Contents
1 Introduction ... 1—1
1.1 Hydraulic boundary conditions... 1—1
1.2 SBW Boundary Conditions Wadden Sea – Project H4803 ... 1—1
1.3 Validation instrument – selection and assessment of data sets for
validation of SWAN for the Wadden Sea ... 1—2 1.4 Objective of this study... 1—3 1.5 Approach ... 1—3 1.6 Outline of the report ... 1—4
2 General guidelines to assess quality of data sets ... 2—1
2.1 Introduction ... 2—1 2.2 Input and output of SWAN ... 2—1 2.2.1 Quantities ... 2—1
2.2.2 Transformation from point measurements to field values... 2—2
3 Wadden Sea: features and related studies ... 3—1
3.1 Wadden Sea: typical features... 3—1 3.2 Related studies ... 3—2
4 Selection and assessment of data sets ... 4—1
5 Conclusions ... 5—1
A Hindcast material ...4.6-A B Test cases in the Oosterschelde Estuary...4.6-A
B.1 Inleiding ...4.6-A B.2 Gegevens behoefte ...4.6-A B.3 Enkele kenmerken Waddenzee en Oosterschelde ...4.6-A B.4 Beschikbare gegevens ... 4.6-B B.4.1 Metingen ... 4.6-B B.4.2 SWAN Hindcast Oosterschelde... 4.6-C B.4.3 Overige gegevens Oosterschelde...4.6-D B.4.4 SWAN Hindcast Westerschelde... 4.6-E B.5 Conclusies ... 4.6-F B.6 Literatuur... 4.6-F
C Dutch inland water systems...G
C.5.3 Wind ... CC C.5.4 Water level ... CC C.5.5 Currents... CC C.5.6 Waves... CC C.5.7 Test cases ... DD C.6 References: Dutch inland water systems ... DD
1
Introduction
1.1 Hydraulic boundary conditions
In compliance with the Flood Defences Act (“Wet op de Waterkering, 1996”), the primary coastal structures must be assessed every five years (2001, 2006, 2011 etc.) for the required level of protection on the basis of the Hydraulic Boundary Conditions (HBC) and the Safety Assessment Regulation (VTV: Voorschrift op Toetsen op Veiligheid). The HBC consist of wave conditions and water levels under extreme situations near the dikes. These conditions occur very rarely, and are not expected to be measured. Therefore one is forced to use computational models. The HBC must be determined anew every five years and established by the Minister of Transport, Public Works and Water Management.
There is a degree of uncertainty concerning the quality of the current HBC of 2001 for the Wadden Sea. This is because they were obtained from an inconsistent set of measurements and design values.
Presently there is insufficient wave data for validation and calibration of SWAN in the Wadden Sea. Consequently, it is not yet possible to obtain reliable and validated HBC with this model in a tidal inlet system such as the Wadden Sea. Furthermore, it is uncertain to what extent the penetration of swell waves penetrate into the Wadden Sea. Measurements near the Emmapolder in Groningen have shown that swell provides a considerable contribution to wave run-up, in the order of 30% (Seijffert, 1991).
1.2 SBW Boundary Conditions Wadden Sea – Project
H4803
The abovementioned problem was the direct motivation for the request from the subproject “Boundary Conditions”, which is part of the main project “Strength and Loading of Coastal
Structures (SBW: Sterkte en Belasting Waterkeringen)”, to WL | Delft Hydraulics to
formulate a Plan of Action in which a strategy would be determined to answer the principle question: “How to arrive at reliable Hydraulic Boundary Conditions for the Wadden Sea for 2011?” In addition to the penetration aspect, the general suitability of the SWAN wave model in the Wadden Sea must be determined and the improvements required to produce reliable HBC in the Wadden Sea should be specified.
The Plan of Action (WL, 2006a) has been formulated with the help of external parties and has been approved by the DG Rijkswaterstaat / RIKZ. The Plan briefly considers the entire chain that is used to derive the Hydraulic Boundary Conditions and focuses on improving the predictions of the wave boundary conditions produced by the wave model that are required to make the transformation from deep water to coastal structures.
In July 2006 RIKZ commissioned WL | Delft Hydraulics for the project “Execution of the Plan of Action SBW Hydraulic Boundary Conditions” (in Dutch: “Uitvoering Plan van Aanpak SBW RVW Wadden Sea”).
The present study forms part of the project ‘Opstellen Programma van Eisen (PvE) en
structuur validatieinstrument en uitbreiding met testcases’1. This project consists of
activities 8.5 and 8.6 of the SBW-RVW Waddenzee project, as described in (WL, 2006a). The project defined under activities 8.5 and 8.6 of SBW-RVW Waddenzee consists of two activities, briefly referred to as ‘validation instrument’ and ‘validation cases’. The first activity is described in (WL, 2006b), and the latter activity is described in the present report.
1.3 Validation instrument – selection and assessment of
data sets for validation of SWAN for the Wadden Sea
The validation instrument is a tool that can be used to validate (new versions of) a spectral wave model, such as SWAN (Booij et al. (1999)). The envisaged validation instrument can be viewed as an extension to, or a replacement of, the quality control systems (test banks) that have been developed so far, namely the SWAN Testbank (WL, 2000) and the ONR Testbed (WL, 2002); see also Kieftenburg (2004a, 2004b) and (WL, 2006b). The validation instrument will play an important role in facilitating the work done for improving SWAN for the Wadden Sea.
The following classification of validation cases can be made (WL, 2002):
• Academic validation cases. These cases are aimed at isolating one or more specific physical phenomena. For these cases, waves are typically monochromatic and unidirectional and comparison is made with analytical solutions.
• Laboratory validation cases. These are idealized situations with focus on wave propagation, wave breaking, non-linear wave-wave interactions and wave-current interactions. Comparisons are made with laboratory observations.
• Field validation cases. These cases are aimed at evaluating the model’s ability to predict wave propagation and transformation in real field situations. These include generic cases, such as growth curves for evaluating idealised fetch-limited wave growth, and application-specific cases related to the particular combination of physical processes that are found in a particular field situation.
The present study considers validation cases of the third class, namely those collected in the field. As mentioned above, the application-specific field validation cases are related to the particular combination of physical process that is found in a particular field situation. For
1 In English: Compilation of Operational Requirements and definition of a structure for a validation
the Wadden Sea, the interacting processes include wave-current interaction, the penetration of long waves in combination with local wind growth, wave propagation across shallow tidal flats and the generation of long waves. Presently, the available test beds contain an insufficient amount of application-specific validation cases of the Wadden Sea. Therefore, in order to assess the performance of SWAN in this complex area, the envisaged validation instrument needs to be extended with such cases.
1.4 Objective of this study
The objective of this study is to compile a collection of data sets with which to populate an envisaged validation instrument which has the purpose of evaluating the performance of spectral wave models (such as SWAN) in the Wadden Sea. This objective comprises two aspects, the first of which is to define selection criteria, for example relevance and quality, by which to identify suitable cases. The second aspect is to investigate whether such criteria are met by the existing data sets for the Wadden Sea, or to areas similar to the Wadden Sea (‘Wadden Sea-like areas’). This leads to a selection of cases that, based on the present knowledge, has sufficient quality for the validation instrument.
