1. Predicting the Probability of Abrupt Changes to Wave‐Generated Seafloor Sand Ripples.
- Author
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Penko, A. M. and Kearney, W. S.
- Subjects
WATER depth ,POINT processes ,TIME series analysis ,STOCHASTIC processes ,OCEAN dynamics ,OCEAN waves - Abstract
A new, non‐dimensional ripple reset parameter and a stochastic point process model is used to estimate the likelihood of propagating ocean waves to form ripples on sandy seabeds. The ripple reset parameter is a function only of water depth, significant wave height, and mean grain size. Ripple formation is estimated by the magnitude of an intensity function based on a time series of the ripple reset parameter. The point process model is trained with a time series of observed waves and ripple change, and is then applied to predict the probability that a ripple field with a different geometry will form within a given time interval from another time series of wave data. The model is trained and tested with four field deployments at three field sites to determine its skill in predicting the ripple formation (a) at one field site over one time period after being trained with observations from the same site over a different time period, and (b) at one field site after being trained with observations from another field site. Results show that while the model is sufficient at predicting ripple formation in both scenarios, it is sensitive to the quality and quantity of the training data. Increasing the amount of training data greatly improves model performance. Employing a stochastic model based on a simple ripple reset parameter reduces tunable model parameters and provides a prediction of the probability for ripple formation given only a water depth, grain size, and time series of wave heights. Plain Language Summary: Ripples are small, hill‐like mounds of sand that are formed by water in the ocean moving back and forth by waves. Sand ripples can cause an increase of sand movement on the seafloor, can cause ocean instrumentation to respond unexpectedly, and can bury and expose objects. Understanding when they form is important for scientists to measure and predict ocean dynamics. If the average height of the waves, the water depth, and the sand grain diameter is known, we can predict whether or not the waves will form ripples with mathematical equations. Previously, the equations to predict ripple formation required a lot of difficult to obtain ocean information. We developed an equation and model to quickly and easily predict the likelihood of ripples forming by waves in a specific size sand and water depth. Key Points: A ripple reset parameter based on wave height, water depth, and grain size is presentedA stochastic model based on the ripple reset parameter can predict the probability of ripple formationThe model accurately predicted ripple resets at three different field sites without calibration [ABSTRACT FROM AUTHOR]
- Published
- 2024
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