1. 0 CDFG SJR Fall-run Chinook Salmon Model CWEMF November 4, 2005

2. 1 Objectives Brief Overview of Model DevelopmentBrief Overview of Model Development Describe Model StructureDescribe Model Structure Describe Model Calibration/ValidationDescribe Model Calibration/Validation Describe Model ScenariosDescribe Model Scenarios

3. 2 Overview San Joaquin River Salmon Production as a function of Spring Vernalis Flow

4. 3 Study Area Courtesy of USFWS

5. 4 Salmon Life History

6. 5 Salmon Cohort “Singe Year Production Indicator” Age 1 Age 2 Age 3 Age 4 Age 5 Brood Year Production Juvenile CWT Recovery Scale Analysis

7. 6 Salmon Escapement “Multi-Year Production Indicator” Age 1 Age 2 Age 3 Age 4 Age 5 Year 1 Year 2 Year 3 Year 4 Year 5 Annual Carcass “Creamer” Survey

8. 7 1995 WQCP Triennial Review  CDFG Challenge: –Are the Flow Objectives Working? –What is the status of the salmon population? –What fraction of juvenile salmon are receiving protection? –What is the status of the VAMP experiment?

9. 8 Populations Are Declining SJ Basin Production Year 1983-1995 Average 42,285 1996-2004 Average 35,004

10. 9 Level of Protection

11. 10 VAMP Implementation VAMP:VAMP: Lock Down Uncertainty at Extremes FirstLock Down Uncertainty at Extremes First Since 2000:Since 2000: Result:Result: Continued uncertaintyContinued uncertainty Need several successive high flow range tests to define/solidify relationshipNeed several successive high flow range tests to define/solidify relationship Flow3200445061007000 Tests3110

12. 11 More Flow = More Salmon

13. 12 2005 SWRCB ’95 WQCP Review CDFG:CDFG: presented its concernspresented its concerns asked for peer reviewasked for peer review SWRCB:SWRCB: peer reviewed already occurredpeer reviewed already occurred CDFG: develop flow recommendationsCDFG: develop flow recommendations

14. 13 Chalkboard What +/- What = More Salmon? E = mc? Harvest? Exports? Disease? Predation? Flow? Gravel?

15. 14 Model Logic Delta ExportsDelta Exports weak correlation to cohort productionweak correlation to cohort production

16. 15 Model Logic Ocean HarvestOcean Harvest weak correlation to cohort productionweak correlation to cohort production Harvest - Sacramento, San Joaquin and CVI Ocean Harvest versus Tuolumne Escapement

17. 16 Model Logic Adult Stock Density LimitationsAdult Stock Density Limitations Previous Year Escapement Mossdale Smolts Ricker Stock-recruit relationship (density dependent mortality governor) appears questionable More females = more juveniles (Tuolumne River)

18. 17 Higher Escapement Higher Spring Flow Higher Cohort Production Higher Escapement Lower Spring Flow Lower Cohort Production Multiple Regression 1973-1999 R-square = 0.75 P =.001 Density Dependent Governor ? Adult Stock Density Limitations year # of females Flow at Modesto Adult Cohort 19803413582217 19816271710019458 19824276984444864 198446543631501 198522580395019373 198635545941303 19874573257125 1988346726370 1996159313769105 19974207470422000 1998403719975310 Tuolumne River data

19. 18 Model Logic Instream FlowInstream Flow Strong correlation to cohort production in relation to spring flow SJB east-side tributaries principle flow & salmon contributor

20. 19 Flow Features  Increased Vernalis Flow Magnitude/Duration/Frequency all projected increased adult salmon production

21. 20 Now What? Challenge: “How to link salmon life history production to flow magnitude & duration?”Challenge: “How to link salmon life history production to flow magnitude & duration?” Solution: “Develop a tool”Solution: “Develop a tool”

22. 21 Model Concept Flow primary driving factor in population (not harvest, exports or adult stock density)Flow primary driving factor in population (not harvest, exports or adult stock density) Quantify relationships between:Quantify relationships between: flow and juvenile productionflow and juvenile production flow and juvenile survivalflow and juvenile survival juvenile survival and adult escapementjuvenile survival and adult escapement Simulate production over timeSimulate production over time

23. 22 Eureka! The Light Bulbs Went On!