The aim of the project is not to gather the data sets nor to perform additional quality assessment. Also, it is not the aim to construct the actual validation simulations. Therefore, for some data sets additional quality assessment must be performed before it can be decided whether to use them for the validation instrument.
1.5 Approach
The approach followed in this study is to first define a set of criteria on the basis of which data sets are to be selected. These criteria include the relevance of a particular data set to the Wadden Sea situation, the completeness of the data set and the quality of the data. Secondly, the typical features of the Wadden Sea area are investigated, and an inventory is made of the relevant wave-related physical processes.
Thirdly, existing reports and data sets are studied in the light of the selection criteria and the typical features of the Wadden Sea to choose a number of data sets that can be used in the wave model validation instrument for this area. Whenever possible, experts are consulted to comment on the quality of these data sets. The following parties and experts contributed to the project:
• RIKZ, in the persons of Annette Kieftenburg, Caroline Gautier and Ester Groenendaal. • RIZA, in the person of Marcel Bottema.
• Coastal Hydraulics Laboratory, Vicksburg, Mississippi, USA, in the person of Barbara Tracy.
1.6 Outline of the report
In Chapter 2, general guidelines for the evaluation of the quality of data sets are given. In Chapter 3, the specific features of the Wadden Sea are discussed. Studies related to the present one are listed here also. In Chapter 4, existing data sets for the Wadden Sea and Wadden Sea-like areas are evaluated.
2
General guidelines to assess quality of data
sets
2.1 Introduction
Validation comprises the quality assessment of a numerical model: to evaluate how well the output of a calibrated numerical model fits observational data. This implies that the field observational data must be of high quality, because otherwise the value of a validation study becomes doubtful.
In the current chapter, general guidelines for assessing the quality of data sets are proposed, with validation of a spectral wave model such as SWAN in mind. As introduction, Section 2.2 lists the input and output quantities of SWAN that are relevant to model validation. This is followed in Section 2.3 by a presentation of the types of field observational data that are generally available for model validation. In Section 2.4, the need for data quality and suitability of data sets for validation cases are discussed. In Section 2.5, criteria for assessing the quality of data sets are presented. Section 2.6 draws together the results of this chapter into a checklist by which to assess the quality of data sets. In Section 2.7, labels indicating the quality of data sets are introduced.
2.2 Input and output of SWAN
2.2.1 Quantities
SWAN predicts wave quantities in a finite computational domain. The following quantities serve as input for a SWAN model:
• Bathymetry (depth). • Water level field.
• Wind field (wind speed and wind direction). • Offshore wave boundary conditions.
• Current field.
The abovementioned data are field quantities, in other words they are a function of the spatial location. SWAN allows the user to model the above quantities as uniform or varying in space and in time.
The output produced by SWAN includes among others the following quantities (either constant or time-dependent), which can readily be compared to field observations:
• Significant wave height. • Several wave period measures. • Peak or mean wave direction. • Directional spreading.
SWAN can give all of the above wave data (and more) in all points in the geographical computational domain (see SWAN User Manual, 2006).
2.2.2 Transformation from point measurements to field values
The SWAN input quantities wind, water level, current, offshore wave boundary conditions and bathymetry are field quantities. This implies that SWAN needs their values in all computational grid points2. In general, these fields of input data must be obtained by means
of interpolation and extrapolation, both in space and time, from point measurements. This inevitably leads to deviations between the actual (‘true’) values and the values used in SWAN. This is even the case if the data set contains no measurement errors. This difficult issue has two components:
• Suitability of the observation locations. • Quality of the interpolation model.
Concerning the first item, at least a sufficient number of measurement locations should be available to represent the spatial variability. In practice, this demand is often not met due to the enormous costs involved in measurement campaigns, see RIZA (2006a). Therefore, careful selection of the limited number of measurement locations is crucial in order to obtain as much spatial representativity as possible.
The issue of interpolating point measurements to field values for all SWAN input quantities is discussed below. Note that SWAN offers the possibility to use tri-linear interpolation on input grids different from the computational grid for the bathymetry, water level, current and wind data. Interpolation techniques are also available for the offshore boundary conditions, see the SWAN User Manual.
Bathymetry
Since the wave field in shallow areas is strongly dependent on the bathymetry, an accurate representation of the bathymetry is essential. Without this, a validation case cannot be made. Ideally, the bathymetry is monitored using a sufficiently fine spatial resolution (order 20 m to 1 km between points, depending on the bottom steepness). It is recommended to allow not more than 10% depth variations between neighbouring points. These measurements are typically repeated annually or bi-annually, but also as soon as possible after a storm (Petten), depending on the expected morphodynamic behaviour. This may then result in interpolation in time. Caution is required when heavy storms are between bathymetry measurements and the considered event. The bathymetry just before such an event is probably the most representative.
2 For the offshore boundary conditions, data is required only in the computational grid points at the
Water level and current
Water level and current data is usually only available in a limited number of points. This complicates interpolation of these quantities.
The water level may often be assumed uniform. Typical exceptions are closed or semi-closed basins where wind may induce significant water level gradients. In the Wadden Sea, the water level can only be assumed uniform in a subdomain in the order of 50 x 50 km2; for
larger areas or during storms the uniform water level approximation is doubtful.
In situations where the currents are small, they are usually neglected. This neglect, however, can be doubtful. For example, in Alkyon (2005) it is shown that in the IJsselmeer a wind-driven current of some tenths m/s can lead to 10% deviation in the significant wave height, depending on the model settings applied in WAQUA/TRIWAQ.
In case that the currents and the variation in the water level cannot be neglected, they must be provided by other means. When currents and nonuniformities in the bathymetry are large, a numerical model like WAQUA/TRIWAQ or Delft3D can be employed to obtain water level and current fields. As mentioned above, the employed settings may strongly influence the outcome, so caution is required. These kind of models must be calibrated and validated both on currents and water levels separately from the wave model. Calibration and validation require that current and water level observational data is available.
Wind data
In present studies, use is made of point measurements, and wind fields are constructed by interpolation through the limited number of wind velocities at these locations. Interpolation of wind data is not only hampered by the limited number of measurement locations, but also by the fact that the measurement locations are usually onshore. The wind speed and direction at land is not necessarily representative for wind on open water. A standard method to scale the wind speed from land to sea is by application of the Charnock relation, see for example Verkaik et al. (2002).
As an alternative, more sophisticated wind fields can be applied. These are obtained from
the HIRLAM model. Its resolution of 11 x 11 km2 is coarse for application in the Wadden
Wave data
Some of the wave data may be used as model input to define offshore boundary conditions. This is necessary when waves generated outside the modelled domain are having a significant influence in the region of interest. This holds, for example, for swell generated at the North Sea while the modelled domain covers (part of) the Wadden Sea. In SWAN, the input wave spectra are linearly interpolated to obtain wave boundary conditions in all computational grid points along the offshore boundary.
Wave data is usually available in a limited number of points. Comparison between wave measurements and simulation results is performed in the measurement locations, hence no spatial interpolation is needed.
