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Tom Rose and Marianne Guerin

Two Applications of the DSM2 Historical Model: Modeling Contaminant Spills and Using Salinity Fingerprints to Improve DSM2 Real-Time Forecasts. Tom Rose and Marianne Guerin. CCWD uses Delta Water WQ Implications Salinity DOC Supply Issues Direct delivery Blending Emergency storage

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Tom Rose and Marianne Guerin

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  1. Two Applications of the DSM2 Historical Model: Modeling Contaminant Spills and Using Salinity Fingerprints to Improve DSM2 Real-Time Forecasts Tom Rose and Marianne Guerin

  2. CCWD uses Delta Water WQ Implications Salinity DOC Supply Issues Direct delivery Blending Emergency storage Forecasting WQ at intakes to help Operations vital G-Model – zero-dimensional DSM2 – accuracy variable at intakes – time-consuming Background

  3. Modeling Contaminant Spills Motivation for the work Methodology Results Future direction Improving Forecasts Background Objective Method Findings Future work – suggestions? Talk Logistics – Using the Historical Model • Background • CCWD Water Supply and Quality Issues • Using DSM2 Historical Model

  4. Motivation Operations – needs to know what to do NOW CCWD informed immediately, or, after the event Operational ?’s: When/Where/How Much/How Long Safety of water supply Toxicity Concentration Ability to deliver water Arrival time at intakes Which intakes (Old River, Rock Slough, Mallard) Cost Using Los Vaqueros reservoir Damage to pumps Duration of shut-down Emergency Modeling: Contaminant Spills

  5. IDEA – For a given set of hydrological and operational conditions, find two+ times in Historical Model that ‘bound’ this hydrology SAC Flow and DCC SJR flow and HORB Exports – SWP and CVP CCWD – Old River, Rock Slough Use PTM to run particle tracking models for first arrival time of contaminant Use Historical Model and PTM

  6. Method: Use prepared plots to find candidate times Use HEC-DSSVue tables to narrow search Run PTM at two (or more) times by injecting particles at source First arrival times at Old River Rock Slough – ‘flux.txt’ Visualize for qualitative information for location of contaminant Method – ‘Quick and Dirty’

  7. Hypothetical Example 03/06/06: spill at 1:00 AM, downstream of Vernalis, ~ DSM2 node 3 • SAC+SJ <83K cfs; Exports: ~6.8K cfs; No HORB or DCC

  8. PTM Results: spill arrives in less than two days

  9. Feb 1999 Mar 2006 Compare Real Result and Closest Scenario

  10. Feb 2000 Feb 1999 Compare Two Scenarios Feb 2000 Feb 1999

  11. ‘Quick and Dirty’ – result in ~ hour Example gave reasonable, fairly conservative estimates BUT, may not be able to find good ‘brackets’ Real-time DSM2 – fairly time-consuming need lots of data (but not EC if just a spill) accuracy variable – see next talk More work with Historical DSM2? Next Step ?

  12. Use DSM2 salinity fingerprinting in conjunction with field data to characterize the sources of modeled salinity error at CCWD intakes in the Delta, and at selected other locations The Objectives are to: Identify systematic bias (seasonal, operational, …) Quantity the error to allow us to put error bars on DSM2 forecasts Possibly develop relationships to correct bias We’re NOT trying to calibrate the model Using the Historical Model to Improve Forecasts

  13. CCWD’s Old River Intake

  14. Compare residual (model – data) with: Salinity fingerprints NDOI (Net Delta Outflow Index) DICU (Delta Island Consumptive Use) Operations DCC (Delta Cross Channel) HORB (Head of Old River Barrier) Exports (SWP, CVP) Look for and characterize bias at: Jersey (RSAN018), Holland (ROLD014), Bacon (ROLD024), CCWD Old R. Intake (ROLD034): Started looking at Jersey Point – easiest Started investigating North, moved South Method

  15. EC-MTZ: Major Contributor to Jersey Point Modeled Error

  16. Residual < 0: Occurs as Salinity is Falling in Late Fall

  17. Residual > 0: Occurs in Fall as Salinity is Rising ….

  18. JP Residual: Interesting Relationship With DICU/NDOI

  19. Holland Tract: EC-MTZ Relationship Evident, But Not as ‘Clean’ as JP

  20. Bacon: Residual Changes w/EC-MTZ Are Messy (at Best), Data Questionable

  21. Old River Intake: Model Usually Underestimates EC, Especially In Summer and Fall as EC-MTZ Peaks

  22. Old River & EC-AG: Residual > 0 in Winter or Spring With Ag Events

  23. There is a suggestive relationship between modeled error and EC-MTZ at all 4 stations investigated: Clearest at Jersey Point Story more complicated as move south along Old River Relationships w/other salinity sources and operations: No apparent relationship with DCC or HORB operations No apparent relationship with EC from SJR, Eastside Not sure about EC from Ag or Sac R. Modeled EC at Old River: Underestimates EC as salinity increases in late summer and fall; related to EC-MTZ Overestimates EC in Winter, Spring; related to EC-AG events Modeled EC at Jersey Point related to DICU/NDOI Error greatest when DICU is a substantial portion of NDOI in the fall Summary of Findings

  24. More work with residuals: Look for relationship with export operations, including CCWD diversions Look at some station on Middle River Include Volumetric Fingerprinting Look closer at ROLD034: Incorporate more data Quantify seasonal error Look for other contributions to error Jersey Point: Quantify error for EC-MTZ Future Work

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