dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  Patrick Jacquet  for instrumental support before and during the campaign. The  DLR  crew is acknowledged for flying operations. This work was funded by the  EU   FP7 EUFAR2 Transnational Access project  and  DACCIWA  project (grant agreement no. 603502 ), the  Labex VOLTAIRE  ( ANR-10-LABX-100-01 ), the  PIVOTS  project provided by the  Région Cenwww. atmos-chem-phys. net/19/11401/2019/Atmos. Chem. Phys. , 19,[ 11401][11402][11403][11404][11405][11406][11407][11408][11409][11410 ][11411] 2019 We thank  Francesco Contino  ( Vrije Universiteit Brussel, Belgium ) for the work undertaken on the simulations of flame. We also thank  Gaëlle De Coetlogon  and  Alain Weill  ( Laboratoire Atmosphères, Milieux, Observations Spatiales -Université Paris-Saclay and CNRS, France ) for their help in determining the MBL height.
Financial support. This work was funded by the  EU   FP7 EUFAR2 Transnational Access project  and  DACCIWA  project (grant agreement no. 603502 ), the  Labex VOLTAIRE  ( ANR-10-LABX-100-01 ), the  PIVOTS  project provided by the  Région Centre -Val de Loire  ( ARD 2020 programme  and  CPER 2015-2020 ) -and the  AP-SOWA  project from the  INSU-LEFE-CHAT  programme. Review statement. This paper was edited by  Mathew Evans  and reviewed by  Joseph Pitt  and one anonymous referee.
Data We use data from the Integrated Climate Data Center (ICDC) at the Hamburg University (http://icdc. cen. uni-hamburg. de/daten/ocean/ar5-slr. html)  [27]. This dataset is based on CMIP5 climate models that were used in the regional sea-level projections of the 5th assessment report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). It provides 1 • × 1 • resolution spatial maps of sea-level rise and its components based on various atmosphere and ocean general circulation models (AOGCMs) and RCP scenarios.
Sterodynamic Sea-Level Changes According to the terminology proposed by  Gregory et al. (2019)   [42] , sterodynamic sea-level changes refer to the sum of the global mean thermosteric sea-level rise (i. e. , sea-level rise due to thermal expansion) and the ocean dynamics corrected from the inverse barometer effect from the atmosphere. In the IPCC assessment, 16 (for RCP2. 6) to 21 (for RCP4. 5 and 8. 5) AOGCMs from the CMIP5 exercise were used to compute this contribution to future sea-level rise. The dataset of the Integrated Climate Data Center at the Hamburg University  [27]  provides the sterodynamic sea-level changes for different AOGCMs and RCP scenarios (see Figure  1  and Appendix A ). Here, we exclude two models (MIROC-ESM and MIROC-ESM-Chem), which are found to simulate anomalously large sterodynamic sea-level rise outside the Mediterranean sea of approximately 50 cm (2100, RCP8.
This "hot-spot" has been suggested to be caused by change in the Gulf Stream  [44] , but the uncertainties in projections of these changes remain very large in climate models  [4] , as emphasized by the large model spread (i. e. , standard deviation) in this sector in Figure  1. In contrast, the model spread is smaller in tropical latitudes compared to mid-and high-latitudes. CMIP5 exercise were used to compute this contribution to future sea-level rise. The dataset of the Integrated Climate Data Center at the Hamburg University  [27]  provides the sterodynamic sea-level changes for different AOGCMs and RCP scenarios (see Figure  1  and Appendix A ). Here, we exclude two models (MIROC-ESM and MIROC-ESM-Chem), which are found to simulate anomalously large sterodynamic sea-level rise outside the Mediterranean sea of approximately 50 cm (2100, RCP8.
Finally, we use the sea-level projections by 2099 and assume they hold for 2100 because some AOGCMs are missing in 2100. The impact of this approximation on our results is lower than 1 cm. In Table  1 , we compare the global mean of sterodynamic sea-level changes used here with those presented in previous studies. Note that for some models (e. g. , MIROC), the global mean of sterodynamic sea-level changes is not exactly equal to the thermosteric sea-level changes because some regions such as the Mediterranean are masked in the ICDC dataset. Marked discrepancies are found among studies listed in Table  1 , because they use different subsets of models, and because some of them superimpose an expert judgment on the top of modelling outcomes. For example, the AR5 and several other studies  [19, 29]  interpreted "the 5 to 95% range of model results as the likely range" or one standard deviation of a t-distribution, which means that the standard deviation of model outcomes is multiplied by 1.