2.3 Types of field wave data for validation
Since SWAN produces wave data as output, validation is done against wave observational data. In field situations, wave observational data can be obtained through a variety of instruments. At sea, devices such as directional wave buoys, omni-directional wave buoys (possibly in an array) and pressure gauges are used. In Dutch lakes, step gauges and capacitance probes are used. These devices yield point data. On the other hand, remote sensing or radar techniques (not often applied in the Dutch setting yet) yield field data. These raw observations can be processed into quantities corresponding to the SWAN outputs listed in Section 2.2, including directional and omni-directional spectra and various integral parameters, to be used for model validation. Note that part of the measured wave data can be used as input for the wave model, namely in the form of offshore wave boundary conditions.
2.4 Quality and suitability of observational data
In Sections 2.2 and 2.3, the typical input and output of SWAN and the generally available types of wave observations were presented. Conceptually, the production of output data by a numerical model can be considered as a process applying certain model formulations, in combination with model settings, to input data. In other words:
Numerical model ( input data, formulations, settings ) = Model output data
The expression above implies also that the quality of the model output data depends on the quality of the model input data, the model formulations and the model settings. The quality of the model settings are part of the calibration procedure, which is accounted for in another part of the SBW project (WL, 2006d,e). In the present study, the focus lies on the quality of the observational data. As discussed in the previous section, observational data is used partly for model input data and partly as objective measure against which the model output is validated. This implies that observational data has to be of undisputably high quality.
another numerical model. This is addressed in more detail in Section 2.2.2. Here the observational data that is used in a SWAN validation case is denoted as a data set for that case.
A validation case consists of an event taking place in a certain geographical region. An example of an interesting event is the 18 January 2007 storm in the Wadden Sea. In other words, a validation case has a geographical and a temporal component. This implies that more than one validation case can be defined for a particular region. Before setting up a validation case, the following questions needs to be answered:
1. Does the geographical region have characteristics relevant for the purpose of the validation?
2. Are data sets of sufficient quality and at suitably chosen locations available? 3. Do the data sets contain data of relevant events (relevant to the defined purpose)? It is only feasible to use a given data set as a validation case if all three of these criteria are fulfilled. In the present chapter, the second criterion is worked out in more detail. The first criterion, namely the characteristics required of a data set to be relevant for the Wadden Sea region, is discussed in Chapter 3 below. The third criterion is not addressed in the present study.
2.5 Quality criteria for data sets
In order to determine the quality of data sets and their suitability for validation of SWAN in the Wadden Sea, a number of criteria have to be fulfilled. The quality of data sets can be assessed against the following criteria:
1. Completeness of the types of data recorded. All relevant information for forcing the model and for evaluating its output must be available. For a typical SWAN simulation, at least the bathymetry, water level, wind and wave data must be available. In some cases, current field data is required as well. If one or more relevant quantities are missing, then the data set is not complete, and one has to question the usefulness of setting up validation cases for this region.
2. Suitability of the observation locations. This means that observations must be performed at a sufficient number of suitably chosen locations. This item is elaborated in Section 2.2.2.
3. Completeness in time. In other words, the observational data for a possibly interesting event must form a complete set. It may occur that there are portions of data missing in a time series, for example due to temporal malfunctioning of wave buoys.
4. Reliability. In other words, the observational data should not contain spurious patterns like noise, spikes or drifting, nor should it have high uncertainty? Since no perfect measurement technique exists, some error is inevitable. This implies that it is crucial not only to know the reliability per data set, but also per case (e.g. per location per day). Many parties that perform observation campaigns have systems in place for quality assurance (for example Waves2004, see Xi (2006)). In the present study such quality reports are considered, but no additional quality analyses have been performed.
this can be judged). And more importantly: the data set should be sufficiently large to allow for an estimate of the representativity of a specific validation case. As for the latter, a given (candidate) validation case should be comparable to other, under similar circumstances (location, wind, current, water level) obtained data, and there should preferrably not be a large unexpected deviation (which may occur due to, for example, natural variability).
It must be noted that data sets are seldom such that one can label them without any doubt as perfect. This implies why quality assessment of data is such a vital element in the process of setting up validation cases.
In Section 2.6, these criteria are put into a specific checklist for the various quantities involved.
Note that there is another form of ‘completeness’ than the one mentioned in Item 1. This concerns completeness related to the parameter range of the data set: the range of hydraulic, wind and wave conditions within the data set must cover the range of interest (in relation to the processes to be tested in SWAN). For example, if one wants to apply the wave model to extreme conditions like storms with winds above 30 m/s, then data sets for such events have to be available. This meaning of ‘completeness’ relates to the third question in the numbered list in Section 2.4, and is not addressed in the present study.
Figure 2.1 Flow diagram illustrating the assessment levels.
be called the ‘geographical level’, and the second the ‘event level’, corresponding to the spatial and temporal components of a validation case introduced in Section 2.4. A flow diagram illustrating these two assessment levels is shown in Figure 2.1. Whilst considering the geographical level, the data sets are evaluated without referring to particular events3. On
this level, Criteria 1 and 2 are assessed. Once a possible event for a validation case is selected, the data set is assessed at the event level. This means that the data set is assessed with focus on this particular event. At the event level, the main focus lies on assessment of Criteria 3, 4 and 5. Also, Criteria 1 and 2 are re-assessed, now with focus on the particular event. For example, are the observation locations suitable, given the characteristics of the event? This re-assessment of Criteria 1 and 2 is expected to be a relatively minor effort compared to the original assessment of these criteria. This separation into assessment levels is particularly useful when one aims at setting up several validation cases for the same geographical region. Assessing the data set quality for new events then takes less effort, since completeness of the data and suitability of the observation locations are already assessed to a large extend.
2.6 Checklist to assess quality of data sets
In the present section, the results of Sections 2.5 are compiled into a checklist to assist in assessing the quality of data sets is given. First, a general checklist is given, which ought to be verified for all quantities. Then, additional checks per quantity are indicated. Note that it is impossible to make an exhaustive list and that exhaustive answering of all the items in this list is not always feasible, since this may take several weeks or months per data set. Experts’ judgement is essential in this process.
2.6.1 General checklist
The following items are to be verified for all quantities (bathymetry, wind, water level, current, wave data). For brevity, the quantity is indicated by the letter Q:
• Is measurement data for Q available? If not, does it then make sense to create a validation case?
• Where does the Q data come from (institute, government, …)? • What measurement technique for Q is employed?
• What is the measurement frequency, and is this sufficient for the event? • Is the measurement equipment leading to Q data reliable?4
• Which standard is procedure used to validate/verify the measured Q data?
• What is the uncertainty of the measured data? Sometimes the uncertainty is known either from the instrument manufacturer or it is available with the data. For example, NDBC (National Data Buoy Center) has information on its web pages (http://www.ndbc.noaa.gov/) for the various devices used in producing its measurements. Nevertheless, often it is hardly possible to answer this question in an objective fashion, since a complete picture of all sources of error is seldom available. In
3 Of course, it is useful to know that possibly interesting events are already included in the available
data sets, or are likely to occur in the near future.
4 In general, the answer will be: sometimes yes, sometimes no, sometimes unknown. It is best to
many situations, one has to deal with doubtful information or possible errors. In addition, many possible sources of error are not investigated explicitly.
