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The role of climate forecast in water resources management - Case studies in Southeast Asia - Shinjiro KANAE Research Institute for Humanity and Nature, Kyoto IIS, The University of Tokyo Acknowledgement to Staff and students of IIS,

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The role of climate forecast in water resources management

  • Case studies in Southeast Asia -

Shinjiro KANAE

Research Institute for Humanity and Nature, Kyoto IIS, The University of Tokyo

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Acknowledgement to

Staff and students of IIS, University of Tokyo

(= Taikan Oki and his group)

Staff and students of University of Yamanashi

(= Yukiko Hirabayashi and her group)

Staff of JMA (Mr. Yamada, Mr. Maeda, ….) Staff of RIHN

  • Dr. Jun Matsumoto, Dr. Akiyo Yatagai.

Many colleagues in agencies in Southeast

Asia (e.g. TMD, RID, …)

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Contents

2006 Floods in the Chao Phraya River One-month climate forecast for Mekong flood. A new gridded precipitation dataset for

validation of climate forecast.

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Chao Phraya

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Introduction: many floods in 2006

  • Aug. ～ the beginning of Oct.
  • Floods

5 times

  • The death

104

  • The completely destroyed

51

  • The partially destroyed

8779

  • The amount of damage

17 billion B

  • The damaged farmland

3856 km2

  • Compensation to farmer

(Mr. Chatchai) 2000 B/rai (Mr. Panya) 500 B/rai

http://www.business-i.jp/news/world- page/news/200610250026a.nwc

1rai = 1600 m2

Ping river Yom river Nan river Pa Sak river Ayutthaya Bangkok Nakhon Sawan Chiang Mai Bung Boraphet Dam Sirikit Dam Bhumipol dam Ang Thong Chao Phraya river Pa Sak Dam

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18th October 2006

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Rule curve of a dam-reservoir

Bhumipolダム

ダム貯水量(×100万m3)

3000 6000 9000 12000 15000

1/1 1/11 1/21 1/31 2/10 3/1 2/20 3/11 3/21 3/31 4/10 4/20 8/8 8/18 8/28 9/7 9/17 9/27 10/7 10/17 10/27 11/6 11/16 11/26 12/6 12/16 12/26 4/30 5/20 5/10 5/30 6/9 6/19 6/29 7/29 7/9 7/19

(=2005) (=2003) (=2004) (=1994) 2006

(総貯水量 1,346,200万m3) (堆砂容量 380,000万m3) （計画貯水量－上限） 計画貯水量－下限）

Bhumipol Dam-reservoir

Maximum

2006

Cross-section middle of Sep.

Plan

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Ping river Yom river Nan river Pa Sak river Ayutthaya Bangkok Nakhon Sawan Bung Boraphet Dam Sirikit Dam Bhumipol dam Ang Thong Chao Phraya river

Flood in Ayutthaya and Ang Thong

100% Reservoir of dam

The rainy season The dry season

Oct. 99%

King’s land Pa Sak Dam Plan Several weeks before dry-season actual Typhoon came!

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Interview in Ang Thong -1

Worse than 1995, 2001

floods

20-30 cm/day increase Information by TV

(3 wks prior to flood)

Temporary walls made

about 3 days before flood (concrete)

Most trouble : toilet

area along the main road of Ang Thong Lives in Shop owner Occupation 40-50s Estimated age Female Sex distinction

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Typhoon “Xangsane” arrived …

2nd Oct., in early dawn, Xangsane landed Thailand and changed to tropical depression

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Typhoon Track

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Introduction

Ping river Yom river Nan river Pa Sak river Ayutthaya Bangkok Nakhon Sawan Chiang Mai Bhumipol dam Ang Thong

100% Reservoir of dam

The rainy season The dry season

Oct.

Few weeks before

One month forecast. Few days forecast. both are important !

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Summary up to here

Numerical climate forecast data is probably

not used for actual purpose in many countries.

Information is useful,

both for “few weeks beforehand” ( climate) and “few days beforehand” ( weather).

We should study:

Current accuracy of few weeks climate forecast. How to use “non-perfect” climate forecast for

water management.

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Current accuracy and uncertainty of

  • ne-month rainfall forecast

Target basin is the Mekong. JMA one-month forecast (hindcast) for 1992-

2001.

