ENSO Impact to the HydrologicalDrought in Lombok Island, Indonesia

Karlina
Kyoto University
2017/11/15

1

Background• Drought occurs in high as well as low rainfall areas (Wilhite and Glantz 1985)
The occurrence of drought,January, 1982 to August, 1983
(Wilhite and Glantz 1985)

Vulnerability map of water sector ofLombok Island (Suroso, Hadi et al. 2009)

Indonesian drought map (BNPB,2010)

2

Background• Lombok Island is one of the 10 biggest rice producers in Indonesia• There is a necessity to study cause and impact of drought to prepare a good mitigationplan of drought in the Island

1200000
1000000(tonnes)

8000006000004000002000000Background• Drought occurrences in the future is likely increasing due to climatechange• In Indonesia, climate change has increase the number and severity ofnatural disaster, such as typhoons, droughts, forest fires, and floods(Measey 2010)• In Lombok Island, the effect of climate change has been already felt
by the increasing of vulnerability of water sector (Suroso, Hadi et al.
2009)

4Background• Mishra and Singh (2010) stated that the droughts around the globe are
related to large-scale climate condition.
• Understanding the relationship between climate indices and drought
will be useful for any drought prediction.
• El Nino worsen the drought condition• The 1997 El Nino drought affected approximately 426.000 hectares ofrice in Indonesia (Measey 2010)• Climate change increase the frequency of El Nino Southern Oscillation(ENSO) event (Suroso, Hadi et al. 2009)• The increasing trend of ENSO frequency will worsen the future drought• In this study, we focus on impact of ENSO to hydrological drought inLombok Island• Hydrological drought is a period of inadequate surface and subsurface
water resources for established water uses of a given water resourcesmanagement system (Wilhite and Glantz 1985).

5

Problem Statement• Is drought getting more severe?

• Is there any direct correlation between ENSO and hydrologicaldrought event?
• Is it possible to use ENSO index for drought prediction in Lombok
Island?

6

Objectives• To understand the historical trend of drought in Lombok Island• To investigate the ENSO - hydrological drought correlation• To assess the lag correlation between ENSO and hydrological drought

7

Data and method• Data used:• Daily discharge data from Lantandaya and Bug Bug Station• The data was collected from River Basin Organization of West Nusa Tenggara• Data period:
Lantandaya 22 years (1986-2010), missing data 1988-1989 & 2009
Bug Bug 19 years (1986-2010), missing data 1989-1992,1994 & 1997

Daily Discharge at Lantandaya Station
7Q (m3/s)

65432
10

Daily Discharge at Bug Bug Station
5040Q (m3/s)

•
•

30
20
1008

Data and method• Hydrological drought event is defined by using discharge index12.00



1.00
0.50

Classification< -1wet/high events-1 ~ 1normal>1dry/low events (drought)

8.00

0.00

6.00

-0.50
-1.00

4.00

-1.50
2.00

-2.00

0.00

-2.50

QIQ

9Qi

Where
Qi = discharge index
𝑄ത = monthly average discharge
Q = monthly discharge in a particular year
𝜎 = standard deviation
Qi

1.50

10.00

Q (m3/s)Qi Drought index of November in Bug Bug station

QQ

Data and method
ENSO (El Nino Southern Oscillation)• Is a naturally occurring phenomenon that involves fluctuating oceantemperatures in the equatorial Pacific• It is a single climate phenomenon that periodically fluctuates between threephases: Neutral, La Niña (cold) or El Niño (warm)
The various "Niño regions" where sea surface temperatures aremonitored to determine the current ENSO phase (warm or cold)

Source: https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst.php

https://climate.ncsu.edu/climate/patterns/ENSO.html

10Data and method
ENSO Classification based on ONI• The ENSO indices:• Oceanic Nino Index (ONI)  3 months
running mean of Extended Reconstructed
Sea Surface Temperature (ERSST).v4 SST
anomalies in the Nino 3.4 region (5°N5°S, 120°-170°W)• Nino3.4  monthly SST in the Nino 3.4region (5°N-5°S, 120°-170°W)• The drought-ENSO correlation isinvestigated by using simple
correlation
ONI
El Nino threshold

ONI (El Nino)
La Nina threshold

ONI (La Nina)

