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 T i m e H o r i z o n Inter-decadal Decision Analysis: Risk + Values Facility Planning Hours 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 Data: Historical, Paleo, Scale, Models Climate Weather
CALIFORNIA Study Area Truckee NEVADA PYRAMID LAKE WINNEMUCCA LAKE (dry) CALIFORNIA Carson DONNER STAMPEDE INDEPENDENCE BOCA PROSSER Truckee Tahoe City NEVADA MARTIS Reno/Sparks Farad Carson City Derby Dam Nixon TRUCKEE RIVER Fernley Ft Churchill TRUCKEE CANAL Fallon LAHONTAN Stillwater NWR Newlands Project CARSON LAKE LAKE TAHOE CARSON RIVER
Motivation US Bureau of Reclamation (USBR) searching for an improved forecasting model for the Truckee and Carson Rivers (accurate and with long-lead lead time) Forecasts determine reservoir releases and diversions Protection of listed species Cui-ui Lahontan Cutthroat Trout Truckee Canal
Outline of Approach Climate Diagnostics Climate Diagnostics To identify relevant predictors to spring runoff in the basins Forecasting Model Forecasting Model Nonparametric stochastic model conditioned on climate indices and snow water equivalent Decision Support System Decision Support System Couple forecast with DSS to demonstrate utility of forecast
Data Used 1949-2003 monthly data sets: Natural Streamflow (Farad & Ft. Churchill gaging stations) Snow Water Equivalent (SWE)- basin average Large-Scale Climate Variables
Winter Climate Correlations Carson Spring Flow 500mb Geopotential Height Sea Surface Temperature
Fall Climate Correlations Carson Spring Flow 500mb Geopotential Height Sea Surface Temperature
Physical Mechanism L 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 Area averages of geopotential height and SST 500 mb Geopotential Height Sea Surface Temperature
Outline of Approach Climate Diagnostics Climate Diagnostics To identify relevant predictors to spring runoff in the basins Forecasting Model Forecasting Model Nonparametric stochastic model conditioned on climate indices and SWE 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 f f y y t yt yt yt p (, 1, 2,..., ) t y t y t y t p = 1, 2,..., f ( y, y 1, y 2,..., y ) dy Estimate conditional PDF and simulate (Monte Carlo, or Bootstrap) K-NN Approach is Used t t t t p t
Model Validation & Skill Measure Cross-validation: drop one year from the model and forecast the unknown value Compare median of forecasted vs. observed (obtain r value) Rank Probability Skill Score k i i 1 RPS( p, d ) = P n d k 1 j= 1 n= 1 n= 1 n RPSS = 1 RPS(forecast) RPS(climatology) Likelihood Skill Score L = N j, i t= 1 N Pc j i t= 1 1 N P,
Forecasting Results Truckee RPSS results Carson RPSS results 1.0 1.0 Median RPSS (all years) 0.8 0.6 0.4 0.2 0.0-0.2 GpH & SWE SWE Nov 1st Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st Month Median RPSS (all years) 0.8 0.6 0.4 0.2 0.0-0.2 GpH & SWE SWE Nov 1st Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st Month 1 Truckee Forecasted vs. Observed Correlation Coeff 1 Carson Forecasted vs. Observed Correlation Coeff Correlation Coeff 0.8 0.6 0.4 0.2 0 GpH & SWE SWE Nov 1st Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st Month Correlation Coeff. 0.8 0.6 0.4 0.2 0 GpH & SWE SWE Nov 1st Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st Month Truckee Likelihood Results Carson Likelihood Results 2.5 2.5 Median Likelihood (all years) 2 1.5 1 0.5 0 GpH & SWE SWE Nov 1st Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st Month Median Likelihood (all years) 2 1.5 1 0.5 0 GpH & SWE SWE Nov 1st Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st Month
