The Surface Water and Ocean Topography (SWOT) altimeter will perform a continuous global water survey with unprecedented resolution and accuracy across its 3-year mission. After being launched on December 16th 2022 with a SpaceX Falcon 9 rocket from Vandenberg in California, it was successfully commissioned followed by a Calibration and Validation (Cal/Val) phase that lasted approximately between April and July 2023. During this period, numerous in-situ measurements were performed across the globe to assess the altimeter's performance. Airborne Light Detection And Ranging (LiDAR) campaigns were conducted off the coasts of Normandy, France as part of other measurements in this region. We carried out 4 different missions, 2 in May and 2 in June, using a Leica ALS 60 airborne sensor aboard 2 different planes, a Piper Navajo and a Swearingen Fairchild Merlin. The flight plans were designed below the SWOT Ka-band Radar Interferometer (KaRIn) along and across the 1-day fast sampling ground track. Ground Control Points (GCP) were acquired under the LiDAR coverage, close to the city of Cherbourg. The plane's trajectory was processed using CNES GINS software, using the integer Precise Point Positioning (iPPP) mode, resulting in centimetric antenna phase positioning. LiDAR data were calibrated using the GCPs with a millimetric average accuracy. First results between SWOT data and airborne LiDAR indicate very good consistency. Indeed, the differences between the SWOT LR 2 km pre-cal product and LiDAR data, averaged over a similar 2 km grid, gives centimetric standard deviation.

Continuer la lecture Partager

KeywordsPhysical modeling; Wave flume; Extreme waves; Wavelet transform; Machine Learning; MLP modelPhysical modeling, spectral analysis, and artificial intelligence techniques were used to study extreme wave behavior and its evolution in shallow waters. A series of physical tests were conducted in a laboratory wave flume using different wave spectra, including JONSWAP (γ = 7), JONSWAP (γ = 3.3), and Pierson-Moskowitz, varying within a broad range of wave amplitudes. The dispersive focusing technique was used to generate these spectral waves. To account for the varying duration of extreme events, one, three, six, and nine wave trains were generated. A total of fifty-one wave gauges, located between 4 m and 14 m from the wave generator, provided comprehensive monitoring of the wave characteristics and their propagation along the wave flume [1]. The analysis incorporates wavelet transform to identify frequency components and their assigned energy using the Maximal Overlap Discrete Wavelet Transform (MODWT) method. The energy of the dominant frequency components, d5 and d4, which represent the peak frequency (fp = 0.75 Hz) and its first harmonic (2fp = 1.5 Hz), respectively, has significantly decreased. In contrast, the energy of the remaining components has increased. By investigating the energy of each frequency component along the wave flume, potential correlations between the dissipation of dominant frequency components and zones of higher energy dissipation are explored. Moreover, using the Multilayer Perceptron (MLP) machine learning algorithm [2], the study confirmed the repeatability of our findings regarding the energy of the frequency components with an accuracy of 98%. This study demonstrates the effectiveness of the MLP algorithm in improving wave prediction using field experimental data.[1] Zhang, J., Benoit, M., Kimmoun, O., Chabchoub, A., & Hsu, H. C. (2019). Statistics of extreme waves in coastal waters: large scale experiments and advanced numerical simulations. Fluids, 4(2), 99.[2] Abroug, I., Matar, R., & Abcha, N. (2022). Spatial Evolution of Skewness and Kurtosis of Unidirectional Extreme Waves Propagating over a Sloping Beach. Journal of Marine Science and Engineering, 10(10), 1475.

Continuer la lecture Partager

Assessing long-term changes in groundwater is crucial for understanding the impacts of climate change on aquifers and for managing water resources. However, long-term groundwater level (GWL) records are often scarce, limiting understanding of historical trends and variability. In this study, we present a deep learning approach to reconstruct GWLs up to several decades back in time using recurrent-based neural networks with wavelet pre-processing and climate reanalysis data as inputs. GWLs are reconstructed using two different reanalysis datasets with distinct spatial resolutions (ERA5: 0.25◦ x 0.25◦ & ERA20C: 1◦ x 1◦) and monthly time resolution, and the performance of the simulations was evaluated. Long term GWL timeseries are now available for northern France, corresponding to extended versions of observational timeseries back to the early 20th century. All three types of piezometric behaviors could be reconstructed reliably and consistently capture the multidecadal variability even at coarser resolutions, which is crucial for understanding long-term hydroclimatic trends and cycles. GWLs'multidecadal variability was consistent with the Atlantic multidecadal oscillation. From a synthetic experiment involving a modified long-term observational time series, we highlighted the need for longer training datasets for some low frequency signals. Nevertheless, our study demonstrated the potential of using DL models together with reanalysis data to extend GWL observations and improve our understanding of groundwater variability and climate interactions.

