how does the choice/configuration of hydrologic models affect the portrayal of climate change...
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How does the choice/configuration of hydrologicmodels affect the portrayal of climate change impacts?
Pablo Mendoza
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Subjectivity in model selection:•How does the choice of model equations impact simulations of hydrologic processes?•Missing processes, inappropriate parameterizations?
Subjectivity in selecting/applying models
• Define a-priori values for model parameters
• Decide what model parameters we adjust, if any
• Decide what calibration strategy we implement, if any
Choice of objective functionChoice of forcing data and calibration period
Model parameters
• Decide which processes to include• Define parameterizations for individual
processes• Define how individual processes combine
to produce the system-scale response• Solve model equations
Model structure
Subjectivity in parameter identification:•How does our choice of model parameters impact simulations of hydrologic processes?•Compensatory effects of model parameters (right answers for the wrong reasons)?
Climate change studies commonly involve several methodological choices that might impact the hydrologic sensitivities obtained. In particular:
Study area
Basins of interest for this study
The Colorado Headwaters Region offers a major renewable water supply in the southwestern United States, with approximately 85 % of the streamflow coming from snowmelt. Hence, we conduct this research over three basins located in this area:
-Yampa at Steamboat Springs
-East at Almont
-Animas at Durango.
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Approach
How do our methodological choices impact the results that we obtain when we evaluate hydrologic sensitivities to climate change?
Master question!
Impact of hydrologic model choice and parameters
Key points-Different hydrologic model structures.-Uncalibrated vs. calibrated model sensitivities.-Model structures vs. parameters.
Impact of spatial forcing resolution on hydrologic sensitivities
Key points-Use of dynamical downscaling outputs at different spatial scales, generated with the same methodology.-Impact of the spatial aggregation of a 4 km gridded dataset on hydrologic sensitivities.
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Model structure selection
Differences in both model architecture and model parameterizations
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Part I:Impact of hydrologic model choice
and parameters
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Research plan: impact of model choice and parameters
Why do different models have different sensitivities to climate change?Is it due to differences in model structure rather than parameter values?
KEY QUESTIONS
Evaluation of uncalibrated model performance and sensitivities to climate change (done)
Model calibration(almost done)
Calculation of calibrated model sensitivities and comparison with uncalibrated(ongoing)
Assessment of differences on hydrologic sensitivities among feasible parameter sets (ongoing)
Task 1
Task 2
Task 3
Task 4
APPROACH 7
Status
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Uncalibrated model simulations(WRF@4km resolution)
How does model performance change with model calibration?
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Calibrated model simulations(WRF@4km resolution)
Calibration process substantially improves streamflow simulation.
Results Model performance
Impact of model structure on climate sensitivity
Impact of model parameters on climate sensitivity
How are observed hydrologic signature measures reproduced by different models?Raw values
10 The choice of a particular objective function (e.g. RMSE) for model calibration does
not necessarily improve simulated signature measures! (example: FMS)
Calibrated
Uncalibrated
Results Model performance
Impact of model structure on climate sensitivity
Impact of model parameters on climate sensitivity
How does hydrologic model choice affect the partitioning of precipitation into ET and runoff?
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Uncalibrated model simulations(WRF@4km resolution)
Calibrated model simulations(WRF@4km resolution)
Uncalibrated models: Climate change signal in Noah (↑ET and ↑Runoff) differs from the rest of models (↑ET and ↓Runoff).
Inter-model differences are larger that climate change, even after calibration process.
Results Model performance
Impact of model structure on climate sensitivity
Impact of model parameters on climate sensitivity
Changes in signature measures
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Uncalibrated
Calibrated
Inter-model differences in signature measure sensitivities don’t necessarily decrease after calibration! (e.g. seasonality and flashiness, especially at East and Yampa).
Results Model performance
Impact of model structure on climate sensitivity
Impact of model parameters on climate sensitivity
How does the impact of model parameters compare with that of model choice?
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Optimal parameter set for NSE (raw space)
Optimal parameter set for objective function
Parameter sets selected
Approach (analysis restricted to VIC):Randomly select 2 points in the parameter space located in the area of maximum values of the objective function (ie. 8 parameter sets in total)
The optimal parameter set may change significantly with the choice of objective function.
Results Model performance
Impact of model structure on climate sensitivity
Impact of model parameters on climate sensitivity
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Impact of parameters (VIC)Impact of model choice (after calibration)
Inter-parameters differences (VIC) have similar magnitudes than inter-model differences when we look at monthly runoff.
Results Model performance
Impact of model structure on climate sensitivity
Impact of model parameters on climate sensitivity
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Impact of parameters (VIC)Impact of model choice (after calibration)
Uncertainty in monthly sensitivities of internal states and fluxes is still substantial, even when evaluating a limited set of model parameters.
