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.