32
10-1-2-3
http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml

11

Streamflow climatology
Discharge in Lantandaya Station

16

3.0142.5

Discharge (m3/s)

Discharge (m3/s)

Discharge in Bug Bug Station

1210864

2.0
1.5

1.0
0.5

200.0JanFebMar

AprMayJunJulAug

Sep

Oct

Nov

Dec

Jan

Discharge in Bug Bug StationFebMar

AprMayJunJulAug

Sep

Oct

Nov

Discharge in Lantandaya Station
2.5

10

2.0

8

Discharge (m 3/s)

Discharge (m3/s)

9765

43

1.5
1.0
0.5

210.00

DJF

MAM

JJA

SON

DJF

MAM

JJA

12 SON

Dec

Seasonal anomalies trend of discharge(Lantandaya station)• We used the anomalies trend to investigate the drought trend
DJF

MAM

1.20

1.00

1.00

0.80

0.80

Anomalies

0.40
0.20
0.00

0.60

Anomaliesy = -0.0243x + 48.66
R² = 0.3044

0.60

-0.20

y = -0.021x + 42.071
R² = 0.2488

0.40
0.20
0.00
-0.20

-0.40
-0.60

-0.40

-0.80

-0.60

JJA

SON

0.80

1.00
0.80

0.60

0.20
0.00

y = -0.0157x + 31.305
R² = 0.2639

Anomalies

Anomalies

0.60
0.40

0.40

y = -0.0195x + 39.017
R² = 0.2974

0.20
0.00
-0.20

-0.20

-0.40

-0.40

-0.6013Seasonal anomalies trend of discharge(Bug Bug station)
DJF
10.00

4.00

8.00

3.00
1.00

Anomalies

4.00
2.00
0.00

0.00
-1.00
-2.00
-3.00

-2.00

-4.00

-4.00

-5.00

-6.00

-6.00

JJA

SON

2.00
1.50

0.50
0.00
-0.50
-1.00

-1.50

y = -0.0131x + 26.041
R² = 0.0254

Anomalies

Anomalies

1.00

y = -0.0323x + 64.672
R² = 0.0202

2.00

y = -0.0711x + 142.06
R² = 0.0569

6.00

Anomalies

MAM

7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
-1.00
-2.00-3.00-4.00

y = -0.0669x + 133.37
R² = 0.0766

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Drought vs ENSO
Lantandaya Station
5432
10-1-2-3-4-5Q Index

Q Index (El Nino)

Q Index (La Nina)

ONI

Q Index (La Nina)

ONI

Bug Bug Station
5432
10-1-2-3-4-5

Q Index

Q Index (El Nino)

15

Drought vs ENSO
4

Lantandaya Station

32
10-1-2Qi
Wet events
Drought eventsONI
El Nino threshold
La Nina threshold-3-416

Drought vs ENSO frequency

Ho : El Nino doesn't affect the drought events
Ha : El Nino affect the drought eventsof freedom
1Thedegrees
frequencyof drought related
El Nino was tested by using chi square test ofsignificance level95%
independence
p0.05
3.841
X 2 a\Example:

if X 2 > X 2 a , we reject the hypothesis
SONData: contingency table
El Nino Non-El NinoDrought
84
Non-drought
1542All
2346
27.263
X

Total
1257
69
statistically significant

Conclusion
El Nino affect the drought events
MAMSeason
Bug
Bug Lantandaya
Rainfall
Data:contingencytable
El Nino Non-El Nino
Total
DJF
Drought
6915
MAM
Non-drought
94554
JJA
All
1554692 SON
3.757
statistically significant
XConclusion

Note

El Nino doesn’t affect the drought events

DJF
Data: contingency table
El NinoNon-El Nino
Drought84
Non-drought
1542
All
2346
2
7.263
X

To
156
statistically sConclusion
El Nino affect the drought events
JJA
Data: contingency table
El NinoNon-El Nino
ToDrought
055
Non-drought
1648
6All
1653
621.627
statistically sX
Conclusion

El Nino doesn’t affect the drought evestatistically not significant
statistically significant

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Monthly correlation
0.800

0.700

Bug Bug Station

0.600
0.500

R-Value

0.400
0.300
0.200
0.100
0.000

Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
ONI
0.079 0.416 0.648 0.315 0.461 0.166 0.189 0.248 0.372 0.473 0.538 0.681
Nino 3.4 0.080 0.410 0.617 0.297 0.381 0.097 0.227 0.205 0.363 0.463 0.495 0.651
R-critical 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404
0.600
0.500

