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

Apresentação em tema: "Models for Managing Climate Risk in Water Management Policy Input from Casey Brown and Assis Francisco F. IRI."— Transcrição da apresentação:

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

2 Application of Seasonal Climate Forecasts to Water Management

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

4 SAHEL Sen declividade = 0.64 Mann-Kendall Tau Test =23 mm mm 2 Tendência =13.20mm 34% Variância =11.21 mm Baixa Freqüência 24% Variância =14.71 Alta Freqüência 42% Variância

5 Sometimes policy is based on a sample that is not 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 Seca 1877 Fortaleza, Brazil

9 Afluência ao Reservatório Orós Fortaleza, Brazil

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

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12 System Risk Perception Reservoir Storage (V) in hm3

13 System Regret in Relation to Perfect Knowledge Zero Flow Perfect KnowledgeClimatologyForecasting Forecasting- Zero (ZF)(PK)(C-Median) (F-Median)(FZ-Median) Average REGRET System (hm3/Year) 52.65015.4526.9730.14 Average REGRET High Priority(hm3/Year) 5.9806.676.58.86 Average REGRET Low Priority(hm3/Year) 46.6908.7720.4722.7

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

15 (a): zero flow (b): climatology (c): perfect knowledge (d): forecast (e): forecast – zero flow 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 total agric (low) urban (high) m 3 /year

16 RESERVEOIR STORAGE JULY

17 Permanence Curve of Reservoir Storage in July for Zero Flow, Climatology, Perfect Knowledge and Forecast

18 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

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

20 CLIMATE DYNAMICAL DOWNSCALING FORECAST SYSTEM FOR NORDESTE PERSISTED GLOBAL SST ANOMALIES ECHAM4.5 AGCM (T42) AGCM INITIAL CONDITIONS UPDATED ENSEMBLES (10+) WITH OBSERVED SSTs Persisted SSTA ensembles 1 Mo. lead Predicted SSTA ensembles 1-4 Mo. lead 10 Post Processing RSM97 (60km) RAMS (40km) HISTORICAL DATA Extended Simulations Observations 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) IRI FUNCEME CPTEC GCM (T42) Hydrologic Models

21 Downscaling (Modo Simulação)

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

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

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

25 Current Reservoir Contents Remaining Water: Agriculture and Hydropower First Priority: Manila Water Urban Centers Low Inflow Business as Usual

26 Reservoir Management Hydropower Water Delivery Storage Spill Inflows

27 Dynamic Rule Curve Inflow Flood

28 More Inflow Greater Flood Risk More Release Possible Wet Forecast

29 Increased Hydropower

30 Irrigation Improvement

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

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

35 Current Reservoir Contents Remaining Water: Agriculture and Hydropower First Priority: Manila Water Urban Centers Low Inflow Business as Usual

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

37 Insurance + Contracts

38 Option Exercise Decision n p ? n p p p + n i p i Observe preseason flows Decide preseason options to exercise Total Cost Observe In- season flows

39 Water Supply Costs