dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This study received support from the  European Union 's  Horizon 2020 EUCP project  (Grant No. GA776613 ). NB UIBK and ETHZ acknowledge  PRACE  for awarding us the access to  Piz Daint  at  Swiss National Supercomputing Center (CSCS, Switzerland ) and the  COSMO, CLM  and  C2SM  communities for developing and maintaining COSMO in climate mode. CM is supported by the  UKRI NERC  funded project  STORMY-WEATHER  (Grant No. NE/V004166/1 ). DB was partially funded by the  Formas  project  EDUCAS  (Grant No. 2019-00829 ). S B, M J R  and E K gratefully acknowledge support from the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  (Grant No. GA01101 ).
Recent evolution of salt marsh -spit systems We used recent shoreline dynamics of the Penouille and Sandy Beach spits to highlight the functioning differences of the two sites (Fig. 2 ). Data used for this purpose was obtained in a previous study of  Bernatchez et al. (2013)  for a report produced for the Canadian natural parks authorities. At each site, shorelines were georeferenced and digitized over the 1948-2010 periods from aerial photographs using the limit of dense herbaceous vegetation (maximum high-water level) or at the top of microcliffs as benchmarks. Shoreline migration rates were then calculated at 50 m horizontal intervals using the DSAS plugin in ArcGIS© software  (Thieler et al. , 2009). The areas of the saltmarshes, beach and spit domains were mapped for Penouille on the 1963-2008 period and for Sandy Beach over the 1948-2008 period using the oldest and most recent aerial photographs available at each site and georeferenced in the ArcGIS© software.
Statistical analyses (i) Identification of coarse sedimentary events Reference cores PEN-ST3-1 and SB-STM-1 were analysed for the intra-class deviation of grain-size data on the organic marsh sections. This allowed to separate the grain-size classes characterized by a continuous presence alongcore from those showing higher variability, and thus to decipher between background sedimentation and punctual coarser sedimentation events (e. g. Sabatier et al. , 2012). All fraction coarser than background minerogenic sedimentation will be referred to in the text as "coarse sedimentation", regardless of their effective coarseness on traditional grain-size charts. In order to get a geochemical characterization of the coarse sedimentation events and to reach a more precise localization these latter downcore, we performed Principal Component Analyses (PCA) on centered and reduced (Z-score) major and trace elements data normalized by Al (PEN-ST3-1 core) or Ti (SB-STM-1 core), D 50 and D 90 grain-size data and LOI data using the "FactomineR"  (Lê et al.
(ii) Spectral analyses of past submersion time-series Reference cores PEN-ST3-1 and SB-STM-1 D 90 grain-size time-series were subjected to spectral analyses to look for periodicities in the occurrence of coarse sedimentation events. The analyses used the Multi-Taper spectrum (MTM;  Thomson, 1990)  and the wavelet transform (Evolutive J o u r n a l P r e -p r o o f Harmonic Analyses, EHA  (Kodama & Hinnov, 2014)  functions of the R software (R Core Team, 2020) Astrochron package  (Meyers et al. , 2014). Prior to the analyses, D 90 grain-size time-series were reinterpolated at a 5-yr step and detrended using a 1/1 LOWESS smoothing (Locally weighted scatterplot smoothing,  Cleveland, 1979) , so as to avoid long-term trends in grain-size data (e. g. a linear increase of washover deposits grain-size linked to a diminishing distance to the coastline) that might obscure the identification of coarse sedimentation events.
Acknowledgement This work was funded by the  Coastal Geoscience Chair  at the  Université du Québec at Rimouski  as part of the Québec Government initiative on natural risks prevention and the Plan d'action 2013-2020 sur les changements climatiques et du Fonds Vert. All data will be made available through the SEANOE repository following publication, with the following DOI: https://doi. org/10. 17882/80577.
The Mid-Holocene period was always considered as a reference, due to its strong response to seasonal orbital forcing. These intercomparisons were extremely efficient and enabled analyses of the robust characteristics using models, but they also favored the development of multi-proxy databases and strengthened model/data comparisons. The development of O 18 water isotope enabled models  [6, 7]  was another important step. However, the interpretation of the isotopic signals proved to be a difficult task  [8]. The paper by Liu et al. [9]  revisits this issue, not only for snapshot periods, but it enlightens the topic considerably by producing a transient simulation of 300 kyr, including several glacial/interglacial cycles. Moreover, this paper focuses on the Asian monsoons with a seasonal interpretation of precipitation δ 18 O (δ 18 O p ) signals in three different regions, central arid Asia (CA), southern Asia (SA) and eastern Asia (EA).
