Unlocking the potential of observations in shoreline modelling through data assimilation

Analyzing the coastal response is a complex problem that usually requires the use of numerical modelling in combination with observations (Alvarez-Cuesta et al., 2023). To this end, data assimilation is a useful tool to blend observational data and models to produce more accurate forecasts.

Here, the performance of different data assimilation algorithms in predicting multiscale shoreline dynamics is studied. Two statistical algorithms based on the Kalman filter (Alvarez-Cuesta et al., 2021) and one variational algorithm named 4DVar (LeDimet, F-X. & Talagrand, O., 1986) are employed together with an equilibrium cross-shore model and a one-line longshore model. A twin experiments procedure is performed to obtain the observation requirements for the different assimilation algorithms in terms of accuracy, length of the data collection campaign and sampling frequency. Similarly, the initial system knowledge needs and the ability of the different assimilation methods to track the system non-stationarity are evaluated under synthetic scenarios.

 With noisy observations, the Kalman filter variants outperform the 4DVar. However, the 4DVar is less restrictive in terms of initial system knowledge and tracks nonstationary parametrizations more accurately for cross-shore processes. Results are demonstrated at two real beaches governed by different processes with different data sources used for calibration and stress the need for assimilating shoreline observations to produce robust forecasts.

REFERENCES

Alvarez-Cuesta, M., Losada, I. J. & Toimil, A. (2023). A nearshore evolution model for sandy coasts: IH-LANSloc. Environmental Modelling and Software, 169, 105827

Alvarez-Cuesta, M., Toimil, A., & Losada, I. J. (2021). Modelling long-term shoreline evolution in highly anthropized coastal areas. Part 1: Model description and validation. Coastal Engineering, 169(July), 103960.

LeDimet, F-X. & Talagrand, O. (1986). Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus a 38.2: 97-110.