Continuer la lecture Partager

Erosion is recognized as a major threat worldwide and can be dramatically observed in Northwestern France as a consequence of water runoff. Recent regional studies in Normandy suggested that off-site erosion and runoff impacts led to significant economic costs over the last 25 years. Even if the regional planning strategy against erosion and runoff impacts could be seen as effective with a cost-benefice balance greater than 1, this strategy will no longer be as effective by 2050 due to climate change effects in the near future. To address this issue and conduct efficient land and water degradation neutrality strategies, local stakeholders now have to identify complementary strategies based on the deployments of nature-based solutions. However, there is a lack of references on the effectiveness of these complementary strategies.In this study, we conducted a modelling exercise with the WaterSed model at the regional scale (12,318 km<SUP>2</SUP>) aiming to: (i) quantify the hydro-sedimentary transfers reaching the karstic systems throughout the 15,000 sinkholes distributed across the territory, (ii) established the first regional cartography of vulnerability of sinkholes to runoff and erosion, and (iii) to evaluate the effectiveness of strategies considering nature-based solutions to prevent land and ground water degradation.The model was calibrated and validated using data of hydro-sedimentary transfer monitoring station on a local catchment. Multiples scenarios were explored (impacts of different nature-based solutions densities, localization of grasslands, ploughing of grass lands, soil and water conservation techniques, etc.) using semi-automatic positioning algorithm.The WaterSed model provided specific outputs like volume of runoff (m3) and volume of sediments (t) reaching the karstic system for different designed storms. The mean runoff per sinkhole was estimated between 7,700 and 23,200 m3 and the mean volume of sediment reached between 0.8 and 4.7 t per sinkhole.Our results suggest that increasing the density of nature-based solutions from 2 to 8 per km<SUP>2</SUP> can reduce from 0.5 to 1.3 % the runoff volume and from 5 to 15 % the sediment load reaching the sinkholes. Our results also suggest that a complement of 20 m to 250 m of grassland upstream a sinkhole can reduce the sediment load from 5 to 13 % and the runoff from 0.5 to 1.5 %. Our results suggest that the localization of ploughed-up grasslands can have a significant impact on the generation of hydro-sedimentary transfers (up to 10 % more sediment discharge).The results of this modeling exercise provided: (i) the first regional cartography of vulnerability of the 15,000 sinkholes to runoff and erosion, and (ii) local thresholds and valuable references to build and conduct efficient land and ground water degradation neutrality strategies with stakeholders.

Continuer la lecture Partager

This study examines the impact of individual storm events, the recovery, and the effect of successive events on pebble beaches. As a first step, storm events in the Normandy region (France) were identified and classified according to their energy content using a 42-year wave height time series. Of the total number of identified storms, 187 were classified as Weak. 74 storms fell under the Moderate category, 25 storms were classified as Significant, 9 storms were labeled as Severe, and 2 storms were characterized as Extreme. A close examination of storm characteristics was done for the 2018-2019 and 2019-2020 winter seasons, where two Severe storms took place in each season. During these periods, the response of the beach was characterized through i) an evaluation of the intertidal beach volume using Digital Elevation Models (DEMs) generated through a video camera platform, and ii) an examination of shoreline change using Sentinel-2 satellite imagery. The analysis revealed distinctive differences between the two winter seasons. The 2018-2019 contained half the storm energy content compared to the 2019-2020 season. During the first winter season, the Severe storm took place by the end of the winter period and encountered an eroded beach. Subsequently, there was a slight volume increase during the summer season which did not fully recover the pre-winter beach volume. As the 2019-2020 winter season commenced, there was further erosion, notably following the impact of the Severe storm (Ciara), which stood out as the most energetic storm during the study period. This event caused the beach to reach its minimum volume in the study, the posterior series of moderate and weak storms arriving at the beach assisted to the partial recovery of the beach volume. By July 2020, the beach volume had reached the pre-winter 2018-2019 values. The assessment of shoreline change using satellite images was used to complement the partial beach coverage of the cameras. Although, this approach was limited by the resolution of satellite images, evidence of shoreline retreat and beach rotation developments were associated to certain storm events, assisting in the evaluation of the beach response to storms.

Continuer la lecture Partager

TerraceM is an open-source software written in MATLAB for mapping and analyzing marine terraces. In this latest release, TerraceM-3 has undergone significant evolution, which leverages the capabilities of machine learning to introduce an automated marine terrace mapping feature. This new version includes a neural network that has been meticulously trained with over 1000 mapped marine terraces. This allows TerraceM-3 users to effortlessly map marine terraces and precisely determine their elevation through the automated mapping of their shoreline angles. In addition, TerraceM-3 incorporates two new functionalities: 1) Photon profile mapping, which includes mapping of satellite LiDAR profiles from the IceSat-2 mission, which broadens the applicability of TerraceM-3 beyond the availability of topographic data. 2) Indicative meaning calculator that accounts for the factors that can alter the initial sea-level position using global datasets (wave conditions and tidal ranges). This method facilitates the direct assessment of uncertainties in the reconstructions of the paleo-sea-level based on marine terraces. TerraceM-3 is a complete toolkit for researchers and students engaged in marine terrace analysis by offering a unique blend of numerical methods, statistical analyses techniques and additional enhanced functionalities to precisely map marine terraces and using them as markers of tectonic deformation.

Continuer la lecture Partager