Mapping of Drought Vulnerability in Bali and Nusa Tenggara Using Remote Sensing Data.
1st Unud/YU Collaboration Seminar
Mapping of Drought Vulnerability in Bali and
Nusa Tenggara Using Remote Sensing Data
I Wayan Nuarsa
Universitas Udayana, 25 May 2015
Outline
Introduction
Questions and Objectives
Research Location
Data Used
Statistic Analysis
Result and Discussion
Conclusions
Introduction
To study drought vulnerability, we need rainfall
data.
Conventionally, rain gauge is the main source
of rainfall data.
Limitation of rain gauge data: not spread
evenly, no data in no people area, no data in
ocean area, and point data source.
Alternatively, we can use the rainfall data from
remote sensing data TRMM data.
The rainfall data from TRMM is needed to
evaluate its accuracy, compared with rain
gauge data before used to estimated drought
disaster.
Types of Drought
Meteorological drought is a prolonged period with
less than average precipitation. Meteorological
drought usually precedes the other kinds of
drought.
Agricultural drought is droughts that affect crop
production or the ecology of the range.
Hydrological drought is brought about when the
water reserves available in sources such as
aquifers, lakes and reservoirs fall below the
statistical average.
Socioeconomic drought is when some supply of
some goods and services such as energy, food
and drinking water are reduced by changes in
meteorological and hydrological conditions.
Questions and Objectives
Questions
Does the TRMM data have enough accuracy to be
used as a source of rainfall data, especially in areas
with limited rainfall data
How to apply Standardized Precipitation Index (SPI)
as a drought indicator in Bali and Nusa Tenggara
Objectives
To evaluate the accuracy of the rainfall data from the
TRMM data compared with rain gauge data.
To Apply Standardized Precipitation Index (SPI) to
map a drought vulnerability in Bali and Nusa
Tenggara Using Remote Sensing Data
Mean Indonesia Annual Rainfall (1998-2010)
Topography
Research Location
Rainfall data from 4 rain gauges data over Bali-Nusa Tenggara islands
(Denpasar (Bali), Montong Gamang, Bima (West Nusa Tenggara), and
Kupang (East Nusa Tenggara)) observed by the Indonesian Meteorology,
Climatology, and Geophysics Agency (BMKG) during 13 years (from January
1998 to December 2010).
Satellite data TRMM 3B43 V6 during 13 years (from January 1998 to
December 2010).
TRMM Sensor
PR
The TRMM is a joint mission between NASA and
the Japan Aerospace Exploration Agency (JAXA)
designed to study the Earth's lands, oceans, air,
ice, and life as a total system.
Sensors:
– Precipitation Radar (PR)
– TRMM Microwave Imager (TMI)
– Visible and Infrared Scanner (VISR)
TMI
– Lightning Imaging Sensor (LIS)
– Cloud and Earth Radiant Energy Sensor
(CERES)
Data: December 1997 – now
PR measures the echo backscattered from rain
and produces very accurate estimates of rain
profiles (vertical distribution). In addition, TMI
measures the microwave radiation emitted by
Earth's surface and by cloud and rain drops.
SPI Calculation
1
g(x) α
xα-1e -x/β
β Γ(α )
x
ln( x)
4 ln x
1
n
1 1
ln(
)
x
3
4 ln( x )
n
( ) x -1e x dx
0
Where:
g(x) = Gamma distribution function
x = Rainfall (mm/month)
Γ(α) = Gamma function
e = Exponential
1
α-1 -x/β
x
e dx
G ( x) g(x) dx α
β Γ(α ) 0
0
x
x
α = Shape parameter (α > 0)
β = Scale parameter (β > 0)
n = Number of rainfall data observation
� = Average of rainfall
(Edwards and McKee, 1997)
SPI Characteristics
The SPI is an index based on the probability of
precipitation for any time scale.
Precipitation is normalized using a probability distribution
so that values of SPI are actually seen as standard
deviations from the median.
The SPI calculation for any location is based on the longterm precipitation record for a desired period. This longterm record is fitted to a probability distribution, which is
then transformed into a normal distribution so that the
mean SPI for the location and desired period is zero.
