Incorporating Large-Scale Climate Information in Water Resources Decision Making Balaji Rajagopalan Dept. of Civil, Env. And Arch. Engg. And CIRES Katrina.

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Presentation transcript:

Incorporating Large-Scale Climate Information in Water Resources Decision Making Balaji Rajagopalan Dept. of Civil, Env. And Arch. Engg. And CIRES Katrina Grantz, Edith Zagona (CADSWES) Martyn Clark (CIRES)

A Water Resources Management Perspective Time HorizonTime Horizon Inter-decadal Hours Weather Climate Decision Analysis: Risk + Values Data: Historical, Paleo, Scale, Models Facility Planning – – Reservoir, Treatment Plant Size Policy + Regulatory Framework – – Flood Frequency, Water Rights, 7Q10 flow Operational Analysis – – Reservoir Operation, Flood/Drought Preparation Emergency Management – – Flood Warning, Drought Response

Study Area TRUCKEE CANAL Farad Ft Churchill NEVADA CALIFORNIA Carson Truckee

Motivation US Bureau of Reclamation (USBR) searching for an improved forecasting model for the Truckee and Carson Rivers (accurate and with long-lead time) US Bureau of Reclamation (USBR) searching for an improved forecasting model for the Truckee and Carson Rivers (accurate and with long-lead time) Truckee Canal Forecasts determine reservoir releases and diversions Forecasts determine reservoir releases and diversions Protection of Protection of listed species Cui-ui Lahontan Cutthroat Trout

Outline of Approach Climate Diagnostics Climate Diagnostics Forecasting Model Forecasting Model Decision Support System Decision Support System Forecasting Model Forecasting Model Nonparametric stochastic model conditioned on climate indices and snow water equivalent Climate Diagnostics Climate Diagnostics To identify relevant predictors to spring runoff in the basins Decision Support System Decision Support System Couple forecast with DSS to demonstrate utility of forecast

Data Used monthly data sets: monthly data sets: Natural Streamflow (Farad & Ft. Churchill gaging stations) Snow Water Equivalent (SWE)- basin average Large-Scale Climate Variables

500mb Geopotential HeightSea Surface Temperature Carson Spring Flow Winter Climate Correlations

Fall Climate Correlations 500mb Geopotential Height Sea Surface Temperature Carson Spring Flow

Physical Mechanism L Winds rotate counter- clockwise around area of low pressure bringing warm, moist air to mountains in Western US Winds rotate counter- clockwise around area of low pressure bringing warm, moist air to mountains in Western US

Climate Indices Use areas of highest correlation to develop indices to be used as predictors in the forecasting model Use areas of highest correlation to develop indices to be used as predictors in the forecasting model Area averages of geopotential height and SST Area averages of geopotential height and SST 500 mb Geopotential HeightSea Surface Temperature

Outline of Approach Climate Diagnostics Climate Diagnostics Forecasting Model Forecasting Model Decision Support System Decision Support System  Forecasting Model Nonparametric stochastic model conditioned on climate indices and SWE Climate Diagnostics Climate Diagnostics To identify relevant predictors to spring runoff in the basins Decision Support System Decision Support System Couple forecast with DSS to demonstrate utility of forecast

The Ensemble Forecast Problem Ensemble Forecast/Stochastic Simulation /Scenarios generation – all of them are conditional probability density function problems Ensemble Forecast/Stochastic Simulation /Scenarios generation – all of them are conditional probability density function problems Estimate conditional PDF and simulate (Monte Carlo, or Bootstrap) Estimate conditional PDF and simulate (Monte Carlo, or Bootstrap) K-NN Approach is Used K-NN Approach is Used

Model Validation & Skill Measure Cross-validation: drop one year from the model and forecast the “unknown” value Cross-validation: drop one year from the model and forecast the “unknown” value Compare median of forecasted vs. observed (obtain “r” value) Compare median of forecasted vs. observed (obtain “r” value) Rank Probability Skill Score Rank Probability Skill Score Likelihood Skill Score Likelihood Skill Score

Forecasting Results

Outline of Approach Climate Diagnostics Climate Diagnostics Forecasting Model Forecasting Model Decision Support System Decision Support System Forecasting Model Forecasting Model Nonparametric stochastic model conditioned on climate indices and SWE Climate Diagnostics Climate Diagnostics To identify relevant predictors to spring runoff in the basins  Decision Support System Couple forecast with DSS to demonstrate utility of forecast

Seasonal Decision Support System Method to test the utility of the forecasts and the role they play in decision making Model implements major policies in lower basin (Newlands Project OCAP) Seasonal time step

Seasonal Model Policies Use Carson water first Use Carson water first Max canal diversions: 164 kaf Max canal diversions: 164 kaf Storage targets on Lahontan Reservoir: 2/3 of historical April-July runoff volume Storage targets on Lahontan Reservoir: 2/3 of historical April-July runoff volume No minimum fish flows (release from upstream reservoir to combat low flows) No minimum fish flows (release from upstream reservoir to combat low flows)

Decision Model Flowchart

Decision Variables Lahontan Storage Available for Irrigation Truckee River Water Available for Fish Diversion through the Truckee Canal

Decision Model Results Canal DiversionWater for Fish Irrigation Water Dec 1 st Forecast Feb 1 st Forecast Apr 1 st Forecast

Dry Year: 1994 April 1 st February 1 st December 1 st Truckee Forecast Carson Forecast Storage for Irrigation Canal Diversion Water for Fish

Wet Year: 1993 April 1 st February 1 st December 1 st Truckee Forecast Carson Forecast Storage for Irrigation Canal Diversion Water for Fish

Normal Year: 2003 April 1 st February 1 st December 1 st Truckee Forecast Carson Forecast Storage for Irrigation Canal Diversion Water for Fish

Exceedance Probabilities

Summary & Conclusions Climate indicators improve forecasts and offer longer lead time Climate indicators improve forecasts and offer longer lead time Water managers can utilize the improved forecasts in operations and seasonal planning Water managers can utilize the improved forecasts in operations and seasonal planning Grantz et al. (2005) – submitted to BAMS Grantz et al. (2005) – accepted in Water Resources Research.

Acknowledgements CIRES and the Innovative Research Project CIRES and the Innovative Research Project Tom Scott of USBR Lahontan Basin Area Office Tom Scott of USBR Lahontan Basin Area Office Funding