Long-term bathymetry changes Quest4D Job Janssens – Flanders Hydraulics Research.

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Presentation transcript:

Long-term bathymetry changes Quest4D Job Janssens – Flanders Hydraulics Research

Available data Interpolation concerns methodology results Analysis of the grids visualization 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: 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 subset data points 2007 grid of 2007 _ Interpolation: methodology _ estimation of interpolation 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

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)