Introduction
Geographical studies are necessary for planning and forecasting. Hence,
it is necessary that a thorough study of topographical features in the
study area should be done. This will help to know the basic parameters
to be kept in mind for the purpose of planning and forecasting events
such as floods and extreme rainfall events. Precipitation is one of
basic and most important parameters if it comes to hydrological
modelling. Therefore, precipitation characteristics of an area have to
be examined properly for modelling of runoff and other hydrological
events. Characteristics of precipitation are known only by measuring it
accurately. Traditional way of measuring precipitation involves usage of
rain gauges. This method requires placing of rain gauges at different
locations and recording the readings manually or automatically every
twenty-four hours. This is a bit tedious work and placement of rain
gauges is not easy if the topography of the region is uneven. Problems
such as maintenance of rain gauge are required from time to time. On the
other hand, if precipitation is accompanied by strong winds then the
amount of rainfall recorded is not accurate. Another precipitation
measuring technique is Tropical Rainfall Measuring Mission (TRMM)
Multi-satellite Precipitation Analysis (TMPA) which gives an alignment
based successive plan for joining rainfall gauges from various
satellites, and in addition check investigations where possible, at
satisfactory scales (0.25° X 0.25°). As per Huffman et al. (2006)
initial approval outcomes are as per the following: the TMPA gives
sensible execution over month to month scales. The TMPA, has bring down
expertise in effectively indicating moderate and light events on brief
time interims, just the same as other fine scale observations.
Illustrations are given of a surge occasion and diurnal cycle assurance.
Joyce et al. (2004) talks about a system using which half-hourly
worldwide precipitation estimates achieved from remote sensing satellite
are circulated by movement vectors got from geostationary satellite
information. The (CMORPH) utilizes movement vectors got from half-hourly
interval geostationary satellite IR symbolism to circulate generally
higher amount of precipitation measurement got from passive microwave
information. Moreover, the shape and intensity of the rainfall
highlights are altered amid the interval in which microwave sensor
examines by using time weighted direct linear interpolation. This
procedure yields both spatial and temporal total microwave-inferred
rainfall investigations, autonomous of the infrared temperature field.
It was seen that CMORPH made improvement in averaging estimations done
by microwave. CMORPH also improved those techniques which uses microwave
and infrared data to estimate precipitation usually when passive
microwave information is not available. According to Ebert et al. (2007)
satellite measurements of precipitation event are most exact amid summer
and at lower latitudes, while the NWP models indicate most noteworthy
expertise a midwinter and at higher latitudes. As a rule, the more the
precipitation regime inclines toward profound convection, the more
(less) precise the satellite (model) measurements are. The approval over
the Joined States additionally recommends that in general the IR-PMW
blended satellite measurements performed and also radar as far as every
day precipitation. We accentuate that these outcomes apply to
precipitation gauges made (for the most part) over land, at day by day
time scales and - 25 km spatial scales. The exactness would surely be
diverse for shorter eras and could enhance or break down depending upon
the regime. The satellite precipitation evaluations might be more exact
over the sea than over land in light of the fact that the PMW
calculations can have the advantage of the microwave emission channels.
Consequently, the decisions with respect to relative exactness of models
versus satellite assessments ought to be rethought for oceanic rainfall,
maybe utilizing TRMM precipitation radar data measurements as approval
for month to month models and satellite precipitation aggregations.
In hilly regions it is not easy to maintain a proper rain gauging
network because of topographical hindrance. Therefore, precise
measurement of rainfall and runoff cannot be done in such regions. In
such regions precipitation over the whole region can be determined using
interpolation. It requires selection of a proper interpolation technique
and then utilising that technique to determine the precipitation over
the entire region. Thus, if we have
a precipitation data measured in a region using rain gauges, then we can
utilise that data set for interpolation and also determine the extent of
precipitation over that region. The approach utilised for interpolation
is geo-statistical technique, this differs from the classical
statistics. The difference between classical statistics and
geo-statistical is that classical approach assumes that every single
data from a group of data is independent and it does not tell about the
other data while geo-statistical approach assumes the spatial data which
is being utilised has a correlation between them this correlation is a
function of distance between the gauging stations. Geo-statistical
techniques have become very popular for interpolation of data where
there is a scarcity of data. Geographical Information System (ArcGIS
10.2.2) provides its users with a variety of interpolation techniques.
Each of these techniques has their own advantages and disadvantages. One
cannot decide prior to use any techniques. After a proper examination of
the data and the topographical features of a region, any one of the
techniques is utilised to perform the interpolation. The superiority of
geo-statistical methods over classical one can be inferred from previous
study done for optimizing monitoring networks.