• Are there strange or unrealistic features in the Q data during the event (noise, spikes, drifting)? Sometimes part of the data set can still be used even if the researcher notes irregularities. It is sometimes necessary to get the raw data before it has been processed to make sure the processing worked correctly. If this cannot be done and it is impossible to remove the irregularities, then this is a good reason to omit this data.
• Are there other sources from which Q data is available, and how do they compare? • What method (e.g., interpolation technique or numerical model) is employed to
transform point data into 2D field data? Is the resolution of the measurement locations sufficient to yield representative field data using this method? What is the quality of the interpolation method?
• Which procedure is used to determine whether the event can be considered as stationary or nonstationary?
• Which procedure is used to determine whether the data can be considered as spatially uniform or not?
• It is helpful to have information from researchers who have used the data set in a successful or unsuccessful situation previously. A ‘data comment’ section can be helpful in this respect.
2.6.2 Additional checklist bathymetry
• At what date is the bathymetry to be used in the validation case measured?
• Morphodynamic behaviour: is the bathymetry during the event expected to be close to the monitored bathymetry? Or is perhaps an interpolation between bathymetries an option? Take into account the occurrence of storms.
2.6.3 Additional checklist wind
• What procedure is used to derive U10,sea from U10,land?
2.6.4 Additional checklist water level
• Is water level data available? Can the water level be considered as spatially uniform or not? If not, where does the employed water level field come from (e.g, a numerical model), and what is its quality?
2.6.5 Additional checklist currents
• Is current data available? Can the current be ngelected or not? If not, where does the employed current field come from (e.g, a numerical model), and what is its quality?
2.6.6 Additonal checklist wave data
measured (e.g. shallow areas, areas with sufficient fetch)? Are the instruments located where the wave motion is not affected by disturbing features, e.g. breaking waves? • Is the wave data representative? Is the data comparable to other, under similar
circumstances (location, wind, current, water level) obtained data, or is there a large spurious deviation due to natural variability?
2.7 Labels for data quality
After assessment of the quality of a data set (either by application of the checklist given above or by similar means), it is useful to label it with a ‘quality label’. The following quality labels are identified:
• Good • Average • Insufficient • Potential
These labels should be assigned to all of the input and output quantities mentioned in Section 2.2, resulting in a final overall label for the testcase as a whole.
Data sets that do have no or only minor doubts associated with them, and whose quality is considered to be good or even excellent, are labelled ‘good’. These data sets are perfectly suitable for validation purposes.
Rejection of all data sets that do not merit the quality label ‘good’ is probably not a good idea: this might result in a too few accepted data sets, so that validation of SWAN is hampered by a lack of observational data. Data sets are labelled as ‘average’ when they have some undesired aspects, but at the same time are considered to be still useful for validation purposes. Some possible examples are data sets with high quality wave data and low quality water level, wind and/or current data, or data sets with high quality measurements, but performed at ill chosen locations. Briefly speaking, data sets that are labelled ‘average’ can be used for validation purposes, but need to be treated with care.
Data sets of insufficient quality are labelled ‘insufficient’, and are to be rejected for validation purposes.
Under the fourth label ‘potential’, the data sets are gathered where the process of quality assessment has not been finalized yet, but whose quality label seems ‘good’ or ‘average’ based on what is known up to date. After quality assessment, the data sets will fall under one of the three categories ‘good’, ‘average’ or ‘insufficient’.
3
Wadden Sea: features and related studies
In this chapter the physical features and wave-related processes that are considered to be typical of the Wadden Sea region are presented (Section 3.1). This inventory of characteristic features is used in Chapter 4 to select field data sets for the validation instrument that are typical for the Wadden Sea. In addition, Section 3.2 gives a summary of studies related to the present one.
3.1 Wadden Sea: typical features
The Wadden Sea is an inter-tidal area located between the Dutch and German north coasts and the line of barrier islands that protects these coasts from the North Sea (Figure 3.1). These barrier islands are separated by tidal gaps, which feature ebb and flood tidal deltas. The flood tidal deltas behind the barrier islands amount to a complex interlocking system of shoals and channels.
Figure 3.1 Overview of the Dutch part of the Wadden Sea.
The complex bathymetry of the Wadden Sea results in complex wave conditions, that feature a number of simultaneous or sequentially occurring wave processes. The following processes can be identified:
• Refraction over shoals and out of canals (penetration of swell). • Nonlinear triad (three-wave) interactions.
• Combination of low-frequency waves (either wind-generated waves from the open sea or swell) and locally generated waves in the tidal basin.
• Wave-current interaction.
• Depth-induced breaking of North Sea waves on flood and ebb tidal deltas and in the surfzone in front of the barrier islands, and of locally generated waves on the shoal and on the foreshore of the Frisian and Groningen coast.
Because of the limited amount of measurement data available for the Wadden Sea, the present study also considers other geographical regions that feature all or some of the physical features and wave processes found in the Wadden Sea. Such regions are identified here as being ‘Wadden Sea-like’.
3.2 Related studies
The present study is related to several other inventory studies. They are briefly introduced here, and the relation to the present study is indicated.
Inventory of shallow water data - Alkyon (2006)
Alkyon (2006) provides an inventory of non-European ‘Wadden Sea-like’ areas. In the present study, the data sets for these areas are included in the collection of data sets for which quality assessment is performed.
Inventory wave data for model calibration – RIKZ (2006a)
RIKZ (2006a) provides an inventory of available measurement data around the North Sea for validation of wave models in shallow areas of the North Sea. In the present study, the ‘Wadden Sea-like’ areas among them are identified, and the data sets are evaluated.
Hindcast material – RIKZ (2006b)
RIKZ (2006b) is a memo compiled by Caroline Gautier (RIKZ), and contains a list of SWAN hindcasts as performed by Dutch parties. Appendix A contains the integral text of this memo. References for ‘Wadden Sea-like’ data sets mentioned in this memo are used in the present study.
Present-day work
4
Selection and assessment of data sets
In this chapter, the available data sets are discussed and their quality is assessed. In this discussion, the investigated field cases are organised on the basis of there relevance to the conditions in the Wadden Sea. Firstly, field cases are presented that are either recorded at the Wadden Sea, or that are sufficiently similar to the Wadden Sea to be considered a near exact match (Section 4.1). Such cases feature coastlines protected by barrier islands, with swell that propagates through tidal gaps (where they are affected by tidal currents) and over a system of canals and shoals, and local generation of wind sea.
Since the number of these cases proves to be limited, a number of additional field cases are considered, each of them featuring some of the processes relevant for the Wadden Sea. Of the relevant processes mentioned in Section 3.1, wave generation in and propagation over shallow regions (Section 4.2), penetration of swell (Section 4.3) and combined swell and wind sea (Section 4.4) are considered. Note that also the other processes mentioned in Section 3.1 (nonlinear triad interactions, wave-current interaction and depth-induced breaking) occur in the additional field cases to a larger or lesser extend. However, they are not included in separate sections.
4.1 Wadden Sea cases
This section presents the cases that are considered to be exact or near exact matches of Wadden Sea conditions. The cases presented are the Amelander Zeegat, the Friesche Zeegat, Norderneyer Seegat, Husum and Willapa Bay. The text on Willapa Bay is a summary of a description appearing in Alkyon (2006).