Results Model performance
Impact of model structure on climate sensitivity
Impact of model parameters on climate sensitivity
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Impact of parameters on the change in hydrologic signature measures (VIC)
Impact of model choice on the change in hydrologic signature measures
Model parameters: larger impact on changes in runoff ratio. Model choice: larger impact on changes in runoff seasonality and flashiness.
Results Model performance
Impact of model structure on climate sensitivity
Impact of model parameters on climate sensitivity
1. Calibration of hydrologic models improves streamflow simulation, but does not necessarily:
i. Improve representation of hydrological processes.ii. Decrease inter-model differences in signature measures change (PGW - CTRL).
2. Inter-model differences in hydrologic sensitivities to climate change:i. Are less pronounced for calibrated models rather than uncalibrated models.ii. May be larger than climate change signals even after calibration.
3. Regarding the role of parameters:i. Model choice (after calibration) and parameter selection from “optimal zones”
provide similar uncertainty in impact of climate change on monthly runoff.ii. Preliminary analysis suggests that uncertainty in monthly variations of specific
fluxes and states (e.g. ET, Soil moisture, SWE) is model-dependent rather than parameter-dependent.
Conclusions: Part I
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Part II:Impact of spatial forcing resolution
on hydrologic sensitivities
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Research plan: impact of forcing resolution
What is the impact of forcing spatial resolution on signature measures?How does forcing spatial resolution affect climate change impact results?
KEY QUESTIONS
Generate forcing datasets for all models:-Raw WRF @4km, 12km and 36km (done).-WRF-4 km data aggregated to 12km and 36 km (done).
Experiment 1: evaluate climate change impact using raw WRF outputs- Uncalibrated model simulations (done). - Calibrated model simulations (ongoing).
Task 1
Task 2
Task 3
APPROACH
Experiment 2: evaluate climate change impact using aggregated WRF-4km outputs.- Uncalibrated model simulations (done). - Calibrated model simulations (ongoing).
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Status
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7-year average cool-season precipitation : 1 October – 31 May
36 km 4 km OBSERVATIONS1000
900
800
700
600
500
400
300
200
100
0
Prec
ipit
atio
n (m
m)
12 km
Fig. Kyoko Ikeda
Results: previous studies
7-year average warm-season precipitation:1 June – 30 September
36 km 4 km OBSERVATIONS700
600
500
400
300
200
100
0
Prec
ipit
atio
n (m
m)
SNOTELGHCN
12 km
Fig. Kyoko Ikeda
Results: previous studies
Results
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How does forcing resolution affect signature measures of hydrologic behavior?RR: Runoff Ratio
Impact of forcing is model dependent (Noah is much more sensitive) Impact of forcing resolution on runoff ratio is also basin dependent.
Calibrated
Impact of forcing resolution on signature measures (historical)
Impact of forcing resolution on climate sensitivity
Y: Yampa River Basin; E: East River Basin; A: Animas River BasinUncalibrated
Raw WRF outputs WRF-4km aggregated
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How does forcing resolution affect signature measures of hydrologic behavior?CTR: Runoff Seasonality
Calibrated
36km resolution datasets tend to produce earlier runoff (less clear for 12km). Aggregation reduces resolution differences. Results depend on basin/model.
Impact of forcing resolution on signature measures (historical)
Impact of forcing resolution on climate sensitivityResults
UncalibratedY: Yampa River Basin; E: East River Basin; A: Animas River Basin
Raw WRF outputs WRF-4km aggregated
Experiment 1: raw WRF output
25 Raw WRF outputs at 12km and 36km change direction of signal (↓Runoff and
↑ET), even after model calibration, to ↑Runoff and ↑ET.
Impact of forcing resolution on signature measures (historical)
Impact of forcing resolution on climate sensitivityResults
Experiment 2: aggregated WRF output
26 Aggregated WRF-4km outputs at 12km and 36km don’t change signals
significantly, but may affect the amplitude (e.g. calibrated Noah).
Impact of forcing resolution on signature measures (historical)
Impact of forcing resolution on climate sensitivityResults
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How will signature measures change across models and forcing resolutions?RR: Runoff Ratio
RR clearly depends on forcing resolution! General decrease of RR in all cases.
Y: Yampa River BasinE: East River BasinA: Animas River Basin
Calibrated
Impact of forcing resolution on signature measures (historical)
Impact of forcing resolution on climate sensitivityResults
Uncalibrated
Raw WRF outputs WRF-4km aggregated
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How will signature measures change across models and forcing resolutions?CTR: Runoff Seasonality
Shift to earlier runoff in all cases (answer does not depend on forcing)
Y: Yampa River BasinE: East River BasinA: Animas River Basin
Calibrated
Impact of forcing resolution on signature measures (historical)
Impact of forcing resolution on climate sensitivityResults
Uncalibrated
Raw WRF outputs WRF-4km aggregated
1. The impact of forcing resolutions on signature measures for historical simulations is reduced when we use spatially aggregated WRF-4km outputs. This implies that physics options in each WRF configuration (4km, 12km and 36 km) dominates hydrological responses.
