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 Study Area & Motivation Outline the Approach Outline the Approach Results Results Summary Summary

3. Study Area TRUCKEE CANAL Farad Ft Churchill NEVADA CALIFORNIA Carson Truckee

4. Study Area Prosser Creek Dam Lahontan Reservoir

5. Basin Precipitation NEVADA CALIFORNIA Carson Truckee 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) 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

7. Current Forecasting Methods Use Snowpack Information Use Snowpack Information Linear regression Linear regression Sometimes subjectively alter the forecast Sometimes subjectively alter the forecast El Nino years El Nino years NRCS official forecast 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) 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) E.g., Pizarro and Lall (2001) Correlations between peak streamflow and Nino3

9. 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

10. Data Used 1949-2003 monthly data sets: 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 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 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

12. Climate Diagnostics Climatology Analysis Climatology Analysis To determine the timing of precipitation and runoff in the basin To determine the timing of precipitation and runoff in the basin Correlation Analysis Correlation Analysis Correlate spring streamflows with winter/ fall ocean- atmosphere variables to identify predictors for the forecasting model Correlate spring streamflows with winter/ fall ocean- atmosphere variables to identify predictors for the forecasting model Composite Analysis Composite Analysis Look at atmospheric circulation patterns in extreme streamflow years– provides explanation of physical mechanism that drives the atmosphere-ocean-land relationship 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) Streamflow in Spring (April, May, June) Precipitation in Winter (November – March) Precipitation in Winter (November – March) Primarily snowmelt dominated basins Primarily snowmelt dominated basins

14. Basin Climatology Spring runoff and winter precipitation are highly variable 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 Winter SWE and spring runoff in Truckee and Carson Rivers are highly correlated April 1 st SWE better than March 1 st SWE April 1 st SWE better than March 1 st SWE

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

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

18. 500mb Geopotential HeightSea Surface Temperature Truckee Spring Flow

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

20. Persistence of Climate Patterns Strongest correlation in Winter (Dec-Feb) Strongest correlation in Winter (Dec-Feb) Correlation statistically significant back to August 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 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 90 th percentile High years: years above 90 th percentile Low years: years below 10 th percentile Low years: years below 10 th percentile

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

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

24. 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

25. 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

26. Forecasting Model Predictors SWE Geopotential Height Sea Surface Temperature

27. Nonlinear Relationship Relationships among variables highly nonlinear Relationships among variables highly nonlinear Correlations strongest when geopotential height index is low 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 Winter/ Fall geopotential heights and SSTs over Pacific Ocean related to Spring streamflow in Truckee and Carson Rivers Physical explanation for this correlation Physical explanation for this correlation Relationships are nonlinear Relationships are nonlinear

29. 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

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

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

32. Local Regression

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

35. 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

36. 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)

37. 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

38. 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

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

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

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

42. Forecasting Results April 1 st forecast

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

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

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

46. Forecast Skill Scores April 1 st 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 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 SWE SWE and Geopotential Height Extremely Wet Years 95th 50th 5th 95th 50th 5th 95th 50th 5th 95th 50th 5th

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

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

50. March 1 st 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)

51. Fall Forecast Results Fall climate forecast captures whether season will be above or below average Fall climate forecast captures whether season will be above or below average Uses only fall (Sept-Nov) geopotential height index Uses only fall (Sept-Nov) geopotential height index

52. Fall Forecast Skill Scores Though not highly accurate, fall forecast improves on climatology Typically no SWE in fall, no forecast available

53. Monthly Disaggregation Important for setting monthly storage targets on Lahontan Reservoir Important for setting monthly storage targets on Lahontan Reservoir Done using K-NN scheme: resampling neighbor’s monthly proportions of total volume Done using K-NN scheme: resampling neighbor’s monthly proportions of total volume

54. 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

55. Truckee-Carson RiverWare Model

56. Truckee RiverWare Model Physical Mechanisms Physical Mechanisms Reservoir releases, diversions, evap, reach routing Reservoir releases, diversions, evap, reach routing

57. Truckee RiverWare Model Physical Mechanisms Physical Mechanisms Reservoir releases, diversions, evap, reach routing Reservoir releases, diversions, evap, reach routing Policies Policies Implemented with “rules” (user defined prioritized logic) Implemented with “rules” (user defined prioritized logic)

58. Truckee RiverWare Model Physical Mechanisms Physical Mechanisms Reservoir releases, diversions, evap, reach routing Reservoir releases, diversions, evap, reach routing Policies Policies Implemented with “rules” (user defined prioritized logic) Implemented with “rules” (user defined prioritized logic) Water Rights Water Rights Implemented in accounting network Implemented in accounting network

59. Simplified Seasonal Model 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 timestep

60. 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 projected April-July runoff volume Storage targets on Lahontan Reservoir: 2/3 of projected April-July runoff volume Minimum Lahontan storage target: 1/3 of average historical spring runoff Minimum Lahontan storage target: 1/3 of average historical spring runoff No minimum fish flows No minimum fish flows

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

62. Seasonal Model Results: 1992 Irrigation Water less than typical– decrease crop size or use drought-resistant crops Irrigation Water less than typical– decrease crop size or use drought-resistant crops Truckee Canal smaller diversion-start the season with small diversions (one way canal) Truckee Canal smaller diversion-start the season with small diversions (one way canal) Very little Fish Water- releases from Stampede coordinated with Canal diversions Very little Fish Water- releases from Stampede coordinated with Canal diversions Ensemble forecast results Climatology forecast results Observed value results NRCS official forecast results

63. Seasonal Model Results:1993 Irrigation Water more than typical– plenty for irrigation and carryover Irrigation Water more than typical– plenty for irrigation and carryover Truckee Canal larger diversion-start the season at full diversions (limited capacity canal) Truckee Canal larger diversion-start the season at full diversions (limited capacity canal) Plenty Fish Water- FWS may schedule a fish spawning run Plenty Fish Water- FWS may schedule a fish spawning run Ensemble forecast results Climatology forecast results Observed value results NRCS official forecast results

64. Seasonal Model Results: 2003 Irrigation Water pretty average: business as usual Irrigation Water pretty average: business as usual Truckee Canal diversions normal: not full capacity, but don’t hold back too much Truckee Canal diversions normal: not full capacity, but don’t hold back too much Plenty Fish Water- no releases necessary to augment low flows, may choose a fish spawning run Plenty Fish Water- no releases necessary to augment low flows, may choose a fish spawning run Ensemble forecast results Climatology forecast results Observed value results NRCS official forecast results

65. Seasonal Model Results All Years

66. Seasonal Model Fall Results All Years

67. 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

68. 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

69. Future Work Couple ensemble forecasts with RiverWare model Couple ensemble forecasts with RiverWare model Temporal disaggregation Temporal disaggregation Forecast improvements Forecast improvements Joint Truckee/Carson forecast Joint Truckee/Carson forecast Objective predictor selection Objective predictor selection Compare results with physically-based runoff model (e.g. MMS) Compare results with physically-based runoff model (e.g. MMS)

70. Acknowledgements Balaji Rajagopalan, Edie Zagona, and Martyn Clark Balaji Rajagopalan, Edie Zagona, and Martyn Clark Paul Sperry of CIRES and the Innovative Reseach Project Paul Sperry of CIRES and the Innovative Reseach Project Tom Scott of USBR Lahontan Basin Area Office Tom Scott of USBR Lahontan Basin Area Office CADSWES personnel CADSWES personnel Advising and support Funding Technical Support

71. Thank You Questions or Comments?

72. Extra Slides Follow

73. Geopotential Height Correlation

74. SST Correlation

75. Sea Surface Temperature Vector Winds High-Low Years Climate Composites

76. PRMS Upper Truckee Vicinity PRMS Basin delineation for Upper Truckee Basin

77. ENSO

78. PDO

79. PNA Pattern Positive PNA has a warm western ridge and cold eastern trough. Negative PNA has a cold western trough and a warm eastern ridge.

80. PNA Pattern Pattern plotted as a regression map. Red, black, and blue contours on the maps indicate positive, zero, and negative isopleths, respectively. Contour interval of 0.35 for correlations and 2 dam for regressions. Variability of the PNA pattern explains over 50% of the December, January, February interannual variability at its four centers of action.