4.1.1 Amelander Zeegat
The Amelander Zeegat is located between the Dutch barrier islands Terschelling and Ameland (Figure 4.1). The inlet consists of a main channel (the “Borndiep”) with local depths of more than 20 m and a secondary channel on the western side of the inlet with bottom depths up to 6 m. The main Borndiep channel is highly instrumented with wave buoys.
Figure 4.1 Amelander Zeegat (Ameland: right; Terschelling: left).
In some of the simulations, spatial variation of the wind fields, water levels and currents are taken into account. On page 3-19 of WL (2006c), it is stated that the quality of the observational data (10 buoys) is sufficient, but their present location is not optimal and there is concern about the the behaviour of the buoys in conditions with strong current. It is recommended in (WL, 2006c) that this hindcast study should be followed up with a detailed analysis (including sensitivity runs) of the presently hindcast storms.
Concluding, there is available data of quality with label ‘average’ that contains interesting events to construct one or more validation cases for the validation instrument.
4.1.2 Friesche Zeegat
The Friesche Zeegat is located between the islands of Ameland and Schiermonnikoog in the Dutch Wadden Sea, see Figure 4.2.
Rijkswaterstaat has recorded wave data in the tidal gap and in the main tidal channel with five wave buoys (plus one offshore) (WL, 2000 & 2002). The wind speed and direction have been recorded at the observation station ‘Huibertgat’, located north of the island of Schiermonnikoog.
• 9 Oct. 1992 (05:00hr) • 9 Oct. 1992 (09:00hr) • 9 Oct. 1992 (11:00hr) 0 0 0 4 m 8 m 8 1 2 3 4 2000 m d 2 m co mputati onal gr id ouput gr id Ameland Schiermonnikoog 5 6 Friesland 4 m 4 m 12 m 16 m 20 m N w av es
Figure 4.2 Friesche Zeegat.
The first case represents a situation with a strong flood current, whereas the second and third cases represent a high water situation (with practically no current) and an ebb current situation respectively. These specific cases have been selected, because (a) at these times, relatively high waves were observed (significant wave height about 3 m), generated by a storm in the northern North Sea, (b) during the period of these observations the wind speed was nearly constant, (c) the frequency spectrum was uni-modal and (d) both tidal currents and water levels were measured (to verify the hydrodynamic computations).
This data set was considered to be of sufficient quality to be taken up in the SWAN Test Bank and ONR Testbed. However, the measured spectra show some spiky behaviour. This may be due to the applied processing of the raw data, but also to measurement inaccuracies. Therefore, a re-analysis of the observed spectra is recommended. The quality label is therefore ‘potential’, with prospects of becoming ‘good’.
4.1.3 Norderneyer Seegat
south and south-west. North of the inlet lies a sandbank on which most waves coming from the North Sea break. Due to wind effects, in the inlet and behind the islands a local wind sea is generated. 0 0 0 0 0 0 4 m 4 m 4 8 m 8 m 12 m 6 9 1000 m 2 m Norderney Juist computatio nal grid 3 8 4 2 1 5 d 7 16 m N waves
Figure 4.3 Norderneyer Seegat.
Hindcasts of the following storms are available: • 16, 17, 19 Nov. 1995
• 5 Feb. 1999 (03:40hr) • 3 Dec. 1999 (18:30hr)
The 16 November 1995 data is hindcast in Ris (1997). The 16, 17 and 19 November 1995 data sets are used in the SWAN Testbank and ONR Testbed (WL, 2000, 2002). These November 1995 data sets later turned out to be unreliable (Kieftenburg, 2004b), and will therefore not be used for the validation instrument. In other words, the quality label for the
November 1995 storms in the Norderneyer Seegat is ‘insufficient’.
Hindcasts of the storms in 1999 were performed in two studies, namely Kaiser and Niemeyer (2001) and WL (2006c), using SWAN versions 40.01 and 40.51 respectively. From pages 2-11 and 2-12 of WL (2006c), the following quote is taken:
[begin quote]
present. Thus, the accuracy with which the measurements considered here depict the sea state at the measurement locations, depends strongly on the accuracy of buoy location information.
• SWAN’s estimates of the integral wave height are good, the periods are fair but the computed and measured spectra do not agree well for most buoy locations, especially in locations in the tidal basin far away from the inlet gorge.
• This underperformance is due to the quality of the bathymetry, the accuracy of the offshore wave conditions and the local wind growth (which is a combination of input and modelled physics). The most determining factors for a successful hindcast are therefore the inputs rather than the modelled physics.
[end quote]
4.1.4 Husum
Figure 4.4 Location of wave stations (ALR Husum) in the waters of the North Sea in Schleswig-Holstein.
North Friesland, from the Eiderstedt peninsula to the border with Denmark, is identified in RIKZ (2006a) as a region comparable with the Dutch Wadden Sea. It consists of several islands and tidal flats between the islands and the mainland, see Figure 4.4.
Figure 4.5 Most recently added wave measurement locations (ALR Husum) around the Island of Pellworm.
Wave data in several pressure sensors has been measured during the 1 November 2006 storm. This data has not been made available to us yet, therefore it is labelled as ‘potential’.
4.1.5 Willapa Bay (Alkyon, 2006)
Willapa Bay, see Figure 4.6, is a tidal inlet on the Pacific Coast of the state of Washington (Smith et al., 2000). A tidal inlet connects this bay with the Pacific Ocean. The Bay is a large estuary with a tidal prism of approximately 6x108 m3, an inlet width of 8 km and a
typical depth of 10m. The entrance is a complex series of shoals and channels. A submerged spit at the northern side of the entrance cyclically grows and breeches. Willapa Bay is a challenging environment for wave modelling. The inlet is subjected to high waves, strong currents, and large variations in tide elevation. In addition, the bathymetry at the Willapa entrance is complex and continually changing.
In the tidal inlet of Willapa Bay, three bottom-mounted pressure gauges are deployed. Further inside the tidal inlet three other wave gauges are situated. Offshore wave data is obtained by a buoy in 40 m depth. In addition, some tidal gauges have been placed to provide information on the water level. Bottom information is regularly updated and available for wave modelling studies. The mean tide range at Willapa Bay is 2.7 m, and current speeds up to 2 m/s are encountered. The wave climate is severe, with a yearly average of 2 m and storm heights up to 9 m. Waves within the bay are strongly modulated by the tide and wave dissipation across the bar. The offshore wave conditions are seasonally dependent. In summer the periods are in the range of 5-10 s, whereas in winter they are in the range from 10-20 s.
Two processes mainly control the wave conditions in Willapa Bay: First, the wave height inside the bay is controlled by the water depth over the bar. Nearly continuous depth-limited wave breaking on the bar leads to an interior wave height modulated by the tide elevation. The second important process is wave-current interaction. According to Smith et al. (2000), currents have a minor effect on the wave conditions inside the bay, except in the navigational channel on the northern edge of the bay. Modulations of the significant wave height of 80 % are found for ebb situations and 20% for flood situations. Local wave generation by wind is only important when the wind speeds exceeds 10 m/s.
