Theoretical Background - Response of Flood Events to Land Use and Climate Change - page 14

sources are very accurate, precise, and have high density, especially if the data are
derived directly into a regular grid (DEM) [ 3 , 11 ].
The quality of a derived DEM could vary largely depending on the data source
and the interpolation technique. The desired quality also depends on which
application the DEM is used; although, a DEM created for one application is often
used for other purposes. Therefore, to create any DEM, it is necessary to consider
the best available data sources and the best processing technique.
Spurious sinks or local depressions in DEMs are frequently encountered and are
a significant source of problems in hydrological applications. Sinks may be caused
by incorrect or insufficient data, or by interpolation techniques that does not
enforce surface drainage. They are easily detected by comparing elevations with
surrounding neighbors [ 11 ]. Therefore, detection of spurious sinks features or local
depressions in DEMs can lead to improvements in DEM generation techniques as
well as detection of errors in source data as indicated above [ 12 ].
More subtle drainage artifacts in a DEM can be detected by performing a full
drainage analysis to derive catchment boundaries and streamline networks, using
the drainage networks creation techniques [ 13 ]. Since, applications of DEMs
depend on representations of surface shape and drainage structure, absolute mea-
sures of elevation error do not provide a complete assessment of DEM quality [ 2 ].
Based on the above description about DEM error, it is necessary to make it clear
that, in science, the word ''error'' does not carry out the usual meaning of the term
''mistake'' or ''blunder''. The error in scientific measurements means the inevitable
uncertainty. As such, errors are not mistakes; the scientists cannot eliminate them;
therefore, the best thing to do is ensure that errors are as small as reasonably
possible with reliable estimate of how large they are [ 14 ]. To this purpose, the
starting point in this research was statistical, spatial and hydrological controlling,
estimating, and correcting the possible errors in the DEM [ 15 ] which will be
explained in the next sectors.
2.3 Interpolation Techniques in Drainage Network
Estimation
Interpolation methods often assume data points are correct and accurate, but it is
assumed that these data points may be subjected to error [ 5 ]. The models appli-
cation to estimate the unknown points may predict the data points exactly
(go precisely through the sample data points) or inexactly (approximate the values
at the data points). If the observed data points are relatively sparse and irregular or
widely spread, interpolation needs to be more sophisticated than for dense,
regularly spaced data. The principles of interpolation are shown in Fig. 2.2 .
However, regularly spaced data may be subject to bias due to intrinsic fre-
quencies in the data [ 5 ]. An interpolation method is working globally, if all data
points are evaluated in the interpolation. Local interpolation techniques use only