Safe nautical charts require a carefully designed bathymetric survey policy,
especially in shallow sandy seas that potentially have dynamic sea floor patterns.
Bathymetric resurveying at sea is a costly process with limited resources,
though. A pattern on the sea floor known as tidal sand waves is clearly present
in bathymetric surveys, endangering navigation in the Southern North Sea because
of the potential dynamics of this pattern. An important factor in an
efficient resurvey policy is the type and size of sea floor dynamics. The uncertainties
of measurement and interpolation associated with the depth values
enable the statistical processing of a time series of surveys, using deformation
analysis. Currently, there is no procedure available that satisfies the Royal
Netherlands Navy requirements. Therefore, a deformation analysis procedure
is designed, implemented and tested in such a way that the procedure works on
bathymetric data and satisfies the Royal Netherlands Navy requirements. Also,
it is necessary to develop a procedure that translates the results into changes of
the resurvey policy, taking into account their confidence intervals.
To describe the sea floor statistically, we assume the sea floor to consist
of a spatial trend function (or characterization) and a residual function (or
dispersion). Such a description is called a representation. The covariances
between positions are expressed in a covariance function, based on the residual
function. The covariance function is used by Kriging, an interpolation procedure
that propagates the variances and covariances of the data points to variances of
the interpolated values. This approach is used widely for spatial analyses, like
the interpolation of a bathymetric data set.
The method that we propose uses Kriging to produce a time series of grids
of depth values and their variances. Subsequently, it uses deformation analysis,
a statistical procedure based on testing theory. Our application of deformation
analysis is particularly aimed at the detection of dynamics in areas with tidal
sand waves, resulting in parameter estimates for the sea floor dynamics, and
their uncertainty. We apply the method to sea floor representations both with
and without a sand wave pattern. A test scenario is set up, consisting of a
survey of an existing area in the Southern North Sea, for which dynamics are
simulated. The results show that the proposed method detects different types
of sea floor dynamics well, leading to satisfactory estimates of the corresponding
parameters.
We show results for the anchorage area Maas West near the Port of Rotterdam,
the Netherlands first. The area is divided into 18 subareas. The results
show that a sand wave pattern is detected for most of the subareas, and a shore ward migration is detected for a majority of them. The estimated migration
rates of the sand waves are up to 7.5 m/yr, with a 95% confidence interval that
depends on the regularity of the pattern. The results are in confirmation with
previously observed migration rates for the Southern North Sea, and with an
idealized process-based model.
Thereafter, we analyze several other areas for which a time series of surveys is
available in the bathymetric archives of the Netherlands Hydrographic Service,
to study the spatial variations in sea floor dynamics. We present results for
several sand wave areas and a single flat area. In some of those areas, dredging
takes place, to guarantee minimum depths. The results indicate sand wave
migration in areas close to the coast, and bed level changes of the order of
decimeters. The dominant wavelength of the sand waves varies. We compare
our results to literature of the same sand wave areas, in which we find similar
migration rates, and different wavelengths.
By formulating four indicators, recommendations are made for the resurvey
policy on the Belgian and Netherlands Continental Shelf. These indicators follow
from the estimates for sea floor dynamics. We present a concept for the
shallowest likely depth surface, on which we base two of the indicators. The
other two indicators act as a warning: they quantify the potentially missed
dynamics, which makes the procedure more robust in case of complicated morphology.
We show clear differences in recommended resurvey frequency between
the five analyzed regions.
We conclude that the designed method is able to use a time series of bathymetric
surveys for the estimation of sea floor dynamics in a satisfactory way.
Those dynamics may be present on the scale of the sea floor, it may be a local
effect, or it may be due to a tidal sand wave pattern. Also, the results are successfully
reduced to a set of four indicators, used to improve a resurvey policy.
Based on these conclusions, we formulate recommendations on the extrapolation
of the results in space and time, on potential adaptations to the designed
procedure, and on implementation of the procedure.