The uncertainties below are those used as input for the global or regional sea-level change computations. Hence, they correspond to one standard deviation of AOGCMs outcomes in this study and in Jackson and Jevrejeva 2016  [45] , but the 5-95% range of AOGCMs outcomes in the AR5, in the Integrated Climate Data Center at the Hamburg University dataset and in  Kopp et al. (2017)   [46].
Mountain Glaciers Several studies have investigated future contributions of glaciers to sea-level rise  [9, 47, 48] , but the projections of Huss and Hock (2015)  [41]  are 15-38% smaller than previous estimates  [31] , except  Hirabayashi et al. (2013)   [49] , who only provide estimates for the scenario RCP8. 5. Huss and Hock (2015)  [41]  forced their global glacier evolution model with 14 AOGCMs from the CMIP5 dataset. They explain their lower projections of glacier melting by their modelling of glacier ice volumes located below sea levels that do not contribute to future sea-level change (one third of the difference with other studies), as well as by different calibration and modelling options such as the framework for glacier geometry change (two third of the difference with other studies).
Greenland For Greenland, we rely on the study of Fürst et al. (2015)  [10] , which delivers global sea-level equivalent mass losses from the Greenland ice-sheet, in mm, relative to the 1986-2005 mean. This study forces an ice-sheet model accounting for surface mass balance effects and dynamic effects with ten AOGCMs from the CMIP5 dataset, considering the effects of vertical ground motions in Greenland. It shows that the dynamic ice melting of Greenland will be limited for large surface melting, because in this case, outlet glaciers currently located in the coastal margins and in contact with the ocean will retreat inland, therefore limiting dynamic effects due to ice in contact with the ocean. Therefore, the contribution of Greenland to sea-level rise is smaller in Fürst et al. (2015)  [10]  than for AR5 (e.
g. , for RCP8. 5 and 2100: 0. 07 to 0. 13 m for Fürst et al. (2015)  [10] , vs. 0. 09 to 0. 28 for AR5), and, therefore, than the well-established probabilistic projections of  Kopp et al. (2014)   [19] , who combined AR5 assumptions with the end-tail of the expert assessment of  Bamber and Aspinall (2013)  [51]. One study delivers slightly smaller Greenland contributions to future sea-level rise for RCP4. 5  [52] , but this may be due to ice-ocean interactions not being fully coupled in this study  [52]. Furthermore, the results are not provided in a probabilistic form  [52]. Finally, it is possible that even the projections of Fürst et al. (2015)  [10]  will be exceeded in the real world because all AOGCMs from the CMIP5 dataset underestimate mass losses observed in Greenland since the 1990s  [53].
Computational Approach and Statistical Dependencies The probabilistic projections presented in this paper are computed by summing the relevant global and regional component of future sea-level rise (Section 3. 1) using a Monte-Carlo approach. The propagation is conducted by sampling uniform random variables (termed as auxiliary variables) Water 2019, 11, 1507 9 of 19 between 0 and 1 and by using the inverse cumulative distribution functions of the corresponding components. In the independent case, six auxiliary variables (one for each of the six components) are randomly and independently sampled. In the fully dependent case, a unique auxiliary variable is used for the thermosteric (or sterodynamic at regional scale), Mountain Glaciers and Greenland components. We use the R HYRISK package (Hybrid Methods for Adressing Uncertainty in Risk Assessments)  [68]  supplemented by functions allowing to perform quasi-random simulations (Sobol' sequence of quasirandom numbers, see  Bratley and Fox (1988)   [69] ) that accelerate the convergence of Monte-Carlo simulations.
Acknowledgments: We thank numerous colleagues for discussions that allowed performing this study, including:  Catherine Ritz ,  Franck Pattyn ,  Angélique Melet ,  Mélanie Becker ,  Gael Durand ,  Robert Nicholls ,  Dewi Le Bars ,  Detlef Stammer ,  Erwin Lambert ,  David Salas-y-Mélia ,  Svetlana Jevrejeva  and the participants to the  WCRP Sea-Level Grand Challenge. We thank especially  Aimée Slangen ,  Benoit Meyssignac  and  Mark Carson  for sharing their data and expertise. We thank two reviewers for their insightful comments that improved our paper. We thank  Anny Cazenave  and  William Llovel  for inviting us to submit an original research work to this special issue.