2. Regarding climate change signal:• Raw WRF outputs at 12km and 36km change direction of signal, even after model
calibration, to ↑Runoff and ↑ET.• Aggregated WRF-4km outputs at 12km and 36km don’t change signals
significantly, but may affect the amplitude (e.g. calibrated Noah).
3. Under a future climate scenario, earlier runoff volumes and a general decrease in runoff ratios is obtained with all forcing datasets. However, results are still model dependent.
Conclusions: Part II
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Thank you
EXTRA
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• Useful information: SIGNATURE MEASURES!!!
• What do they represent?i. RR: overall water balance.ii. FMS: vertical redistribution of soil moisture.iii. FHV: watershed response to large precipitation events.iv. FLV: Long term baseflow.v. FMM: Mid-range flow levels.vi. CTR: runoff seasonality.
Casper et al. (2012)
EXAMPLES
RR: Runoff Ratio (Q/P)FMS: Slope of mid-segment in FDC (0.2 < Pexc < 0.7)FHV: High segment volume in FDC (0 < Pexc < 0.02)FLV: Low segment volume in FDC (0.7 < Pexc < 1)FMM: Median value of simulated streamflowCTR: Centroid of avg. water year daily hydrograph (days since Oct 1)
Approach: diagnostic signatures
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Results: impact of model choice and parameters
Uncalibrated model simulations(WRF@4km resolution)
How does model performance change with model calibration?
33Calibrated model simulations(WRF@4km resolution)
Results: impact of model choice and parameters
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How are observed hydrologic signature measures reproduced by different models?Raw valuesUncalibrated
Calibrated
Results: impact of model choice and parameters
How are observed hydrologic signature measures reproduced by different models?CTRL - Observed
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Uncalibrated
Calibrated
Results: impact of model choice and parameters
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How are observed hydrologic signature measures reproduced by different models?CTRL - ObservedUncalibrated
Calibrated
Monthly total runoff values for different basins/models
Results: impact of model choice and parameters
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Uncalibrated model simulations(WRF@4km resolution)
Calibrated model simulations(WRF@4km resolution)
Monthly differences (PGW-CTRL) in mm for specific fluxes/states
Results: impact of model choice and parameters
38Uncalibrated model simulations(WRF@4km resolution)
Calibrated model simulations(WRF@4km resolution)
Current (CTRL) and Future (PGW) signature measures
Results: impact of model choice and parameters
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Uncalibrated
Calibrated
Current (CTRL) and Future (PGW) signature measures
Results: impact of model choice and parameters
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Changes in signature measures
Results: impact of model choice and parameters
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Changes in signature measures (PGW vs. CTRL runs)
Results: impact of model choice and parameters
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Changes in signature measures (PGW vs. CTRL runs)
Results: impact of model choice and parameters
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Results: impact of model choice and parametersWhat is the impact of the objective function on the optimal parameter set?
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Optimal parameter set for NSE (raw space)
Optimal parameter set for objective function
Kling-Gupta Efficiency (KGE)
Nash-Sutcliffe Efficiency (NSE)
Results: impact of model choice and parameters
So… how will our hydrologic sensitivities change if we arbitrarily select parameter sets within the optimal region?
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Optimal parameter set for NSE (raw space)
Optimal parameter set for objective function
Parameter sets selected
Approach:Randomly select 2 points in the parameter space located in the area of maximum values of the objective function (ie. 8 parameter sets in total)
Results: impact of model choice and parameters
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Model performance for CTRL simulations (Sep/2002 – Oct/2008)
Results: impact of model choice and parameters
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Model performance for CTRL simulations (Sep/2002 – Oct/2008)East River Basin
Results: impact of model choice and parameters
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How are observed hydrologic signature measures reproduced by different parameter sets?Raw values
Results: impact of model choice and parameters
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How are observed hydrologic signature measures reproduced by different parameter sets?CTRL - Observations
Results: impact of model choice and parameters
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Impact of parameters on partitioning of precipitation into ET and Runoff
Results: impact of model choice and parameters
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How will hydrologic signature measures change in a future climate?PGW vs. CTRL values for 8 different parameter sets (VIC)
Results: impact of model choice and parameters
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Impact of parameters on the change in hydrologic signature measures (VIC)
Impact of model choice on the change in hydrologic signature measures
Results: impact of model choice and parameters
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How will hydrologic signature measures change in a future climate?Current and future raw values
Results: impact of model choice and parameters
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How will hydrologic signature measures change in a future climate?Future - Current
Results: impact of model choiceHow are observed hydrologic signature measures reproduced by different
models/datasets?Raw values
55Experiment 1: raw WRF output & uncalibrated models
Results: impact of model choice
56Experiment 1: raw WRF output & uncalibrated models
How are observed hydrologic signature measures reproduced by different models/datasets?
