Butuan City Rainfall Spatial Analysis Using GIS
Jerald L. RUTA and Anamarie J. PONDOG, Philippines
Key words: Rainfall Spatial Analysis, Thiessen Polygon, StatPlanet
SUMMARY
This paper examines the rainfall spatial characteristics of Butuan City during the
whole year of 2014. The monthly precipitation database was comprised of four (4) automated
rain-gauge stations situated in the study area. Monthly rainfall concentrations have been
studied in the context of their mean values and spatial diversification in order to establish the
spatial distribution of rainfall patterns and to detect homogeneous areas with similar rainfall
evolution. The location of automate rain gauges installed in Butuan City was determined and
Thiessen polygon was created to compute the average rainfall over the basin. GIS, as an
essential tool that helps to demonstrate and understand the hydrological analysis of an area,
was used to view, edit and analyze rainfall data. StatPlanet on the other hand, was employed
for an attractive interactive visualizations that facilitate the interpretation of information and
to promote evidence-based decision making by improving and facilitating the communication
and interpretation of information. Results showed that the precipitation distribution over
Butuan City is not uniform. There was a wide variations among seasons and locations noticed.
It was found out that Brgy. Dugyaman, Anticala had the highest recorded rainfall precipitation
of 267.31 mm while the lowest recorded precipitation of 174. 92 mm was in Brgy. Sumile and
the average precipitation of Butuan City on 2014 was 202.04 mm.
.
13th South East Asian Survey Congress
Expanding the Geospatial Future
28th – 31st July 2015
Marina Bay Sands, Singapore
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Butuan City Rainfall Spatial Analysis Using GIS
Jerald L. RUTA and Anamarie J. PONDOG, Philippines
1. INTRODUCTION
Every year, Philippines experiences strong typhoons with heavy rains and flooding
during monsoon season. In past, these natural phenomena have plagued the country, claiming
many lives and million worth of properties. In order to prevent excessive damages, obtaining
timely and accurate data is a key to disaster mitigation. In preparation for such calamities, the
Advanced Science and Technology Institute (ASTI), in cooperation with the Philippine
Atmospheric, Geophysical, Astronomical Services Administration (PAGASA), both attached
to the DOST, have enhanced our country’s capability in monitoring real-time weather
disturbances by developing and producing low-cost system solutions and instrumentations.
All rainfall data from the automated rain gauge stations are collected on a central
database server of https://fmon.asti.dost.gov.ph/weather/predict and further analyzed for
possible flooding if the intensity of rainfall exceeds a pre-determined threshold or critical
level. The processed data are readily available and easily accessible to the local authorities
especially the disaster response unit over the internet, on a real-time basis. A web-based
visualization tool enables quick information or warning dissemination and disaster
preparedness, as well as provides easy mapping to pinpoint which areas are affected in a finer
scale.
With the application of GIS, data management and retrieval, data modeling (e.g.,
contouring), spatial analysis, and presentation can be performed. Data modeling functions
include contouring of groundwater levels or dissolved contaminant concentrations, and terrain
modeling for watershed analysis. Data modeling also can be used to develop subsurface
structure for a groundwater model or a stream network within a surface water model. Beyond
data modeling, the real strength of GIS is in spatial analysis, determining relationships
between different information in a spatial context. Inherent in spatial analysis is the process of
overlaying different datasets to determine relationships between them. An example might be
an analysis of regional land use (which might be a grid-based raster dataset), and its effects on
groundwater quality (a set of contours resulting from modeling of point sample data) [1].
2. RESULTS AND DISCUSSION
Figure 1 showed the process flow of the
study which employed to attain the objectives of
this study. The process flow comprises of three
sections namely; Pre-processing, Joining and
Visualization of Data and Post Processing of Data.
The researchers conducted courtesy call
prior to site visit to ensure security and to officially
informed the local officials regarding the research.
Figure 1. Process Flow of the Study
13th South East Asian Survey Congress
Expanding the Geospatial Future
28th – 31st July 2015
Marina Bay Sands, Singapore
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The locations of the automated rain gauges were took and projected through the
projection of the maps in ArcGIS. The coordinates of the specific location were very
important in the analysis of rainfall distribution using Theissen polygon method and the
coordinates is shown in Figure 2. It shows that the ARG is installed dirtributed throughout the
city. There are four (4) ARGs installed and located in Brgy. Anticala, Brgy. Ampayon, Brgy.
Bancasi and Brgy. Sumile. Basically, these ARGs were used to monitor the rainfall in the
coverage area. The estimate areal distribution of precipitation depth in the study area was also
determined using Theissen Polygon. Figure 3 shows the rainfall distribution of Butuan City.
The polygons represent the catchment area of the rain gauge. For agriculture, this information
find significant. It can provide information on the areas regarding the crops water
requirement. It helps to determine the amount of water need for irrigation or to be drain when
there is excess of water.
Figure 1. Coordinates of Automated Rain
Gauges
Figure 3. Rainfall Spatial Analysis in
Butuan City using Thiessen Polygon
Method
Moreover, the collected average rainfall of Butuan City for 2014 is also presented in
Table 1. Table shows the bisectional area of Thiessen polygon, measured average
precipitation of the station and the rainfall volume. The average precipitation over the
catchment was computed by the total of column 4 to the total area in column 2. Thus, the
average precipitation of Butuan City on 2014 was 202.04 mm.
