logging in or signing up Clim Application Water Lindon Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 55 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: January 07, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Slide1: Models for Managing Climate Risk in Water Management Policy Input from Casey Brown and Assis Francisco F. IRISlide2: Application of Seasonal Climate Forecasts to Water ManagementSlide3: Managing The Full Range of Variability common assumption of a static policy storage level) SAHEL: SAHEL Sen declividade = 0.64 Mann-Kendall Tau Test s=23 mm s2 = 516 mm2Sometimes policy is based on a sample that isnot representative of the true expectation. From Meko: Sometimes policy is based on a sample that is not representative of the true expectation. From Meko Colorado River, western U.S.From Connie Woodhouse: From Connie WoodhouseVazão do Rio Colorado em Lees Ferry : Vazão do Rio Colorado em Lees Ferry Precipitação em Fortaleza 1849-2006: Precipitação em Fortaleza 1849-2006 Seca 1877 Fortaleza, BrazilAfluência ao Reservatório Orós: Afluência ao Reservatório Orós Fortaleza, BrazilCorrelação das Vazões Afluentes ao Oros e a Temperatura da Superfície do Mar: 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, BrazilSystem Risk Perception: System Risk Perception Reservoir Storage (V) in hm3 System Regret in Relation to Perfect Knowledge : System Regret in Relation to Perfect Knowledge Slide14: (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 1995Slide15: (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) m3/yearRESERVEOIR STORAGE JULY: RESERVEOIR STORAGE JULYPermanence Curve of Reservoir Storage in July for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast” : Permanence Curve of Reservoir Storage in July for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast” 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: 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 climatologyRelation between the storage in July (hm3) and Volume release between July and December (hm3) for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast”. : Relation between the storage in July (hm3) and Volume release between July and December (hm3) for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast”. Slide20: CPTEC GCM (T42) Hydrologic ModelsDownscaling(Modo Simulação): Downscaling (Modo Simulação)Esquema de Previsão Climática de Vazões: Propoagação de Incertezas “END to END” : 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 Condições Iniciais Estrutura do ModeloInflow to Angat Reservoir: Inflow to Angat Reservoir 3-months lag correlation (Nino3.4,QJJAS) = -0.20 (Nino3.4,QOND) = -0.51 JJAS – 30% OND – 46% (Arumugam et al., submitted) Another Setting: Near Manilla, PhilippinesSeasonal Climate Forecast: Expected skill for a 3-month season: Seasonal Climate Forecast: Expected skill for a 3-month seasonSlide25: Current Reservoir Contents Remaining Water: Agriculture and Hydropower First Priority: Manila Water Urban Centers Low Inflow “Business as Usual”Reservoir Management: Reservoir Management Hydropower Water Delivery Storage Spill InflowsSlide27: Dynamic Rule Curve Inflow FloodWet Forecast: More Inflow Greater Flood Risk More Release Possible Wet ForecastIncreased Hydropower: Increased HydropowerIrrigation Improvement: Irrigation ImprovementDry Forecast: Dry Forecast Less Inflow Less Flood Risk More Storage Possible - but not sufficientSlide34: 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 IrrigationSlide35: Current Reservoir Contents Remaining Water: Agriculture and Hydropower First Priority: Manila Water Urban Centers Low Inflow “Business as Usual”Slide36: Current Reservoir Contents Probabilistic Inflow Forecast Dry Year Option Contracts Contracts w/ Dry Year Option Insurance + Contracts: Insurance + Contracts Option Exercise Decision: Option Exercise Decision np ? nppp + nipi Observe preseason flows Decide preseason options to exercise Total Cost Observe In-season flowsWater Supply Costs: Water Supply Costs You do not have the permission to view this presentation. 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Clim Application Water Lindon Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 55 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: January 07, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Slide1: Models for Managing Climate Risk in Water Management Policy Input from Casey Brown and Assis Francisco F. IRISlide2: Application of Seasonal Climate Forecasts to Water ManagementSlide3: Managing The Full Range of Variability common assumption of a static policy storage level) SAHEL: SAHEL Sen declividade = 0.64 Mann-Kendall Tau Test s=23 mm s2 = 516 mm2Sometimes policy is based on a sample that isnot representative of the true expectation. From Meko: Sometimes policy is based on a sample that is not representative of the true expectation. From Meko Colorado River, western U.S.From Connie Woodhouse: From Connie WoodhouseVazão do Rio Colorado em Lees Ferry : Vazão do Rio Colorado em Lees Ferry Precipitação em Fortaleza 1849-2006: Precipitação em Fortaleza 1849-2006 Seca 1877 Fortaleza, BrazilAfluência ao Reservatório Orós: Afluência ao Reservatório Orós Fortaleza, BrazilCorrelação das Vazões Afluentes ao Oros e a Temperatura da Superfície do Mar: 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, BrazilSystem Risk Perception: System Risk Perception Reservoir Storage (V) in hm3 System Regret in Relation to Perfect Knowledge : System Regret in Relation to Perfect Knowledge Slide14: (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 1995Slide15: (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) m3/yearRESERVEOIR STORAGE JULY: RESERVEOIR STORAGE JULYPermanence Curve of Reservoir Storage in July for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast” : Permanence Curve of Reservoir Storage in July for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast” 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: 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 climatologyRelation between the storage in July (hm3) and Volume release between July and December (hm3) for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast”. : Relation between the storage in July (hm3) and Volume release between July and December (hm3) for “Zero Flow”, “Climatology”, “Perfect Knowledge” and “Forecast”. Slide20: CPTEC GCM (T42) Hydrologic ModelsDownscaling(Modo Simulação): Downscaling (Modo Simulação)Esquema de Previsão Climática de Vazões: Propoagação de Incertezas “END to END” : 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 Condições Iniciais Estrutura do ModeloInflow to Angat Reservoir: Inflow to Angat Reservoir 3-months lag correlation (Nino3.4,QJJAS) = -0.20 (Nino3.4,QOND) = -0.51 JJAS – 30% OND – 46% (Arumugam et al., submitted) Another Setting: Near Manilla, PhilippinesSeasonal Climate Forecast: Expected skill for a 3-month season: Seasonal Climate Forecast: Expected skill for a 3-month seasonSlide25: Current Reservoir Contents Remaining Water: Agriculture and Hydropower First Priority: Manila Water Urban Centers Low Inflow “Business as Usual”Reservoir Management: Reservoir Management Hydropower Water Delivery Storage Spill InflowsSlide27: Dynamic Rule Curve Inflow FloodWet Forecast: More Inflow Greater Flood Risk More Release Possible Wet ForecastIncreased Hydropower: Increased HydropowerIrrigation Improvement: Irrigation ImprovementDry Forecast: Dry Forecast Less Inflow Less Flood Risk More Storage Possible - but not sufficientSlide34: 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 IrrigationSlide35: Current Reservoir Contents Remaining Water: Agriculture and Hydropower First Priority: Manila Water Urban Centers Low Inflow “Business as Usual”Slide36: Current Reservoir Contents Probabilistic Inflow Forecast Dry Year Option Contracts Contracts w/ Dry Year Option Insurance + Contracts: Insurance + Contracts Option Exercise Decision: Option Exercise Decision np ? nppp + nipi Observe preseason flows Decide preseason options to exercise Total Cost Observe In-season flowsWater Supply Costs: Water Supply Costs