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Long-term impact of dredging and beach nourishment works on benthic communities
No full textInternational audienceDredging and rainbowing techniques are commonly used to replenish sandy beaches and protect the coast against erosion. Since 2003, such operations have been conducted every other year on Pyla Beach, Arcachon Bay (French Atlantic Coast). The evolution of macrobenthic communities subjected to regular disturbance was analyzed once in springtime at dredging and disposal sites, as well as in a control area, over 21 years. The overall benthic community was dominated by the same few species. The dredged area harbors a benthic community whose characteristics suggest the maintenance of a disturbed status, compared to the control area, whose community follows a trajectory possibly influenced by the general decrease of organic matter in the sediment. Regarding the disposal site, species richness is slowly declining. In both disturbed areas, the community was dominated by species able to recolonize rapidly (polychaetes, peracarid crustaceans), while the control area rather favored bivalves
Climates evolution at the Permian transition in northern Gondwana
No full textInternational audienceThe late Carboniferous and early Permian climate (ca. 320 to 270 Ma) was characterized by adistinctive global warming that resulted in a transition from icehouse to greenhouse conditions.This global warming resulted in the melting of major continental ice sheets that formed at highlatitudes during the Late Paleozoic Ice Age (LPIA) and impacted both the continental andmarine biosphere. At that time, the northern (Laurussia) and southern (Gondwana) parts ofPangea were joined by the Central Pangean Mountains (CPMs), including the Variscan belt inthe present-day western and central Europe. At low latitudes, in the tropical belt, global- scaleclimate simulations and qualitative climate proxies suggest an increased aridity during the earlyPermian deglaciation. Qualitative and quantitative regional-scale climate reconstructions havealso been performed using sedimentological or biological proxies, oxygen isotope, and bytransfer functions linking chemical weathering intensity to land surface temperature. Thesereconstructions, which are based on sedimentary archives from the north of the CMPs andfrom the North China Block, evidenced regional-scale and temporal climate variabilities in thetropical belt. So far, no quantitative climate studies have been performed in the southern sideof the CMPs, despite the importance of this region for biosphere evolution. To reconstructclimate around the Carboniferous–Permian transition in north Gondwana, we investigatedsedimentary archives from the San Giorgio, Perdasdefogu, and Guardia Pisano basins,Sardinia, Italy. We first established a reliable chronological framework using U-Pbgeochronology on zircon. U-Pb geochronological analyses on zircon show that the SanGiorgio, Perdasdefogu and Guardia Pisano basins, Sardinia, Italy, were infilled at300.9±4.0/5.0 Ma, 295.7±0.7/3.0 Ma and 289.6±0.6/2.9 Ma, respectively. This stratigraphicrange encompasses the Carboniferous–Permian transition and allows to document theevolution of surface conditions in subequatorial north Gondwana, where these basins werelocated at that time. Second, we assessed weathering intensities through whole-rockgeochemical analyses on mudrocks and inferred temperature and precipitation levels of20±1°C and 1469±37 mm during the infilling of the San Giorgio Basin, 18±2°C and1370±56 mm for the Perdasdefogu Basin and 23±2°C and 1588±97 mm for the GuardiaPisano Basin. Third, we compiled paleobotanical data available from literature, whichcorroborate the ‘cooling-drying – warming-wetting’ climate signal inferred from geochemicalproxies. These climate changes could reflect local tectonic events that occurred in the southernVariscan foreland during the late Carboniferous to early Permian. However, the ‘cooling-drying– warming-wetting’ early Permian climate trend in Sardinia is also consistent with the stepwisedemise of the LPIA recorded elsewhere and could thus record a global climate change
Identification of Natural Hydrogen Seeps: Leveraging AI for Automated Classification of Sub‐Circular Depressions