Since the wave observations in the tidal inlet was made by bottom-mounted pressure gauges, a pressure correction has been applied to the measured spectrum. Smith et al. (2000) show that this correction function is strongly affected by the ambient current, especially at ebb (opposing current). This affects the accuracy of the measured spectra at frequencies at the first harmonic and higher. As a result, the use of this data set for the validation of mean
period measures (such as Tm01) and the generation of superharmonics (through triad
interaction) is not recommended. However this data set may be reliable in terms of wave height and peak period.
Concluding, the data sets of Willapa Bay are useful but must be treated with care. Data is not yet available. Therefore, its label is ‘potential’, with prospect of becoming ‘average’.
4.1.6 Romø
4.2 Wave generation in and propagation over shallow
regions
This section presents the field cases that feature wave generation in and wave propagation over shallow regions, including Lake George, Lake IJssel, Lake Sloten and Lake Ponchartrain. The text on Lake George and Lake Ponchartrain is a summary of descriptions appearing in Alkyon (2006).
4.2.1 Lake George (Alkyon, 2006)
Lake George is a shallow water lake in south-eastern Australia with a size of approximately 20 km by 10 km (size depends on water level and rainfall), see Figure 4.7. The water depth varies seasonally from 2 m down to 0.2 m. A detailed bottom topography is available. This lake is ideally suited to investigating fetch-limited wave growth in finite-depth water. Since 1990 wave measurement have been performed by the Australian Defence Force Academy, the University of New South Wales and the University of Adelaide. The quality of this data is high. The Lake George studies were done in two parts, namely a study of wave evolution (Young and Verhagen, 1996) and a study of the physical processes that aimed at improving the formulations of source functions in SWAN (Young et al., 2005).
1 m 2 m 2 m 2 m 2 m 1 2 3 4 5 6 7 8 N 2 m 1.5 m 0.5 m 500 m d
Figure 4.7 Lake George, including bathymetry and measurement site during the campaign of 1990 to 1994.
parameterisation of the directional spreading. The typical wind speeds in these experiments ranged from 5 m/s to 18 m/s.
In the ONR Testbed (WL, 2002), the following events are studied: • 19 Dec. 1993 (22:00hr)
• 3 Oct. 1993 (17:00hr) • 21 Nov. 1992 (16:00hr)
The field campaign to study the SWAN source terms (especially wind input and white capping) was carried out from September 1997 to August 2000. This was achieved by building a measurement platform at the eastern side of the lake, about 50 m from the shore, to record waves arriving from the west. The water level was 1.5 m at the start of the measurements and dropped to about 0.20 m in late 2000. This provides a wide range of depths. The maximum wind speed during this experiment was about 20 m/s. The wave measurements were carried out with an array of 8 probes (capacitance wires).
The Lake George data sets are considered to be applicable to validate wave growth over the shoals in the Wadden Sea. The quality of these data sets are considered to be very high (quality label: ‘good’).
4.2.2 Lake IJssel
Lake IJssel is a NW-SE oriented lake of roughly 20 km by 60 km in the north of The Netherlands (see Appendix C.2). The lake bed is on average about 5 m below the reference datum (NAP). The bathymetry, last measured in 1999, is rather irregular in the NW-half and rather flat in the SE-half. The spatially averaged lake levels are typically about –20 cm NAP, but may range from about –40 cm to +40 cm NAP.
From mid-1997, well-documented wave observations have been made by Rijkswaterstaat using a network of five measurement locations, named FL2, FL5, FL9, FL25 and FL26, distributed over the lake (e.g. Bottema, 2006). This observation network features both short-, mid- and long-fetch locations (less than 1 to over 25 km), and water depths that vary from 1.2 m to 6 m. Two locations (FL25/FL375 and especially FL5) are in relatively shallow
water. The amount of data from mid-1997 up to mid-2006 is equivalent to about 76 months at the FL2-location and about 68 months for the remaining locations. Suitable data sets feature wind speeds ranging between 10 m/s and 23 m/s (records with weaker wind are typically less uniform, less stationary and not well suited for the present purpose). From 2001 onwards, the availability of wave data is generally over 80% of the considered period. Wind observations were made at the stations FL2, FL26 and, from mid-2003, also at FL25/FL37. Station FL2 is generally more suitable as a reference location than FL26. In the early years (until 2000) there are much more FL2 data available, while the number and duration of periods with failing wind equipment is much smaller than at FL26. No measurements of wind-induced currents are available for Lake IJssel. As for all situations where the waves are predominantly locally generated, this may lead to order 5-10%
uncertainty in model hindcasts. This is because wind-driven currents are expected to have some influence on the waves (Alkyon, 2005).
Figure 4.8 Lake IJssel. FL25**: until mid-2006; FL37*: since mid-2006.
In addition, some interesting nonstationary cases are selected: • 9 Sept. 2005
• 24 May 2006
The Lake IJssel are considered to be applicable to validate wave growth over the shoals in the Wadden Sea. The quality of these data sets is considered to be high (quality label: ‘good’). The following quality criteria were used in the selection of these data sets: (a) completeness (at least 4 locations) and reliability of all data, (b) stationarity at 10% level for at least two hours, (c) uniform wind field (within 5-10%), (d) thermally neutral atmosphere (in accordance with assumptions in SWAN) and (e) representativeness of typical storm conditions at Lake IJssel.
In Appendix C.2, a more extensive description is given.
4.2.3 Lake Sloten
Lake Sloten is a 3 km by 5 km lake in the Northeast of The Netherlands (see Appendix C.1). The lake bed is about 2.2 m below the NAP datum on average. Together with an average water level of about 0.5 m below the NAP datum, this yields an average water depth of about 1.7 m.
Figure 4.9 Lake Sloten.
wave growth. Such conditions are considered to also occur on the tidal flats and some foreshores of the Wadden Sea.
Reference water levels are measured with a pressure sensor, which are considered reliable from March 2001 onwards. There are no measurements of wind-induced set up of the mean water level. For hindcasts, the SL29 still water level is usually taken as a reference. The resulting model error is expected to be a few percent at most since departures from the depth-limited equilibrium situation are expected to be small because of the mild bottom slope. No measurements of wind-induced currents are available for Lake Sloten. As for all situations where the waves are predominantly locally generated, this may lead to order 5-10% uncertainty in model hindcasts (Alkyon, 2005). The bathymetry of Lake Sloten has last been measured in 2003.
The following stationary data sets have been selected for this area (see Appendix C.1): • 10 Feb. 2002 • 12 Feb. 2002 • 26 Feb. 2002 • 10 Oct. 2002 • 27 Oct. 2002 • 20 Mar. 2004
In addition, some nonstationary cases are selected: • 10 Feb. 1999
• 2 Oct. 1999 • 2 Oct. 2001 • 8 Nov. 2001 • 2 May 2003
The quality of these data sets is good, hence the label ‘good’. The following quality criteria were used in the selection of these data sets: (a) reliability of all data, (b) representativeness of typical storm conditions at Lake Sloten, (c) stationarity (within 10%) for at least a few hours Only for the cases with over 18 m/s wind, this requirement was slightly relaxed to avoid the rejection of most cases with the largest available wind speeds.
