In rainfall data. In order to obtain a

In the sense of
identifying the likely behavior of several actual hydraulic features, the
Hydrological Modelling plays a vital role in Hydraulic Designing. While being
the main source of water, precipitation involves considerably in these kind of
hydraulic related designing works. The problem associated here is the
non-uniformity in the rainfall both in spatially and temporally.


There are
several methods for spatial interpolation of rainfall data. In order to obtain
a regionalized value, it is very important to the best method suitable for
doing the interpolations. Mainly there are two categories for spatial
interpolation. They are,

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1.      Deterministic
Interpolation Methods

2.      Geostatistical
Interpolation Methods


Simple Average
Method, Thiessen Polygon Method (THI) which also named as Neighbor Method (NN),
Inverse Distance Weighting Method (IDW) are some of major Deterministic Methods
which are widely used in practice. Apart from those, Polynomial Interpolation
Method (PI) and Moving Window Regression (MWR) also categorize under this type.
Kriging can be discussed under Geostatistical Interpolation Methods which
provides some measurement of accuracy also in the process. (Ly, Charles,
& Degre, 2013)
Due to the availability of different methods, as there is a wide variety of the
used concepts, the final output may vary from method to method.


Due to the
uncertain factors like topographic details, these kind of variations can happen
considerably and that is the reason to use a proper method for the
interpolation of rainfall data. (Chen, et al., 2017) As the outcome from
the rainfall interpolation is supposed to be used as the design data for
hydraulic designs, it is very important to ensure the accuracy and reliability
of the values. Major concept of selecting the most suitable way of analyzing is
straightly depends on the availability of rainfall data. With much
representative data values some methods may give the most suitable data values
for the designing purposes. (Ly, Charles, & Degre, 2013)