Spatial resolution is 2.5 degree. Evaluation is based on one-month rainfall.

(basically 10day-forecast*3 =30day is used.) (sometimes 30day-forecast is used.)

Observed precipitation data is collected

mostly as a part of GAME/MAHASRI.

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MMR 10/20 10/18 8/15 10/5 LAO 8/31 8/11 5/4 9/18 8/15 9/8 THA 10/9 8/26 9/30 7/31 KHM 11/4 11/18 8/28 7/7 10/9 VNM 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992

Major Flood disasters in the Mekong

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Observation stations Flooded points

○2.5-degree grid and observation stations

上流域・・・格子A、B、C、D 中流域・・・格子E、F、G、H、I、J、K、L、O 下流域・・・格子M、N

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August September

Correlation between observation and forecast (Precipitation)

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Forecast Error (anomaly is compared) Ensemble Uncertainty

％ ％ Flood of 2000 July 7th: Forecast error and Ensemble uncertainty

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Error by 10-day*3 Error by 30-day

％ ％ Flood of 2000 July 7th: Forecast error based on 10-day forecast and 30-day f ％

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％ ％

Flood of 2000 July 7th: Forecast error based on 10-day forecast and 30-day f

Ensemble uncertainty by 10-day*3 Ensemble uncertainty by 30-day

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For the validation of forecast,

  • bserved precipitation dataset is very

important!!

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(Asian Precipitation－Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources)

Global Environmental Research Fund by the Ministry of Environment, Japan (Project B062 Approved as a three year project; May 2006 – March 2009) Principal Investigator: Dr. Akiyo Yatagai (RIHN) Research Institute for Humanity and Nature (RIHN) Co-PI: Dr. Akio Kitoh, Meteorological Research Institute (MRI)/ Japan Meteorological Agency (JMA) Members: Akiyo YATAGAI, Shinjiro KANAE, Tsugihiro WATANABE, Jumpei KUBOTA, Itsuki HANDOH (RIHN) Akio KITOH, Kenji KAMIGUCHI, Osamu ARAKAWA (MRI/JMA) Project Home Page: http://www.chikyu.ac.jp/precip/aphrodite.htm

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East Asia Gauge-Based Daily Precipitation Analysis Climatology (Xie et al. 2004)

Yatagai et al. (2005)

(mm/day)

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Xie et al. (2004) In each 0.5

  • deg. box
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Cross Validation Tests

  • Cross validation tests are conducted for 365 days of 1997;
  • Each time, daily precipitation observations at 10% randomly

selected stations are withdrawn and data at the remaining 90% stations are used to define the daily analyses at 0.05o lat/lon grid resolution;

  • This is repeated for 10 time so that each station is dropped
  • nce;
  • Withdrawn station observations are compared to analyzed

values at the gauge location to examine the accuracy of the daily analyses;

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Accuracy – Gauge Network Density

  • Analyses were made

without 10% stations (10 times)；

  • Correlation between the

withdrawn station

  • bservations and the

analysis is calculated for each station;

  • Scatter plots between

correlation and the distance to the closest station are examined;

0 100 200 300 400 500 Distance (km)

Observation at each 100-km is preferable.

Horizontal distribution of CCs will be used for Developing observational Network in future

Xie et al. (2004,2007)

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Conclusion

Let’s share climate-forecast dataset. (database for

everyone is preferable.)

Let’s make a good observation dataset of

precipitation over Asia. (Gridded data is easy to use!)

Let’s investigate from the viewpoint of

  • current accuracy
  • required accuracy for application
  • remove bias, and consider ensemble-uncertainty
  • importance of time-scale for application

(several weeks? several days? hours?)

My analysis is preliminary. Your help is necessary.

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Thank you very much

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観測 2000年7月7日の洪水 ・・・ （2000/6/8-2000/7/7の30日積算降水量の比較） 予測 約10日先の予測 ○アンサンブル予報のばらつき 予測降水量は観測降水量の半分程度と過小評価気味

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％ ％ 予測スコアは概ね－20.0～20.0の範囲 予測スコアの増大に偏りはない 変動係数は一様にとても大きい ⇒ 降水量予測のしにくかった状態 2000年7月7日の洪水 ・・・ （2000/6/8-2000/7/7の30日積算降水量の比較） 予測スコア 30日先の予測 アンサンブルのばらつき 30日先の予測