Chaiwat Ekkawatpanit, Weerayuth Pratoomchai Department of Civil Engineering

King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

Naota HanasakiNational Institute for Environmental Studies, Tsukuba, Japan

So KazamaTohoku University, Sendai, Japan

24-25 November, 2015

Climate Change Impact on Water Resources using Global Climate and Hydrological Model

2015 APEC Typhoon Symposium (APTS)Lessons Learned from Disastrous Typhoons

Outline of presentation

Introduction

Objective of the study

Study area

Methodology

Results and discussion

Conclusions

2

Introduction

There is a 95% (IPCC, 2013) consensus among the scientific community that climate change is real and human activity is the main cause (anthropogenic climate change)

In fact, there are uneven temporal and spatial distributions of climate change impacts ?

3

Objective

This study aim to investigate the impacts of climate change on water resources in the Upper Chao Phraya River Basin in Thailand, which concerned among climatology and river discharge.

4

Study area: The Upper Chao Phraya River basin (UCP)

The basin covers an area of 109,973 km2 or 22% of the country’s area

o 60.0% is foresto 35.6% is agricultural areao 4.4% is classified to other,

e.g., urban, water bodies

5

Models and Data

K10 data

Hanasaki et al., 2008; Hanasaki and Mateo (2012)

Kotsuki et al., 2010

CMIP5 (GCMs data)

H08

Land Surface Module

River Routing Module

Reservoir Operation Module

Crop Growth Module

Withdrawal Module

Environmental Flow Module

Groundwater Recharge Model

Groundwater Flow Model

Groundwater Level

Groundwater Storage

Aquifer properties (T, S)

Rushton and Ward (1979)

Prickett and Lonnquist (1971)

- Rainfall- Air temperature- Wind speed- Specific humidity- Surface air pressure- Longwave downward radiation- Shortwave downward radiation

River induced infiltration

model

Q

Qn

R, E, Ro

Effective porosity

R = RainfallE = EvaporationRo = RunoffQ = River discharge

Qn = Recharge/ Discharge from riverbedQi = Recharge from infiltration

Qi

Where

Three modules that not cover in this study

7 climate variables:

Methodology (Mathematical models):

Kazama et. al. 2007

CMIP5 - Coupled Model Intercomparison Project Phase 5 5 GCMs

under 3 scenarios

RCP 2.6RCP 4.5RCP 8.5

6

Land Surface Hydrology Module (LSM):

Soil water balance Energy balance

inf sm s sb

dWRa Snowf Q E Q Q

dt

4(1 ) sSW LW T lE H G

Soil water balance

Energy balance

The model was developed by Hanasaki et al., 2008; 2012; 2014

7

Schematic of H08’s river module

tRivOutxAQRivInfRivSto tot )(

River Module:

8

Reservoir Operations Module:

• In this study, we focused on Bhumibol and Sirikit Reservoirs only.

• In reality, reservoir operations are very complex

• We propose an idealized simple reservoir model.

• Although simple, this simulation offers good insight into river management and planning.

Bhumibol Dam

Sirikit Dam

Nakhon SawanChainat

Ayutthaya

Bangkok

Chao Phraya River Basin

N1

Y1CW21

P1

W4AY4

Y16N5A

P17 N67

C2

C13

C35Rojana

Ping

Wan

g

Yom

Nan

Pa

Sak

Chao P

hraya

Tha C

hin

Elev. (m)

0-10

10-20

20-30

30-50

50-100

100-150

500-1,000

1,000-1,500

1,500-2,000

2,000-2,572

9

10

Climate change conditions: 5 GCMs used in this study

GCMs Institutions Resolution (lon × lat)

Original Applied in the study

MIROC-ESM-CHEM National Institute for Env. studies 2.81° × 2.81° 5.0’ × 5.0’

HadGEM2-ES Met office Hadley centre 1.87° × 1.24° 5.0’ × 5.0’

GFDL-ESM2M Geophysical fluid dynamics Lab. 2.50° × 2.00° 5.0’ × 5.0’

IPSL-CM5A-LR Institute Pierre Simon Laplace 3.75° × 1.87° 5.0’ × 5.0’

NorESM1-M Norwegian Climate Centre 2.50° × 1.87° 5.0’ × 5.0’

Used linear interpolation to interpolate the original resolution of GCM data to the study grid size of 5’ x 5’ or about 10 km x 10 km

Shifting and scaling method was used for removing systematic biases of the original GCM data (e.g., Alcamo et al., 2007; Hanasaki et al., 2013)

Results: Model Calibration

N

Bhumipol Dam

Sirikit Dam

C.2

0

2,000

4,000

6,000

8,000

10,000

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Annu

al Infl

ow (M

CM)

0

2,000

4,000

6,000

8,000

10,000

1970 1975 1980 1985 1990 1995 2000 2005

Annu

al Infl

ow (M

CM)

0

1,000

2,000

3,000

4,000

5,000

6,000

1950 1960 1970 1980 1990 2000 2010

Flood

pea

k (CM

S)

0

50

100

150

200

250

300

350

Jan-

86

Jan-

87

Jan-

88

Jan-

89

Jan-

90

Jan-

91

Jan-

92

Jan-

93

Jan-

94

Jan-

95

Jan-

96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Dis

char

ge (m

3se

c-1)