In Appendix C.1, a more extensive description is given.
4.2.4 Lake Ponchartrain (Alkyon, 2006)
Figure 4.10 Lake Ponchartrain (http://www.mpcnetwork.com/marinersinfo/charts/gm/2neworleans.gif).
Of particular interest are the measurements collected during hurricane Katrina (29 August, 2005). During this hurricane, wave measurements were collected at two of the measurement locations. Both gauges are in water with a depth of 4 m, during the passage of the hurricane the depth increased by 2 m to 3 m due to set-up effects. The wind speed was about 30 m/s from the North. The significant wave height peaked at about 2.7 m with a peak period of 7 sec. The sampling period of the wave records are rather short (8.5 min), causing a lot of scatter in the results.
The wave height measurements are probably affected by excessive tilt due to the extreme winds or they may have been submerged or overturned. Smith (2006) gives an analysis of this data set and modelling attempts with the STWAVE model. The wave heights are probably biased and therefore not useable.
Concluding, the data set obtained during hurricane Katrina is labelled ‘insufficient’ for validation purposes. It must be noted that hurricane Katrina is such an extreme and unique (and therefore, from a scientific point of view, interesting) event, that it is worthwhile to put a lot of effort in reducing the uncertainties in the data sets in order to improve its quality.
4.2.5 Other data sets featuring shallow regions
Wave observation has recently started at Lake Tai-Hu near Shanghai (China). Lake Tai-Hu has a size of 2250 km2 and an average depth of 2 m. Considering Lake Tai-Hu’s large size
Other Dutch lakes, including Lake Ketel and Lake Marker, and also Dutch river systems and inland estuarine areas were considered, see Appendices C.3, C.4 and C.5). These data sets were either considered to be of insufficient quality to be included in the validation instrument, or to be insufficiently relevant to the specific conditions at the Wadden Sea.
4.3 Penetration of swell
This section presents the field cases that feature penetration of swell, including the Westerschelde and the Oosterschelde.
4.3.1 Westerschelde
The Westerschelde Estuary in Zeeland, see Figure 4.11, features a system of canals and shoals that resemble those found in the Wadden Sea.
Figure 4.11 Westerschelde and Oosterschelde.
The Westerschelde Estuary features certain physical processes in common with the Wadden Sea, such as swell penetration with refraction over shoals and out of canals. Svašek (2003) describes hindcasts of the following storms in the Westerschelde (for each, five appropriate moments were selected):
Concerning the quality of these data sets, the following has been concluded (Svašek (2003); C. Gautier, 2006, pers. comm.):
• Parts of the wave data have to be treated with some caution, but this posed no large problems. It is advised to re-evaluate the spectra if a Westerschelde validation case is created.
• The employed bathymetry is considered to be sufficiently accurate.
• Various wind fields and water levels are available, including both uniform fields and spatially varying fields. The latter are derived from HIRLAM (wind) and WAQUA (water level). According to Gautier, these quantities may need some renewed attention. Concluding, after reassessment of the available wave data and re-addressing the wind fields and water level, the above-mentioned storm situations in the Westerschelde are likely to offer validation material with sufficient quality. The quality label is therefore ‘potential’,
with prospects of becoming ‘good’.
4.3.2 Oosterschelde
The Oosterschelde Estuary in Zeeland, like the Westerschelde, features a system of canals and shoals that resemble those found in the Wadden Sea, see Figure 4.11 and see Appendix B. The western part of the Oosterschelde, seaward of the flood barrier, could be suitable for evaluating the penetration of swell into complex shallow water areas such as those found in the Wadden Sea. The eastern part of the Oosterschelde could be suitable for evaluating swell penetration in combination with local wind growth. Here it must be noted that the influence of the barrier then needs to be taken into account.
The only available hindcast study is done by Kamsteeg et al. (2001), and concerns the 25 January 1990 storm. This data set is not very useful for the objective of the present work, since propagation of swell was not measured.
Since 1980 continuous observations have been made of waves, wind and water levels. These are available on the website of the Hydro Meteo Centre of Zeeland (www.hmcz.nl). Earlier observations have been made, but are considered to be of insufficient quality for the present purposes. The most recent bathymetrical survey of this area has been in 2000 and 2001, see (WL, 2005), which is rather old. The quality of the data available at Hydro Meteo Centre of Zeeland is not verified, but may be considered to be sufficient since it is part of Rijkswaterstaat Directie Zeeland, and therefore subject to certain quality procedures. Measured current information is not available, and will have to be obtained from numerical simulation. A selection of suitable data sets for validation has not yet been made.
4.4 Combined swell and wind sea
This section presents the field cases that feature combined swell and wind sea. These include the Westerschelde Estuary presented above, and two additional cases, namely Lunenburg Bay and the Gulf of Tehuantepec, presented below. The text on these two cases is a summary of descriptions appearing in Alkyon (2006).
4.4.1 Lunenburg Bay (Alkyon, 2006)
Lunenburg Bay, Nova Scotia, is a coastal embayment on the southern shore of Nova Scotia approximately 8 km long and 4 km wide, see Figure 4.12. It is an ideal site for studying local wind growth in the presence of a background swell. Lunenburg Bay has an irregular bathymetry, characterized by a typical depth of 10 m and it is exposed to wave energy from the North Atlantic Ocean. A 2 m to 5 m deep shoal exists near the mouth of the bay, composed of long, narrow and steep-walled bedrock ridges that are separated by deeper channels in-filled with gravel-sized sediments. A digital bottom topography is available for Lunenburg Bay.
Observational data were collected over three field seasons to date (2003, 2004, 2005) and included several large wave events, notably Hurricane Fabian (Sept. 2003), Hurricane Juan (Sept. 2003, with a deep-water significant wave height of 9 m), Tropical Storm Nicole (Oct. 2004), Tropical Storm Ophelia (Sept. 2005) and Hurricane Wilma (Oct. 2005). Some storms occurred hundreds of kilometres southward of Lunenburg Bay and generated long-period swell in Lunenburg Bay.
Figure 4.12 Lunenburg Bay.
resolving wave spectra, two single-point velocity-meters with co-located pressure sensors and standard acoustic Doppler profilers, and a surface-moored directional wave-rider buoy. The set of instruments is capable of observing directional spectra at 0.5-hr intervals for wave events at 5 locations in Lunenburg Bay. Wave boundary conditions are supplied by an offshore directional wave-rider buoy, maintained by NDBC. In 2005 these observations were supplemented with an array of wave and current instruments in the shoal region. Based on Mulligan et al. (2006) and Van der Westhuysen (2007), the quality label for Lunenburg Bay is ‘good’.
4.4.2 Gulf of Tehuantepec (Alkyon, 2006)
The Gulf of Tehuantepec is located in northwestern Mexico near the town of Tehuantepec. It is well known for having strong offshore winds, which occur predominantly during the winter months, when significant atmospheric pressure differences develop between the Gulf of Mexico and the Pacific Ocean, forcing winds through a mountain gap at the head of the gulf. These strong winds cause fetch-limited wave growth in the Pacific Ocean. In many cases a background swell is present. The wave data collected in GOTEX is therefore important for studying fetch-limited wave growth in very strong winds (velocities larger than 30 m/s), in the presence of opposing swells.
