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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

Two Applications of the DSM2 Historical Model: Modeling Contaminant Spills and Using Salinity Fingerprints to Improve DSM2 Real-Time Forecasts

Tom Rose


Marianne Guerin

CCWD uses Delta Water

WQ Implications



Supply Issues

Direct delivery


Emergency storage

Forecasting WQ at intakes to help Operations vital

G-Model – zero-dimensional

DSM2 – accuracy variable at intakes – time-consuming

talk logistics using the historical model
Modeling Contaminant Spills

Motivation for the work



Future direction

Improving Forecasts





Future work – suggestions?

Talk Logistics – Using the Historical Model
  • Background
    • CCWD Water Supply and Quality Issues
    • Using DSM2 Historical Model
emergency modeling contaminant spills

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



Ability to deliver water

Arrival time at intakes

Which intakes (Old River, Rock Slough, Mallard)


Using Los Vaqueros reservoir

Damage to pumps

Duration of shut-down

Emergency Modeling: Contaminant Spills
use historical model and ptm
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
method quick and dirty

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’
hypothetical example
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
compare two scenarios

Feb 2000

Feb 1999

Compare Two Scenarios

Feb 2000

Feb 1999

next step
‘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 ?
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

Compare residual (model – data) with:

Salinity fingerprints

NDOI (Net Delta Outflow Index)

DICU (Delta Island Consumptive Use)


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

old river intake model usually underestimates ec especially in summer and fall as ec mtz peaks
Old River Intake: Model Usually Underestimates EC, Especially In Summer and Fall as EC-MTZ Peaks
summary of findings
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
future work
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