On the Investigation of the Sea-Level Variability in Coastal Zones Using SWOT Satellite Mission: Example of the Eastern English Channel (Western France)

Abstract

The future mission of surface water and ocean topography (SWOT), launched in 2020 over a period of 3–5 years, will be designated to address the issue of combining surface water hydrology with physical oceanography aiming to present new perspectives of applications for coastal areas. The extent to which the synthetic SWOT measurements can reproduce the temporal variability of the sea level was investigated. The eastern English Channel (NW France) was considered as a case of application. The hourly sea-level records were filtered from the aliased harmonic tides by classic harmonic analyses to obtain the nontidal residual. This residual was used to simulate synthetically the satellite samples based on the number of overpasses per repeat cycle at each geographical station. Both real and synthetic SWOT measurements were compared by the use of different approaches of inference statistics and wavelets. The statistical behavior, deduced from the functions of probability density (pdf) and cumulative distribution (cdf), shows correlations between 65% and 75% for hourly measurements, which increase to 85% for monthly average ones. The frequency of positive and negative extreme values is under-estimated with an order less than 25%. The potential use of SWOT depends on the number of measurements and the sampling interval between SWOT overpasses per repeat orbit. In the time–frequency domain, the wavelet multiresolution analysis of the nontidal sea level displays four components: 1) 1 year; 2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 4–7 month; 3) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 2–3 month; and 4) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt;2$</tex-math></inline-formula> month bands. Such modes seem to be well manifested by SWOT samples with a mean explained variance more than 75%. The aliasing frequency of the altimeter generates a dispersion and an overexpression of the energy spectrum, which increases with the number of overpasses per repeat orbit and the high frequency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim $</tex-math></inline-formula> 2–3 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt;2$</tex-math></inline-formula> month bands). The reconstructed wavelet components evidence the capacity of SWOT to estimate the annual and the seasonal variability of the nontidal sea level. In particular, SWOT is able to reproduce the most of extreme storm surges in the English Channel. The main finding of this research clearly shows the utility of SWOT satellite altimetry in observing and understanding the sea-level variability and storm surges, complementing tide-gauge observations for the validation and improvement of coastal models.