No full textInternational audienceHydrogen has long been used as an energy vector, but the recent discovery of natural hydrogen (H 2 ) opens the door for its use as a direct energy source. Identifying H 2 seepages is therefore crucial to advance exploration. Although the scientific community does not yet fully understand the parameters controlling H 2 leaks from underground, sub‐circular depressions (SCDs) appear to be key indicators associated with these emissions. However, distinguishing SCDs from similar landforms remains a challenge. This study leverages open‐source multispectral and high‐resolution imagery to train a deep learning model (YOLOv8) for classifying rounded landforms and detecting H 2 ‐related structures (i.e., SCDs). The model achieved 90% accuracy with Google Maps© imagery, outperforming Sentinel‐2 multispectral data. Applied to a pre‐existing data set from Brazil, the model allowed a large‐scale screening, discarding 52% of the structures as non‐H 2 emitting ones and pinpointing high‐potential areas for field validation. Future enhancements, including, for example, higher‐resolution input data and morphometric analysis, would aim to reduce false positives and boost predictive accuracy. This approach significantly improves H 2 exploration efficiency, with global applicability including some region‐specific adjustments during post‐processing analyses
Environmental and climatic evolution of a river-proximal peatland in the Cuvette Centrale, Congo Basin
No full textInternational audienceTropical peatlands play a crucial role in the global carbon cycle, yet their formation and response to environmental changes remain poorly understood. The Congo Basin, containing the World's largest tropical peatlands, holds about 30 billion tons of carbon in the Cuvette Centrale. Initial studies revealed diverse peat deposits in different geomorphic and hydrologic settings, with extensive peat domes in interfluvial basins and river-proximal peatlands following hydrographic networks. Here, we present a comprehensive analysis of a peat core from a peatland in close vicinity of the Momboyo River within the Cuvette Centrale. Using bulk organic carbon and nitrogen analyses, Rock-Eval® thermal analysis, and plant wax carbon and hydrogen isotope compositions, we reconstructed this peatland's evolution and associated past hydroclimatic conditions.Our findings reveal distinct phases of peat accumulation, transitioning from an organic carbon-rich river floodplain to a forested marshland, and finally to a closed-canopy swamp forest. Rock-Eval® parameters indicate in-situ peat accumulation in the marshland and swamp forest phases. Peat initiation occurred around 10.6 calibrated kiloanni before present (cal ka BP) under increasing precipitation and around 8.7 cal ka BP the terrestrialization of the peatland coincided with the establishment of a closed canopy forest. Notably, we also identify a trend to drier climatic conditions from 5 cal ka BP onwards and the previously described "Ghost Interval" -an interval of heavily decomposed peatin this core, indicating this period left its mark across peatlands in different parts of the Cuvette Centrale. Last, a short-lived vegetation change occurred between 1.7 and 1.5 cal ka BP that did not impact peat formation. Regarding the climatic evolution, the plant wax hydrogen isotope (δD n-C29 ) record corroborates regional climatic patterns observed in previous studies, emphasizing the close linkage between precipitation regimes and peatland development. The strong agreement between our findings and a δD n-C29 record from the offshore Congo fan further supports the regional nature of climatic evolution.</p
Holocene glacier evolution in the Pyrenees based on 36Cl cosmic-ray exposure dating (Troumouse Cirque, Pyrenees National Park)
No full textInternational audienceWhile the Late Glacial evolution of Pyrenean glaciers is rather well known, Holocene glacier behaviour is much less constrained, except for the Little Ice Age period. In this study, we attempt to bridge this knowledge gap by dating four moraines in the Western-Central Pyrenees using in-situ chlorine-36 cosmic-ray exposure dating of moraine boulders. Ages of 11.2 ± 1.5 ka, 7.8 ± 1.1 ka, 3.0 ± 0.6 ka and 972 ± 208 yr were obtained, constituting the most robust directly dated Holocene glacier chronology in the Pyrenees. The moraine age of 7.8 ± 1.1 ka (n=5) is a novel finding, as no evidence for significant contemporary glacial advance exists at any other site in the Pyrenees or in the Alps/elsewhere in Europe. We tentatively link this moraine to the 8.2 ka cold event. We propose a palaeoclimatic interpretation of this Holocene glacial evolution, in conjunction with the radiocarbon ages from nearby peat bog cores. Further, we hypothesise on the role of Mid-Holocene warm periods on the melting of permafrost within rock glacier complexes and how they translate into peat bog deposits in the vicinity of the moraines. Finally, based on archaeological evidence (construction and ceramic remains) collected in the centre of Troumouse Cirque, the possible influence of its glacier variability on first human settlements in the region is discussed
