Generalized Parameter Estimation for Large-Scale Hydrologic Models:
Application to the VIC model
Naoki Mizukami1, Martyn Clark1, Andy Newman1,
Kevin Sampson1, Andy Wood1
Bart Nijssen2, and Luis Samaniego3
1. National Center for Atmospheric Research
2. University of Washington
3. Helmholtz Centre for Environmental Research – UFZ
AGU Fall Meeting 2014
H42A-06 – December 18, 2014
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Outline
 Introduction
 Motivation
 Goals
 Parameter estimation methods
 Current approaches and Transfer Function-Rescaling approaches
 Example of Variable Infiltration Capacity (VIC) model
 Spatial parameter fields
 Simulations over the upper-Colorado River Basin
 Summary and Future work
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Outline: 1. Introduction 2. Parameter estimations 3. VIC examples
Current approach for parameter estimations at a large scale modeling
 A- priori (base) parameter estimations + basin-wide calibrations (i.e. basin-wide multipliers)
(e.g., SAC/SNOW17 – NWS River Forecast, VIC- USBR- climate change assessment)
 Issues
 Discontinuity in spatial distributions
of model parameters.
 Transferability of calibrated
parameters to the other basins?
 Not counting for sub-grid variability
of soil properties.
Goal and approach
 Improve continental parameter
estimates for multiple hydrologic
models.
 Initial work: Parameter estimation
for the Upper Colorado River basin
using streamflow from headwater
basins
For example, the nationwide VIC simulations used for
water security assessments based on a single model with
spatially inconsistent parameter estimates
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Outline: 1. Introduction 2. Parameter estimations 3. VIC examples
Traditional approach
Transfer Function-Rescaling approach
Samaniego et al. 2010, WRR
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Outline: 1. Introduction 2. Parameter estimations 3. VIC examples
The Samaniego rescaling methodology
Soil Data
(e.g., STATSGO space)
Model Params
(e.g., STATSGO space)
βi
Pi
Ztot
Model Params
(e.g., 3 Layers)
Model Layers
(e.g., 3 Layers)
𝑃𝑖
𝑃𝑖
simulations
bedrock
(Pedo-) transfer function
𝑃𝑖 = 𝑇𝐹 𝒂, 𝛽𝒊
Vertical rescaling
𝑃𝑖 =𝑂 𝑤 ∙ 𝑃𝑖
w: weight of intersecting
Soil Layer
Horizontal rescaling
𝑃𝑖 = 𝑂 𝑤 ∙ P𝑖
w: weight of intersecting
Soil polygon
Adjust TF coefficients
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Outline: 1. Introduction 2. Parameter estimations 3. VIC examples
VIC calibration over 9 basins in the upper Colorado
Calibration basins
Parameter
TF
Related process/states
Binfilt
a1
Infiltration to the top layer
Ds
a2
Baseflow
a3*slope*Ksat
Baseflow
Ws
a4
Baseflow
Ksat
a5*ks
Validation basins
Dsmax
AR
d1
a6*Ztot
d2
a7*Ztot
d3
Ztot*(1-a6-a7)
Bulk Density
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Model grid: NLDAS 12km
Met: Maurer et al. 2002
Period: 10/1990-9/2000
Calib algorithm: SCE with RMSE
Percolation btw layers
Max. soil moisture storage
Baseflow
a8*ρbulk
ai : coefficient of PTF
Italics: STATSGO soil properties
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Outline: 1. Background 2. Parameter estimations 3. VIC examples
3 calibration strategies
Traditional, individual basins
Traditional, regional basins
Calibration region
TF Rescaling
Calibration region
Calibration basins
Validation basins
Parameter multiplier
Cp (basini) where i =1…n
Different parameter
multipliers for each basin
Parameter multiplier
Cp (region)
Single set of parameter
multipliers for the entire
region
TF coefficient
ap (region)
Single set of adjusted TF
coefficients
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Outline: 1. Introduction 2. Parameter estimations 3. VIC examples
Example parameter estimates: traditional, individual basins
Max Soil Moisture Storage in bottom layer
Base parameter- NLDAS
Calibrated
Calibratedparameters
parameters– –Interpolated
only basins
Calibrated multiplier
• Cρbulk(basini)
• Cd1(basini)
• Cd2(basini)
• Cztot(basini)
i = 1,…15
Nearest Neighbor
Outline: 1. Introduction 2. Parameter estimations 3. VIC examples
Example parameter estimates: traditional, regional
Max Soil Moisture Storage in bottom layer
Calibrated
region
Calibratedparameters
parameters– –entire
only basins
Base parameter -NLDAS
Calibrated multiplier
• Cρbulk(region)
• Cd1(region)
• Cd2(region)
• Cztot(region)
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Outline: 1. Background 2. Parameter estimations 3. VIC examples
Example parameter estimates: TF-Rescaling
Max Soil Moisture Storage in bottom layer
Estimation with default TF coefficients
Estimation with calibrated TF coefficients
Calibrated TF coef.
• aρbulk
• ad1
• ad2
• aztot
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Outline: 1. Background 2. Parameter estimations 3. VIC examples
NSE -Transferred parameter
Preliminary result: Transferability of calibrated parameters
NSE -basin
Include only validation basins
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Summaries and future direction
1. Implemented framework of TF-Rescaling method for large-scale
hydrologic model parameter estimation.
2. TF-Rescaling produces physically reasonable spatial variability of
parameters.
3. Tested transferability of estimated parameters over Upper Colorado.
 initial performance was mixed (with small sampled basins).
1. Conduct robust examinations of transferability across different
hydroclimate regions.
2. Calibrate using multiple hydrologic signatures and objective functions.
3. Examine multi-scale behavior.
4. Build a library of (improved) TFs for multiple hydrologic models.
5. Test with high resolution SSURGO data (and other high-resolution
datasets).
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EXTRA
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Default soil parameters used for CMIP5 climate change assessment
Binfilit parameter
Default max. soil water storage
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Parameters derived from calibrated coefficients of transfer function
Binifilt - Calibrated TF-upscale
Max. soil water storage - Calibrated TF-upscale
Issue
• Spatially constant Binfilit
• Infiltration depends on slope and soil property
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Relate Binfilit to topography
Adjusted Binifilt (i,j) = Binifilt * stdele(i,j) /stdele(cal grid boxes)
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•
•
•
adjusted Binifilt (i,j) - topographically adjusted pixel at grid box (i,j)
Binifilt - spatially constant parameter (calibrated)
Stdele(I,j) - standard deviation of 1km elevation within grid box (I,j)
Stdele(cal grid boxes) - standard deviation of 1km elevation within grid boxes used for calibration
Binifilt - Calibrated TF-upscale + adjusted by std of elev.
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