Positive SPI values indicate greater than median
precipitation, and negative values indicate less than
median precipitation
SPI Classification
SPI Value
–2.00
Drought Classification
Extreme drought
–1.99 - –1.50
Severe drought
–1.49 - –1.00
Moderate drought
–0.99 – 0.99
Normal
1.00 – 1.49
Moderately wet
1.50 – 1.99
Very wet
2.00
Extremely wet
Statistic Analysis
(S
n
r
i 1
i
- S ) (G i - G )
(n - 1) σ S σ G
1 n
MBE ( Si - Gi )
n i 1
1 n
2
RMSE ( Si - MBE - Gi )
n i 1
Where:
r
= Coefficient of correlation
MBE
= Mean Bias Error
RMSE
= Root Mean Square Error
Si
= Data from the Satellite (TRMM)
Gi
= Data from rain gauge
σS and σG = Standard deviations of S and G, respectively
n
= Number of data pairs.
Rainfall (mm month-1)
Jul-00
Jan-01
Jul-01
Jan-02
Jul-02
Jan-03
Jul-03
Jul-03
Jan-04
Jan-04
Jul-04
Jul-05
Jul-05
Jan-06
Jan-06
Jul-06
Jul-06
Jan-07
Jan-07
Jul-07
Jul-07
Jan-08
Jul-08
Jan-08
Jul-08
Jan-09
Jan-09
Jul-09
Jul-09
Jan-10
Jul-10
TRMM
Jul-10
TRMM
Jan-10
Rain Gauge
Jan-05
Rain Gauge
Jan-05
Result and Discussion
Month
Month
Jan-03
Jul-04
Rain Gauge of Denpasar
Jan-00
800
Jul-02
Jul-99
600
Jan-02
Jan-99
400
Jul-01
Jul-98
200
Jan-01
0
Jul-00
Jan-98
Rain Gauge of Montong Gamang
Jan-00
800
Jul-99
600
Jan-99
400
Jul-98
200
0
Jan-98
Rainfall (mm month-1)
Rainfall (mm month-1)
Rainfall (mm month-1)
Jul-00
Jan-01
Jul-99
Jan-00
Jul-00
Jan-01
Jul-01
Jul-01
Jul-02
Jul-02
Jan-03
Jan-03
Jan-03
Jul-03
Jul-03
Jul-03
Jan-04
Jan-04
Jan-04
Jul-04
Month
Jan-02
Month
Month
Jan-02
Jul-02
Jul-04
Jul-04
Jan-05
Jan-05
Jul-05
Jul-05
Jul-05
Jan-06
Jan-06
Jan-06
Jul-06
Jul-06
Jul-06
Jan-07
Jan-07
Jan-07
Jul-07
Jul-07
Jul-07
Jul-08
Jan-08
Jul-08
Jul-09
Jul-09
Jul-09
Jan-10
Jan-10
Jan-10
Jul-10
Jul-10
Jul-10
TRMM
Jan-09
TRMM
Jan-09
TRMM
Jan-09
Rain Gauge
Rain Gauge
Jul-08
Jan-08
Rain Gauge
Jan-05
Jan-08
Rain Gauge of Bima
Jan-00
Jan-99
800
Jul-99
Jul-98
600
Jan-99
Jan-98
400
0
Jul-98
Rain Gauge of Kupang
Jan-98
200
800
600
Jan-02
400
Jul-01
200
Jan-01
0
Jul-00
Average of Fourth Rain Gauge
Jan-00
800
Jul-99
600
Jan-99
400
Jul-98
200
0
Jan-98
Rainfall (mm month-1)
Relationship Between Rain Gauge and TRMM
Rain Gauge of Denpasar
Rain Gauge of Montong Gamang
400
200
y = 1.2883x - 9.5628
R² = 0.84
0
400
400
200
y = 0.9613x + 19.373
R² = 0.50
0
200
400
600
0
800
TRMM (mm month-1)
200
400
y = 0.9715x + 5.5101
R² = 0.78
200
0
600
0
TRMM (mm month-1)
Rain Gauge of Kupang
200
TRMM (mm month-1)
Average of Fourth Rain Gauge
600
800
600
400
200
y = 1.3363x + 0.3782
R² = 0.8
0
Rain Gauge (mm month-1)
0
Rain Gauge (mm month-1)
Rain Gauge (mm month-1)
600
Rain Gauge (mm month-1)
Rain Gauge (mm month-1)
Rain Gauge of Bima
600
800
400
200
y = 1.489x - 1.5135
R² = 0.89
