research inst. for humanity and nature chikyu.ac.jp (also at iis, university of tokyo)

http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/

Research Inst. for Humanity and Naturehttp://www.chikyu.ac.jp

(also at IIS, University of Tokyo)

Taikan Oki

Water Resources Application Project

(WRAP)


Criteria: the most critical problemCriteria: the most critical problem

Recovery Cost

Duration of problem

Frequency of occurrence

Future perspective

(the most critical problem)

Drought

Water storage elevation in Bhumipol Dam in drought years (1992-1994)

210

220

230

240

250

260

270

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

Month

Wa

ter

Sto

rag

e e

lev

ati

on

(m,m

sl)

Dead Storage

Upper Rule Curve

1992

Lower Rule Curve

19941993

Data Source: RID, 2001; PAL and Panya ,1999.

Almost dry up !!!Almost dry up !!!

1. Water Resources and Water Problems in the Study Area

8 Sub River Basin

6 =Ping

7 =Wang

8 =Yom

9 =Nan

10 =Main Chao Phraya

11 =Sakakrang

12 =Pasak

13 =Thachin

Made by RID

Study Area Water-related Problems

Flooding

Drought

Water Pollution

Excessive Groundwater Extraction

Bhumipol

Sirikit


5,357

10,034

5,964

8,326

4,371

15,607

8,500

4,821

2,000

5,615

10,531

7,163

-

4,000

8,000

12,000

16,000

20,000

NormalYear

Drought(1992)

Drought(1993)

Drought(1994)

MC

M

Stored Water on 1st Jan.Released WaterInflow

Water Situation in Drought 1992-1994

Data Source: EGAT (for whole year)

Planning ProblemPlanning Problem

Possible Solution: Reliable Long-term Hydroclimatic PredictionPossible Solution: Reliable Long-term Hydroclimatic Prediction


• Rainfall (RF) and StreamflowRainfall (RF) and Streamflow

• Southern Oscillation Index (SOI)Southern Oscillation Index (SOI)

• Sea Surface Temperature (SST)Sea Surface Temperature (SST)

2.DATA AND METHODOLOGY USED IN HYDROCLIMATIC PREDICTION

Sea Surface Temperature (SST)

The British Atmospheric Data Centre (BADC), UK

-GISST_2.3B Dataset (1 degree x 1 degree)

SOI

-35

-25

-15

-5

5

15

25

35

1960 1965 1970 1975 1980 1985 1990 1995 2000

SOI

SOI = (Standardized Tahiti - Standardized Darwin) / MSD

Bureau of Meteorology (BOM) Australia, AU

99 100 101

14

15

16

17

18

19C hiangm ai

Lam pangPhrae

N an

U ttaradit

Tak

M ae Sot

Bhum ibol Dam

Phitsanulok

Phetchabun

Nakhon Saw an

Lopburi

D on M uang

Suphanburi

Kanchanaburi

Bangkok

P.1

C.2

R aingauge station (by TM D )

Stream flow station (by R ID )

Sub-river basin

R iver

100 105

10

15

20

Thai Meteorological Department (TMD), TH

Royal Irrigation Department (RID), TH

Global Energy and Water Cycle Experiment (GEWEX), Asian Monsoon Experiment - Tropics (GAME-T)

Data UsedData Used (Monthly 1960-2000)

aP.1

98°0'E

98°0'E

98°30'E

98°30'E

99°0'E

99°0'E

99°30'E

99°30'E

100°0'E

100°0'E

18°30'N 18°30'N

19°0'N 19°0'N

19°30'N 19°30'N

Mae NgatReservoir

:

2

5

9

aP.1

98°0'E

98°0'E

98°30'E

98°30'E

99°0'E

99°0'E

99°30'E

99°30'E

100°0'E

100°0'E

18°30'N 18°30'N

19°0'N 19°0'N

19°30'N 19°30'N

Mae NgatReservoir

:

2

5

9

Upper Ping River Basin


Model Used for Long-term Hydroclimatic PredictionModel Used for Long-term Hydroclimatic Prediction

PURPOSE TYPE MODEL

Physically Based Model

Understand the physical mechanisms

Climate Model (GCM, AGCM, etc.)

Statistically Based Model

More Accurate Predicted Result

(for real application)

Linear Regression ModelGeneralized Additive ModelArtificial Neural Networks,

etc.

