Mapping of Drought Vulnerability in Bali and Nusa Tenggara Using Remote Sensing Data.

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1

st

Unud/YU Collaboration Seminar

Universitas Udayana, 25 May 2015

Mapping of Drought Vulnerability in Bali and

Nusa Tenggara Using Remote Sensing Data

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Outline



Introduction



Questions and Objectives



Research Location



Data Used



Statistic Analysis



Result and Discussion

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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

(4)

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

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Questions and 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

Objectives



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

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Topography

(7)



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).

(8)



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)

–

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.

PR

TMI

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SPI Calculation

-x/

β

α-α

α

x

e

β

g(x)

1

)

Γ(

1



 





















































3

)

ln(

ln

4

1

)

ln(

)

ln(

4

1

n

x

n

x







x











0

1

-x

)

(



e

x

dx









x

-x/

β

α-α

x

dx

e

x

α

β

g(x) dx

x

G

0

1

0

Γ(

)

1

)

(

Where:

g(x) = Gamma distribution function

x = Rainfall (mm/month)

Γ(α)

= Gamma function

e = Exponential

α

= Shape parameter (

α > 0)

β

= Scale parameter (

β > 0)

n = Number of rainfall data observation

�

= Average of rainfall

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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

term precipitation record for a desired period. This

long-term 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.

(11)

SPI Classification

SPI Value

Drought Classification

–

2.00

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

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Where:

r

= Coefficient of correlation

MBE

= Mean Bias Error

RMSE

= Root Mean Square Error

S

i

= Data from the Satellite (TRMM)

G

i

= Data from rain gauge

σS

and

σG

= Standard deviations of S and G, respectively

n

=

Number of data pairs.

G

S

i

σ

G

-

G

S

-

S

1)

-(n

)

(

)

(

r

n

1

i









n

i

G

S

n

i

1

(

-

)

1

MBE



















n

1

i

2

)

-MBE

-(

1

RMSE

S

i

G

i

n

(13)

0 2 0 0 4 0 0 6 0 0 8 0 0 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10

Rainfall (mm month-1₎

Mo n th

Ra

in

G

a

u

g

e

o

f D

e

n

p

a

sa

r

R ai n G au g e TR M M 0 2 0 0 4 0 0 6 0 0 8 0 0 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10

(14)

0 2 0 0 4 0 0 6 0 0 8 0 0 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10

Mo n th

Ra

in

G

a

u

g

e

o

f K

u

p

a

n

g

Mo n th

A

v

e

ra

g

e

o

f F

o

u

rt

h

Ra

in

G

a

u

g

e

(15)

y = 1.2883x - 9.5628 R² = 0.84 0

200 400 600 800

0 200 400 600 800

Ra

in G

aug

e

(mm month

-1

)

TRMM (mm month

-1

)

Rain Gauge of Denpasar

y = 0.9613x + 19.373 R² = 0.50 0

200 400 600

0 200 400 600

R

ain Ga

ug

e

(mm m

onth

-1

)

TRMM (mm month

-1

)

Rain Gauge of Montong Gamang

y = 0.9715x + 5.5101 R² = 0.78

0 200 400

Ra

in G

aug

e

(mm month

-1

)

TRMM (mm month

-1

)

Rain Gauge of Bima

y = 1.3363x + 0.3782 R² = 0.8 0

200 400 600 800

0 200 400 600 800

R

ain Ga

ug

e

(mm m

onth

-1

)

TRMM (mm month

-1

)

Rain Gauge of Kupang

y = 1.489x - 1.5135 R² = 0.89 0

200 400 600

0 200 400 600

R

ain Ga

ug

e

(mm m

onth

-1

)

TRMM (mm month

-1

)

Average of Fourth Rain Gauge

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Location

r

R

2

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

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-3 -2 -1 ₀ ₁ ₂ ₃ Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 SPI-1 Mo n th R ai n G au g e TR M M SP I-1

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-3 -2 -1 ₀ ₁ ₂ ₃ Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 SPI-6 Mo n th Ra in G au g e S P I-6 TR M M SP I-6

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-3 -2 -1 ₀ ₁ ₂ ₃ Jan-98

Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10

SPI-12

Mo

n

th

R

ai

n

G

au

g

e

SP

I-1

2

TR

M

SP

I-1

2

V

a

ria

b

ili

ty

o

f S

P

I i

n

S

c

a

le

o

f 1

2

M

o

n

th

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y = 0.7589x + 0.0456 R² = 0.62

-3 -2 -1 0 1 2 3

R

ain Ga

ug

e

SPI

-1

TRMM SPI-1

y = 0.8871x + 0.0005 R² = 0.79

-3

-2

-1

0

1

2

3

-3

-2

-1

0

1

2

3

R

ain Ga

ug

e

SPI

-6

TRMM SPI-6

y = 0.8604x - 0.0019 R² = 0.74

-3 -2 -1 0 1 2 3

R

ain Ga

ug

e

SPI

-3

TRMM SPI-3

y = 0.8746x + 8E-06 R² = 0.76

-3

-2

-1

0

1

2

3

-3

-2

-1

0

1

2

3

R

ain Gaug

e

SPI

-9

TRMM SPI-9

y = 0.8686x + 0.0003 R² = 0.75

-3

-2

-1

0

1

2

3

-3

-2

-1

0

1

2

3

R

ain Ga

ug

e

SPI

-12

TRMM SPI-12

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SPI Scale

r

R

2

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

10 20 30 40

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

RM

S

E

(

%

)

SPI scale

-10 -8 -6 -4 -2 0 2

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

M

BE

(

%

)

SPI scale

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D

ro

u

g

h

t a

n

d

W

e

t P

a

tte

rn

o

f S

P

I-6

D

u

rin

g

1

9

8

a

n

d

2

0

10

-3

-2

-1

0

1

2

3

Jun-98

Jun-99

Jun-00

Jun-01

Jun-02

Jun-03

Jun-04

Jun-05

Jun-06

Jun-07

Jun-08

Jun-09

Jun-10

SPI-6

M

o

n

th

T

R

MM

S

P

(23)

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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 R

2

and

most lowest error compared with other SPI scale.

3. In 20012005, study area indicate drought, 1998

-2000, and 2010 tends wet, and other year is normal.

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Finish