0
0
200
400
600
TRMM (mm month-1)
800
0
200
400
TRMM (mm month-1)
600
400
Accuracy and Error of TRMM Data
r
R2
MBE
RMSE
Denpasar
0.92
0.84
-17.44
46.37
Montong Gamang
0.71
0.50
-9.85
70.02
Bima
0.89
0.78
-3.58
48.99
Kupang
0.92
0.80
-25.37
60.10
Average
0.94
0.89
-32.06
43.56
Location
SPI-1
Jul-99
Jan-00
Jan-00
Jul-00
Jul-00
Jan-01
Jan-01
Jul-01
Jul-01
Jan-02
Jan-02
Jul-02
Jul-02
Jul-03
Jan-04
Jan-04
Jul-04
Jan-05
TRMM SPI-3
Jul-05
Jan-06
Jul-06
Jul-06
Jan-07
Jan-07
Jul-07
Jul-07
Jan-08
Jan-08
Jul-08
Jul-08
Jan-09
Jan-09
Jul-09
Jul-09
Jan-10
Jan-10
Jul-10
Jul-10
TRMM SPI-1
Jan-05
Jan-06
Jul-03
Month
Month
Jul-04
Jul-05
Jan-03
Rain Gauge
Jan-03
3
Jul-99
2
Jan-99
1
Jan-99
0
Jul-98
-1
Jul-98
Rain Gauge SPI-3
Jan-98
-2
-3
3
2
1
0
-1
-2
-3
Jan-98
Variability of SPI in Scale of 1 and 3 Months
SPI-3
SPI-6
Jan-00
Jan-00
Jul-00
Jul-00
Jan-01
Jan-01
Jul-01
Jul-01
Jan-02
Jan-02
Jul-02
Jul-02
Jan-03
Jul-03
Jan-04
Jan-05
Jul-04
Jul-05
Jan-06
Jul-06
Jan-07
Jan-07
Jul-07
Jul-07
Jan-08
Jan-08
Jul-08
Jul-08
Jan-09
Jan-09
Jul-09
Jul-09
Jan-10
Jan-10
Jul-10
Jul-10
TRMM SPI-6
Jul-06
Jan-04
Jan-05
TRMM SPI-9
Jan-06
Jul-03
Month
Month
Jul-04
Jul-05
Jan-03
Rain Gauge SPI-6
Jul-99
3
Jul-99
2
Jan-99
1
Jan-99
0
Jul-98
-1
Jul-98
Rain Gauge SPI-9
Jan-98
-2
-3
3
2
1
0
-1
-2
-3
Jan-98
Variability of SPI in Scale of 6 and 9 Months
SPI-9
SPI-12
3
2
1
0
-1
-2
-3
Jan-98
Jul-98
Jan-99
Jan-00
Jul-00
Jan-01
Jul-01
Jan-02
Jan-03
Jul-03
Jan-04
Rain Gauge SPI-12
Jul-02
Month
Jul-04
Jan-05
Jan-06
Jul-06
Jan-07
Jul-07
Jan-08
Jul-08
Jan-09
Jul-09
Jan-10
Jul-10
TRMM SPI-12
Jul-05
Variability of SPI in Scale of 12 Months
Jul-99
Relationship SPI from Rain Gauge dan TRMM
3
3
y = 0.7589x + 0.0456
R² = 0.62
2
-1
-2
Rain Gauge SPI-6
0
0
-1
-2
-3
-1
0
1
2
3
-3
-2
-1
TRMM SPI-1
0
0
-1
-2
1
2
3
-3
-2
-1
TRMM SPI-3
y = 0.8746x + 8E-06
R² = 0.76
2
0
-1
-2
-3
-3
-2
-1
0
1
TRMM SPI-9
y = 0.8686x + 0.0003
R² = 0.75
2
1
2
3
0
1
TRMM SPI-6
3
3
Rain Gauge SPI-12
-2
1
-3
-3
-3
y = 0.8871x + 0.0005
R² = 0.79
2
1
Rain Gauge SPI-3
1
Rain Gauge SPI-9
Rain Gauge SPI-1
2
3
y = 0.8604x - 0.0019
R² = 0.74
1
0
-1
-2
-3
-3
-2
-1
0
1
TRMM SPI-12
2
3
2
3
Accuracy and Error of TRMM SPI
SPI Scale
r
R2
MBE
RMSE
SPI-1
0.79
0.62
-1.57
20.93
SPI-3
0.86
0.74
0.03
17.64
SPI-6
0.89
0.79
-0.03
15.88
SPI-9
0.87
0.76
-0.002
16.71
SPI-12
0.87
0.76
-0.01
17.08
40
2
MBE (%)
RMSE (%)
0
30
-2
-4
-6
20
-8
-10
10
SPI-1
SPI-3
SPI-6
SPI scale
SPI-9
SPI-12
SPI-1
SPI-3
SPI-6
SPI scale
SPI-9
SPI-12
SPI-6
3
2
1
0
-1
-2
-3
Jun-98
Jun-00
Jun-01
Jun-02
Month
Jun-04
Jun-05
Jun-06
Jun-07
Jun-08
Jun-09
Jun-10
TRMM SPI-6
Jun-03
Drought and Wet Pattern of SPI-6 During 1998 and 2010
Jun-99
Spatial Pattern of SP1-6 in Bali and Nusa Tenggara