Artificial Neural Network (ANN)

• Use limited data• Computational skill in complex problem• No assumption needed as other statistical models• Updating parameters process

By Manusthiparom (2003)Manusthiparom (2003)


Methodology Used for Hydroclimate Prediction

Influence of ENSOOn rainfall and streamflow

1.1 El Niño/La Niña composites1.2 Categorical contingency analysis 11

Prediction process 2.1 Prediction by ANN modeling22

Improvement and extension3.1 Prediction using additional predictors3.2 Input Sensitivity Analysis3.3 Spatial rainfall prediction

33

Potential use of prediction for improved WRM system

4.1 Irrigation Water Demand Forecasting44


http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/3. LONG-TERM RAINFALL PREDICTION

BY ANN MODELING APPROACH

Input layer Hidden layer Output layer

I1

Ini

I2

Bias

H1

Hnh

H2

Bias

O2

O1

Onmax

Feed-Forward Direct Multi-step Network

Difficulties in Using ANN ModelingDifficulties in Using ANN Modeling

Physical considerations,Correlation analysis

Determination of Input Nodes1 SST&RF, SOI& RF,

RF& RF

Trial and error processDetermination of

No.of Hidden Layers and Hidden Nodes

2 Hidden layer=1No. of hidden node=5-10

Adaptive process with changing initial

weighting parameters

Training Process(Weighting factors)3 Good pattern=97 %

(target error=15%)


Predicted Rainfall Anomaly (12 months-ahead)

-200

-100

0

100

200

300

400

500

396 408 420 432 444 456

month

rain

fall

an

om

aly

(m

m/m

on

th)

CASE 1, Bias=3.3 mm/ EI=55.3%CASE 2, Bias=1.8 mm/ EI=76.4 %CASE 3, Bias=1.7 mm/ EI=91.5 %Observed

1997 19981996

Rainfall Anomaly Prediction 12 months ahead12 months ahead

Observed and Predicted Rainfall (12 months ahead)

-

100

200

300

400

500

600

- 100 200 300 400 500 600

Predicted Rainfall (mm/month)

Ob

se

rve

d R

ain

fall

(mm

/mo

nth

)Testing

Predicted Rainfall (12 months ahead)

-

100

200

300

400

500

600

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385

month

Ra

infa

ll (m

m/m

on

th)

Predicted

ObservedTraining

Case 1Case 1: Train (1962-1979, 18 yrs), Test (1980-1999, 20 yrs)

Case 2: Train (1962-1989, 28 yrs), Test (1990-1999, 10 yrs)

Case 3: Train (1962-1994, 33 yrs), Test (1995-1999, 5 yrs)

SSTs: 3 areasSSTs: 3 areas


-

100

200

300

400

500

600

- 100 200 300 400 500 600


Ob

se

rve

d R

ain

fall

(mm

/mo

nth

)

Predicted rainfall for 12 months ahead (Testing)

0

100

200

300

400

500

600

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Ra

infa

ll (m

m/m

on

th)

Predicted

Observed

Case 3:

Train (1962-1994, 33 yrs), Test (1995-1999, 5 yrs)

Target

Error=

15%

Predicted rainfall for 12 months ahead (Testing)

0

100

200

300

400

500

600

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Ra

infa

ll (m

m/m

on

th)

Predicted Rainfall

Observed Rainfall

-

100

200

300

400

500

600

- 100 200 300 400 500 600


Ob

se

rve

d R

ain

fall

(mm

/mo

nth

)Case 2:

Train (1962-1989, 28 yrs), Test (1990-1999, 10 yrs) Large Error

Drought

Target

Error=

15%

Smaller Error

Bad Pattern

Bad Pattern

Rainfall Prediction 12 months ahead12 months ahead SSTs: 3 areasSSTs: 3 areas


O bserved ra in fa ll anom aly (August 1997) (a)

O bserved rain fa ll anom a ly (August 1998) (b )

P red ic ted ra in fall anom a ly (A ugust 1997) (d)

P red ic ted ra in fa ll anom a ly (A ugus t 1998) (e )

O bserved rainfall anom a ly (August 1999) (c)

P red ic ted rain fall anom a ly (August 1999) (f)

9 8 9 9 1 0 0 1 0 1

1 4

1 6

1 8

2 0

9 8 9 9 1 0 0 1 0 1

1 4

1 6

1 8

2 0

9 8 9 9 1 0 0 1 0 1

1 4

1 6

1 8

2 0

9 8 9 9 1 0 0 1 0 1

1 4

1 6

1 8

2 0

9 8 9 9 1 0 0 1 0 1

1 4

1 6

1 8

2 0

9 8 9 9 1 0 0 1 0 1

1 4

1 6

1 8

2 0

Rainfall (m m )

