RAINFALL ESTIMATE FOR FLOOD MANAGEMENT USING
METEOROLOGICAL DATA FROM SATELLITE IMAGERY
SAISUNEE BUDHAKOONCHAROEN
Water Resources Engineering Division, Civil Engineering Department
Mahanakorn University of Technology
51 Cheum Samphan Rd., Nong Chok, Bangkok 10530 Thailand
Tel. 661- 3095187 Fax. 662- 9883666 Ext. 281-5 Ext.313
E-mail : saisunee@mut.ac.th
This article presents the investigation of the relation between rainfall measured by the
synoptic stations and that forecasted from the meteorological satellite image taken by
Japan’s Geostationary Meteorological Satellite, GMS-5. The north basin of Pasak River
in central Thailand was the area selected for this study. The observed rainfall from the
synoptic stations located at Muang, Lomsak and Wichianburi Districts, Petchabun
Province during wet season (May to October) of the years 1997 and 1998 were analyzed
to find out the relations to the cloud top temperature taken from the meteorological
satellite image during the same period of time. As the results, the relation between rainfall
and cloud top temperature could be revealed by the equations: P = 2.52 x 10 28 Tp– 11.411, P
= 7.57 x 1024 Tp– 9.555 and P = 1.796 x 1026 Tp–10.525 with the correlation coefficient (R2) of
0.7527, 0.6829 and 0.8149 for rain gauge stations at the districts of Muang, Lomsak and
Wichianburi respectively, where P is the value of rainfall in millimeter and T p is the value
of the cloud top temperature in degree Kelvin. Then, the empirical equations were used to
estimate the rainfall during the wet season of the years 1999 and 2000 in comparison with
the actual rainfall measured from the corresponding synoptic stations during the same
period of time. Such comparison gave the correlation coefficient (R2) of 0.8249, 0.6591
and 0.8411 respectively. This result revealed that the estimated rainfall gave an
acceptable relation with the values of actually measured at the same period of time. The
result of this rainfall estimate is expected to challenge the further study to transform the
estimated values of rainfall into the flood hydrograph using appropriate rainfall-runoff
model. It can be conclude that this initiation is therefore, alternatively served as an initial
challenge for further development of real time flood forecast using more sufficient and
appropriate meteorological data from satellite imagery or other types of remote sensing
observation.
INTRODUCTION
In the past decades, many urban areas around the world have been experiencing a
dramatic increase in disaster due to flood. Flood is one of the extreme hydrological events
which has been a major concern since the dawn of human civilization. Especially the flash
flood, it can cause the greatest number of deaths and the greatest damage in a sudden. The
impact of flood on people’s livelihood may be much longer than the water take to recede.
The effect of lost and degraded assets can also have very much long-term impact on
society and ecosystems. It is obvious that flood incident has become the limiting factor to
economic and social development. Therefore, various efforts have been set out for
establishment of the proper prevention and mitigation to reach sustainable level to relief
flood stress in such a way that avoid the past mistakes and satisfy the widest range of
needs and maintain the natural ecosystem (Budhakooncharoen, S., [2]). The current
situation to cope with this type of water related extreme event is diversified into four
levels, namely disaster preparedness, prevention and mitigation, emergency response and
disaster recovery. Among promising response strategies, the role of forecast and warning
system is widely taken into consideration in several research studies to contribute to the
systematic preparedness of urban flood management. This calls for a sequence of several
steps (Todini, E., [12]), namely establishing an effective monitoring to detect all possible
formation of storm through meteorological conditions and hydrological process which
may lead to the extreme event. Future river flow condition and water stage are forecasted
on the place, time, magnitude, areal extension, duration and time span of crisis based
mathematical modeling including meteorological, hydrological and hydraulic models
(Bronstert, A., [1]). Warning is sent to the appropriate authorities and to the public. The
population at risk and the authorities responsible for the defense responds to the warning.
Finally, assistance during and after the disaster hit is taken place, eg. provision of food,
drinking water, shelter and medical care including reconstruction of infrastructure,
rehabilitation of the environment and of the defense system. The challenge lies in the
improvement of meteorological model to assess in detail the location and the magnitude
of rainfall especially the local convective heavy thunderstorm that may cause flash flood.