Hydrological measurements (piezometric probes), studies of the lichen trimline and of high-water marks The characterisation of the current seasonal and interannual hydrological fluctuations of the civales was based on two complementary methods. The first consisted of installing two observation wells (piezometric probes) in two civales (PP1 and PP2, Fig. 1E ). The probes measured the cumulative hydrostatic and atmospheric pressures as well as the water temperature at a frequency of one measurement every 45 min, during the periods April 2015eApril 2019 (PP1) and April 2015eApril 2016 (PP2). A barometric correction was made using data acquired by a barothermal probe installed in the study area. The measured stages were repositioned in relation to the mean sea level (a. s. l. ), thanks to positioning using LiDAR DEM.
Sedimentological, pedological, geochemical and isotopic studies The study is based on the analysis of three sediment series, CS1eS2C,  CS2eS1 and CS2eS10 (Figs. 1, 3, 4, 6e8). It includes several indicators of environmental change. The grain size of the deposits was interpreted using the percentage of grain size fractions and the D50 diameter of each sample grain-size distribution. The latter was measured by laser diffraction with a Beckman-Coulter LS230, after the destruction of organic matter (OM). Grain-size data allows us to interpret the energy of the depositional environment (e. g. the lutites, in x 4. 3. 1. 1) but also of the fraction of carbonate authigenic minerals (e. g. the silt fraction, in x 4. 3. 1. 2). In order to characterise sediment composition, we measured the amount of total carbon/total sediment (TC) and the amount of organic carbon/leached sediment.
Age of deposits and chrono-stratigraphic interpretations Comparison of the ages estimated by the radiocarbon method on land-plant OM with those estimated on potentially mixed OM (of land-plant and aquatic plant origin) and/or with the ages of the archaeological material (ceramic sherds) revealed a chronological coherence within and between the sediment zones of each series. No hard water effect was identified in the whole dataset studied (Table  1 ). The amount of radiocarbon dates on charcoal and OM from paleosoils, as ell as the intermittent dynamics of the lake system may explain this coherence. The morpho-stratigraphy and chronological markers allowed the following chrono-stratigraphic interpretations. E4, the top of which is dated to around [3522e3193] BCE in CS2eS1, has a mid-Holocene and possibly also early Holocene age  (Figs.
Acknowledgements and funding This work was supported by the programme  Pet en Norte NAACHTUN  (2010e2022, dir. P. Nond ed eo, supports:  Minist ere de l'Europe et des Affaires Etrang eres MEAE ,  Centre National de la Recherche Scientifique CNRS, Universit e Paris 1 Panth eon-Sorbonne, Pacunam foundation, Perenco company, Simone et Cino Del Duca foundation ), by the project  MAYANAT  (2020e2021, coord. C. Castanet, supports:  Institut Ecologie et Environnement INEE  of the  CNRS ) and the project  HYDROAGRO  (2014e2016, coord. E. Lemonnier ,  C. Castanet , supports:  Universit e Paris 1 Panth eon-Sorbonne). Financial support from the  LabEx DynamiTe  ( ANR-11-LABX-0046 ), in the framework of the  programme "Investissements d'Avenir ". It forms part of  Centro franc es de Estudios Mexicanos y Centroamericanos (CEMCA Guatemala) activities in Central America.
This study investigates the capability of high and medium spatial resolution ocean color satellite data to monitor the transport of suspended particulate matter (SPM) along a continuum from river to river mouth to river plume. An existing switching algorithm combining the use of green, red and near-infrared satellite wavebands was improved to retrieve SPM concentrations over the very wide range (from 1 to more than 1000 g. m -3 ) encountered over such a continuum. The method was applied to time series of OLI, MSI, and MODIS satellite data. Satisfactory validation results were obtained even at the river gauging station. The river liquid discharge is not only related to the SPM concentration at the gauging station and at the river mouth, but also to the turbid plume area and SPM mass estimated within the surface of the plume.
m -2 ) and was mounted on a vertical mast away from shadow effects. The sensors spanned the 350-950 nm spectral range with a spectral resolution of 3. 3 nm. Data were recorded using TriOS GmbH MSDA software using the nominal calibration coefficients set in October 2014. Calibrated data for Ed, Lt, and Ls were then interpolated to 1 nm intervals before processing. Measurements last 2 to 12 min for each station. Simultaneous spectra from the three sensors were acquired every 10 s and the remote-sensing reflectance signal (Rrs, sr -1 ) was computed as:.
