Numerical models of coastal dune growth encode feedbacks and nonlinearities between sediment transport and plant growth. The range of processes and tunable parameters involved make model calibration an important step when using models for prediction. In this paper we outline a method to calibrate models of coastal dune formation and describe the process from end to end. The first step is collection of both topographic and vegetation data at two time periods with photogrammetry using the technique of structure-from-motion. Using the first topographic and vegetation capture as the model initial condition, the free parameters in the model are th...  more

Numerical models of coastal dune growth encode feedbacks and nonlinearities between sediment transport and plant growth. The range of processes and tunable parameters involved make model calibration an important step when using models for prediction. In this paper we outline a method to calibrate models of coastal dune formation and describe the process from end to end. The first step is collection of both topographic and vegetation data at two time periods with photogrammetry using the technique of structure-from-motion. Using the first topographic and vegetation capture as the model initial condition, the free parameters in the model are then tuned by running the model many times and adjusting the free parameters with a genetic algorithm, a machine learning technique. A set of parameters is found that produces the lowest prediction error — and in this way the model is calibrated for local conditions. We outline this routine, provide an example, and direct the reader to the open source software developed as part of the workflow presented here, which can be used with other dune models and/or other datasets.