Developing Climate Resilient Systems in the Asia Pacific Region

Transcription

1 Developing Climate Resilient Systems in the Asia Pacific Region Dr Yahya Abawi 1,2, Dr Sunil Dutta 1, Dr David McClymont 1, Dr Simon White 1, Ms Janita Pahalad 2, Colleagues from Indonesia and the Pacific Island Countries 1 Queensland Climate Change Centre of Excellence 2 National Climate Centre, Bureau of Meteorology

2 Key Challenges and Research Motivation Developed Industrialized nations are largely responsible for GHG emissions and global warming, yet the poor countries are more vulnerable to the impact of climate change and have little capacity to adapt to climate change impacts- We have an ethical responsibility to help these people. At least 80% of humanity lives on less than $10 a day. About 20% lives on less than $1 a day The poorest 40 percent of the world s population accounts for 5 percent of global income. The richest 20 percent accounts for 75 percent of the world income. About 30,000 children die each day due to poverty (UNICEF)

3 Millennium Development Goal (MDG) In the year 2000 the world leaders pledged to reduce world poverty by half from 20% to 10% by 2015 ; More than half way through this target date 17% of the world population lives in poverty although some progress has been made in other key targets. Goal 1: Eradicate extreme poverty and hunger Goal 2: Achieve universal primary education Goal 3: Promote gender equality and empower women Goal 4: Reduce child mortality Goal 5: Improve maternal health Goal 6: Combat HIV/AIDS, malaria and other diseases Goal 7: Ensure environmental sustainability Goal 8: Develop a Global Partnership for Development

4 Additional Pressures to Achieving MDG Population growth (150 per minute) Globalization Urbanization Water scarcity Declining yield Climate Change Modernization of agriculture has lagged behind industrialization in developing countries Transfer of land from the production of food to production of fuel Transfer of land to livestock (high protein food) Biosecurity issues affecting FTA

5 Key Challenges and Research Motivation Food Security Availability (threatened by impact of climate change on yield and distribution) Accessibility Stability (threatened by climate variability and climate extremes) Utilization Food security is not about food aid (Australian White Paper on International Aid) accelerating economic growth fostering functioning and effective states investing in people promoting regional stability and cooperation. International Agricultural Research is the key in dealing with the world food crisis and food security.

6 The Australian Response Australian Centre for International Agricultural Research (ACIAR) Australian Government Overseas Aid Program (AusAid) NGO s Water Management (Kiribati, Fiji, Tonga,Tuvalu, Indonesia, Cook Islands, Vanuatu) Agriculture (Indonesia, PNG, Fiji,Tonga) Renewable Energy (Samoa,Fiji) Human Health (Solomon Islands, PNG, Vanuatu) Climate has a significant impact on all sectors

7 Seasonal climate forecasting for better irrigation system management in Lombok

8

9

10 Data (Systems) Modelling - Lombok

11 Lombok Systems Approach

12 Pacific Islands - Climate Prediction Project Rainwater management Tuvalu Case Study Hydropower Samoa Groundwater Kiribati, Tonga Surface Water Cook Islands, Fiji, Vanuatu Agriculture PNG, Tonga, Fiji Malaria study Solomon Islands

13 Pacific Islands Climate Prediction Project Develop a software SCOPIC (Seasonal Climate Outlook for Pacific Island Countries) to provide local NMS with the ability to issue seasonal climate forecasts specific to their country Training in SCF and Risk Management Conduct pilot project on the impact of climate on vulnerable sectors in each participating country

14 SCOPIC Seasonal Climate Outlooks for Pacific Island Countries To develop capacity in PIC NMS to provide SCO s for climate sensitive sectors and the general public Simple Operation 1. Organize Data 2. Explore Data 3. Analyze Relationships 4. Test Skill 5. Generate Report 6. Drought analysis Tarawa (Kiribati) Apia (Samoa) Lead time (months) Lead time (months) Season Season

15 El Nino based droughts in Samoa and Australia Warning Success Rate by ENSO type % 80.00% 60.00% 40.00% 20.00% El Nino, 52.80% La Nina, 11.80% Other, 21.10% 0.00%

16 z z z Relationship between ENSO Forecast Skill, and Drought Occurrence (Droughts summarised using 4, 6, 12 month accumulation periods) Neutral, 24% La Nina, 7% Droughts by ENSO Type El Nino, 69% Warning Success Rate by ENSO Type Neutral, 42% Droughts by ENSO Type El Nino, 33% La Nina, 25% Warning Success Rate by ENSO Type Neutral, 26.9% Droughts by ENSO Type El Nino, 26.9% La Nina, 38.5% Warning Success Rate by ENSO Type El Nino, 50.68% La Nina, 25.00% Neutral, 14.94% El Nino, 48.94% La Nina, 34.69% Neutral, 29.59% El Nino, 25.45% La Nina, 39.22% Neutral, 16.28%

17

18

19 Pacific Islands Climate Prediction Project Hydropower management Samoa Case Study Aims Determine the utility of SCF in the management of hydro-power generation for the Afulilo Dam. identify management strategies to maximise the use of hydropower generation relative to thermal production. Key points Energy demand increasing 4-5% p.a. In 1992, Hydropower supplied 80% of demand Currently 50% of energy demand is sourced from thermal (diesel)