m -2 ) and was mounted on a vertical mast away from shadow effects. The sensors spanned the 350-950 nm spectral range with a spectral resolution of 3. 3 nm. Data were recorded using TriOS GmbH MSDA software using the nominal calibration coefficients set in October 2014. Calibrated data for E d , L t , and L s were then interpolated to 1 nm intervals before processing. Measurements last 2 to 12 min for each station. Simultaneous spectra from the three sensors were acquired every 10 s and the remote-sensing reflectance signal (Rrs, sr -1 ) was computed as: Rrs = L t -ρL s E d ( 1 ) where ρ is the air-water interface reflection coefficient  [27]. This coefficient may vary strongly with wind speed for clear sky conditions because of reflection of brighter parts of the sky in presence of waves  [27]  but is approximately independent of wind speed under overcast skies.
• The MODIS sensors aboard the polar-orbiting Terra (MODIS-T) and Aqua (MODIS-A) satellite platforms launched in 1999 and 2002, respectively. They provide multispectral data with a revisiting time of one day at the latitude of the study area (thus 2 MODIS images per day with a ~2 h gap between them), with three spatial resolutions of 250 m, 500 m, and 1 km, depending on the spectral band. To provide representative and statistically robust results, this study is based on large MODIS, OLI, and MSI datasets covering several years between 2013 and 2020 with a wide range of Rhône River discharge conditions and SPM concentrations measured in Arles (Figure  2 ). For OLI and MSI, we downloaded cloud-free (<10%) images acquired over the study area between 2013 and 2018. Given the low temporal resolution of OLI, all cloud-free images available over this period were downloaded and processed.
For MSI, only images acquired during the 2016 and 2018 years corresponding to simultaneous in situ SPM concentration measurements at the SORA station were selected. To increase the amount of data with high SPM concentration, OLI and MSI images acquired during Rhône River high turbidity periods (SPM concentration measured at SORA station > 70 g. m -3 ) in 2019 and 2020 were added to the dataset. Note that the study area was covered by two successive MSI tiles switching at ~43. 25 • N. In order to limit the data processing time, we only kept the tile covering the Rhône River from Arles to the northern part of the river plume (latitude > 43. 25 • N). Orthorectified and terrain-corrected Level-1T OLI and Level-1C MSI products were obtained from the Landsat-8 portal on the ESA website (https://landsat8portal.
eo. esa. int/portal/ (accessed on 22 February 2022)) and from the Copernicus open access hub (https://scihub. copernicus. eu/ (accessed on 22 February 2022)), respectively. Both were processed using the ACOLITE software (release of 26 March 2019) (https://odnature. naturalsciences. be/remsem/software-and-data/acolite (accessed on 22 February 2022)) using the dark spectrum fitting (DSF) atmospheric correction method (e. g. ,  [30] [31] [32] ) to obtain the multispectral water-leaving reflectance signal (ρ w ). The DSF method automatically selects the band producing the lowest atmospheric path reflectance to largely avoid amplification of glint and adjacency effects in the atmospheric correction. This also means that pixels and bands with severe sun glint are discarded from the estimation of the atmospheric contribution and the glint signal is thus still present in the resulting water-leaving reflectance that can drastically affect the derived SPM concentration.
The glint reflectance is thus estimated and removed using a glint correction implemented in the ACOLITE software and based on the shortwave infrared (SWIR) band  [31, 33]. This glint correction results in the decrease in the water-leaving reflectance values by about 0. 004 (on average) in the green, red, and NIR bands, which correspond to less than 10% of the total signal in the visible bands and up to 30% in the NIR band for moderate and high turbid waters. For very low turbid waters (<10 g. m -3 ), this effect can nevertheless reach 40% and 100% in the visible and NIR bands, respectively. In addition, a non-water masking (top-of-atmosphere reflectance ρt at 1600 nm > 0. 0215) is applied to remove pixels corresponding to land, above-water objects, clouds, or haze affected by residual glint effects.
