Theoretical Background - Response of Flood Events to Land Use and Climate Change - page 15

Fig. 2.2 The principles of
interpolation technique [ 16 ]

z

z 2

z ?
0

z 3

z 1

y

(x ,y ) 33

(x ,y ) 00

(x ,y )
22

(x ,y )
11

x

data points in a certain neighborhood of the estimated point [ 16 ]. The selection of
interpolation methods depends primarily on the nature of the variable and its
spatial variation [ 16 ].
There have been many studies that compare the effectiveness of alternative
interpolation techniques, using a wide range of different test datasets and condi-
tions. Overall, it has been found that a number of well-defined factors have a major
influence on the quality of interpolation: data measurement accuracy; data density;
data distribution; and spatial variability.
These factors are fairly predictable findings, but prior examination of each of
these elements may assist in choosing the most appropriate technique for the problem
at hand and/or be used in guiding sampling of new or supplementary data sets.
Interpolation quality can often be substantially improved through the use of ancillary
information, such as remote sensing data or additional environmental information
(e.g., location of stream networks) [ 17 ]. Having obtained the best possible data set,
within budget and time constrains, achieving the maximum usage and value is very
important. Hence more general spatial interpolation is required [ 17 ]:

• To convert from one level of data resolution or orientation to another (resam-
pling). Usually, resolution is reduced to the coarsest in a set, but resolution can
be increased using a suitable interpolator.
• To convert from one representation of a continuous surface to another, e.g.,TIN
to grid or point or contour to grid [ 17 ].

There are two interpolation techniques; Deterministic and Geostatistical:

• Deterministic interpolation is directly based on the nearby measured values or
on specified mathematical formulas that determine the smoothness of the
resulting surface.
• Geostatistical interpolation is based on statistical models that include autocor-
relation (statistical and spatial relationships among the measured points).

Different interpolation techniques characteristics are summarized in Table 2.1
[ 17 , 19 ]: