Using Large-Scale Climate Information to Forecast Seasonal Streamflows in the Truckee and Carson Rivers

1. Using Large-Scale Climate Information to Forecast Seasonal Streamflows in the Truckee and Carson Rivers Katrina A. Grantz Dept. of Civil, Architectural, and Environmental Engineering Masters Defense Fall 2003

2. The Research Project • Study Area & Motivation • Outline the Approach • Results • Summary

3. Study Area PYRAMID LAKE WINNEMUCCA LAKE (dry) CALIFORNIA NEVADA Nixon Stillwater NWR Derby Dam STAMPEDE Reno/Sparks Fernley Fallon TRUCKEE RIVER INDEPENDENCE BOCA Newlands Project PROSSER Truckee CARSON LAKE MARTIS LAHONTAN Carson City DONNER Tahoe City CARSON RIVER LAKE TAHOE NEVADA Truckee Carson CALIFORNIA TRUCKEE CANAL Farad Ft Churchill

4. Study Area Lahontan Reservoir Prosser Creek Dam

5. Basin Precipitation NEVADA Truckee Carson CALIFORNIA Average Annual Precipitation

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

7. Current Forecasting Methods • Use Snowpack Information • Linear regression • Sometimes subjectively alter the forecast • El Nino years • NRCS official forecast

8. Hydroclimate in Western US • Past studies have shown a link between large-scale ocean-atmosphere features (e.g. ENSO and PDO) and western US hydroclimate (precipitation and streamflow) • E.g., Pizarro and Lall (2001) Correlations between peak streamflow and Nino3

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

10. Data Used • 1949-2003 monthly data sets: • Natural Streamflow (Farad & Ft. Churchill gaging stations) • Snow Water Equivalent (SWE)- basin average • Large-Scale Climate Variables

11. 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

12. Climate Diagnostics • Climatology Analysis • To determine the timing of precipitation and runoff in the basin • Correlation Analysis • Correlate spring streamflows with winter/ fall ocean-atmosphere variables to identify predictors for the forecasting model • Composite Analysis • Look at atmospheric circulation patterns in extreme streamflow years– provides explanation of physical mechanism that drives the atmosphere-ocean-land relationship

13. Basin Climatology • Streamflow in Spring (April, May, June) • Precipitation in Winter (November – March) • Primarily snowmelt dominated basins

14. Basin Climatology • Spring runoff and winter precipitation are highly variable Spring Streamflow (1949-2003) Winter SWE (1949-2003)

15. SWE Correlation • Winter SWE and spring runoff in Truckee and Carson Rivers are highly correlated • April 1st SWE better than March 1st SWE

16. Correlation Analysis • Correlate spring streamflows in Truckee and Carson Rivers with ocean-atmosphere variables from the preceding Winter/Fall

17. Winter Climate Correlations Carson Spring Flow 500mb Geopotential Height Sea Surface Temperature

18. Winter Climate Correlations Truckee Spring Flow 500mb Geopotential Height Sea Surface Temperature

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

20. Persistence of Climate Patterns Strongest correlation in Winter (Dec-Feb) Correlation statistically significant back to August

21. Composite Analysis • Look at atmospheric circulation patterns in extreme streamflow years– provides explanation of physical mechanism that drives the atmosphere-ocean-land relationship • High years: years above 90th percentile • Low years: years below 10th percentile

22. Climate Composites Vector Winds Low Streamflow Years High Streamflow Years

23. Climate Composites Sea Surface Temperature Low Streamflow Years High Streamflow Years

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

25. 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

26. Forecasting Model Predictors SWE Geopotential Height Sea Surface Temperature

27. Nonlinear Relationship • Relationships among variables highly nonlinear • Correlations strongest when geopotential height index is low

28. Climate Diagnostics Summary • Winter/ Fall geopotential heights and SSTs over Pacific Ocean related to Spring streamflow in Truckee and Carson Rivers • Physical explanation for this correlation • Relationships are nonlinear

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

30. Forecasting Model Requirements • Forecast spring streamflow (total volume) • Capture linear and nonlinear relationships in the data • Produce ensemble forecasts (to calculate exceedence probabilities)

31. Modified K- Nearest Neighbor(K-NN) • Uses local polynomial for the mean forecast (nonparametric) • Bootstraps the residuals for the ensemble (stochastic) Benefits • Produces flows not seen in the historical record • Captures any non-linearities in the data, as well as linear relationships • Quantifies the uncertainty in the forecast (Gaussian and non-Gaussian)

32. Local Regression

33. Local Regression

34. Residual Resampling e * t y * t xt* yt* = f(xt*) + et*

35. Model Validation & Skill Measure • Cross-validation: drop one year from the model and forecast the “unknown” value

36. 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)

37. 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

38. 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 • Likelihood Skill Score

39. Model Predictors • SWE • Geopotential Height • SWE • Geopotential Height • SST ? OR • Compare model results between using SST and not using SST in the predictor set

40. Model Predictors • SWE • Geopotential Height • SWE • Geopotential Height • SST ? OR • Little or no improvement in results with SST index • Possibly due to redundancy in physical mechanism driving correlation • Possibly due to relatively decreased correlation coefficient • In the interest of parsimony, use SWE and geopotential height only

41. Forecasting Results Predictors • April 1st SWE • Dec-Feb geopotential height 95th 50th 5th 95th 50th 5th April 1st forecast

42. Forecasting Results April 1st forecast

43. Forecasting Results • Ensemble forecasts provide range of possible streamflow values • Water manager can calculate exceedence probabilites Wet Year Dry Year

44. RPSS All Years Wet Years Dry Years • Beats climatology in most years • Performs best in wet years 0 1

45. Likelihood Skill Score All Years Wet Years Dry Years • Beats climatology in most years • Performs best in wet years 0 1 3

46. Forecast Skill Scores April 1st forecast • Median skill scores significantly beat climatology in all year subsets, both Truckee and Carson • Truckee slightly better than Carson

47. Use of Climate Index in Forecast Extremely Wet Years • In general, the boxes are much tighter with geopotential height (greater confidence) • Underprediction w/o climate index– might not be fully prepared with flood control measures 95th 95th 50th 50th 5th 5th 95th 95th 50th 50th 5th 5th SWEand Geopotential Height SWE

48. Use of Climate Index in Forecast Extremely Dry Years • Tighter prediction interval with geopotential height • Overprediction w/o climate index (esp. in 1992) • Might not implement necessary drought precautions in sufficient time 95th 95th 50th 50th 5th 5th 95th 95th 50th 50th 5th 5th SWEand Geopotential Height SWE

49. March 1st Forecast Uses March 1st SWE and Dec-Jan geopotential height index Skill decreases with March 1st forecast (less SWE information) Still pretty good results

50. March 1st Forecast Skill • Median skill scores significantly beat climatology in all year subsets, both Truckee and Carson • RPSS and Likelihood show mixed results on which river is better (Truckee or Carson)