24. 23 Model Features Excel spreadsheet platformExcel spreadsheet platform Links life history stages by numerical production at each phaseLinks life history stages by numerical production at each phase Predicts adult escapement (1967-2000)Predicts adult escapement (1967-2000) Flow duration & magnitude variableFlow duration & magnitude variable Predicts escapement change & water volumePredicts escapement change & water volume Has 95% confidence level predictionsHas 95% confidence level predictions Customizable input parametersCustomizable input parameters

25. 24 Model Refinements Limits predictions to data setLimits predictions to data set Uses San Joaquin Basin data for age cohort reconstructionUses San Joaquin Basin data for age cohort reconstruction Smolt Outmigration pattern WY Type specificSmolt Outmigration pattern WY Type specific Allows for HORB/non-HORB smolt survivalAllows for HORB/non-HORB smolt survival 95% confidence levels predictions95% confidence levels predictions

26. 25 Conceptual Model Vernalis Flow Mossdale Smolts Delta Survival Chipps Smolts Adult Cohort Escapement Reconstruction Annual Escapement (spawners)

27. 26 Vernalis Flow - Mossdale Smolts

28. 27 Outmigration Pattern

29. 28 Conceptual Model Vernalis Flow Mossdale Smolts Delta Survival Chipps Smolts Adult Cohort Escapement Reconstruction Annual Escapement (spawners)

30. 29 Delta Survival

31. 30 Conceptual Model Vernalis Flow Mossdale Smolts Delta Survival Chipps Smolts Adult Cohort Escapement Reconstruction Annual Escapement (spawners)

32. 31 Adults versus Outmigrants

33. 32 Reconstruct Adult Escapement EscapementYearEscapement SmoltProductionYearCohort# Age Cohort %'s Age 1 Age 2 Age 3 Age 4 Age 5 0.05%30.00%55.35%14.00%0.60% 1967 1968276083153392 1968 1969986034929,58154,57713,804592 1969 1970140314217761968 1970 1971111913366201577 1971 19724610138255643 197214,9191973263817911,46036916 19731,5471974364521,0942,01851022 19741,21319753304 29911,82946320

34. 33 SJR Scale vs CV CWT

35. 34 Repeat Cycle Combine spawners with Vernalis flow to predict Mossdale Smolt Production Vernalis Flow Mossdale Smolts Delta Survival Chipps Smolts Adult Cohort Escapement Reconstruction Annual Escapement (spawners)

36. 35 Model Calibration & Validation Two Approaches Two Approaches Calibrate with subset/validate with remainingCalibrate with subset/validate with remaining Calibrate with entire data setCalibrate with entire data set ParametersParameters Escapement patternEscapement pattern Time period averageTime period average Replacement ratioReplacement ratio Stay within 95% CIStay within 95% CI

37. 36 SJ Basin Escapement 1967-2000 -----Historic -----Modeled

38. 37 95% Confidence Intervals Historic Upper 95% CI Lower 95% CI

39. 38 Model Scenarios Ten model runsTen model runs Four presentedFour presented

40. 39 What Scenarios Possible Goal Attainment ScenariosPossible Goal Attainment Scenarios Vary Flow with Window ConstantVary Flow with Window Constant VAMP (3200-7000 with 31 day window)VAMP (3200-7000 with 31 day window) Constant Flow & Window ConstantConstant Flow & Window Constant 10,000 all WY Types & 31 day window10,000 all WY Types & 31 day window Constant Flow & Variable WindowConstant Flow & Variable Window 10,000 all WY Types & Variable WY Window10,000 all WY Types & Variable WY Window Variable Flow & Variable WindowVariable Flow & Variable Window 5-20K Flow & 31-90 day Window5-20K Flow & 31-90 day Window

