Global sensitivity analysis

Definition

Global sensitivity analysis is a statistical method used to assess how the variability in the output of a model can be attributed to different sources of uncertainty in its input parameters. It provides insights into which parameters are most influential on model predictions and helps in understanding the relationships between inputs and outputs. This method is crucial during model calibration, validation, and uncertainty analysis, as it allows for identifying the key factors that drive the model's performance and informs decision-making processes.

5 Must Know Facts For Your Next Test

1. Global sensitivity analysis evaluates all input parameters simultaneously, allowing for a comprehensive view of their combined effects on output variability.
2. It can identify both linear and non-linear relationships between inputs and outputs, making it valuable for complex models.
3. This analysis helps prioritize which parameters need precise estimation and which can be treated with more uncertainty.
4. Incorporating global sensitivity analysis during model validation enhances the robustness of the conclusions drawn from the model.
5. It is essential for effective communication of uncertainties in model predictions to stakeholders involved in decision-making.

Review Questions

• How does global sensitivity analysis enhance the process of model calibration?
  • Global sensitivity analysis enhances model calibration by identifying which input parameters have the most significant impact on model outputs. By focusing on these key parameters, researchers can prioritize their efforts in obtaining accurate estimates during calibration. This targeted approach improves model performance and ensures that resources are used effectively to fine-tune the model.
• Discuss the role of global sensitivity analysis in uncertainty analysis and its importance for model validation.
  • Global sensitivity analysis plays a critical role in uncertainty analysis by quantifying how uncertainties in input parameters influence the model's outputs. This understanding is vital for validating models because it allows researchers to assess the reliability and robustness of their predictions. By recognizing which parameters contribute most to uncertainty, they can better communicate risks and inform stakeholders about potential limitations in their findings.
• Evaluate the significance of conducting global sensitivity analysis before finalizing a hydrological model for practical applications.
  • Conducting global sensitivity analysis before finalizing a hydrological model is significant as it provides insights into the most influential parameters, guiding decisions on where to focus calibration efforts and data collection. This step not only enhances the credibility of the model but also supports informed decision-making in water resource management. Moreover, by understanding parameter sensitivities, practitioners can better anticipate how changes in inputs might affect outputs, ultimately leading to more effective strategies for addressing water-related challenges.

Related terms

Model Calibration: The process of adjusting model parameters to improve the agreement between model predictions and observed data.

Uncertainty Analysis: The assessment of the impact of uncertainty in input parameters on model outputs, often used to quantify confidence in model predictions.

Parameter Estimation: The process of determining the values of parameters within a model based on observed data and statistical techniques.