The final dataset is composed of 56 OLI images and 86 MSI images (Table  1 ) and provides decent coverage of the period from 2013-2018 and a large SPM concentration range at SORA [0. 5-1389 g. m -3 ]. The MODIS dataset is composed of 4 years of MODIS-A and MODIS-T images  (2014, 2015, 2016, and 2018). The years 2014 to 2016 correspond to our sea campaigns while MODIS-A images for the year 2018 were added to increase the dataset in common with OLI and MSI acquisitions. Level 1A MODIS-A and MODIS-T data were downloaded from the oceancolor. gsfc. gov website then processed using SeaDAS (version 7. 0) software (seadas. gsfc. nasa. gov, accessed on 3 March 2022) to generate geolocation and Level 1B files. Rrs(555), Rrs(645), and Rrs(859) Level 2 products were then generated using the l2gen function.
The MUMM  [34]  atmospheric correction was applied  [12]. However, this correction is only valid for moderately turbid waters and can lead to an underestimation of SPM concentration in highly turbid waters sometimes found in the Rhône River mouth. Based on visual inspection, images corresponding to highly turbid waters close to the river mouth were reprocessed using the SWIR atmospheric correction  [35]. l2gen flags were used to mask clouds (reflectance threshold at 2130 nm > 0. 018) and glint. As MODIS images were used to estimate the Grand Rhône River plume surface, images with less than 80% of valid pixels on the region of plume presence (ROPP) were discarded from the dataset (see  [15] ). The ROPP was identified using all available images, corresponding to pixels where at least 5% of the SPM concentrations were above 3 g.
m -3  [15]  (Figure  A1A ) and with a longitude higher than 4. 5 • E (to focus on the Grand Rhône turbid plume only). In addition, reflectance products showing abnormally high values in the red band all over the study area, likely due to unmasked glint, were also removed from the dataset. Therefore, for each year, the mean water reflectance value in the red band was computed for each image in an offshore SPM free rectangle of the study area (Figure  A1A ). These "offshore reflectances" were plotted as a function of days and fitted with a Gaussian function. Images for which the difference between the offshore reflectances and fitted reflectances was larger than 1. 5 times the standard deviation of the offshore reflectances were considered outliers (Figure  A1B ). The final dataset is composed of 916 MODIS images, covering 580 days over 4 years.
The dataset is well-distributed over seasons, with about 19% of the dataset acquired in winter, 31% in summer and about 25% in spring and autumn (Figure  A2A ). The distribution of this dataset as a function of the freshwater discharge in Arles is also highly representative, as it is identical to those observed in the 2005-2020 period (61% for dry conditions (Q < 1500 m 3. s -  ) and 39% for wet conditions (Q > 1500 m 3. s -  )) (Figure  A2B ). Only spectral bands useful for SPM retrieval (see Section 2. 3) were considered, i. e. , the green, red, and near-infrared (NIR) bands at 561/560/555 nm, 655/665/645 nm, and 865/865/859 nm, respectively, for the OLI, MSI, and MODIS sensors, after applying atmospheric corrections (using NIR and SWIR bands). The MODIS red and NIR bands are provided with a spatial resolution of 250 m.
The green band has a native lower resolution of 500 m but, for our purposes, it was recomputed at a 250 m resolution for consistency with red and NIR bands. Similarly, the 865 nm NIR band of MSI is provided with a 20 m resolution and was recomputed at a 10 m resolution to be consistent with the MSI green and red bands resolution. The three sensors' specifications and datasets are summarized in Table  1.
The SPM SA concentration is then computed as: SPM SA = α × SPM G + β × SPM R + γ × SPM NIR ( 4 ) Remote Sens. 2022, 14, 2026 12 of 37 weakly affected by adjacency effects, notably based on computations made using the SIMilarity Environment Correction (SIMEC) code  [41]. Results obtained show that adjacency effects on the water reflectance can be as low as 5% at 560 and 665 nm and 10% at 865 nm (for the most favorable case of turbid waters and low reflective land, i. e. , winter period in our area) and as high as 20% at 560 and 665 nm and 80% at 865 nm for the worst case (clear waters and highly reflective land, e. g. , summer period). Therefore, we assume the adjacency effects on satellite-derived water reflectance values and on SPM concentrations to be moderate along the river (up to about 20%), including at the SORA station where the ρw(NIR) are consistent with the ρw(G) and ρw(R) values (Figure  A4 ).