During the Gulf of Tehuantepec Experiment (GOTEX), conducted in February 2004, surface-wave measurements were made using a scanning lidar (Airborne Topographic Mapper, ATM) on the NSF/NCAR C-130 aircraft during fetch-limited conditions with wind speeds ranging from 10 to 25 m/s. Wave data are collected with an ASIS buoy located near the 60 m depth contour and 3 ADCP located along the 20 m depth contour. The experiments were carried out by Scripps institution near San Diego and the University of Ensenada, Mexico, in the framework of the GOTEX experiment.
Preliminary results of the GOTEX experiment were presented at the 2005 WISE meeting and at the AGU 2005 meeting (Romero and Melville, 2005). Based on these preliminary results, the quality of the data set appears to be good. Therefore, the quality label is ‘potential’. However, this is a proprietary data set, which has not yet been released by the principle researchers.
4.5 Remaining cases
The field cases of Petten and Haringvliet in The Netherlands are also evaluated, but are not considered relevant for the present purpose.
4.6 Summary of evaluation
To conclude the evaluation presented above, this section offers a summary of the availability and quality of data sets, organised per geographical region and event in tabular form. The last column concerns the advice whether to include the data sets in the validation instrument.
The following definitions are used:
• Events are dated as follows: [day]-[month]-[year] (hour:minute). For example, 08-02-2004 (22:20) means: 8 February 2004, at 20 minutes past 10 in the evening. In case no particular events are selected yet, this is indicated by an empty space.
• The qualification ‘Yes‘ for Avail (availability) means that the digital data is available at RIKZ, RIZA and/or WL | Delft Hydraulics. ‘No’ means that this data is not yet available for the aforementioned parties.
• The quality labels ‘good’, ‘average’, ‘insufficient’ and ‘potential’ are discussed in Section 2.7.
• Under ‘To be included’, the advice concerning inclusion in the validation instrument is given: ‘Yes’, ‘No’ and ‘Further research’. The latter means that further research is required before deciding to include it.
Table 4.1 Summary of data set evaluation. Wadden Sea cases.
Region Event(s) Avail. Quality To be included
Norderneyer Seegat 16-11-1995 17-11-1995 19-11-1995 05-02-1999 (03:40) 03-12-1999 (18:30) Yes , , , , , , , , Insufficient , , , , , , , , No , , , , , , , ,
Husum 01-11-2006 No Potential Further research
Willapa Bay Various dates No Potential Further research
Romø Various dates No Potential Further research
Table 4.2 Summary of data set evaluation. Wave generation in and propagation over shallow regions.
Region Event(s) Avail. Quality To be included
Lake George 19-12-1993 (22:00) 03-10-1993 (17:00) 21-11-1992 (16:00) 1997 to 2000 Yes , , , , No Good , , , , , , Yes , , , , Further research Lake IJssel – stationary 02-10-1999 22-02-2002 27-10-2002 12-11-2002 02-04-2003 18-04-2004 08-01-2005 12-02-2005 13-02-2005 01-11-2006 Yes , , , , , , , , , , , , , , , , , , Good , , , , , , , , , , , , , , , , , , Further research to decide which of these cases to include. Lake IJssel - nonstationary 09-09-2005 24-05- 2006 Yes , , Good , , Yes , , Lake Sloten – stationary 10-02-2002 12-02-2002 26-02-2002 10-10-2002 27-10-2002 20-03-2004 Yes , , , , , , , , , , Good , , , , , , , , , , Further research to decide which of these cases to include. Lake Sloten – nonstationary 10-02- 1999 02-10-1999 02-10-2001 08-11-2001 02-05-2003 Yes , , , , , , , , Good , , , , , , , , Further research to decide which of these cases to include.
Lake Ponchartrain 29-08-2005 No Insufficient No
Lake Tai-Hu Yet none, but
potentially interesting
--- --- ---
Lake Ketel Yes Insufficient No
Lake Marker Yes Insufficient No
Dutch river systems Yes Insufficient No
Inland estuarine areas
Table 4.3 Summary of data set evaluation. Penetration of swell.
Region Event(s) Avail. Quality To be included
Westerschelde 28-05-2000 28-12-2001 27-10-2002 Yes , , , , Potential , , , , Further research , , , ,
Oosterschelde Various dates Yes Potential
Table 4.4 Summary of data set evaluation. Combined swell and wind sea.
Region Event(s) Avail. Quality To be included
Westerschelde See Table 4.3 above --- --- ---
Lunenburg Bay Various dates Yes Good Yes
Gulf of Tehuantepec
5
Conclusions
This study has been conducted to identify a number of data sets from field measurements that can serve as validation cases in an envisaged validation instrument for spectral wave models such as SWAN, with the ultimate aim of delivering reliable wave estimates for the Wadden Sea. To this aim, selection criteria for data sets have been defined, and the typical characteristics of the Wadden Sea have been identified. Main criteria for the selection of data sets were defined, namely (a) whether the geographical region has characteristics relevant for the purpose of the validation, (b) whether the data sets are of sufficient quality and taken at suitable locations and (c) whether the data sets contain relevant events. Under item (b), the quality of data sets was further detailed as the requirement for completeness of the data sets, suitability of the measurement locations, reliability and representativity. The following quality labels have been identified:
• Good • Average • Insufficient • Potential
Data sets labelled ‘good’ are perfectly suitable for validation purposes. ‘Average’ data sets can be used for validation purposes as well, but need to be treated with care. Data sets labelled ‘insufficient’ are to be rejected for validation purposes. The label ‘potential’ for a data set means that its quality assessment has not been finalised yet.
Subsequently, these selection criteria were applied to a number of existing data sets for the Wadden Sea and, since the number of data sets for the latter proves to be limited, for areas similar to the Wadden Sea. The results of this evaluation are summarised in tabular form in Section 4.6. From these results the following conclusions can be drawn:
• A number of data sets that were recorded at the Wadden Sea, or in regions that closely resemble it, have been identified. The Amelander Seegat data set (years 2004 and 2005) of this class is considered to be of average quality for use in the validation instrument. The quality of the Norderneyer Seegat data set (years 1995 and 1999) is insufficient. The existing data sets for the Friesche Zeegat (year 1992), Husum, Willapa Bay and Romø are labelled ‘potential’.
• Additional existing data sets have been identified that do not fully match the geographical situation of the Wadden Sea, but do feature some of the physical processes occurring there. These processes are wave generation over shallow regions, penetration of swell and the occurrence of combined swell and wind sea.
areas. Lake Tai-Hu is identified as an interesting case for future application. However, no data sets are available at this moment for that region.
• Two cases were identified that features the penetration of swell into a system of canals and shoals, namely the Westerschelde and the Oosterschelde Estuaries. The quality of the existing data sets for both of these cases is ‘potential’.
• Three cases that are characterised by combined swell and wind sea conditions have been identified. These are the Westerschelde Estuary (mentioned above), Lunenburg Bay and the Gulf of Tehuantepec. The Lunenburg Bay data set is considered to be ‘good, and the Westerschelde and Gulf of Tehuantepec data sets are considered to be ‘potential’.