Station
Bisectional Area
(Km2)
Sumile
Dugyaman Anticala
Ampayon
PAGASA
Compound, Bancasi
Total
265.132
51.734
213.89
286.544
817.3
13th South East Asian Survey Congress
Expanding the Geospatial Future
28th – 31st July 2015
Marina Bay Sands, Singapore
Measured
Precipitation
(mm)
174.92
184.63
267.31
181.55
Rainfall volume
(mm- Km2)
46376.89
9551.65
57174.94
52022.06
165125.54
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Possible Small Water Impounding Project (SWIP) in Butuan City was also
determined. In Figure 4, potential SWIP (small water impounding project) with the use of
watershed tool in ArcGIS is shown. Also with the used of DEM, it identified its water
accumulation and elevation. Result shows that in Brgy. Maibu, Bilay and Maguinda are
potential for small water impounding project.
Figure 4. Small Water Impounding Project using GIS
Furthermore, this study utilized StatPlanet software for the interactive visualization. In
Figure 5, the rainfall spatial analysis of Butuan City using StatPlanet is shown. Figure shows
the monthly rainfall data of four stations. This tool promotes evidence-based decision making
by improving and facilitating the communication and interpretation of information through
provision of attractive interactive visualizations which facilitate the interpretation of
information. Analysis and interpretation of data was more easier.
Figure 5. Rainfall Spatial Analysis of Butuan City using StatPlanet
13th South East Asian Survey Congress
Expanding the Geospatial Future
28th – 31st July 2015
Marina Bay Sands, Singapore
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3. CONCLUSIONS AND RECOMMENDATIONS
3.1 Conclusions
1) The use of rainfall data of the automated rain gauge and GIS in analyzing the spatial
distribution of precipitation has facilitated the consideration of spatially distributed
parameters that could help people especially the farmers to increase their production
by knowing the weighted average distribution of rain.
2) GIS and raster file DEM is an effective tool to evaluate the flow direction and
accumulation of streams or rivers using the stream ordering.
3) With the aid of GIS and DEM, generation of SWIP Map provides water for
supplemental irrigation, domestic purposes and livestock production in critical, less
accessible upland areas and isolated, vulnerable resource-poor communities.
4) StatPlanet is a very valuable tool in interpreting the data in a way that anyone
understand easily and to help any agencies to have a real time or interactive maps that
could help in decision making in times needed.
3.2 Recommendations
GIS possess great capabilities that were partially used in this study. The researchers
1) It is strongly recommended to conduct similar studies on different water resources
problems to further explore this technology.
2) In creating a SWIP maps, it needs to have a ground verification if the project could be
efficient for impounding water.
3) The ARG should be cleaned at least twice a month or as needed, especially after
inclement weather occurrences for the accuracy of the data.
4. ACKNOWLEDGEMENT
For the whole journey that lives may bring back and forth through the trials, up’s and
down, Almighty God motivates us all. “God will never leave me nor forsake me”. Most and
foremost, we thank GOD for all the blessings, favor and guidance to conquer all our needs.
Also, our heartfelt thanks to our consultant Sir NIGEL ZANORIA, Sir PAUL BAANG,
ENGR. RALF TABANYAG and PHilDHRRA- AMCCAP family for lending their time and
efforts in helping us in the completion of this project. Lastly, to EMIEERALD Projects under
DOST-PCIEERD, the researcher is grateful for the approval of this research and for the funds.
5. REFERENCES
HongjieXie, Xiaobing Zhou, Enrique R. Vivoni, Jan M.H., "“GIS-based NEXRAD Stage
III," 2005, p. pp. 65–76.
Dr. Neeraj Bhargava1, Dr. Ritu Bhargava2, Prakash Singh Tanwar3, Ankit Sharma4,
"Rainfall Spatial Analysis using GIS".
Saud Taher and Abdulmohsin Alshaikh, "Spatial Analysis of Rainfall," in Nordic
Hydrology, 1998, pp. 91-104.
S. Alehaideb, "Precipitation distribution in the southwest of Saudi Arabia," Arizona State
University..
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13 South East Asian Survey Congress
Expanding the Geospatial Future
28th – 31st July 2015
Marina Bay Sands, Singapore
F. Pockels, "The Theory of the Formation of Precipitation on Mountain Slopes, Mon.,"
1981.
BIOGRAPHY
Engr. Anamarie J. Pondog is an Instructor 2 and the Chairperson of Engineering Sciences
Department in the College of Engineering and Information Technology of Caraga State
University, Butuan City, Agusan del Norte, Mindanao, Philippines. She is currently pursuing
her master’s degree in MS in Engineering major in Land and Water Resources Engineering
and Technology at University of Southeastern Philippines, Apokon, Tagum City, Davao del
Norte, Philippines. Engineer Pondog is an Agriculture Engineer by profession, graduated at
Caraga State University on 2010. At present, she is a Project Staff of Phil-LiDAR 2.2.14:
LiDAR Data Processing, Modelling and Validation by HEIs for the Detailed Resources
Assessment in Mindanao: CARAGA Region (Region 13) funded by DOST-PCIEERD.
CONTACTS
ANAMARIE J. PONDOG
Chairperson, ES Dept
Caraga State University
Ampayon
Butuan City
Philippines
Email: ajpondog@carsu.edu.ph
JERALD L. RUTA
Caraga State University
Ampayon
Butuan City
Philippines
Email: jerald.ruta@gmail.com
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28th – 31st July 2015
Marina Bay Sands, Singapore
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