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New Tools and Resources for Streamflow Forecasting Applications JISAO Climate Impacts Group and Department of Civil and Environmental Engineering University of Washington October, 2003 Alan F. Hamlet Andy Wood Seethu Babu Marketa McGuire Dennis P. Lettenmaier Overview of Recent Advances in Streamflow Forecasting Applications at the UW Extension of driving data records and removal of temporal inhomogeneities Retrospective data now available from 1915-2000 – Adds 35 years. Near real-time data assimilation Updating of snow simulations using SNOTEL and remote sensing Extension of locations where we can provide hydrologic forecast products Climate change scenarios Seasonal to interannual streamflow forecasts Bias correction software Accurate linkages to specific reservoir models Tools for creating water management applications Importance of Long Records Old New 450000 Cool Cool Warm Warm 350000 300000 250000 200000 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 150000 1900 Apr-Sept Flow (cfs) 400000 Importance of Increased Sample Size for Forecast Interpretation Blue = all years 1900-2002 Green = all enso neutral 1900-2002 (33) Red = enso neutral 1961-2002 (12) April-September Average Streamflow (cfs) 450000 400000 350000 300000 250000 200000 Monte Carlo 90% confidence limits for 25th percentile 150000 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Probability of Exceedence 0.9 1.0 Removal of Temporal Inhomogeneities 800000 700000 600000 500000 400000 300000 Hist 200000 100000 0 19 25 19 25 19 25 19 26 19 26 19 27 19 27 19 27 19 28 19 28 19 29 19 29 19 30 19 30 19 30 19 31 19 31 19 32 19 32 19 32 19 33 19 33 19 34 19 34 Before temporal corrections VIC 800000 700000 600000 500000 300000 200000 100000 0 19 25 19 25 19 25 19 26 19 26 19 27 19 27 19 27 19 28 19 28 19 29 19 29 19 30 19 30 19 30 19 31 19 31 19 32 19 32 19 32 19 33 19 33 19 34 19 34 After temporal corrections 400000 VIC Hist Basic Routing Network for the Columbia Basin Extended PNW VIC Domain and Routing Locations VIC Routing Network and Flow Simulation Nodes Snake River Upstream of Ice Harbor Calibration of Small Scale Basins Owyhee Basin Before Calibration Owyhee Basin After Calibration Issues with Hydrologic Model Bias 900000 800000 700000 600000 500000 400000 300000 200000 100000 0 VIC 1999 1999 1998 1997 1997 1996 1995 1995 1994 1993 1993 1992 1991 1991 1990 1989 1989 1988 1987 1987 1986 1985 Observed 1985 Streamflow (cfs) Columbia River at The Dalles Quantile-Based Bias Correction (Wood et al. 2002) VIC Input = 19000 35000 30000 Flow (cfs) 25000 20000 obs 15000 vic 10000 5000 0 0 0.2 0.4 0.6 Probability of Exceedence Bias Corrected Output = 10000 0.8 1 Bias Correction Objectives: Raw Bias Corrected Result: Bias corrected hydrologic simulations are quite consistent with observed streamflows in absolute value and climate change signals are translated without significant distortion. Linkages to PNW Reservoir Models ColSim (UW) integrated model of the Columbia River Basin GENESYS (NWPPC) hydropower analysis model for the Columbia main stem SnakeSim (UW and IDWR) integrated planning model for the Snake River basin Applications: Climate Change Assessments Long-Range Planning Seasonal to Interannual Forecasts Water Management Applications Tools for Incorporating Probabilistic Forecasts in Water Management Plans: An example for Libby Dam in the Columbia River basin CRB Current Operations use the Energy Content Curve (Rule curves for Libby Dam in a wet year are shown) 7000000 Forecast Information Used 6000000 refill 4000000 3000000 2000000 1000000 crit flood No Forecast Information Used ECC Drought is assumed jul jun may apr mar feb jan dec nov oct sep 0 aug Storage (acre-ft) 5000000 Constructing a New Reservoir Rule Curve called the “Refill to Least Flood” Curve Least Amount of Flood Evacuation Expected Based on Forecast Refill Curve to Least Flood Target Based on Lowest Streamflow Forecast 7000000 Flood 1 6000000 Flood 2 5000000 4000000 Flood 3 3000000 Flood 4 2000000 Flood 5 1000000 Ju n Ap r Fe b De c O ct Au g 0 Refill to Least Flood (wet) Refill to Least Flood Curve for Libby Dam for the 2nd Lowest Ensemble Member in the 2004 Forecast (“1994”) (~85% likelihood of refill to April 1 Flood Curve) 7000000 Flood 1 6000000 5000000 Flood 3 4000000 3000000 Flood 4 2000000 Flood 5 1000000 Refill to Least Flood (wet) 0 n Ju r Ap b Fe ec D O ct g Status Quo Au Storage (acre-ft) Flood 2 Refill to Least Flood Curve for Libby Dam for the Lowest Ensemble Member in the 2004 Forecast (“1979”) (~95 % likelihood of refill to April 1 Flood Curve) 7000000 Flood 1 6000000 5000000 Flood 3 4000000 Flood 4 3000000 2000000 Flood 5 1000000 Refill to Least Flood (wet) Status Quo n Ju r Ap b Fe ec D O ct g 0 Au Storage (acre-ft) Flood 2 Interpretation: Libby is an important project for maintaining fish flows (one of three projects which supply supplementary water to help maintain flows at McNary Dam under the BiOp). If Libby does not refill, fish flows may be strongly impacted. The Refill to Least Flood rule curves (based on the 2004 forecast) show that there is about a 15% risk of Libby Dam not refilling to the flood evacuation target in April if the normal flood draft in December occurs. Hedging in the fall and early winter to keep storage levels high may therefore be an appropriate management action in 2003-2004 to help protect fish in the lower basin. Conclusions The opportunities for developing improved hydrologic forecasts for water management applications is enhanced by: •Extending the length of temperature and precipitation records and removing temporal inhomogeneities •Assimilating observed data to improve estimates of initial conditions (especially snow) •Extending the domain of hydrologic models and the number of streamflow routing locations •Calibrating the hydrologic model •Bias Correcting streamflow simulations •Creating custom linkages to specific water management models and analytical tools for interpreting probabilistic forecasts for water management applications.