Then, the hydrological model plays an important role to simulate the transformation of
rainfall into run-off as part of a real time flood prediction system. Hydraulic model
provides details of flow and stage for different locations and times of flood wave
propagation. Improvement and coupling of all modeling system will allow for increased
early warning lead-times to alleviate flood damage. To contribute in this research study to
the early step of real time flood forecast, it has led to the option using remote sensing data
to estimate the rainfall in a pilot study area.
REAL–TIME FLOOD FORECASTING
Rainfall is a major factor controlling the hydrology in the region. It is the main input of
flood. The study of rainfall is thus of fundamental importance especially for flash flood
disaster management. Among the promising response strategies to cope with urban flood
management, the role of watch, forecast and warning system is widely taken into
consideration in several research studies to contribute to the systematic preparedness. In
addition to those mentioned in the abstract, calls for a sequence of several steps in the
earlier years to estimate the precipitation such as Grose et.al. [3], V.Thorne et.al. [11],
Gruber et.al. [4], Krajewski et.al. [7], etc. with greatest emphasis on use of satellite
imagery.
To contribute in this research study to the early step of real time flood forecast, it has
led to the accomplish of term projects of civil engineering students at Mahanakorn
University of Technology. The main purpose of study is to estimate the convective
rainfall using meteorological satellite image. And this paper is also attempted to compare
the accuracy of estimated rainfall with that measured by the synoptic stations.
In this paper, the concept of using estimated rainfall for application in real – time
flood forecasting is described. The data used in the study is summarized. The results of
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the study consisting of analyses of estimated rainfall along with estimate verification
results are presented. Finally, summary conclusions are made in this paper.
The increase in damage caused by flood is due to the area vulnerable to higher risk of
flood disaster. In the other side, it can’t help accusing the weakness in natural disaster
management in the past which concentrate in a reactive approach especially dealing with
rescue operation and rehabilitation. One should rethink to establish a better and more
holistic concept such as the proactive approach in disaster management which
incorporates a full management cycle of flood disaster preparedness, readiness,
emergency response and recovery / rehabilitation.
The concept of real – time rainfall estimate using satellite data provides guidance to
hydrologist issuing flash flood watch, forecast and warning. This is due to the reason that
use of conventional rain gauge measurements is limited because their distribution is
sparse and not available in mountainous and remote areas. The rainfall estimate should be
accomplished on real time basis from remote sensing data. Appropriate rainfall – runoff
model is used. Discharge and water level along the river in any sub–basin are simulated
using the hydrodynamic model. If the real time data is available, flood forecast and
warning will be automatically issued through a pre-defined time span.
METHODOLOGY AND CASE STUDY
The cloud of cold top produces more rainfall than that with warmer top. The convective
thunderstorm is characterized by very low cloud top temperature (195 – 210 Kelvin)
(Scherer and Hudlow, 1971 ; Scofield, 1987). Due to its acceptable relation, the power –
law regression fit between cloud top temperature and precipitation rate was studied in
many research activities, such as Goodman et.al (1994), Martin et.al (1990), Gagin et.al.
(1985) etc.
In this study, rainfall estimate was initially accomplished by compute the power – law
regression relationship between cloud top temperature and observed rainfall rate. This
regression was derived from a statistical record of cloud top temperature (202 – 244
Kelvin) converted from the consecutive hourly GMS – 5 satellite images in infrared band
and the 3 – hour rainfall rate measured at the synoptic stations north of Pasak river basin
in Central Thailand during wet season (May to October) of the years 1997 and 1998 (see
Figure 1).
In this study, only convective rainfall rate of greater than 10 mm. per 3 hours was
considered. Every satellite image is coincident within time difference of one hour. Next,
verification of the empirical equations obtained were made by using to estimate the
rainfall during the wet season of the years 1999 and 2000 in comparison with the actual
rainfall measured from the corresponding synoptic stations during the same period of time
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Figure 1. Pilot study area, Pasak river basin in central Thailand
(Note: The bar graphs in the figure are mean monthly rainfall in mm.)