-200

-150

-100

-50

0

50

100

150

200

250

300

Spatial Rainfall PredictionSpatial Rainfall Prediction

Rainfall Anomaly in August 1997-1999Rainfall Anomaly in August 1997-1999

ObservationObservation

• Software: Surfer 8

• Total: 16 stations

• Gridding method: Kriging

• Variogram model: Linear

• Slope =1.0, Aniso= 1,0

• Kriging type: point

• Drift type: None

• No search: Use all data (16)

PredictionPrediction

One MonthOne Month AheadAhead

1997 1998 1999



Date: 2 November 2002Project: Krasieo Operation and Maintenance Project, Royal Irrigation DepartmentLocation: Suphanburi, Thailand

Learning the existing system of WRM Period: 4-15 November 2002Tutor: Mr. Sombat Sontisri (Irrigation Eng.)Chief: Mr. Pongsak ArunwichitsakulWater Allocation GroupOffice of Hydrology & Water ManagementRoyal Irrigation Department (RID), Thailand

Water Manager

Irrigation Eng.

Learning Existing Learning Existing System of WRM fromSystem of WRM from Water ManagerWater Manager

Meeting and InterviewMeeting and Interview Water Water UsersUsers

Water Users (Agriculture)Water Users (Agriculture)

• DroughtDrought is the most serious problem is the most serious problem

• They want to know how much water They want to know how much water will be will be availableavailable for them in next growing season for them in next growing season

Water Manager (RID)Water Manager (RID)

• They want to know how much They want to know how much water will be water will be availableavailable in next season in next season

• They want to improve the existing system if it is They want to improve the existing system if it is easy to understandeasy to understand and and easy to doeasy to do in practice) in practice)

5. POTENTIAL USE OF PREDICTION IN IMPROVING WATER RESOURCES MANAGEMENT SYSTEM


Mean Prediction1991 Drought 84.27 79.79 -4.47 1992 Drought 73.06 94.81 21.751993 Drought 66.25 95.39 29.141994 Transit 75.92 96.43 20.501995 Flood 86.06 94.59 8.52

YearEI (%)

YearDifference

(%)

1993

0

50

100

150

200

250

300

350

400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rai

nfa

ll (

mm

)

Long-term Mean Observation Prediction

1993

-200

-150

-100

-50

0

50

100

150

200


Rai

nfa

ll A

no

mal

y (m

m)


1993

0

5

10

15

20

25

30


Irri

gat

ion

Wat

er D

eman

d (

mcm

)

Observation Long-term Mean Prediction

1993

-10

-5

0

5

10


Irri

gat

ion

Wat

er D

eman

d A

no

mal

y (m

cm)


Rainfall

Irrigation Water Demand (IWD)

An

omal

y V

alu

e

Ab

solu

te V

alu

eDrought:1993

Forecasted Irrigation Water Demand

Using long-term mean: 120.61 mcm

Using observation: 150.25 mcm

Using prediction: 147.61 mcm

Mae Ngat Irrigation Project, Chiang Mai

Using predicted rainfall is better

Using predicted rainfall is worse

12-month ahead forecasted IWD12-month ahead forecasted IWD



5,357

10,034

5,964

8,326

4,371

15,607

8,500

4,821

2,000

5,615

10,531

7,163

-

4,000

8,000

12,000

16,000

20,000

NormalYear

Drought(1992)

Drought(1993)

Drought(1994)

MC

M

Stored Water on 1st Jan.Released WaterInflow

Water Situation in Drought 1992-1994

Forecasted Irrigation Water Demand

Using long-term mean: 120.61 mcm

Using observation: 150.25 mcm

Using prediction: 147.61 mcm

1993

0

5

10

15

20

25

30


Irri

gati

on

Wate

r D

em

an

d (

mcm

)

Observation Long-term Mean Prediction

IWD

Adjustment of Irrigation Area for 120.61 mcm

Based on long-term mean: 30,000 rai (120.61/120.61*30,000)

Based on observation: 24,081 rai (120.61/150.25*30,000)

Based on prediction: 24,502 rai (120.61/147.61*30,000)

1 rai =1,600 m2 1 km2 = 625 rai

Drought:1993Potential Use of Forecasted IWD to improve Planning System

Water scarcity situation in 1994

should have been improved.

5,953 6,2024,373



Summary ANN can predict monthly rainfall a year

ahead with fairly good accuracy based on SST, SOI, and preceding rainfall.

Seasonal prediction of rainfall will substantially contribute for better water resources/reservoir operations.

GAME-T Database is there:

http://game-t.nrct.go.th/GAME-T/ New research opportunities under GAME-

Tropics/Phase II and WRAP for everybody!


Thank you!

research inst. for humanity and nature chikyu.ac.jp (also at iis, university of tokyo)

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