Morphotectonic evolution of the north-western margin of the Paris Basin Evolution morphotectonique de la marge nord-occidentale du Bassin de Paris

International audienceThe north-western margin of the Paris Basin and particularly the chalk plateau of the Pays de Caux constitute a essential hinge zone of north-western Europe. This area, located between the Armorican and Ardennes tectonic blocks, has been strongly influenced by the graben dynamics of the English Channel and also been influenced by the by-effects of Pyrenean and Alpine compressions. The plateau thus shows a complex morphology in spite of the relative monotony of its topography. The recent results of research on its geomorphology and geology, presented in this paper, allow a detailed reconstruction of the morphotectonic evolution of the region from the Paleogene to the Quaternary, emphasizing the existence of current tectonic movements at an amplitude of the order of 2 to 5 meters per 100,000 years

Spatio-temporal variability of the morpho-sedimentary dynamics observed on two gravel beaches in response to hydrodynamic forcing

(IF 2.9; Q2)International audienceThis article aims to investigate the 3D morpho-sedimentary dynamics of two gravel beaches in relation to hydrodynamic forcing, using a multi-sensor approach. Study sites, namely Etretat and Hautot-sur-Mer, are both located in Normandy, France. Thus, they face similar wave conditions of the English channel's eastern side, with megatidal ranges and channelized wave orientations. However, they differ in gravel size (D50 Etretat = 5.2 cm; D50 Hautot-sur-Mer = 7.0 cm), vertical composition (Etretat is a purely gravel beach, Hautot-sur-Mer is a composite one with a low tide terrace) and wave exposure (Etretat is an embayed beach, oriented 47?N, Hautotsur-Mer is a semi-open beach, oriented 71?N). Used data include shoreline positions automatically extracted from coastal Video Monitoring Systems (VMS) images between 2018 and 2020, wave data provided by the Wave Watch 3 model, and gravel size maps derived from UAV-imagery at different dates (one in Etretat, three in Hautot-sur-Mer). First, an Empirical Orthogonal Function (EOF) analysis was performed on the shoreline position data to extract the Principal Components (PC) describing mechanisms of morphological changes in the shoreline shape at different elevations (-2 to +3 m in Etretat and + 1 to +3 m in Hautot-sur-Mer). Four mechanisms spread within five PCs were found in Etretat: cross-shore translation (PC1), rollover (PC2), scale/elevation dependent rotation (PC3 and PC4) and breathing (PC5). Four PCs describing three mechanisms were identified in Hautot-sur-Mer: right-centered beach cell rotation (PC1), left-centered beach cell rotation (PC2), large scale rotation (PC3) and rollover (PC4). Interpretation of the PCs were supported by significant correlations with morphological parameters such as average beach width (BW), beach orientation angle (BOA) and beach slope (BS). The main mid-term morphological periods of variability include 2, 3, 5 and 8+ months in Etretat and 2, 3 and 6 months in Hautot-sur-Mer (all parameters included), which essentially corresponds to the variability of the wave energy. Finally, the analysis of surface grain size spatial variability revealed the presence of textural patterns with spatial and temporal variations in sorting and average grain size up to 1 cm in two months

Long-run forecasting surface and groundwater dynamics from intermittent observation data: An evaluation for 50 years

(IF 10.75; Q1)International audienceThe accurate prediction of water dynamics is critical for operational water resource management. In this study, we propose a novel approach to perform long-term forecasts of daily water dynamics, including river levels, river discharges, and groundwater levels, with a lead time of 7–30 days. The approach is based on the state-of-the-art neural network, bidirectional long short-term memory (BiLSTM), to enhance the accuracy and consistency of dynamic predictions. The operation of this forecasting system relies on an in-situ database observed for over 50 years with records gauging in 19 rivers, the karst aquifer, the English Channel, and the meteorological network in Normandy, France. To address the problem of missing measurements and gauge installations over time, we developed an adaptive scheme in which the neural network is regularly adjusted and re-trained in response to changing inputs during a long operation. Advances in BiLSTM with extensive learning past-to-future and future-to-past further help to avoid time-lag calibration that simplifies data processing.The proposed approach provides high accuracy and consistent prediction for the three water dynamics within a similar accuracy range as an on-site observation, with approximately 3 % error in the measurement range for the 7 day-ahead predictions and 6 % error for the 30 d-ahead predictions. The system also effectively fills the gap in actual measurements and detects anomalies at gauges that can last for years. Working with multiple dynamics not only proves that the data-driven model is a unified approach but also reveals the impact of the physical background of the dynamics on the performance of their predictions. Groundwater undergoes a slow filtration process following a low-frequency fluctuation, favoring long-term prediction, which differs from other higher-frequency river dynamics. The physical nature drives the predictive performance even when using a data-driven model