Lantandaya Station

0.400

R-value

0.300
0.200
0.100

0.000

Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
ONI0.039 0.459 0.436 0.382 0.305 0.382 0.182 0.156 0.091 0.025 0.292 0.287
Nino 3.4 0.065 0.478 0.397 0.395 0.360 0.428 0.240 0.225 0.040 0.080 0.297 0.298
R-critical 0.413 0.413 0.413 0.413 0.413 0.413 0.413 0.413 0.413 0.413 0.413 0.413

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Seasonal correlation
Bug Bug Station
0.700

0.600

•The strongest correlation is seen during SON season.

•Some previous research of rainfall-ENSO correlationin Indonesia found out the highest correlation ofR

0.500
0.400

rainfall and ENSO happens during dry season (JJA)

0.300(Hendon 2003)(Haylock and McBride 2001)(Kirono,

0.200

0.100
0.000
ONI
Nino 3.4
R-critical

Butler et al. 2015)
DJF
0.495
0.488
0.404

MAM
0.401
0.362
0.404

JJA
0.298
0.290
0.404

SON
0.612
0.587
0.404

The results shows possibility of lag correlation ofdischarge and ENSO

Lantandaya Station

0.450

Unclear correlation of ENSO and discharge:

0.4000.350•0.300Q•

the location of the discharge in the upstream may

0.250

give significant impact to the river discharge which

0.200

may be strongly affected by

0.150
0.100

•

Orographic rainfall

0.050

•

Groundwater that is affected by land use type

0.000
ONI
Nino 3.4
R-critical

DJF
0.377
0.373
0.413

MAM
0.388
0.423
0.413

JJA
0.105
0.188
0.413

SON
0.217
0.245
0.413

which is typically forest and there is natural
reservoir in the mountain19

Lag-correlation between ENSO anddischarge in Bug Bug station
Time lag of Nino3.4(months prior to)
9630*significant at 95%
Time lag of ONI(months prior to)
9630*significant at 95%DJF(wet season)

Nino3.4 - Qi lag correlation (R)Rainfall Season
DJF
MAM
JJA-.058.413 *-.014-.176.405 *.299-.257.021-.136.549 *.559 *.316SON.548 *-.280.539 *.595 *ONI - Qi lag correlation (R)Rainfall Season
DJF
MAM
JJA-.109.432 *.012-.200.443 *.281-.265.055-.097.524 *.597 *.305

SON.561 *-.207.541 *.633 *

MAM

ENSO

discharge

JJA

SON(dry season)
20

Conclusions• The trend of drought in Lombok Island isincreasing.
• The ENSO impact to discharge in Bug Bug station
is more significant than in Lantandaya station.
• The strongest ENSO-discharge correlation in Bug
Bug station happens during SON season and theweakest one happens during JJA season.
• There is a lag correlation between JJA ENSO andSON Discharge in Lantandaya Station.
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References•

Wilhite, D. A., & Glantz, M. H. (1985). Understanding: the drought phenomenon: the role of definitions.
Water international, 10(3), 111-120.

•Measey, M. (2010). Indonesia: a vulnerable country in the face of climate change. Global Majority E-Journal, 1(1), 31-45.
•Suroso, D., Hadi, T. W., Sofian, I., Latief, H., Abdurahman, O., & Setiawan, B. (2009). Vulnerability of small
islands to climate change in Indonesia: a case study of Lombok Island, Province of Nusa Tenggara Barat.

•Hendon, H. H. (2003). Indonesian rainfall variability: Impacts of ENSO and local air-sea interaction.

Journal of Climate, 16(11), 1775-1790.
•Haylock, M., & McBride, J. (2001). Spatial coherence and predictability of Indonesian wet season rainfall.
Journal of Climate, 14(18), 3882-3887•Kirono, D. G., Butler, J. R., McGregor, J., Ripaldi, A., Katzfey, J., & Nguyen, K. (2015). Historical and futureseasonal rainfall variability in Nusa Tenggara Barat Province, Indonesia: implications for the agriculture
and water sectors. Climate Risk Management.

•Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1), 202-216.

22Thank you so much for your kind attention…

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