However, the ρ w (NIR) vs. ρ w (R) relationship shows an unexpected high scatter at low ρ w (R) values (ρ w (R) < 0. 09) caused by an abnormal increase in NIR reflectance values in the river. These abnormally high NIR reflectance values mainly appear on images recorded during the spring and summer seasons and are likely caused by adjacency effects from surrounding vegetation and/or residual glint (Figure  A4 ). Fortunately, the SPM concentrations are mainly estimated using the red and green bands as the water is moderately turbid during these seasons (Figure  A4 ), and these bands are only weakly affected by adjacency effects, notably based on computations made using the SIMilarity Environment Correction (SIMEC) code  [41]. Results obtained show that adjacency effects on the water reflectance can be as low as 5% at 560 and 665 nm and 10% at 865 nm (for the most favorable case of turbid waters and low reflective land, i.
The same trend as  [15]  is observed with our data, with percentile 95 values slightly increasing from 1. 5 g. m -3 to a plateau around 2. 5-3 g. m -3 for large areas, and then suddenly increasing towards the Rhône River mouth (Figure  A6 ). As in  [15] , we considered that this SPM R = 3 g. m -3 concentration plateau corresponds to the limit between the turbid plume and the ambient SPM concentration observed outside, and was thus selected as the threshold. To estimate the sensitivity of the metrics (i. e. , plume surface and mass) to this threshold, we also computed the plume boundaries for SPM R threshold values of 2 g. m -3 and 4 g. m -3 and used them to estimate uncertainties on the turbid plume surface and SPM mass (Figure  5 ). Once the threshold was applied to the dataset, we developed a routine to (1) identify the plume contour that corresponds to the Grand Rhône River plume, (2) extract its surface area, and (3) compute the corresponding plume surface mass.
As for the SPM concentration, the relationship between the SPM mass in the surface (0-1 m depth) plume and the river discharge is best fitted with a power law. This SPM mass was estimated based on a very simplified assumption (the surface plume is 1 m thick and the SPM concentration is constant within this surface layer). It varies from 2 tons (minimum river discharge generating a plume that can be detected using MODIS satellite data) to more than 40,000 tons during the maximum peak flood events covered by our dataset.
The scatterplot (Figure  11A ) first shows that all points are below the 1:1 line (except very few points on this line). Points are far below the 1:1 line for the minimum solid discharges then move closer as the solid discharge increased. Therefore, the relationship is not really linear over the whole dataset, despite a significant determination coefficient of 0. 68. Depending on the definition of the river plume limits, the corresponding slopes of the linear relationships vary from 0. 04 to 0. 09, which means that, as a rough approximation, less than 10% of the total SPM transported by the river through the SORA gauging station ends up in the surface river plume. This implies that a massive sinking and probably settling of SPM occurred in the prodelta zone, i. e. , right after the river mouth (as highlighted in Figure  9 ), probably resulting from intensive flocculation processes when SPM reached salty waters.
SPM concentration relationships for the plume and for the SORA station. The relationship calibrated using data acquired at the SORA station (river) is characterized by higher A ρ and lower C ρ coefficients than those calibrated with data from the river plume, resulting in a water reflectance saturation occurring at lower water-leaving reflectance values. The same trends were observed for the NIR relationships, even if high SPM concentration measurements in the river plume were lacking to make a decent comparison (Figure  A7C ). As already discussed in Section 3. 1. 2, the difference between these two relationships can be partly explained by an overcorrection of glint effects by the ACOLITE software. However, this difference is lower but still significant without the glint correction and thus cannot be attributed to an overcorrection of glint effects only.
Acknowledgments: Thank you to  P. Raimbault  and the  MOOSE program  ( Mediterranean Oceanic Observing System  for the Environment) now included in the RI ILICO for providing the SORA dataset (permission has been obtained In situ data of SPM properties were acquired through field campaigns supported under national programs TUCPA ( EC2CO DRILL 2013 ) and  MATUGLI  ( ANR-14-ASTR-0021 ).
s -1 ). measurements and 4 h high frequency measurements during high 2005-2020 SORA station (Arles)-Rhône River SPM concentration (g. m -3 ) Autonomous measurements at SORA station: daily sampling and 4 h (Q > 3000 m 3. s -1 ). high frequency sampling during high flooding events 2005-2020 In situ Dataset SORA station (Arles)-Rhône River POC concentration (g. m -3 ) (Q > 3000 m 3. s -1 ). Autonomous measurements at SORA station: daily sampling and 4 h high frequency sampling during high flooding events 2005-2020 Rhône River plume SPM concentration (g. m -3 ) Sampling during field campaigns. February 2014 February 2015 February 2016 Rhône River plume Above-water reflectance Measured with TriOS portable sensor simultaneously with SPM sampling during field campaigns. February 2014 February 2015 February 2016.