Conclusions
1. Rainfall data from TRMM produces high relationship
with rain gauge for both monthly rainfall and SPI.
2. SPI in scale of 6 months give the highest r and R2 and
most lowest error compared with other SPI scale.
3. In 2001-2005, study area indicate drought, 1998 2000, and 2010 tends wet, and other year is normal.
4. TRMM 3B43 are potentially used as source of rainfall
data especially in data-sparse regions.
Finish
Thank You
Mapping of Drought Vulnerability in Bali and
Nusa Tenggara Using Remote Sensing Data
I Wayan Nuarsa
Universitas Udayana, 25 May 2015
Outline
Introduction
Questions and Objectives
Research Location
Data Used
Statistic Analysis
Result and Discussion
Conclusions
Introduction
To study drought vulnerability, we need rainfall
data.
Conventionally, rain gauge is the main source
of rainfall data.
Limitation of rain gauge data: not spread
evenly, no data in no people area, no data in
ocean area, and point data source.
Alternatively, we can use the rainfall data from
remote sensing data TRMM data.
The rainfall data from TRMM is needed to
evaluate its accuracy, compared with rain
gauge data before used to estimated drought
disaster.
Types of Drought
Meteorological drought is a prolonged period with
less than average precipitation. Meteorological
drought usually precedes the other kinds of
drought.
Agricultural drought is droughts that affect crop
production or the ecology of the range.
Hydrological drought is brought about when the
water reserves available in sources such as
aquifers, lakes and reservoirs fall below the
statistical average.
Socioeconomic drought is when some supply of
some goods and services such as energy, food
and drinking water are reduced by changes in
meteorological and hydrological conditions.
Questions and Objectives
Questions
Does the TRMM data have enough accuracy to be
used as a source of rainfall data, especially in areas
with limited rainfall data
How to apply Standardized Precipitation Index (SPI)
as a drought indicator in Bali and Nusa Tenggara
Objectives
To evaluate the accuracy of the rainfall data from the
TRMM data compared with rain gauge data.
To Apply Standardized Precipitation Index (SPI) to
map a drought vulnerability in Bali and Nusa
Tenggara Using Remote Sensing Data
Mean Indonesia Annual Rainfall (1998-2010)
Topography
Research Location
Rainfall data from 4 rain gauges data over Bali-Nusa Tenggara islands
(Denpasar (Bali), Montong Gamang, Bima (West Nusa Tenggara), and
Kupang (East Nusa Tenggara)) observed by the Indonesian Meteorology,
Climatology, and Geophysics Agency (BMKG) during 13 years (from January
1998 to December 2010).