41. 40 10 Chosen Model Scenarios ScenarioFlow RangeWindow DurationNotes 15-7K31 DaysNo HORB 25-7K31 DaysHORB 35K30, 45, 60, 75, 90 DaysDuration & magnitude constant 410K30, 45, 60, 75, 90 DaysDuration & magnitude constant 515K30, 45, 60, 75, 90 DaysDuration & magnitude constant 65-10K30, 45, 60, 75, 90 DaysDuration constant & magnitude variable 75-15k30, 45, 60, 75, 90 DaysDuration constant & magnitude variable 85-20K30, 45, 60, 75, 90 DaysDuration constant & magnitude variable 95-15KVariable with WY TypeDuration & magnitude variable 105-20KVariable with WY TypeDuration & magnitude variable

42. 41 Existing Flow Magnitude

43. 42 Vernalis Flow thousand-cfs 2-7 Duration (days) 30 Predicted Escapement (25 year average) 17,097 Additional Predicted Escapement (25 year average) 579 Percent Increase in Escapement 4% Replacement Ratio 1.12 Average Annual acre-feet 65,978 Percent Additional Water 10% Percent of Water Available 2% Salmon per 1000 acre-feet 9 D-1641 Flows (No HORB)

44. 43 Vernalis Flow thousand-cfs 2-7 Duration (days) 30 Predicted Escapement (25 year average) 17,888 Additional Predicted Escapement (25 year average) 1,370 Percent Increase in Escapement 8% Replacement Ratio 1.12 Average Annual acre-feet 65,978 Percent Additional Water 10% Percent of Water Available 2% Salmon per 1000 acre-feet 16 D-1641 Flows (With HORB)

45. 44 Constant Flow

46. 45 Vernalis Flow thousand-cfs 10 Duration (days) 60 Predicted Escapement (25 year average) 24,903 Additional Predicted Escapement (25 year average) 8,385 Percent Increase in Escapement 51% Replacement Ratio 1.10 Average Annual acre-feet 635,797 Percent Additional Water 43% Percent of Water Available 21% Salmon per 1000 acre-feet 13 Constant Flow

47. 46 Flow Varies by Water Year Type

48. 47 Vernalis Flow thousand-cfs 5-15 Duration (days) 60 Predicted Escapement (25 year average) 25,929 Additional Predicted Escapement (25 year average) 9,411 Percent Increase in Escapement 57% Replacement Ratio 1.16 Average Annual acre-feet 593,693 Percent Additional Water 42% Percent of Water Available 14% Salmon per 1000 acre-feet 16 Flow varies by Water Year Type

49. 48 Variable Flow and Duration

50. 49 Variable Flow and Duration Vernalis Flow thousand-cfs 5-20 Duration—Variable by WY Type (days) 30-90 Predicted Escapement (25 year average) 37,181 Additional Predicted Escapement (25 year average) 20,663 Percent Increase in Escapement 125% Replacement Ratio 1.22 Average Annual acre-feet 991,795 Percent Additional Water 46% Percent of Water Available 17% Salmon per 1000 acre-feet 21

51. 50 Conclusion Stanislaus River San Joaquin River

52. 51 What Now? Model DocumentationModel Documentation Release Model/ReportRelease Model/Report Model RefinementsModel Refinements Peer ReviewPeer Review Develop questionsDevelop questions

53. 52 Peer Review Questions What is the best way to test model results?What is the best way to test model results? Is there a better population health barometer than “replacement ratio and escapement”?Is there a better population health barometer than “replacement ratio and escapement”? What is the influence of cascading confidence intervals?What is the influence of cascading confidence intervals? Other Questions?Other Questions?

54. 53 Input & Questions Provide comments and questions to: dmarston@dfg.ca.govProvide comments and questions to: dmarston@dfg.ca.gov dmarston@dfg.ca.gov

55. 54 Q & A