Transcript Document 7540013

Calibration
DOH Science Conference
July 17, 2008
Mike Smith, Victor Koren, Zhengtao Cui,
Seann Reed, Fekadu Moreda
Current Status
HL-RDHM
DHM-TF
AWIPS DHM
P& ET
P, T & ET
(Forecast)
Auto
SNOW -17
Calibration rain + melt
ICP
SAC-SMA, SAC-HT
SAC-SMA
surface runoff
surface runoff
base flow
Hillslope routing
Channel routing
Flows and state variables
Calibration
rain
base flow
Hillslope routing
Channel routing
Flows and state variables
Forecasting
Mods
Manual and Auto Calibration
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Adjustment of parameter scalar multipliers
Use manual and auto adjustment as a strategy
Start with hourly lumped calibration
Model parameters optimized in auto calb:
– SAC-SMA
– Hillslope and channel routing
– Snow-17
• Search algorithms
– Simple local search
• Objective function: Multi-scale
• Limited to headwater basins
Calibration Approach
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Multiply each grid
value by the same scalar
factor.
x 2 =
Preserve Spatial
Pattern of Parameters
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Example: Ith parameter out of N total model parameters
Calibrate distributed model by uniformly adjusting all grid values
of each model parameter (i.e., multiply each parameter grid value by the same factor)
1. Manual: manually adjust the scalar factors to get desired hydrograph fit.
2. Auto: use auto-optimization techniques to adjust scalar factors.
HL-RDHM
P, T & ET
SNOW -17
rain + melt
SAC-SMA, SAC-HT
Auto
Calibration
surface runoff
base flow
Hillslope routing
Channel routing
Flows and state variables
Execute these components
in a loop to find the set of
scalar multipliers that
minimize the objective
function
Multi-Scale Objective Function (MSOF)
Emulates multi- time scale nature of manual calibration
 1 
J    
k 1   k 
n
2
 q
mk
i 1
o , k ,i
 qs ,k ,i  X 
2
• Minimize errors over hourly, daily, weekly, monthly
intervals (k=1,2,3,4…n…user defined)
• q = flow averaged over time interval k
• n = number of flow intervals for averaging q
• mk = number of ordinates for each interval
• X = parameter set
1 =
k
Weight:
-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.
Calibration: MSOF Time Scales
Average monthly flow
Average weekly flow
Average daily flow
Multi-scale objective
Hourly flow
function represents different
frequencies of streamflow
and its use partially imitates
manual calibration strategy
Auto Calibration: Case 1
Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2
Arithmetic Scale
After autocalibration
Before
autocalibration
of a priori
parameters
Observed
Auto Calibration: Case 1
Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2
Semi-Log Scale
Observed
After autocalibration
Before
autocalibration
of a priori
parameters
Auto Calibration: Case 2
Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2
Arithmetic Scale
Before
autocalibration
of a priori
parameters
After autocalibration
Observed
HL-RDHM and ICP
• Display time series
• ICP modifications
– Run MCP3 or HL-RDHM
– Copy optimized parameters to HLRDHM input file
– Re-run HL-RDHM