long-term bathymetry changes quest4d job janssens – flanders hydraulics research
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Long-term bathymetry changes
Quest4D
Job Janssens – Flanders Hydraulics Research
• Available data• Interpolation
concerns
methodology
results
• Analysis of the gridsvisualization of the depth lines
trend analysis
chart differencing
conclusions
Outline
Available data
Selection of historical navigation charts:(charts available at Hydrography Department, Flemish Authorities)
2007 chart high resolution grid (20m x 20m)
other charts irregular pattern of data points
Example:Chart of 1938
Available data
Example:Chart of 1938
datapoints 1938 digitized in ArcGIS
Available data
Example:Chart of 1938
datapoints 1938 digitized in ArcGIS
datapoints 1908 digitized in ArcGIS
Available data
Example:Chart of 1938
datapoints 1938 digitized in ArcGIS
datapoints 1908 digitized in ArcGIS
different charts have datapoints on different locations
interpolation of each set of datapoints to a grid
Available data
ArcGIS interpolation techniques:
IDW
kriging
natural neighbor
Problems associated with interpolation:
are averaging techniques
average value cannot be greater than highest or less than lowest input
in sparse data sets:
interpolation cannot reproduce ridges or troughs!
seafloor morphology flattened by interpolation
Interpolation: concerns
“straightforward” interpolation: test case
• 2007 data point set: - high resolution (20m x 20m) - no interpolation needed
Interpolation: concerns
“straightforward” interpolation: test case
• 2007 data point set: - high resolution (20m x 20m) - no interpolation needed
• subset of the 2007 data point set
Interpolation: concerns
“straightforward” interpolation: test case
• 2007 data point set: - high resolution (20m x 20m) - no interpolation needed
• subset of the 2007 data point set
• interpolation of this subset
Interpolation: concerns
“straightforward” interpolation: test case
difference chart: 2007 - 2007 interpolated
• ridges less higher than they are• troughs less deeper than they are
Conclusion:
interpolation of sparse data set flattens morphology
interpolation error correlated with location
Interpolation: concerns
Solution: use high resolution data of 2007 to estimate interpolation error
Example: grid of 1938
grid of 1938 = interpolation of data points 1938
interpolation of sub- set data points 2007
grid of 2007_
Interpolation: methodology
_
estimation of inter- polation error
! Basic assumption:
data points of 2007, but only at locations of the 1938 data points
stable morphology: no major changes in location of ridges/troughs
Interpolation: illustration methodology
Interpolation: illustration methodology
Interpolation: illustration methodology
Interpolation: illustration methodology
Interpolation: illustration methodology
Interpolation: illustration methodology
Interpolation: illustration methodology
Interpolation: illustration methodology
1866
Interpolation: results
1908
Interpolation: results
1938
Interpolation: results
1969
Interpolation: results
2007
Interpolation: results
1) visualization of different depth lines
2) trend analysis
3) chart differencing
Erosion/sedimentation patterns studied through:
Analysis of the grids
1) visualization of depth lines:
Analysis of the grids
Example: Middelkerkebank, 8m depth lines
2) Trend analysis:
Analysis of the grids
linear least square fit on time series of depth values
sedimentation trend
~ 0.03 m/year
(time series of 5 depth values for each grid cell)
2) Trend analysis:
Analysis of the grids
3) Chart differencing:
Analysis of the grids
Conclusions:
Analysis of the grids
Morphologic changes:
• Anthropogenic:
dredging: of navigation channels (Scheur, Pas van ‘t Zand)
dumping: S1 (Sierra Ventana), …
influence of breakwaters Zeebrugge harbour: bay of Heist
• Natural:
no significant movement of the banks sedimentation/erosion: coastal banks form the most dynamic zone
sedimentation of the ridges (e.g. Oostendebank)
erosion of the troughs (e.g. Grote Rede, Kleine Rede)
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