Raw values
Results: impact of model choiceHow are observed hydrologic signature measures reproduced by different
models/datasets?Raw values
57Experiment 2: aggregated WRF output & uncalibrated models
Results: impact of model choice
58Experiment 2: aggregated WRF output & uncalibrated models
How are observed hydrologic signature measures reproduced by different models/datasets?
Raw values
Results: impact of model choice
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How does forcing resolution affect signature measures of hydrologic behavior?FMS: Flashiness of runoff
Uncalibrated models Calibrated modelsY: Yampa River BasinE: East River BasinA: Animas River Basin
Results: impact of model choice
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How does forcing resolution affect signature measures of hydrologic behavior?FHV: Response to large precipitation events
Uncalibrated models Calibrated modelsY: Yampa River BasinE: East River BasinA: Animas River Basin
Results: impact of model choice
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How does forcing resolution affect signature measures of hydrologic behavior?FLV: Long-term baseflow
Uncalibrated models Calibrated modelsY: Yampa River BasinE: East River BasinA: Animas River Basin
Results: impact of model choice
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How does forcing resolution affect signature measures of hydrologic behavior?FMM: Mid-range flow levels
Uncalibrated models Calibrated modelsY: Yampa River BasinE: East River BasinA: Animas River Basin
Results: impact of model choice
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How will signature measures change across models and forcing resolutions?FMS: Flashiness of runoff
Y: Yampa River BasinE: East River BasinA: Animas River Basin
Uncalibrated Calibrated
Results: impact of model choice
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How will signature measures change across models and forcing resolutions?FHV: Response to large precipitation events
Y: Yampa River BasinE: East River BasinA: Animas River Basin
Uncalibrated Calibrated
Results: impact of model choice
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How will signature measures change across models and forcing resolutions?FLV: Long-term baseflow
Y: Yampa River BasinE: East River BasinA: Animas River Basin
Uncalibrated Calibrated
Results: impact of model choice
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How will signature measures change across models and forcing resolutions?FMM: Mid-range flow levels
Y: Yampa River BasinE: East River BasinA: Animas River Basin
Uncalibrated Calibrated
Results: impact of forcing spatial resolution
Experiment 1: raw WRF output67
Total runoff (uncalibrated) Total runoff (calibrated)
Results: impact of forcing spatial resolution
Experiment 2: aggregated WRF output68
Total runoff (uncalibrated) Total runoff (calibrated)
Results: impact of forcing spatial resolution
Experiment 1: raw WRF output69
Evapotranspiration (uncalibrated) Evapotranspiration (calibrated)
Results: impact of forcing spatial resolution
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Evapotranspiration (uncalibrated) Evapotranspiration (calibrated)
Experiment 2: aggregated WRF output
Results: impact of forcing spatial resolution
Experiment 1: raw WRF output71
SWE (uncalibrated) SWE (calibrated)
Results: impact of forcing spatial resolution
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SWE (uncalibrated) SWE (calibrated)
Experiment 2: aggregated WRF output
Results: impact of forcing spatial resolution
Experiment 1: raw WRF output73
Soil moisture (uncalibrated) Soil moisture (calibrated)
Results: impact of forcing spatial resolution
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Soil moisture (uncalibrated) Soil moisture (calibrated)
Experiment 2: aggregated WRF output
NSE surfaces: PRMS
Nsim = 10,000 (100 x 100 points)
East River Basin
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NSE surfaces: PRMS
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: PRMS
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: PRMS
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: PRMS
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: PRMS
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: PRMS
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: PRMS
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: PRMS
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: VIC
Nsim = 10,000 (100 x 100 points)
East River BasinIs this related to spatial parameterization or to the model?No, because thick2 is spatially constant in the basin
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NSE surfaces: VIC
Default
Calibration strategy: One multiplier for each parameter (binfilt, Ds, Dsmax, Ws, depth2, depth3)
Before discontinuity
After discontinuity
The conflictive parameter is spatially constant!!
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Results: impact of model choiceWhat is the impact of the objective function on the optimal parameter set?
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NSE surfaces: VIC
Nsim = 2,500 (50 x 50 points)
East River Basin
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NSE surfaces: VIC
Nsim = 2,500 (50 x 50 points)
East River Basin
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EXTRA: the VIC experiment
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