Observation

Simulation

P.1 (Ping River)IOA = 0.96

0

50

100

150

200

250

300

350

400

Jan-

86

Jan-

87

Jan-

88

Jan-

89

Jan-

90

Jan-

91

Jan-

92

Jan-

93

Jan-

94

Jan-

95

Jan-

96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Dis

char

ge (m

3se

c-1)

Observation

Simulation

W.4A (Wang River)IOA = 0.89

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

Jan-

86

Jan-

87

Jan-

88

Jan-

89

Jan-

90

Jan-

91

Jan-

92

Jan-

93

Jan-

94

Jan-

95

Jan-

96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Dis

char

ge (m

3se

c-1)

Observation

Simulation

C.2 (Basin outlet)IOA = 0.93

0

100

200

300

400

500

600

700

800

Jan-

86

Jan-

87

Jan-

88

Jan-

89

Jan-

90

Jan-

91

Jan-

92

Jan-

93

Jan-

94

Jan-

95

Jan-

96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Dis

char

ge (m

3se

c-1)

Observation

Simulation

Y.6 (Yon River)IOA = 0.91

11

12

Results: Annual mean air temperature

Current period(1986-2000)

Projection period(2026-2040)

RCP2.6average from 5 GCMs

Change(Future – Current)

Surface Air Temperature change ( )

RCP 2.6 RCP 4.5 RCP 8.5

Results: Annual mean air temperature

13

C0

C0 C0

C00

0.5

1

1.5

2

2.5

MIROC HadGEM GFDL IPSL NorESM

Su

rfac

e ai

r te

mp

erat

ure

ch

ang

es (

°C)

RCP2.6

RCP4.5

RCP8.5

Results: Annual mean air temperature

14

The increasing of surface air temperature in the near future was in a range of 0.9-2.31 which had a 25.38 as a mean annual surface air temperature. C0

C0

Results: Surface water balance from the LSM

Average annual rainfall, evaporation, and runoff (1986-2000)

Rainfall = 987 mmEvaporation = 810 mm or 82% of annual rainfallSurface runoff = 177 mm or 18% of annual rainfall

15

Results: Water balance

0

200

400

600

800

1,000

1,200

MIR

OC

Had

GE

M

GF

DL

IPS

L

Nor

ES

M

MIR

OC

Had

GE

M

GF

DL

IPS

L

Nor

ES

M

MIR

OC

Had

GE

M

GF

DL

IPS

L

Nor

ES

M

History RCP2.6 RCP4.5 RCP8.5

(mm

.) Rainfall

Runoff

Evaporation

16

MIROC and NorESM GCMs showed increasing trend for all variables

Results: Rainfall

17

Current period(1986-2000)

Projection period(2026-2040) RCP2.6

average from 5 GCMs

Change(Future – Current)

Results: Rainfall

18

RCP 2.6 RCP 4.5 RCP 8.5

Annual Rainfall change

There were both increase and decrease in projected rainfall changes except RCP4.5 scenario. This scenario showed that over the whole basin rainfall might be reduced by 20 mm to 50 mm.

19

Result: River discharge at Chiang Mai

0

50

100

150

200

1 2 3 4 5 6 7 8 9 10 11 12

Dis

char

ge (

m3

sec-1

)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

P.1 (RCP2.6)

0

50

100

150

200

1 2 3 4 5 6 7 8 9 10 11 12

Dis

char

ge (

m3

sec-1

)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

P.1 (RCP4.5)

0

50

100

150

200

1 2 3 4 5 6 7 8 9 10 11 12D

isch

arge

(m

3se

c-1)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

P.1 (RCP8.5)

From January to June, the river discharge projections from the GCMs decreased.In contrast, during the second monsoon period (August to October), river discharges in the upper area (mountainous region) showed significantly increased.

20

Result: River discharge at Kampangphet

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9 10 11 12

Dis

char

ge (

m3

sec-1

)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

P.7A(RCP2.6)

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9 10 11 12

Dis

char

ge (

m3

sec-1

)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

P.7A(RCP4.5)

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9 10 11 12

Dis

char

ge (

m3

sec-1

)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

P.7A(RCP8.5)

March to June, river discharge projections of river discharges from the GCMs are decreased. In contrast, during July to February,the river discharges in the downstream showed significantly increased.

21

Result: River discharge at Nakorn Sawan

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12

Dis

char

ge (

m3

sec-1

)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

C.2 (RCP2.6)

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12

Dis

char

ge (

m3

sec-1

)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

C.2 (RCP4.5)

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12

Dis

char

ge (

m3

sec-1

)

Month

Area2 Area1

Min Max

Obs (Past) MIROC

HadGEM GFDL

IPSL NorESM

One standard deviation range

C.2 (RCP8.5)

River discharge in C.2 quite stable from January to May because this period was controlled by reservoir operations. During the wet season (May to October), the river discharge at the basin outlet station was peak in October but the rainfall was maximum in September.

Conclusions

The increasing of annual surface air temperature in the near

future (2026-2040) was in a range of 0.9-2.31°C, which had a

25.38 °C as a mean annual surface air temperature.

Maximum air surface temperature is projected to increase

by 1.77-2.31 °C in the projected period related to the

reference period (1986-2000).

Rainfall tended to decrease in the near future, on average.

For the river discharge projection, Chiang Mai and

Kampangphet will increase in the risk of both drought (first

monsoon) and flood (second monsoon) but Nakorn Sawan

province might predominate by drought.

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Thank you for your kind attention.

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