I have elevation data for a region consisting of a lake and it is surrounded by a wetland. Now for using the data for forming a hydrodynamic model, I need to smoothen the bathymetric data for the lake area. How can I do so without disturbing the characteristic bed geometry?
It depends on what you mean by 'geometry'. Volume? Mean depth? Surface area? Shape of hypsometric curve?
I assume from the question that your model grid resolution is coarser than the data resolution, in which case the simplest approach would be to form a simple average of observed values within each grid cell.
Averaging has problems if the data distribution is non-uniform. Under such conditions (which you might have from soundings) you have what might be considered to be a classic gridding problem. There is a large literature on such problems, and many possible solutions (Venables & Ripley 1999). If smoothness is desired, you might try using the "Barnes" method, which is used a lot in oceanography (Koch et al. 1983; Barnes 1994).
REFERENCES
The citations given below may be useful, either directly or as entries to the literature.
@book{venables1999mass,
Address = {New York, NY, USA},
Author = {W. N. Venables and B. D. Ripley},
Edition = {Third},
Publisher = {{S}pringer-{V}erlag},
Title = {Modern applied statistics with {S}-plus},
Url = {http://www.stats.ox.ac.uk/pub/MASS4},
Year = {1999}}
@article{koch1983aibo,
Author = {S. E. Koch and M. DesJardins and P. J. Kocin},
Journal = Journal of Climate and Applied Meteorology,
Pages = {1487-1503},
Title = {An interactive {B}arnes objective map analysis scheme for use with satellite and conventional data},
Volume = {22},
Year = {1983}}
@article{barnes1994aobo1,
Author = {S. L. Barnes},
Journal = {Journal of Atmospheric and Oceanic Technology},
Pages = {1433--1447},
Title = {Application of {B}arnes objective analysis scheme. {P}art {I}: effects of undersampling, wave position, and station randomness},
Volume = {11},
Year = {1994}}
I got rid of ripples in my bathymetry with a moving average. Depending on the window size, more or less detail is kept. I used the convolution (http://en.wikipedia.org/wiki/Convolution) function in Python (Numpy module) to do it. You can also do it in Matlab.
My bathymetry was 1D but there are 2D convolution functions in Python and Matlab.
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