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Parameter values for RHESSys inputs

Tables are provided for vegetation functional types including parameters that are typically calibrated, range of values to test, parameters that should be established from literature derived estimates (rather than calibrated), and the level of impact that varying a parameter may have.

• Minimum value - lower end of range to test
• Maximum value - upper end of range to test
• Importance - parameter has a high, low, or medium impact on performance/processing
• Empirical estimation - value should be determined by using real-world data and statistical analysis (yes) or calibration/sensitivity analysis should be used to determine acceptable values (no)
• Modification - parameter is typically calibrated (standard) or the default value should be used unless certain expertise is involved (specialized)

Canopy Strata Parameters

Tree
Shrub
Grass

For parameter descriptions see the Parameter Definition Files page

Calibration

Vegetation parameters play a critical role in determining the behavior of key processes like evapotranspiration, carbon cycling, energy and water balance. Calibration of these parameters is essential to ensure that models provide realistic, reliable, and meaningful results.

• Calibration ensures that key processes are realistically represented by tuning parameters to match observed behavior, thereby improving the model’s accuracy.
• Vegetation characteristics vary significantly across different ecosystems due to differences in species composition, soil properties, and climate. Default parameter values often fail to capture local variations, resulting in poor model performance. Calibration allows the model to be customized to specific regions, ensuring that local conditions are properly represented.
• Calibration helps reduce inherent uncertainty due to variability in inputs by aligning parameter performance with observed data, and improve confidence in the model’s predictions.
• Calibration can help ensure long term model stability as some parameter combinations can cause vegetation instability, oscillations, or crashes.
• Calibration helps capture non-linear interactions by fine-tuning parameters that model complex feedbacks such as vegetation-climate interactions.
• A calibrated model that can realistically simulate current and past conditions is more likely to provide reliable predictions under future scenarios.