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DSD-SEA 2019 Bridging Policy Gaps through Software and Modelling - Weesakul

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Presentation by Dr. Sutat Weesakul, Hydro-Informatics Institute (Thailand) at the Seminar Hydro Software to support policy development and real-time decision making, during the Deltares Software Days South-East Asia 2019. Wednesday, 27 November 2019, Bangkok.

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DSD-SEA 2019 Bridging Policy Gaps through Software and Modelling - Weesakul

  1. 1. Bridging Policy Gaps through Software and Modelling Dr. Sutat Weesakul Director of Hydro – Informatics Institute (Public Organization) Ministry of Higher Education, Science, Research and Innovation Deltares Software Days South-East Asia 2019, 27th November 2019, Bangkok
  2. 2. 2 | Bridging Policy Gaps through Software and Modelling Contents 1 2 3 Principles on Water Governance Examples of Modelling for Bridging the Gaps • Supporting decision making under uncertainty • Improving communication among stakeholders • Warning and preparation for crisis readiness • Reaching information in real-time Conclusions
  3. 3. Principles on Water Governance
  4. 4. 4 | Bridging Policy Gaps through Software and Modelling Water governance cycle Actions Water governance Policies and strategies formulation Implementation Monitoring Evaluation Principles Indicators Bridging gap New instruments or improvements Assessing gaps Source: OECD (2015), OECD Principles on Water Governance
  5. 5. 5 | Bridging Policy Gaps through Software and Modelling Multi-level governance gaps in water policy Policy gap Accountability gap Funding gap Capacity gap Information gap Administrative gap Objective gap Governance gaps • Policy gap - Sectoral fragmentation of water-related tasks across ministries and agencies. • Accountability gap - Insufficient users commitment and lack of concern, awareness, participation. • Funding gap - Unstable or insufficient funding is undermining the water policy implementation. • Capacity gap - Insufficient scientific, technical, infrastructural capacity of actors. • Information gap - Asymmetries of information between different stakeholders involved in water policy. • Administrative gap - Geographical mismatch between hydrological and administrative boundaries. • Objective gap - Different rationales creating obstacles for adopting convergent targets. Source: OECD (2011), Water Governance in OECD: A Multi-Level Approach
  6. 6. Modelling for Policy Gaps Bridging
  7. 7. 7 | Bridging Policy Gaps through Software and Modelling Adaptive flood risk management plan for Sukhothai Province Supporting decision making under uncertainty
  8. 8. 8 | Bridging Policy Gaps through Software and Modelling Causes of the flood in Sukhothai ▪ Topography and land use changes. ▪ High intensity of rainfall. ▪ Quick runoff and high peak discharge from upstream. ▪ River capacity reduction. Sukhothai Phrae Phitsanulok Phichit Capacity of Yom River Average annual rainfall of Yom River Yom River Basin Avg. Annual Rainfall 1,204 mm. Avg. Annual Discharge 4,143 MCM. Sukhothai Province Avg. Annual Rainfall 1,196 mm. Avg. Annual Discharge 2,946 MCM. Kaeng Sua Ten Dam Storage: 1,175 MCM. Annual inflow: 978 MCM. Storage : Inflow = 1 : 0.8 Upper Yom Dam Storage: 166 MCM. Annual inflow: 911 MCM. Storage : Inflow = 1 : 5.5 Yom Dam Storage: 588 MCM. Annual inflow: 1,382 MCM. Storage : Inflow = 1 : 2.4 Sukhothai Phrae
  9. 9. 9 | Bridging Policy Gaps through Software and Modelling Adaptation pathway for flood risk management Example of planed decision Local and regional actions ▪ Reduction of flood vulnerability of land use. ▪ Raising embankments Sukhothai. Upstream actions ▪ Large upstream reservoir. ▪ Optimized operation. Stepwise development of strategies ▪ Early implementation measures (e.g. bypass around Sukhothai, flood retardation) ▪ No regret measures (e.g. reduction of flood vulnerability of land use) ▪ Easily adaptable measures (e.g. diversion of peak flows, increase discharge capacity through dredging) ▪ Reservoir measures (e.g. build medium or large reservoir, optimized operation) ▪ Regional measures (e.g. raising embankments) (%)
  10. 10. 10 | Bridging Policy Gaps through Software and Modelling Development of Delft3D FM flood model for Ayutthaya City Improving communication between stakeholders
  11. 11. 11 | Bridging Policy Gaps through Software and Modelling The great flood in 2011 ▪ The widespread flooding in the Chao Phraya River Basin. ▪ Ayutthaya City suffered from serious damages with almost all area were under water for months. Ayutthaya City Ayutthaya City