BEETROOTS: Spatially regularized Bayesian inference of physical parameter maps. Application to Orion
No full textInternational audienceContext. The current generation of millimeter (mm) receivers is capable of producing cubes of 800 000 pixels over 200 000 frequency channels to cover a number of square degrees over the 3 mm atmospheric window. Estimating the physical conditions of the interstellar medium (ISM) with an astrophysical model on the basis of such large datasets is challenging. Common approaches tend to converge to local minima and end up poorly reconstructing regions with a low signal-to-noise ratio (S/N) in most cases. This instrumental revolution thus calls for new scalable data analysis techniques with more advanced approaches to statistical modeling and methods.Aims. Our aim is to design a general method to reconstruct large maps of physical conditions from the rich datasets produced by new and future instruments. The requirements of the method include the ability to scale to very large maps, to be robust to varying S/N, and to escape from the local minima. In addition, we want to quantify the uncertainties associated with our reconstructions to produce reliable analyses.Methods. We present BEETROOTS, a PYTHON software that performs Bayesian reconstructions of maps of physical conditions based on observation maps and an astrophysical model. It relies on an accurate statistical model, exploits spatial regularization to guide estimations, and uses state-of-the-art algorithms. It can also assess the ability of the astrophysical model to explain the observations, providing feedback to improve ISM models. In this work, we demonstrate the power of BEETROOTS with the Meudon PDR code on synthetic data. We then apply it to estimate physical condition maps in the full Orion molecular cloud 1 (OMC-1) star-forming region based on Herschel molecular line emission maps.Results. The application to the synthetic case shows that BEETROOTS can currently analyze maps with up to ten thousand pixels, addressing large variations among the S/N values within the observations while escaping from local minima and providing consistent uncertainty quantifications. On a personal laptop, the inference runtime ranges from a few minutes for maps of 100 pixels to 28 hours for maps of 8100 pixels. Regarding OMC-1, our reconstructions of the incident UV radiation field intensity, G0, are consistent with those obtained from FIR luminosities. This demonstrates that the considered molecular tracers are able to constrain G0 over a wide range of environments. In addition, the obtained thermal pressures are high in all dense regions of OMC-1 and positively correlated with G0. Finally, the Meudon PDR code successfully explains the observations and the obtained G0 values are reasonable, which indicates that UV photons control the gas physics and chemistry across the rim of OMC-1.Conclusions. This work paves the way toward systematic and rigorous analyses of observations produced by current and future instruments. Subsequent efforts still need to be made in parallelizing the algorithm and thereby gaining two orders of magnitude for the map sizes
Knowledge-inspired fusion strategies for the inference of PM 2.5 values with a neural network
No full textInternational audienceAbstract. Ground-level concentrations of particulate matter (more precisely PM2.5) are a strong indicator of air quality, which is now widely recognised to impact human health. Accurately inferring or predicting PM2.5 concentrations is therefore an important step for health hazard monitoring and the implementation of air-quality-related policies. Various methods have been used to achieve this objective, and neural networks are one of the most recent and popular solutions. In this study, a limited set of quantities that are known to impact the relation between column aerosol optical depth (AOD) and surface PM2.5 concentrations are used as input of several network architectures to investigate how different fusion strategies can impact and help explain predicted PM2.5 concentrations. Different models are trained on two different sets of simulated data, namely, global-scale atmospheric composition reanalysis provided by the Copernicus Atmosphere Monitoring Service (CAMS) and higher-resolution data simulated over Europe with the Centre National de Recherches Météorologiques ALADIN model. Based on an extensive set of experiments, this work proposes several models of knowledge-inspired neural networks, achieving interesting results from both the performance and interpretability points of view. Specifically, novel architectures based on boundary condition generative adversarial networks (BC-GANs, which are able to leverage information from sparse ground observation networks) and on more traditional UNets, employing various information fusion methods, are designed and evaluated against each other. Our results can serve as a baseline benchmark for other studies and be used to develop further optimised models for the inference of PM2.5 concentrations from AOD at either the global or regional scale
