Fedor Baart

The Effect of the 18.6-Year Lunar Nodal Cycle on Regional Sea-Level Rise Estimates

Abstract BAART, F.; VAN GELDER, P.H.A.J.M.; DE RONDE, J.; VAN KONINGSVELD, M., and WOUTERS, B., 2012. The effect of the 18.6-year lunar nodal cycle on regional sea-level rise estimates. Sea-level rise rates have become important drivers for policy makers dealing with the long-term protection of coastal populations. Scenario studies suggest that an acceleration in sea-level rise is imminent. The anticipated acceleration is hard to detect because of spatial and temporal variability, which consequently, have become important research topics. A known decadal-scale variation is the 18.6-year nodal cycle. Here, we show how failing to account for the nodal cycle resulted in an overestimation of Dutch sea-level rise. The nodal cycle is present across the globe with a varying phase and a median amplitude of 2.2 cm. Accounting for the nodal cycle increases the probability of detecting acceleration in the rate of sea-level rise. In an analysis of the Dutch coast, however, still no significant acceleration was found. The nodal cycle causes sea level to drop or to rise at an increased rate; therefore, accounting for it is crucial to accurately estimate regional sea-level rise.

Trends in Sea-Level Trend Analysis

Abstract BAART, F.; VAN KONINGSVELD, M., and STIVE, M., 2012. Trends in sea-level trend analysis. Discussions on sea-level rise trend estimates as, for example, the one recently published in this Journal of Coastal Research, reveal different perspectives on proper methods of deriving sea-level trend estimates. This editorial discusses various methodological considerations and proposes a number of best practices for sea-level trend analysis.

A Comparison between WCS and OPeNDAP for Making Model Results and Data Products Available through the Internet

Numerical models produce output with a large number of variables, grid cells and time steps. The same applies to algorithms that produce gridded datasets from sparse or abundant raw data. Further use of the resulting data products has been challenging, especially for dissemination outside the institute of origin. Due to the gradually increasing size of data products, simply downloading copies of them is becoming impossible. A gradual transition from traditional download methods to web services is therefore observed. Web services allow for on-the-fly access to subsets of data that were hitherto considered as indivisible granules. Here we compare the most mature candidates to serve gridded data through the web: the Open-source Project for a Network Data Access Protocol (OPeNDAP) and Web Coverage Service (WCS) protocols. In the framework of the new Dutch National Model and Data Centre (NMDC.eu) a distributed data storage has been created by coupling OPeNDAP servers. A WCS service layer is provided for the same data. This allows us to compare OPeNDAP and WCS. Using several use cases, we compare the usability, performance and features of the two protocols.

OpenEarth : Using Google Earth as outreach for NCK's data

In 2003 various projects at Deltares and the TU-Delft merged their toolboxes for marine and coastal science and engineering into one toolbox, culminating in 2008 in an open source release, known as OpenEarthTools (OET). OpenEarth adopts the wikipedia approach to growth: web 2.0 crowd sourcing. All users are given full write access to help improve the collection. Quality is assured by version control, tracking all changes. OpenEarth started as a social experiment to investigate whether crowd sourcing was possible in our community of marine and coastal researchers. The answer is yes: over 1000 users registered, now enjoying over 5000 contributions from over 100 contributors. The most important asset is a general toolbox to plot any data type in Google Earth. With this toolbox it has become very easy for marine and coastal experts to disseminate their data via Google Earth. It enables the NCK community to make its data available to end-users and the general public with only little effort. They can now consume our data as simple as watching YouTube: DataTube. In this paper it is shown that OpenEarth has added important value by analyzing a wide range of marine and coastal data types from NCK simultaneously in Google Earth. To match the traditional gap between specialist knowledge and end users, Google Earth is shown to be a very powerful tool. The possibilities for outreach by NCK are manifold.

Morphological impact of a storm can be predicted three days ahead

People living behind coastal dunes depend on the strength and resilience of dunes for their safety. Forecasts of hydrodynamic conditions and morphological change on a timescale of several days can provide essential information to protect lives and property. In order for forecasts to protect they need be relevant, accurate, provide lead time, and information on confidence. Here we show how confident one can be in morphological predictions of several days ahead. The question is answered by assessing the forecast skill as a function of lead time. The study site in the town of Egmond, the Netherlands, where people depend on the dunes for their safety, is used because it is such a rich data source, with a history of forecasts, tide gauges and bathymetry measurements collected by video cameras. Even though the forecasts are on a local scale, the methods are generally applicable. It is shown that the intertidal beach volume change can be predicted up to three days ahead.

The State of the World’s Beaches

Coastal zones constitute one of the most heavily populated and developed land zones in the world. Despite the utility and economic benefits that coasts provide, there is no reliable global-scale assessment of historical shoreline change trends. Here, via the use of freely available optical satellite images captured since 1984, in conjunction with sophisticated image interrogation and analysis methods, we present a global-scale assessment of the occurrence of sandy beaches and rates of shoreline change therein. Applying pixel-based supervised classification, we found that 31% of the world’s ice-free shoreline are sandy. The application of an automated shoreline detection method to the sandy shorelines thus identified resulted in a global dataset of shoreline change rates for the 33 year period 1984–2016. Analysis of the satellite derived shoreline data indicates that 24% of the world’s sandy beaches are eroding at rates exceeding 0.5 m/yr, while 28% are accreting and 48% are stable. The majority of the sandy shorelines in marine protected areas are eroding, raising cause for serious concern.

Intertidal beach classification in infrared images

ABSTRACT Hoonhout, B.M., Baart, F., Van Thiel de Vries, J.S.M. 2014. Intertidal Beach Classification in Infrared Images. In: Green, A.N. and Cooper, J.A.G. (eds.), Proceedings 13th International Coastal Symposium (Durban, South Africa), Journal of Coastal Research, Special Issue No. 70, pp. 657–662, ISSN 0749-0208. Digital imagery is a powerful data source for coastal monitoring, maintenance and research. It provides high-resolution measurements in both time and space. The size and resolution of long-term imagery datasets provide great opportunities, but also pose problems of tractability in the data analysis. In order to fully use the possibilities of these datasets, reliable and automated classification of images is essential. This paper discusses an automated classification approach based on Conditional Random Fields (CRF). The algorithm is applied in pixel space only. Therefore it does not rely on in-situ measurements, nor is there a need for image rectification. The algorithm consists of three steps: segmentation, feature extraction and model training and prediction. We applied the method to a coastal thermal infrared image stream that monitors the wetting and drying of the upper intertidal beach in relation to tide and meteorological parameters. Classification of the upper intertidal beach provides information on the potential sources of Aeolian sediment. The use of 62 extracted features and structured learning proves to provide significantly better classification results compared to algorithms solely based on intrinsic intensity features.

Author Correction: The State of the World’s Beaches

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.