Satellite data TRMM 3B43 V6 during 13 years (from January 1998 to
December 2010).
TRMM Sensor
PR
The TRMM is a joint mission between NASA and
the Japan Aerospace Exploration Agency (JAXA)
designed to study the Earth's lands, oceans, air,
ice, and life as a total system.
Sensors:
– Precipitation Radar (PR)
– TRMM Microwave Imager (TMI)
– Visible and Infrared Scanner (VISR)
TMI
– Lightning Imaging Sensor (LIS)
– Cloud and Earth Radiant Energy Sensor
(CERES)
Data: December 1997 – now
PR measures the echo backscattered from rain
and produces very accurate estimates of rain
profiles (vertical distribution). In addition, TMI
measures the microwave radiation emitted by
Earth's surface and by cloud and rain drops.
SPI Calculation
1
g(x) α
xα-1e -x/β
β Γ(α )
x
ln( x)
4 ln x
1
n
1 1
ln(
)
x
3
4 ln( x )
n
( ) x -1e x dx
0
Where:
g(x) = Gamma distribution function
x = Rainfall (mm/month)
Γ(α) = Gamma function
e = Exponential
1
α-1 -x/β
x
e dx
G ( x) g(x) dx α
β Γ(α ) 0
0
x
x
α = Shape parameter (α > 0)
β = Scale parameter (β > 0)
n = Number of rainfall data observation
� = Average of rainfall
(Edwards and McKee, 1997)
SPI Characteristics
The SPI is an index based on the probability of
precipitation for any time scale.
Precipitation is normalized using a probability distribution
so that values of SPI are actually seen as standard
deviations from the median.
The SPI calculation for any location is based on the longterm precipitation record for a desired period. This longterm record is fitted to a probability distribution, which is
then transformed into a normal distribution so that the
mean SPI for the location and desired period is zero.
Positive SPI values indicate greater than median
precipitation, and negative values indicate less than
median precipitation
SPI Classification
SPI Value
–2.00
Drought Classification
Extreme drought
–1.99 - –1.50
Severe drought
–1.49 - –1.00
Moderate drought
–0.99 – 0.99
Normal
1.00 – 1.49
Moderately wet
1.50 – 1.99
Very wet
2.00
Extremely wet
Statistic Analysis
(S
n
r
i 1
i
- S ) (G i - G )
(n - 1) σ S σ G
1 n
MBE ( Si - Gi )
n i 1
1 n
2
RMSE ( Si - MBE - Gi )
n i 1
Where:
r
= Coefficient of correlation
MBE
= Mean Bias Error
RMSE
= Root Mean Square Error
Si
= Data from the Satellite (TRMM)
Gi
= Data from rain gauge
σS and σG = Standard deviations of S and G, respectively
n
= Number of data pairs.
