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Models for Managing Climate Risk in Water Management Policy

Models for Managing Climate Risk in Water Management Policy. Input from Casey Brown and Assis Francisco F. IRI. Application of Seasonal Climate Forecasts to Water Management. FOREFITED OPPORTUNITY. HARDSHIP. CRISIS.

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Models for Managing Climate Risk in Water Management Policy

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  1. Models for Managing Climate Riskin Water Management Policy Input from Casey Brown and Assis Francisco F. IRI

  2. Application of Seasonal Climate Forecasts to Water Management

  3. FOREFITED OPPORTUNITY HARDSHIP CRISIS Managing The Full Range of Variability common assumption of a static policy storage level)

  4. Tendência s=13.20mm 34% Variância Baixa Freqüência 24% Variância s=11.21 mm Alta Freqüência 42% Variância s=14.71 SAHEL s=23 mm s2 = 516 mm2 Sen declividade = 0.64 Mann-Kendall Tau Test

  5. Sometimes policy is based on a sample that isnot representative of the true expectation. From Meko Colorado River, western U.S.

  6. From Connie Woodhouse

  7. Vazão do Rio Colorado em Lees Ferry

  8. Precipitação em Fortaleza 1849-2006 Fortaleza, Brazil Seca 1877

  9. Afluência ao Reservatório Orós Fortaleza, Brazil

  10. Correlação das Vazões Afluentes ao Oros e a Temperatura da Superfície do Mar Fortaleza, Brazil A variabilidade hidrológica esta associada a fenômenos climáticos em escala planetária.

  11. System Risk Perception Reservoir Storage (V) in hm3

  12. System Regret in Relation to Perfect Knowledge

  13. (b): climatology (a): zero flow (c): perfect knowledge (d): forecast Plots show storage, from 1912 to 1995 (e) forecast – zero flow Reservoir Storage: (a) “Zero Fllow”, (b)”Climatology”, (c)”Perfect Knowledge”, (d)”Forecast”, (e) “forecast-Zero”

  14. m3/year (b): climatology (a): zero flow total agric (low) urban (high) (d): forecast (c): perfect knowledge Demand Suplly for High and Low Priority and for the system simulated in: (a) “Zero Fllow”, (b)”Climatology”, (c)”Perfect Knowledge”, (d)”Forecast”, (e) “forecast-Zero” (e): forecast – zero flow

  15. RESERVEOIR STORAGE JULY

  16. Permanence Curve of Reservoir Storage in July for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast”

  17. Probability of Shortfall will be less than some value in the system. Using the forecast provides the possibility that the shortfall will be less than the shortfall using climatology

  18. Relation between the storage in July (hm3) and Volume release between July and December (hm3) for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast”.

  19. CLIMATE DYNAMICAL DOWNSCALING FORECAST SYSTEM FOR NORDESTE • HISTORICAL DATA • Extended Simulations • Observations PERSISTED GLOBAL SST ANOMALIES ECHAM4.5 AGCM (T42) Persisted SSTA ensembles 1 Mo. lead 10 Post Processing PREDICTED SST ANOMALIES Tropical Pacific Ocean (LDEO Dynamical Model) (NCEP Dynamical Model)(NCEP Statistical CA Model) Tropical Altantic Ocean (CPTEC Statistical CCA Model) Tropical Indian Ocean (IRI Statistical CCA Model) Extratropical Oceans (Damped Persistence) Predicted SSTA ensembles 1-4 Mo. lead 10 RSM97 (60km) RAMS (40km) AGCM INITIAL CONDITIONS UPDATED ENSEMBLES (10+) WITH OBSERVED SSTs IRI FUNCEME Hydrologic Models CPTEC GCM (T42)

  20. Downscaling(Modo Simulação)

  21. Esquema de Previsão Climática de Vazões: Propoagação de Incertezas “END to END” Temperatura Superfície do Mar Modelos de Circulação Geral Estrutura do Modelo Condições Iniciais Modelos Climáticos Regionais Estrutura do Modelo Condições Iniciais Correção Estatística “Weather Generation” Estrutura do Modelo Modelos Hidrológicos Calibração/Validação (incerteza parâmetros) Condições Iniciais Combinação de Multi-Modelos Previsão de Vazão

  22. Another Setting: Near Manilla, Philippines Inflow to Angat Reservoir JJAS – 30% OND – 46% 3-months lag correlation (Nino3.4,QJJAS) = -0.20 (Nino3.4,QOND) = -0.51 (Arumugam et al., submitted)

  23. Seasonal Climate Forecast: Expected skill for a 3-month season

  24. “Business as Usual” Urban Centers Low Inflow Current Reservoir Contents First Priority: Manila Water Remaining Water: Agriculture and Hydropower

  25. Reservoir Management Hydropower Water Delivery Storage Spill Inflows

  26. Dynamic Rule Curve Flood Inflow

  27. Wet Forecast Greater Flood Risk More Inflow More Release Possible

  28. Increased Hydropower

  29. Irrigation Improvement

  30. Dry Forecast Less Flood Risk Less Inflow More Storage Possible - but not sufficient

  31. Impacts on Irrigation 1998 (1) - 86.60 % 1998 (2) - 43.94 % Irrigated Palay Production in AMRIS 1 – First Semester Harvest (Nov – Mar cropping season/dry) 2 – Second Semester Harvest (Jun – Oct cropping season/wet)

  32. “Business as Usual” Urban Centers Low Inflow Current Reservoir Contents First Priority: Manila Water Remaining Water: Agriculture and Hydropower

  33. Dry Year Option Contracts Probabilistic Inflow Forecast Contracts w/ Dry Year Option Current Reservoir Contents

  34. Insurance + Contracts

  35. Option Exercise Decision Observe preseason flows Observe In-season flows np ? Decide preseason options to exercise Total Cost nppp + nipi

  36. Water Supply Costs

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