ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
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GIS-BASED SYSTEM FOR RAINFALL ESTIMATION USING RAINGAUGE DATA: A PROTOTYPE 
Yinchai WANG 
Faculty of Information Technology, Universiti Malaysia Sarawak, 
94300 Kota Samarahan, Sarawak, MALAYSIA 
Tel:+60-82-671000 ex377, Fax:+60-82-672301, E-mail:ycwang@fit.unimas.my 
Teck Kiong SIEW 
Faculty of Information Technology, Universiti Malaysia Sarawak, 
94300 Kota Samarahan, Sarawak, MALAYSIA 
ABSTRACT 
High-quality rainfall measurements on a variety of temporal and spatial scales are desirable for a wide range of applications. Reliable 
values of rainfall are not yet available for most areas over Sarawak because of scarcity of gauge measurements. This research 
developed the optimal rainfall estimation model to estimate monthly rainfall from the surrounding rainfall stations. The technique 
pursued was a geostatistical approach by taking into account the effect of topography factor on spatial variation of rainfall. But, there 
are problems with the topography factor especially in dealing with geographical data and spatial analysis. These problems, that can not 
be solved manually, were overcome with the aid of GIS which has full capabilities in generating geographical data and performing 
spatial analysis. Thus, the whole system was implemented using GIS. The procedure involved was the interpolation method applied 
over the determined best area of interpolation in which all the surrounding rainfall stations were significantly correlated to the point 
which rainfall was to be estimated. Regression analysis was applied for model selection. The optimal rainfall estimation model 
selected was the best multiple linear regression equation that consists of topography parameters as independent variables, applied over 
the best area of interpolation. Cross-validation was used for the optimal model evaluation and for comparing with six alternate 
estimation methods. The optimal model provided the best cross-validation result. The output was a GIS based Rainfall Estimation 
System for estimating point and are-average monthly rainfall at an unmonitored site, and for distributing point rainfall estimates to 
regular spaced grid cells in producing isohyets of monthly rainfall over the study area. 
1. INTRODUCTION 
In Sawarak, many human and economic activities involving 
agriculture, industry, commerce and transportation are 
extremely reliant on the weather conditions. Observation of 
rainfall includes raingauge measurement that is the direct and 
quantitative measurement of rainfall, provided by all the rainfall 
stations. Space observation of rainfall refers to infrared (IR) 
satellite and radar data that give an indirect measurement of 
rainfall in terms of temperature and reflectivity, provided by 
satellite and radar at Kuching Meteorological Station only. 
Quantitative measurement of rainfall are preferred and 
required by many activities and models. The main problem 
with raingauge observations lies in the lack of dense network. 
As compared to vast landmass and complex terrain, there is a 
lack of conventional gauge measurements in Sarawak, which 
prevents adequate sampling of rainfall observations for many 
applications such as agriculture and hydrological models. In 
view of this scarcity of observation, estimates of the amount 
and spatial distribution of rainfall are critical inputs to a variety 
of these models. However, obtaining reliable estimates is 
particularly difficult when the area coverage provided by the 
surrounding stations is sparse and when rainfall varies greatly 
with locations due to topographic effects which involve normal 
land or sea breeze effects and upslope or downslope motion 
of the monsoon flow over the mountains. In order to take into 
account these topographic effects, which involve spatial data, 
into rainfall estimation, there is a need of analytical tools 
dealing with spatial data and analysis, which can not be done 
manually, to improve rainfall estimation. This acts as a main 
hindrance in an effort to develop rainfall estimation model to 
estimate rainfall. Until recently, the advancement in computer 
technology especially the recent development of GIS provides 
tools tackling the spatial data. GIS, systems which deal with 
spatial information, map processing, spatial database and 
spatial analysis, can play an important role in dealing with 
organizing and integrating apparently disparate data sets 
together by geography and performing more complex spatial 
analysis. 
The main objective of this research is to estimate rainfall at 
unmonitored site using rainfall data from surrounding 
raingauge stations using the interpolation procedure with the 
integration of Geographic Information System (GIS). With the 
aid of GIS, topography factor itself, consists of other variables 
such as elevation, barriers and land-sea distribution, affecting 
the rainfall and its distribution greatly can be analyzed to study 
its extent of effects to rainfall estimation which, otherwise, is a 
very difficult task. Thus, GIS enables rainfall estimation 
technique to include as many parameters of the topography 
factor which involves spatial data. This study will focus on 
how to incorporate spatial analysis into the rainfall estimation 
and monitoring using raingauge data. 
The rest of this paper is organized as follows: Section 2 
consists of literature review on the rainfall estimation 
techniques. Section 3 discusses the components of the 
methodology in deriving the optimal rainfall estimation model 
and developing GIS-based system. Section 4 describes the 
implementation of the whole system. Analysis and conclusion 
are given in sections 5 and 6 respectively. 
2. LITERATURE REVIEW 
2.1. Rainguage - Rainfall Estimation 
Raingauge networks measure rainfall accumulations on the 
ground at fixed locations. There is a tendency to 
underestimate rain amount due to physical and human errors 
such as wind direction, surrounding object, overflow and 
observation errors. Inspite of this, raingauges still give very 
good point accuracy. However, raingauges also suffer serve 
limitation in sampling especially over ocean and remote land 
areas as most of raingauges tend to be distributed with a 
pronounced spatial bias toward populated areas along the 
coast and against areas with high elevation and/or slope . In 
spite of this, several studies have demonstrated the utility of 
simple models based on the point data to diagnose factors 
such as topography, geology, hydrographic and vegetation 
which are observed to cause rainfall variations even over short 
distances such as a diagnostic model for estimating rainfall 
[1].