Strategy for

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Strategy for Interactive Calibration of
the Sacramento Model Using NWSRFS Interactive
Calibration Program (ICP)
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General Considerations when using ICP

• Change duration/scale depending on flow
• components/parameters being examined
• Remove large errors in parameter values
  whenever
• detected
• Periodically return to previous steps to
  recheck results
• Primary statistics to periodically examine
• annual bias
• seasonal bias
• flow interval bias
• Remain flexible
• Think! Ask questions before you view the
  simulation
• What do I expect to see?
• What secondary effects could I see?
• Why didnt I see what I expected?

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Sacramento Model Calibration Strategy

• Start with a priori parameters
• 1. Remove large errors, especially volume
• 2. Adjust low flow parameters to get a
• reasonable baseflow simulation
• 3. Adjust tension water capacities
• 4. Adjust parameters that primarily affect storm
  runoff
• 5. Final adjustments to improve seasonal
• and flow interval bias patterns

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Snow-17 Calibration Strategy

• Remove large errors,
• timing of snowmelt runoff.
• form of precipitation
• Adjust major parameters
• MFMAX, MFMIN
• SCF
• UADJ
• SI

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Sacramento Model Calibration

• Each parameter is designed to represent a
  specific portion of the hydrograph under certain
  moisture conditions
• Concentrate on having each parameter serve its
  primary function rather than overall goodness of
  fit.

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Sacramento Model Calibration Strategy
Step 1 Remove Large Errors

• Check water balance annual bias should be less
  than 10-20
• Check for large timing errors, most common
• Storm runoff/baseflow ration in error (raise or
  lower entire percolation curve)
• Amount of surface runoff incorrect (adjust UZFWM)
• Improper channel response function for Sacramento
  Model (remove interflow from channel response
  unit hydrograph)

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Sacramento Model Calibration StrategyStep 2
Reasonable baseflow simulation

• Identify primary baseflow component
• Adjust key parameters
• LZPK, LZSK, LZFPM, LZFSM, PFREE
• May need to adjust ZPERC and REXP
• Determine if riparian evaporation exists and
  determine general magnitude of RIVA (then set
  RIVA back to zero)

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Sacramento Model Calibration StrategyStep 3
Tension water capacities

• Determine UZTWM and LZTWM based on periods when
  maximum soil moisture deficits occur.
• While examining UZTWM, check and adjust value of
  PCTIM

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Sacramento Model Calibration StrategyStep 4
Storm runoff simulation

• Get proper division between surface runoff and
  interflow by changing UZFWM
• Adjust UZK to get correct timing of interflow.
• Refine percolation curve over large range of
  LZDEFR values
• Primarily adjust ZPERC and REXP
• Use ICP percolation analysis feature
• Determine if ADIMP is needed. If so, determine
  proper value.

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Sacramento Model Calibration StrategyStep 5
Final Adjustments

• Determine value of RIVA if riparian evaporation
  exists
• Adjust ET-Demand values to improve seasonal bias
  pattern (alter by changing monthly PE adjustment
  curve).
• Refine timing of peaks by modifying channel
  response (unit hydrograph)
• Raise or lower percolation curve to improve flow
  interval bias pattern by changing LZFSM and LZFPM
  by the same ratio.

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Parameter Relationships when Watershed Divided
into Sub-Areas

• Keep parameter values the same, except when the
  hydrograph from one sub-area can be isolated
  (then can modify parameters for the sub-area
  influencing that response).
• Relationships can be established between certain
  values based on soils, vegetation, etc, (then
  maintain that ration (ratio, diff) as parameter
  values are adjusted.

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Calibration of HL-RDHM
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Calibration of SAC Parameters with Scalar
Multipliers

• Use of scalar multipliers (assumed to be uniform
  over a basin) greatly reduces the number of
  parameters to be calibrated. This assumes the
  spatial distribution of a-priori parameters is
  realistic.
• Parameters from 1-hour, lumped model calibrations
  can be a good starting point. Lumped model
  parameters, if derived at the 1 hour time scale,
  can be used to derive initial scalar multipliers,
  i.e.
• multiplier lumped model parameter/basin
  average of gridded a-priori parameter values
• Scalar multipliers are adjusted using similar
  strategies and objectives to those for lumped
  calibration
• Both manual and a combination of automatic and
  manual calibration on scalar multipliers have
  proven effective

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Comparison Between Calibration Steps for
Distributed and Lumped Modeling
Distributed
Lumped
Distributed
Lumped
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Manual Headwater Calibration

• Follow similar strategies to those used for
  lumped calibration except make changes to
  multipliers, e.g. from Anderson (2002)
• Remove large errors
• Obtain reasonable simulation of baseflow
• Adjust major snow model parameters, if snow is
  included\
• Adjust tension water capacities
• Adjust parameters that primarily affect storm
  runoff
• Make final parameter adjustments

Can still use PLOT-TS and STAT-QME

• Stat-Q event statistics summarize how well you
  do on bias, peaks, timing, and RMSE, etc over any
  of selected events.
• R scripts assist with routing parameter
  adjustment.

See HL-RDHM User Manual for a detailed example.
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HL-RDHM
P, T ET
SNOW -17
rain melt
Auto Calibration
SAC-SMA, SAC-HT
surface runoff
Execute these components in a loop to find the
set of scalar multipliers that minimize the
objective function
base flow
Hillslope routing
Channel routing
Flows and state variables
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Multi-Scale Objective Function (MSOF)
Emulates multi-scale nature of manual calibration

• Minimize errors over hourly, daily, weekly,
  monthly intervals (k1,2,3,4n)
• q flow averaged over time interval k
• n number of flow intervals for averaging
• mk number of ordinates for each interval
• X parameter set

-Assumes uncertainty in simulated streamflow is
proportional to the variability of the observed
flow -Inversely proportional to the errors at the
respective scales. Assume errors approximated by
std.
Weight
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Auto Calibration Case 2
Example of HL-RDHM Auto Calibration ELDO2 for
DMIP 2 Arithmetic Scale
After autocalibration
Before autocalibration of a priori parameters
Observed
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Possible Strategy

• Start with best a-priori or scaled lumped
  parameters
• Run automatic calibration
• Make manual adjustments (particularly for
  routing parameters) to get the preferred storm
  event shapes

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