RAINFALL RATE & CLOUD TOP TEMPERATURE
The statistical relationship between GMS cloud top brightness temperature and 3 – hourly
rainfall measurement over the study area during wet season (May to October) of the years
1997 and 1998 with one - hour time lag is presented in Figure 2. The result of power –
law fit between instantaneous observed cloud top temperature and rainfall rate is shown in
Figure 3 and could be revealed by the equations in Table 1
Table 1. Power – law regression fit between cloud top temperature and rainfall
observations over the study area during wet season of the years 1997 and 1998
Synoptic stations
Muang (379201)
Lomsak (379401)
Wichianburi (379402)
Power – law regressions
P = 2.52 x 1028 Tp– 11.411
P = 7.57 x 1024 Tp– 9.555
P = 1.796 x 1026 Tp–10.525
Correlation coefficient (R2)
0.7527
0.6829
0.8149
where P is the value of convective rainfall rate in millimeter per three hours,
Tp is the value of the cloud top temperature from the consecutive hourly GMS
satellite images in infrared band in degree Kelvin.
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Rainfall measurement (m m. per three hours )
Station code 379201
(Muang)
Station code 379401
(Lomsak)
Station code 379402
(Wichianburi)
200
200
200
150
150
150
100
100
100
50
50
50
0
0
0
200
220
240
200
Cloud top temperature
220
200
240
220
240
Tp (Kelvin)
Figure 2. Statistical relationship between cloud top temperature and rainfall
observations over the study area during wet season of the years 1997 and 1998
Station code 379201
(Muang)
1000
Station code 379402
(Wichianburi)
Station code 379401
(Lomsak)
1000
100
100
100
10
10
10
Rainfall measurement (mm. per three hours)
1000
28
P = 2.52 x 10 Tp
-11.411
24
P = 7.57 x 10 Tp
2
R = 0.7527
240
Cloud top temperature
200
-10.525
2
R = 0.8149
1
220
P =1.796 x 10 Tp
R = 0.6829
1
200
26
-9.9555
2
1
220
240
200
220
240
Tp (Kelvin)
Figure 3. Power - law regression fit between cloud top temperature and rainfall
observations over the study area during wet season of the years 1997 and 1998
5
Station code 379201
(Muang)
Estimated rainfall (mm. per three hours)
100
Station code 379402
(Wichinburi)
Station code 379401
(Lomsak)
100
100
75
75
R2 = 0.8249
75
R2 = 0.6591
50
50
50
25
25
25
0
0
0
25
50
75 100
Observed rainfall
R2 = 0.8411
0
0
25
50
75
100
0
25
50
75
100
(mm.per three hours)
Figure 4. Comparison between estimated and observed rainfall rates during the wet
season of the years 1999 and 2000
VERIFICATION
Verification was made by estimating the rainfall rate through the above power – law
regression during the wet season of the years 1999 and 2000 in comparison with the
actual rainfall measured from the corresponding synoptic stations during the same period
of time as shown in Figure 4. The correlation coefficient (R2) of 0.8249, 0.6591 and
0.8411 were respectively achieved for rain gauge stations at the districts of Muang,
Lomsak and Wichianburi.
CONCLUSIONS AND DISCUSSIONS
This paper describes the development of power – law regression relationship between
cloud top brightness temperature provided by the GMS – 5 satellite and the observed
rainfall rate collected from three adjacent synoptic stations in Central Thailand. The aim
of this early step study is to estimate rainfall for further study in flash flood forecasting.
Preliminary result of this study on the potential application of this technique to flash flood
/ heavy rainfall prediction is encouraging. It is indicated from the result that the
relationship is likely to be regionally dependent. A relationship derived from one special
case will not accurately fit another. However, the result of study has demonstrated the
viability for additional validation and sensitivity analyses. Since rain tends to be a random
variable, the accurate estimate thus depends not only on instantaneous rainfall amount for
a point. Cloud top temperature distribution as well as areal and temporal variation of the
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cloud system will help locate the going to rain area. A great deal of work is still required
to strengthen the study introduced in this paper. It is expected to model a relationship that
is able to extract all possible variables that are related to the rainfall process. The model
will be applicable to real time flash flood watch if it is able to rapidly process the large
amount of satellite imagery during the operation mode. In addition, it should be
adaptively adjust to the diverse characteristics of rainfall for different geographic of the
region.
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