  12. 12. 12 | Bridging Policy Gaps through Software and Modelling Delft3D FM model outcome ❑ Provide understanding of the event and verify the effectiveness of possible measures in an easy way. ❑ Improve communication among professionals, decision makers and various stakeholders.
  13. 13. 13 | Bridging Policy Gaps through Software and Modelling Operational forecasting and early warning system for the Gulf of Thailand Warning and preparation for crisis readiness
  14. 14. 14 | Bridging Policy Gaps through Software and Modelling Ocean forecasting system ▪ Hydrodynamic Model (Delft3D FM) ▪ Wave Model (SWAN) ▪ Weather Prediction Model (WRF-ROMS) ▪ Early Warning System (Delft-FEWS) Early Warning System based on Delft-FEWS
  15. 15. 15 | Bridging Policy Gaps through Software and Modelling ▪ 31 Dec 2018 - A disturbance in the southern of the South China Sea was upgraded to a tropical depression. ▪ 1 Jan 2019 - It was upgraded to the first tropical storm of the 2019 and was named Pabuk. ▪ 2-3 Jan 2019 - Pabuk accelerated west-northwestward and entered the Gulf of Thailand. ▪ 4 Jan 2019 - Pabuk had made landfall over Pak Phanang, Nakhon Si Thammarat. ▪ 5 Jan 2019 - Pabuk had weaken and moved westward to Andaman Sea. Tropical Storm Pabuk 31/12/18 1/1/192/1/193/1/19 4/1/19 5/1/19 Legends TD TS
  16. 16. 16 | Bridging Policy Gaps through Software and Modelling Water level forecast in the Gulf of Thailand Pak Phanang, Nakhon Si Thammarat 03/Jan/19 Pak Phanang, Nakhon Si Thammarat Forecast date: 4 Jan 2019 Reanalysis date: 30 Jan 2019
  17. 17. 17 | Bridging Policy Gaps through Software and Modelling Water level forecast in the Gulf of Thailand KoLak station Pak Phanang station Rayong station Ko Samui Station Forecast 3 days ahead, Computation time: 2.5 hrs. Forecast 7 days ahead, Computation time: 6 hrs.
  18. 18. 18 | Bridging Policy Gaps through Software and Modelling Comparison of observed and forecasted water level on 5th Jan 2019 Highest observed water level Highest forecasted water level Ko Lak station Actual water level Pak Phanang station Rayong station Ko Samui station Actual water level Actual water level Actual water level
  19. 19. 19 | Bridging Policy Gaps through Software and Modelling 4 Jan 2019 - Pak Phanang, Nakhon Si Thammarat 5 Jan 2019 - The Upper Gulf of Thailand Effects of Tropical Storm Pabuk
  20. 20. 20 | Bridging Policy Gaps through Software and Modelling HARRIET (1962) PABUK (2019) Landfall at Pakpanang, Nakhon Si Thammarat with wind speed 95 km/hr Evacuation: None Losses: 911 casualties 22,296 households destroyed 50,775 households damage Landfall at Pakpanang, Nakhon Si Thammarat with wind speed 85 km/hr Evacuation: 31,665 people were evacuated Losses: 5 casualties 405 households destroyed 53,008 households damage HARRIET (1962) vs PABUK (2019)
  21. 21. 21 | Bridging Policy Gaps through Software and Modelling Reaching information in real-time Rainfall estimation and nowcasting system using composite weather radar data and its application
  22. 22. 22 | Bridging Policy Gaps through Software and Modelling POD FAR CSI 7-Jul-19 F1 0.525 0.456 0. F2 0.433 0.619 0. F3 0.400 0.684 0. URBAN RAINFALL (BANGKOK) 7 June 2019 Rainfall estimation and nowcasting system using composite weather radar data BKK
  23. 23. 23 | Bridging Policy Gaps through Software and Modelling 23rd July 2019 Heavy rainfall event in Bangkok **BMA reported highest rainfall observed in Nongjok and Klong Samwa Rainfall estimation and nowcasting system using composite weather radar data
  24. 24. 24 | Bridging Policy Gaps through Software and Modelling COMPOSITE RADAR ON LINE APPLICATION Rainfall estimation and nowcasting system using composite weather radar data Composite Radar Features: - Push rainfall warnings - User’s request interaction
  25. 25. 25 | Effectiveness Clear water policy goals and targets at different level. Efficiency Maximize the benefits of sustainable water management. Trust and Engagement Building public confidence and inclusiveness of stakeholders. Adaption pathway Delft3D FM model Ocean forecasting Composite weather radar Source: OECD (2015), OECD Principles on Water Governance Conclusions
  26. 26. 26 | Policy gap Accountability gap Funding gap Capacity gap Information gap Administrative gap Objective gap Governance gaps Adaption pathway Delft3D FM model Ocean forecasting Composite weather radar Conclusions
  27. 27. 27 | Hydro – Informatics Institute (Public Organization) Ministry of Higher Education, Science, Research and Innovation, THAILAND tel +66 2 5180901 fax +66 2 5180910 www.hii.or.th www.thaiwater.net

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