20 Use of Climate Seasonal Forecasts for Hydropower Management in Samoa Afulilo Dam Daily Water Balance (Nov 06 - March 08) Dam-level (m) Theoretical Dam Level Rainfall(mm) /11/ /11/ /12/2006 3/01/ /01/2007 Elevation - Storage curve for Afulilo Dam Resevior 14/02/2007 y = x x R 2 = /03/ /03/ /04/2007 9/05/ /05/ /06/ /07/2007 1/08/ /08/ /09/2007 3/10/ /10/ /11/2007 5/12/ /12/ /01/2008 6/02/ /02/ /03/ Date Elevation (metres above MRL) y = Ln(x) R 2 = y = Ln(x) R 2 = Curve 1 (< ) Curve 2 ( ) Curve 3 (> 313) Log. (Curve 1 (< )) Log. (Curve 2 ( )) Poly. (Curve 3 (> 313)) ,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000 Storage Volume (ML)

21 Assessing the potential of seasonal climate forecasting to better manage groundwater resources in Kiribati (and Tonga) 1. Collect, collate and digitise historical groundwater test data. 2. Develop software to transform historical groundwater EC measurements into time-series of freshwater lens volume. 3. Assess the forecasting potential of seasonal rainfall and seasonal average freshwater lens volume. 4. Develop guidelines for freshwater lens management based on different ENSO conditions using SCOPIC.

22 Cross-validated Tercile LEPS Scores (3mth Predictand Totals) Using 2mth avg NINO3.4 SST Anomalies Tarawa Rainfall (56-59yrs ) Lead-time (months) Jan -Mar Feb -Apr Mar -May Apr -Jun May -Jul Jun -Aug Jul -Sep Aug -Oct Sep -Nov Oct -Dec Nov -Jan Dec -Feb

23 Developing Lens software

24

25

26 Linear Interpolation- Limited data

27 Results Forecasting Skill

28 Pacific Islands Climate Prediction Project Prediction of Vector-born diseases (Malaria) Aims Determine whether malaria epidemics in the Solomon Islands are related to the ENSO, rainfall and other hydro-climatic variables; and Determine if such relationship can be used as an early warning system for predicting heightened risk of a malarial epidemic and therefore in assisting targeted control strategies.

29 Malaria Snapshot 100 countries, 40% of world population live in areas where malaria transmission occurs million cases each year world wide 750,000 2 million deaths each year Plasmodium falciparum accounts for 60-70% of all cases in SI. Transmitted by Anopheles Mosquitoes Ideal breeding condition (25-30 C, RH 60%)

30 Positive Incidence Ratio (PIR) per 1000 Population in Solomon Island PIR Central Regions PIR Western & Choiseul PIR Malaita PIR Makira PIR Temotu PIR Solomon Islands Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

31 Average PIR(JFMAM) related to November rainfall Average PIR(JFMAM) related to November rainfall at Solomon Islands (triangle is El Nino, and square is La Nina and filled circle is Non-ENSO year) Median rain (JFM) y = x R 2 = PIR Median PIR November rainfall, mm

32 Average PIR(JFMAM) vs rainfall in SONDJ Average PIR(JFMAM) related to SONDJ average rainfall at Solomon Islands (triangle is El Nino, and square is La Nina and filled circle is Non-ENSO year) 45 PIR Median rain (JFM) y = x R 2 = Median PIR SONDJ average rainfall, mm Average PIR(JFMAM) vs rainfall in JFM Average PIR(JFMAM) related to JFM rainfall at Solomon Islands (triangle is El Nino, and square is La Nina and filled circle is Non-ENSO year) 45 PIR Median rain (JFM) y = x R 2 = Median PIR JFM average rainfall, mm

33 Confirmed to unconfirmed malaria cases in the Solomon islands ( ) 0.80 Control program El Nino La Nina Independence Ethnic Tension Ratio of Confirmed and Unconfirmed malaria cases

34 Annual Log PIR as a function of climate and non-climate variables in Solomon Islands (control measures , 1984, , )

35 Pacific Islands Climate Prediction Project Water Management Cook Island Avatiu Aims Predict streamflow volumes for the management of domestic and agricultural water supplies Develop SCF-based management guidelines using SCOPIC Challenges Limited streamflow data (2-3 years) Long term rainfall data ( ) Climate data (temperature, solar radiation, RH) Required data disaggregation and generation using Weatherman

36 Simulated and observed streamflow for Avatiu catchment during calibration period (1/8/ /7/2002) r2=0.86

37 Simulated and observed streamflow for Avatiu catchment ( ) IHACRES (identification of unit hydrograph and component flows from rainfall, evaporation and streamflow)

38 Cross-validated Tercile LEPS Scores 3mth avg SOI Values Monthly Streamflow For Avatiu (78-79 Years) Lead-time (months) Forecasting Skill Streamflow Jan -Mar Feb -Apr Mar -May Apr May Jun Jul Aug Sep -Jun -Jul -Aug -Sep -Oct -Nov Monthly Rainfall For Avatiu (78-79 Years) Oct -Dec Nov -Jan Dec -Feb Lead-time (months) Forecasting Skill Rainfall Jan -Mar Feb -Apr Mar -May Apr -Jun May -Jul Jun -Aug Jul -Sep Aug -Oct Sep -Nov Oct -Dec Nov -Jan Dec -Feb Worse than Climatology As good as Climatology Better than Climatology Monthly average values M onthly stream flow for A vatiu Higher Skill in streamflow forecasting Darker Shaded Blues Average Monthly Rainfall Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec M o n th ly ra in fa ll fo r A v a tiu Average Monthly Streamflow Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

39 Work in progress (Tuvalu, Tonga, Fiji, PNG, Vanuatu) Pacific Islands Climate Prediction Project Acknowledgements ACIAR, AusAid, QCCCE, Bureau of Meteorology and Colleagues from Pacific Islands National Met Services Thank You