Calibration and Optimization#

SYMFLUENCE provides comprehensive calibration capabilities for hydrological models through automated parameter estimation, multi-objective optimization, and robust evaluation frameworks.

Overview#

Model calibration in SYMFLUENCE follows a systematic approach:

  1. Parameter Selection — Define which parameters to calibrate

  2. Objective Functions — Choose appropriate metrics for optimization

  3. Algorithm Configuration — Select optimization strategy

  4. Evaluation — Assess calibrated model performance

Basic Calibration Setup#

Configure calibration in your project YAML:

# Calibration periods
CALIBRATION_PERIOD: "2018-01-01,2018-06-30"
EVALUATION_PERIOD: "2018-07-01,2018-12-31"

# Parameters to calibrate
PARAMS_TO_CALIBRATE: [k_snow, fcapil, newSnowDenMin]

# Optimization settings
OPTIMIZATION_ALGORITHM: DE
OPTIMIZATION_METRIC: KGE
POPULATION_SIZE: 48
NUMBER_OF_ITERATIONS: 30

Parameter Selection#

SUMMA Parameters

Common parameters for calibration:

PARAMS_TO_CALIBRATE:
  - k_snow          # snow thermal conductivity
  - fcapil          # capillary fringe thickness
  - newSnowDenMin   # minimum new snow density
  - theta_sat       # soil porosity
  - theta_res       # residual soil moisture
  - vGn_alpha       # van Genuchten alpha parameter
  - vGn_n           # van Genuchten n parameter

FUSE Parameters

For FUSE model calibration:

SETTINGS_FUSE_PARAMS_TO_CALIBRATE:
  - alpha           # baseflow recession parameter
  - beta            # percolation parameter
  - k_storage       # storage coefficient
  - qbrate_2c       # baseflow rate
  - percfrac        # percolation fraction

NextGen Parameters

Noah-OWP parameters:

NGEN_NOAH_PARAMS_TO_CALIBRATE:
  - bexp            # pore size distribution
  - dksat           # saturated hydraulic conductivity
  - psisat          # saturated soil potential
  - refkdt          # reference infiltration parameter

Optimization Algorithms#

Differential Evolution (DE)

Robust global optimizer. Recommended for most applications.

OPTIMIZATION_ALGORITHM: DE
POPULATION_SIZE: 48
NUMBER_OF_ITERATIONS: 30
DE_SCALING_FACTOR: 0.5
DE_CROSSOVER_RATE: 0.9

Dynamically Dimensioned Search (DDS)

Efficient for high-dimensional problems.

OPTIMIZATION_ALGORITHM: DDS
NUMBER_OF_ITERATIONS: 1000
DDS_R: 0.2

Particle Swarm Optimization (PSO)

Good for continuous optimization problems.

OPTIMIZATION_ALGORITHM: PSO
POPULATION_SIZE: 30
NUMBER_OF_ITERATIONS: 50
PSO_INERTIA_WEIGHT: 0.7
PSO_COGNITIVE_PARAM: 1.5
PSO_SOCIAL_PARAM: 1.5

Multi-Objective (NSGA-II)

For multiple competing objectives.

OPTIMIZATION_ALGORITHM: NSGA-II
NSGA2_PRIMARY_METRIC: KGE
NSGA2_SECONDARY_METRIC: NSE
POPULATION_SIZE: 100
NUMBER_OF_ITERATIONS: 50

Objective Functions#

Single Objective

OPTIMIZATION_METRIC: KGE

Available metrics: - KGE — Kling-Gupta Efficiency (recommended) - NSE — Nash-Sutcliffe Efficiency - RMSE — Root Mean Square Error - PBIAS — Percent Bias - R2 — Coefficient of Determination

Multi-Objective (NSGA-II / MOEA/D)

Multi-objective algorithms optimize two metrics simultaneously:

OPTIMIZATION_ALGORITHM: NSGA-II
NSGA2_PRIMARY_METRIC: KGE
NSGA2_SECONDARY_METRIC: NSE

Multivariate Objective

Combine multiple target variables with per-variable weights and metrics:

OBJECTIVE_FUNCTION: MULTIVARIATE
OBJECTIVE_METRICS:
  streamflow: KGE
  swe: NSE
OBJECTIVE_WEIGHTS:
  streamflow: 0.7
  swe: 0.3

Calibration Execution#

Command Line

# Run calibration step
symfluence workflow step calibrate_model --config my_project.yaml

# Run full workflow including calibration
symfluence workflow run --config my_project.yaml

# Check workflow status
symfluence workflow status --config my_project.yaml

Python API

from symfluence import SYMFLUENCE

# Initialize SYMFLUENCE with configuration
sf = SYMFLUENCE('my_project.yaml')

# Run calibration step
sf.run_individual_steps(['calibrate_model'])

# Or run the optimization manager directly
from symfluence.optimization.optimization_manager import OptimizationManager

opt_manager = OptimizationManager(config, logger)
results = opt_manager.run_optimization_workflow()

# Get best parameters from results
best_params = results.get('best_parameters', {})
best_score = results.get('best_score', None)

Results and Evaluation#

Output Files

Calibration produces:

  • calibration_results.csv — Parameter evolution

  • best_parameters.yaml — Optimal parameter set

  • objective_history.png — Convergence plot

  • parameter_sensitivity.csv — Sensitivity analysis

Validation

Performance on the independent EVALUATION_PERIOD is reported automatically alongside the calibration-period score using OPTIMIZATION_METRIC.

Best Practices#

  1. Parameter Bounds

    Override default parameter ranges where needed:

    PARAMETER_BOUNDS:
  k_snow: [0.01, 1.0]
  theta_sat: [0.3, 0.6]

  2. Computational Efficiency

    Run trials in parallel across worker processes:

    NUM_PROCESSES: 16

Troubleshooting#

Common Issues

  • Slow convergence: Increase population size or iterations

  • Parameter bounds: Check realistic ranges for your domain

  • Memory issues: Reduce the number of parallel processes

  • Poor performance: Verify observation data quality

Example Workflows#

Basic Single-Objective

CALIBRATION_PERIOD: "2015-01-01,2017-12-31"
EVALUATION_PERIOD: "2018-01-01,2020-12-31"
PARAMS_TO_CALIBRATE: [k_snow, fcapil, theta_sat]
OPTIMIZATION_ALGORITHM: DE
OPTIMIZATION_METRIC: KGE
POPULATION_SIZE: 30
NUMBER_OF_ITERATIONS: 50

Multi-Objective with Validation

CALIBRATION_PERIOD: "2010-01-01,2015-12-31"
EVALUATION_PERIOD: "2016-01-01,2018-12-31"
PARAMS_TO_CALIBRATE: [alpha, beta, k_storage, qbrate_2c]
OPTIMIZATION_ALGORITHM: NSGA-II
NSGA2_PRIMARY_METRIC: KGE
NSGA2_SECONDARY_METRIC: NSE
POPULATION_SIZE: 100
NUMBER_OF_ITERATIONS: 100

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See Also