Rainfall (mm month-1)
Jul-00
Jan-01
Jul-01
Jan-02
Jul-02
Jan-03
Jul-03
Jul-03
Jan-04
Jan-04
Jul-04
Jul-05
Jul-05
Jan-06
Jan-06
Jul-06
Jul-06
Jan-07
Jan-07
Jul-07
Jul-07
Jan-08
Jul-08
Jan-08
Jul-08
Jan-09
Jan-09
Jul-09
Jul-09
Jan-10
Jul-10
TRMM
Jul-10
TRMM
Jan-10
Rain Gauge
Jan-05
Rain Gauge
Jan-05
Result and Discussion
Month
Month
Jan-03
Jul-04
Rain Gauge of Denpasar
Jan-00
800
Jul-02
Jul-99
600
Jan-02
Jan-99
400
Jul-01
Jul-98
200
Jan-01
0
Jul-00
Jan-98
Rain Gauge of Montong Gamang
Jan-00
800
Jul-99
600
Jan-99
400
Jul-98
200
0
Jan-98
Rainfall (mm month-1)
Rainfall (mm month-1)
Rainfall (mm month-1)
Jul-00
Jan-01
Jul-99
Jan-00
Jul-00
Jan-01
Jul-01
Jul-01
Jul-02
Jul-02
Jan-03
Jan-03
Jan-03
Jul-03
Jul-03
Jul-03
Jan-04
Jan-04
Jan-04
Jul-04
Month
Jan-02
Month
Month
Jan-02
Jul-02
Jul-04
Jul-04
Jan-05
Jan-05
Jul-05
Jul-05
Jul-05
Jan-06
Jan-06
Jan-06
Jul-06
Jul-06
Jul-06
Jan-07
Jan-07
Jan-07
Jul-07
Jul-07
Jul-07
Jul-08
Jan-08
Jul-08
Jul-09
Jul-09
Jul-09
Jan-10
Jan-10
Jan-10
Jul-10
Jul-10
Jul-10
TRMM
Jan-09
TRMM
Jan-09
TRMM
Jan-09
Rain Gauge
Rain Gauge
Jul-08
Jan-08
Rain Gauge
Jan-05
Jan-08
Rain Gauge of Bima
Jan-00
Jan-99
800
Jul-99
Jul-98
600
Jan-99
Jan-98
400
0
Jul-98
Rain Gauge of Kupang
Jan-98
200
800
600
Jan-02
400
Jul-01
200
Jan-01
0
Jul-00
Average of Fourth Rain Gauge
Jan-00
800
Jul-99
600
Jan-99
400
Jul-98
200
0
Jan-98
Rainfall (mm month-1)
Relationship Between Rain Gauge and TRMM
Rain Gauge of Denpasar
Rain Gauge of Montong Gamang
400
200
y = 1.2883x - 9.5628
R² = 0.84
0
400
400
200
y = 0.9613x + 19.373
R² = 0.50
0
200
400
600
0
800
TRMM (mm month-1)
200
400
y = 0.9715x + 5.5101
R² = 0.78
200
0
600
0
TRMM (mm month-1)
Rain Gauge of Kupang
200
TRMM (mm month-1)
Average of Fourth Rain Gauge
600
800
600
400
200
y = 1.3363x + 0.3782
R² = 0.8
0
Rain Gauge (mm month-1)
0
Rain Gauge (mm month-1)
Rain Gauge (mm month-1)
600
Rain Gauge (mm month-1)
Rain Gauge (mm month-1)
Rain Gauge of Bima
600
800
400
200
y = 1.489x - 1.5135
R² = 0.89
0
0
200
400
600
TRMM (mm month-1)
800
0
200
400
TRMM (mm month-1)
600
400
Accuracy and Error of TRMM Data
r
R2
MBE
RMSE
Denpasar
0.92
0.84
-17.44
46.37
Montong Gamang
0.71
0.50
-9.85
70.02
Bima
0.89
0.78
-3.58
48.99
Kupang
0.92
0.80
-25.37
60.10
Average
0.94
0.89
-32.06
43.56
Location
SPI-1
Jul-99
Jan-00
Jan-00
Jul-00
Jul-00
Jan-01
Jan-01
Jul-01
Jul-01
Jan-02
Jan-02
Jul-02
Jul-02
Jul-03
Jan-04
Jan-04
Jul-04
Jan-05
TRMM SPI-3
Jul-05
Jan-06
Jul-06
Jul-06
Jan-07
Jan-07
Jul-07
Jul-07
Jan-08
Jan-08
Jul-08
Jul-08
Jan-09
Jan-09
Jul-09
Jul-09
Jan-10
Jan-10
Jul-10
Jul-10
TRMM SPI-1
