory.

http://www.oceanleadership.org – The Consortium for Ocean Leadership.

http://www.ncof.co.uk – The National Centre for Ocean Forecasting.