Vulnerable but not equal: Mountain lakes exhibit heterogeneous patterns of phytoplankton responses to climate change
No full textInternational audienceAbstract While climate change affects the phytoplankton biodiversity at both local and global scales, predicting phytoplankton community responses to warming is impaired by their polyphyletic complexity. High mountain lakes are highly vulnerable systems, partly due to their limited biodiversity, and forecasting their ecological trajectories is a key challenge for scientists and conservation managers. We evaluated the phytoplankton's sensitivity to temperature in 24 high‐altitude lakes over a multi‐year (average 7‐year) study. We detected assemblage‐specific responses to warming, with different trends in biovolume and diversity observed among the diatom‐dominant, mixed‐mixotrophs dominant, and colonial‐green dominant assemblages. The environmental settings partly governed assemblage responses, highlighting the role of the landscape filters in determining the response to warming. The biological stability of lakes, that is, their ability to resist shifts in their phytoplankton assemblage, is therefore determined both by the lake characteristics and warming intensity
Assimilation of volcanic sulfur dioxide products from IASI and TROPOMI into the chemical transport model MOCAGE: case study of the 2021 La Soufrière Saint Vincent eruption with the March 2022 version of MOCAGE
No full textInternational audienceAbstract. Sulfur dioxide emitted during volcanic eruptions can be hazardous for aviation safety. As part of their activities, the Volcanic Ash Advisory Centres (VAACs) are therefore interested in the real-time atmospheric monitoring of this gas. A recent development aims at improving the forecasts of volcanic sulfur dioxide quantities made by the MOCAGE (Modèle de Chimie Atmosphérique à Grande Échelle) chemistry transport model. For this purpose, observations from both TROPOMI (Tropospheric Monitoring Instrument) and IASI (Infrared Atmospheric Sounding Interferometer; B and C) located on separate polar-orbiting satellites are assimilated into the model. These sulfur dioxide measurements are based on the eruption event of the La Soufrière Saint Vincent volcano in April 2021. Observations from OMI (Ozone Monitoring Instrument) are considered validation data. The resulting assimilation experiments show that the combined assimilation of IASI and TROPOMI observations always leads to a better forecast compared to the independent assimilation of data from each instrument. Sulfur dioxide atmospheric field forecasts are better when the available observations are numerous and cover a long time window
The nucleardatapy toolkit for simple access to experimental nuclear data, astrophysical observations, and theoretical predictions
No full textLA-UR-25-20848Systematic comparisons across theoretical predictions for the properties of dense matter, nuclear physics data, and astrophysical observations (also called meta-analyses) are performed. Existing predictions for symmetric nuclear and neutron matter properties are considered, and they are shown in this paper as an illustration of the present knowledge. Asymmetric matter is constructed assuming the isospin asymmetry quadratic approximation. It is employed to predict the pressure at twice saturation energy-density based only on nuclear-physics constraints, and we find it compatible with the one from the gravitationalwave community. To make our meta-analysis transparent, updated in the future, and to publicly share our results, the Python toolkit nucleardatapy is described and released here. Hence, this paper accompanies nucleardatapy, which simplifies access to nuclearphysics data, including theoretical calculations, experimental measurements, and astrophysical observations. This Python toolkit is designed to easily provide data for: i) predictions for uniform matter (from microscopic or phenomenological approaches); ii) correlation among nuclear properties induced by experimental and theoretical constraints; iii) measurements for finite nuclei (nuclear chart, charge radii, neutron skins or nuclear incompressibilities, etc.) and hypernuclei (single particle energies); and iv) astrophysical observations. This toolkit provides data in a unified format for easy comparison and provides new meta-analysis tools. It will be continuously developed, and we expect contributions from the community in our endeavor.</div