Fifteen Years (1993–2007) of Surface Freshwater Storage Variability in the Ganges-Brahmaputra River Basin Using Multi-Satellite Observations

International audienceSurface water storage is a key component of the terrestrial hydrological and biogeochemical cycles that also plays a major role in water resources management. In this study, surface water storage (SWS) variations are estimated at monthly timescale over 15 years (1993-2007) using a hypsographic approach based on the combination of topographic information from Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hydrological Modeling and Analysis Platform (HyMAP)-based Global Digital Elevation Models (GDEM) and the Global Inundation Extent Multi-Satellite (GIEMS) product in the Ganges-Brahmaputra basin. The monthly variations of the surface water storage are in good accordance with precipitation from Global Precipitation Climatology Project (GPCP), river discharges at the outlet of the Ganges and the Brahmaputra, and terrestrial water storage (TWS) from the Gravity Recovery And Climate Experiment (GRACE), with correlations higher than 0.85. Surface water storage presents a strong seasonal signal (~496 km 3 estimated by GIEMS/ASTER and~378 km 3 by GIEMS/HyMAPs), representing~51% and 41% respectively of the total water storage signal and it exhibits a large inter-annual variability with strong negative anomalies during the drought-like conditions of 1994 or strong positive anomalies such as in 1998. This new dataset of SWS is a new, highly valuable source of information for hydrological and climate modeling studies of the Ganges-Brahmaputra river basin

Multi-timescale dynamics of extreme river flood and storm surge interactions in relation with large-scale atmospheric circulation: Case of the Seine estuary

The present work investigates the multi-timescale dynamics of extreme fluvial-surge interactions (EFS) in a rivertide environment, in the case of the Seine estuary. This environment is considered an excellent natural laboratory to analyze river-surge interaction because of its time-varying flow and the available water-level records provided by tide gauges along the estuary. A spectral approach has been used to investigate the multi-timescale changes in EFS, governed by fluvial and marine contributions, in relation to the historical events of flood-storm concomitance and the large-scale atmospheric circulation. The spectral components of EFS, calculated at five stations along the estuary, highlighted a series of variability modes varying from the inter-month (-3-6 months) to the inter-annual (-2-, -3-5- and -6-8-years) scales and exhibiting, respectively, 55% and 20% of the total variability. The contribution of marine and fluvial effects in the EFS varies along the estuary and according to the timescale from seasons, when the interaction is governed by tidal deformation, to years. The connection of the historical flood-storm events with the EFS signal changes in their spectral signature according to their severity as well as the energetic physical drivers acting in each event: events with high return period are manifested at larger scales while events with low return period are limited to small scales.Finally, the examination of the physical relationships between the EFS and the global climate mechanisms has demonstrated the key role of the Sea Level Pressure (SLP) and the North Atlantic Oscillation (NAO) acting, respectively, in anti-phase at -1-2-yr and in phase at scales larger than 3-yr. The signature of the climate drivers operates differently according to the timescale; they are identified within the -80% of the inter-annual EFS. This signature is more significative since the 2000s when the increase in the NAO generates a rise in EFS variability. -20% of EFS would be related to the non-linear effects of the timescale interactions and other local mechanisms operating at such scales.This finding highlights the non-stationarity of the multi-timescale dynamics of marine storms and fluvial floods, and the relevance of the climate connection use for assessing the compound multi-hazard events at largescales