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Estimation of possible damages due to catastrophic flooding for long-term disaster mitigation planning

Estimation of possible damages due to catastrophic flooding for long-term disaster mitigation planning. Contents. Introduction - 1D-Hydrodynamic modeling with MIKE 11 Development of an improved method for loss of life ( LOL ) estimation

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Estimation of possible damages due to catastrophic flooding for long-term disaster mitigation planning

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  1. Estimation of possible damages due to catastrophic flooding for long-term disaster mitigation planning

  2. Contents • Introduction - 1D-Hydrodynamic modeling with MIKE 11 • Development of an improved method for loss of life (LOL) estimation • Loss of life (LOL) estimation for different scenarios • Conclusions and Suggestions

  3. Introduction • Role of climate change in disaster management • Possible extreme changes in climate as guidelines for the development of new concepts for disaster mitigation • Drastic weather change • Heavy rainfall • Catastrophic flooding downstream of the dam • Risk to people and property

  4. Introduction cont‘d

  5. Introduction cont‘d • Jhelum river valley downstream of Mangla dam in Pakistan • One of largest earth and rock-fill dams in world • Main dam height ~125 m high above riverbed (by Google earth)

  6. Introduction cont‘d

  7. 1D-Hydrodynamic modeling with MIKE 11 Jhelum Bridges Mangla dam Kahan River Suketar Nallah Bandar Kas Rasul Barrage Bunha River Jabba Kas Malikwal Bridge • Project Reach: about 329km • Different Hydraulic • structures • Five tributaries between • Mangla and Rasul Barrage; • No gauges are existing there • 1D-modeling for unsteady • flow conditions Khushab Bridge Chenab River Confluence Point Upstream Trimmu Barrrage

  8. 1D-Hydrodynamic modeling with MIKE 11cont‘d

  9. 1D-Hydrodynamic modeling with MIKE 11cont‘d Rasul Barrage

  10. 1D-Hydrodynamic Modeling with MIKE 11cont’d

  11. 1D-Hydrodynamic Modeling with MIKE 11cont’d Rasul Barrage

  12. Development an improved LOL estimation method LOLi = PARi x FATBASE x Fsv x Fage x Fmt x Fst x Fh x Fwar x Fev LOLi = loss of life at a particular location ´´i`` downstream of the dam PARi = Population at risk at a particular location ´´i`` downstream of the dam FATBASE = Base Fatality rate of 0.15 (worst case of medium severity) (Graham, 1999), assuming an average value of 1.0 for all other factors with average conditions.

  13. Development an improved LOL estimation method Fsv= Flood Severity factor High Severity very likely 1.0 Medium Severity unlikely 0.3 Low Severity very unlikely 0.1 Fage = Age risk factor A (<10yrs+ (>=65yrs)),B (10-15)yrs and C (15-64)yrs Fage = 1.25 *A% +1.1* B%+ 0.8* C% (general form) Fmt = Material risk factor Fmt = 1 * X % + 1.5 * Y % (general form) Where, X= % of other type of houses, Y= % very low strength houses

  14. Development an improved LOL estimation method Fst = Storey risk factor Fst = 1 (for high severity and all house types) Fst = 1- S % (for medium and low severity) Where, S= % of more storey houses Fh = Health risk factor; 3% disabled people Fh = 1 *H % + 1.25*D % (general form) Where, H= % of PAR with avg. health, D= % of disabled PAR

  15. Development an improved LOL estimation method Fwar = Warning factor (Graham,1999) Warning Flood Severity understanding Fwar No No 1 Some (15-60min) Vague/unclear 0.7 Adequate (>60min) Precise/clear 0.3 Fev= Ease of evacuation factor Warning Ease of evacuation Fev No No 1 Some (15-60min) Some 0.7 Adequate(>60min) Good 0.3

  16. Loss of Life estimation Estimated PAR is related to the highest flood event in the past

  17. Loss of Life estimation

  18. Loss of Life estimation

  19. Loss of Life estimation

  20. Conclusions and Suggestions • Severe climate change can cause extreme flooding downstream of a • dam • Estimation of possible damages is an important part of any dam • safety study • - Loss of life increases with the delay in warning initiation with respect • to dam failure • For all dam failure cases, maximum LOL (~80%) occurs in first • 50 km downstream of Mangla dam • % total LOL for the worst case of Mangla dam failure is close to 4% • which seems to be very high

  21. Conclusions and Suggestions • - LOL results clearly show the need of improvement in existing risk • Reduction measures in order to reduce possible LOL due to Mangla • dam failure • More research is required to estimate • - ease of evacuation • - risks posed by age groups • - very low strength houses and more storey houses • - Realistic estimation of possible LOL due to natural hazards like • floods helps in long-term disaster mitigation planning

  22. THANKS FOR YOUR ATTENTION QUESTIONS?? Saqib.Ehsan@iws.uni-stuttgart.de www.iws.uni-stuttgart.de Lehrstuhl für Wasserbau und Wassermengenwirtschaft Institut für Wasserbau, Universität Stuttgart

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