Jan-05
Jan-06
Jul-03
Month
Month
Jul-04
Jul-05
Jan-03
Rain Gauge
Jan-03
3
Jul-99
2
Jan-99
1
Jan-99
0
Jul-98
-1
Jul-98
Rain Gauge SPI-3
Jan-98
-2
-3
3
2
1
0
-1
-2
-3
Jan-98
Variability of SPI in Scale of 1 and 3 Months
SPI-3
SPI-6
Jan-00
Jan-00
Jul-00
Jul-00
Jan-01
Jan-01
Jul-01
Jul-01
Jan-02
Jan-02
Jul-02
Jul-02
Jan-03
Jul-03
Jan-04
Jan-05
Jul-04
Jul-05
Jan-06
Jul-06
Jan-07
Jan-07
Jul-07
Jul-07
Jan-08
Jan-08
Jul-08
Jul-08
Jan-09
Jan-09
Jul-09
Jul-09
Jan-10
Jan-10
Jul-10
Jul-10
TRMM SPI-6
Jul-06
Jan-04
Jan-05
TRMM SPI-9
Jan-06
Jul-03
Month
Month
Jul-04
Jul-05
Jan-03
Rain Gauge SPI-6
Jul-99
3
Jul-99
2
Jan-99
1
Jan-99
0
Jul-98
-1
Jul-98
Rain Gauge SPI-9
Jan-98
-2
-3
3
2
1
0
-1
-2
-3
Jan-98
Variability of SPI in Scale of 6 and 9 Months
SPI-9
SPI-12
3
2
1
0
-1
-2
-3
Jan-98
Jul-98
Jan-99
Jan-00
Jul-00
Jan-01
Jul-01
Jan-02
Jan-03
Jul-03
Jan-04
Rain Gauge SPI-12
Jul-02
Month
Jul-04
Jan-05
Jan-06
Jul-06
Jan-07
Jul-07
Jan-08
Jul-08
Jan-09
Jul-09
Jan-10
Jul-10
TRMM SPI-12
Jul-05
Variability of SPI in Scale of 12 Months
Jul-99
Relationship SPI from Rain Gauge dan TRMM
3
3
y = 0.7589x + 0.0456
R² = 0.62
2
-1
-2
Rain Gauge SPI-6
0
0
-1
-2
-3
-1
0
1
2
3
-3
-2
-1
TRMM SPI-1
0
0
-1
-2
1
2
3
-3
-2
-1
TRMM SPI-3
y = 0.8746x + 8E-06
R² = 0.76
2
0
-1
-2
-3
-3
-2
-1
0
1
TRMM SPI-9
y = 0.8686x + 0.0003
R² = 0.75
2
1
2
3
0
1
TRMM SPI-6
3
3
Rain Gauge SPI-12
-2
1
-3
-3
-3
y = 0.8871x + 0.0005
R² = 0.79
2
1
Rain Gauge SPI-3
1
Rain Gauge SPI-9
Rain Gauge SPI-1
2
3
y = 0.8604x - 0.0019
R² = 0.74
1
0
-1
-2
-3
-3
-2
-1
0
1
TRMM SPI-12
2
3
2
3
Accuracy and Error of TRMM SPI
SPI Scale
r
R2
MBE
RMSE
SPI-1
0.79
0.62
-1.57
20.93
SPI-3
0.86
0.74
0.03
17.64
SPI-6
0.89
0.79
-0.03
15.88
SPI-9
0.87
0.76
-0.002
16.71
SPI-12
0.87
0.76
-0.01
17.08
40
2
MBE (%)
RMSE (%)
0
30
-2
-4
-6
20
-8
-10
10
SPI-1
SPI-3
SPI-6
SPI scale
SPI-9
SPI-12
SPI-1
SPI-3
SPI-6
SPI scale
SPI-9
SPI-12
SPI-6
3
2
1
0
-1
-2
-3
Jun-98
Jun-00
Jun-01
Jun-02
Month
Jun-04
Jun-05
Jun-06
Jun-07
Jun-08
Jun-09
Jun-10
TRMM SPI-6
Jun-03
Drought and Wet Pattern of SPI-6 During 1998 and 2010
Jun-99
Spatial Pattern of SP1-6 in Bali and Nusa Tenggara
Conclusions
1. Rainfall data from TRMM produces high relationship
with rain gauge for both monthly rainfall and SPI.
2. SPI in scale of 6 months give the highest r and R2 and
most lowest error compared with other SPI scale.
3. In 2001-2005, study area indicate drought, 1998 2000, and 2010 tends wet, and other year is normal.
4. TRMM 3B43 are potentially used as source of rainfall
data especially in data-sparse regions.
Finish
Thank You