Conclusions

This case study demonstrates how a best-practice modeling framework helps ensure that modeling is well considered, well documented, and transparent. The approach increases the evidence on which the model development may be accepted by the stakeholders. Regardless of the model's final success in relation to its predictive purposes, the ten-step process should ensure benefits to all involved, in the form of better insight into the system, the data, and the scope and limitations of modeling.

Requirements for transparency may be summarized as

• clear statements of the stakeholders and their objectives;

• documentation of the nature, scope, and quality of the data;

• a strong rationale for the choice of model families and features;

• justification of the methods and criteria employed in calibration and parameter estimation, including readiness to acknowledge, critically, informal or unorthodox methods and criteria where circumstances require them;

• thorough analysis of the performance relative to the resources and application;

• documentation of the model's utility, assumptions, accuracy, limitations, and need and potential for improvement; and

• adequate reporting of the above to inform criticism and review of the model.

See also: Conceptual Diagrams and Flow Diagrams; Parameters; Participatory Modeling; Remote Sensing; Sensitivity and Uncertainty; Statistical Prediction; Sensitivity, Calibration, Validation, Verification; Wetland Models.

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