Outline of Approach Climate Diagnostics Climate Diagnostics To identify relevant predictors to spring runoff in the basins Forecasting Model Forecasting Model Nonparametric stochastic model conditioned on climate indices and SWE Decision Support System 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 Max canal diversions: 164 kaf 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)
Decision Model Flowchart Ensemble Forecasts Repeat for each ensemble member Truckee Forecast Carson Forecast Is Truckee Forecast > Max Diversion? Is Carson Forecast > Lahontan Target? No Yes No Yes Truckee Avail for Diversion = Truckee Forecast Truckee Avail for Diversion = Max Diversion Diversion Requested = Target Carson Forecast Diversion Requested = 0.0 kaf Is Avail for Diversion > Diversion Request? No Yes Truckee Canal Diversion = Avail for Diversion Truckee Canal Diversion = Diversion Requested Water Available for Fish = Truckee Fcst Truckee Canal Diversion Water Available for Irrigation = Carson Fcst + Truckee Canal Diversion
Decision Variables Lahontan Storage Available for Irrigation Truckee River Water Available for Fish Diversion through the Truckee Canal
0 100 300 500 Decision Model Results r=0.17 0 100 300 500 Perfect Forecast Irrigation Water (kaf) r=0.23 0 50 100 150 Median of Ensemble Irrigation Water(kaf) 0 50 100 150 r=0.36 0 200 400 600 Perfect Forecast Diversion (kaf) Median of Ensemble Diversion(kaf) Median of Ensemble Fish Water(kaf) 0 200 400 600 r=0.54 0 100 200 300 400 500 Perfect Forecast Fish Water (kaf) 0 100 200 300 400 500 Perfect Forecast Irrigation Water (kaf) r=0.38 r=0.62 0 50 100 150 Median of Ensemble Irrigation Water(kaf) 0 50 100 150 0 200 400 600 Perfect Forecast Diversion (kaf) Median of Ensemble Diversion(kaf) Median of Ensemble Fish Water(kaf) 0 200 400 600 r=0.79 0 100 200 300 400 500 Perfect Forecast Fish Water (kaf) 0 100 300 500 Perfect Forecast Irrigation Water (kaf) r=0.78 0 50 100 150 Median of Ensemble Irrigation Water(kaf) 0 50 100 150 r=0.93 0 200 400 600 Perfect Forecast Diversion (kaf) Median of Ensemble Diversion(kaf) Median of Ensemble Fish Water(kaf) 0 200 400 600 Dec 1 st Forecast Feb 1 st Forecast Apr 1 st Forecast Irrigation Water Canal Diversion Water for Fish Perfect Forecast Fish Water (kaf)
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 1994 (Dry Year) Apr 1st Feb 1st Dec 1st Historical Irrigation Water mean value (kaf) 94 161 214 264 264 kaf Irrigation Water exceedance probability 4% 14% 18% 50% Fish Flow mean value (kaf) 0 42 39 199 60.5 kaf Fish Flow exceedance probability 0% 57% 58% 87% Canal Diversion mean value (kaf) 52 107 121 84 1993 (Wet Year) Apr 1st Feb 1st Dec 1st Historical Irrigation Water mean value (kaf) 291 332 246 264 264 kaf Irrigation Water exceedance probability 73% 73% 31% 50% Fish Flow mean value (kaf) 452 391 138 199 60.5 kaf Fish Flow exceedance probability 100% 99% 81% 87% Canal Diversion mean value (kaf) 8 29 101 84 2003 (Normal Year) Apr 1st Feb 1st Dec 1st Historical Irrigation Water mean value (kaf) 261 268 225 264 264 kaf Irrigation Water exceedance probability 40% 49% 26% 50% Fish Flow mean value (kaf) 76 223 71 199 60.5 kaf Fish Flow exceedance probability 61% 91% 69% 87% Canal Diversion mean value (kaf) 126 106 108 84
Summary & Conclusions Climate indicators improve forecasts and offer longer lead time 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 Funding CIRES and the Innovative Research Project Tom Scott of USBR Lahontan Basin Area Office