2 ). The magmatic record of the oldest cratonic nuclei, exemplified by the Pilbara carton and the Barberton terrane of the Kaapvaal craton, respectively in Australia and South Africa, typically spans over one billion years (Nd model ages ranging from 4. 0 to 3. 0 Ga calculated according to  DePaolo (1981)  using data from the Georoc database and references therein) and UePb ages on zircon ranging from 3. 7 to 2. 7 Ga (Georoc database and references therein and  Zeh et al. , 2007 Zeh et al. , , 2009 Zeh et al. , , 2011;; Fig. 3 ). The Pilabara and Barberton are probably the most emblematic and best studied Archaean terranes and might have been part of a single Archaean craton  (Bleeker, 2003; Cheney, 1996)  representing a surface of more than 500 000 km 2 , which is similar to the Altiplano.
3. 9 3. 7 3. 5 3. 3 3. 1 2. 9 2. 7 2. 5 Ages (Ga) extracted from the GEOROC database with additional data from  Zeh et al. , 2007 Zeh et al. , , 2009 Zeh et al. , , 2011. The Pilbara and Barberton probably corresponded to a single Archaean cratonic nuclei  (Cheney, 1996)  representing a surface of more than 500 000 km 2. Nd model ages (T DM ) are calculated according to  DePaolo (1981). Nd model ages suggest a continuous transfer of magmas from the mantle to the crust from the Hadean to the MesoArchaean. UePb ages point to a continuous record of magmatic differentiation and/or crustal reworking for close to 1 Ga from the EoArchaean to the transition with the Proterozoic. available data on the present-day composition, structure and thermal characteristic of the oldest parts of Archaean cratons (Table  1 ) in order to define generic Archaean cratonic nuclei.
5 W m À1 $K À1 and at 3. 5 W m À1 $K À1 , respectively. The heat production is nil in the mantle. b) Geometric characteristics and boundary conditions that serve as a basis for calculating the thermal evolution with time of a generic Archaean craton. The mantle heat flow, qm, is considered to be the same as the present-day one and the heat production at a given time, AðtÞ, is calculated taking into account the natural radioactive decay of heat producing isotopes, 238 U, 235 U, 232 Th, 40 K. The thickness of the thermal lithosphere, z l , and the surface heat flow, qs, underlined in grey, are left free. Values for surface heat flow and lithospheric thickness are extracted from the dataset of  Artemieva and Mooney (2001) ; crustal thicknesses are extracted from the map of  Mooney et al. (2001) ; and the oldest known ages for each cratons are extracted from the database of  Artemieva (2006).
The present-day thickness of the thermal lithosphere is constrained by (i) pressureetemperature conditions retrieved from subcratonic mantle xenolith carried back to the surface in recent lavas, and (ii) the interpretation of tomographic data  (Poupinet and Shapiro, 2009; Poupinet et al. , 2003; Roy et al. , 1968). Xenolith data are consistent with a present-day thickness of the thermal lithosphere between 200 and 250 km for most cratons  (Artemieva and Mooney, 2001). Tomographic data corroborate these estimates at first order but tend to yield a thicker lithosphere (Table  1 ), with the notable exception of the China and Siberia cratons that are underlain by a thinner lithospheric root  (Poupinet and Shapiro, 2009). We consider xenolith data to be more representative of the long-term thermal lithosphere and an average of 225 km has been chosen for the present-day thickness of the thermal lithosphere beneath the Archaean generic craton.
74 mW m À3 (Fig. 6 ) yielding a crustal radiogenic heat flow of 29. 6 mW m À2. These values are at first order consistent with the ones determined today by geophysical data and experimental means, namely between 400 and 600 C for the Moho temperature and a range of values between 0. 29 and 1. 00 mW m À3 for the radiogenic crustal heat production in Archaean regions  (Artemieva and Mooney, 2001; Rudnick et al. , 1998; Stern, 2008; Turcotte and Schubert, 1982). This consistency gives some credit to our underlying assumptions and thus to the thermal evolution that we model for the generic Archaean craton. Taking into account the decay of HPI, the calculated geothermal gradient for the generic Archaean craton at 3. 5 Ga is characterised by a Moho temperature of 900 C, a thermal lithosphere thickness of 132 km (Fig.
Acknowledgments This paper is the result of a ten years long scientific journey that involved a number of Master students, including  S everine Blouin ,  Julie Garry ,  Paul Grauwin ,  Sylvain Michel ,  Sophie Audonneau ,  Luca Guillaumot , and  Madeline Olivier. Bruno Scaillet  is warmly thanked for smooth editorial handling. The current version of this paper benefited from constructive reviews by  Laurent Jolivet  and an anonymous reviewer. The scientific demonstration, although similar at first order, was significantly improved to respond to the critical comments of  Patrice Rey  and of an anonymous reviewer on a previous version of this paper. This contribution is part of the  URCO  project supported by the  NEEDS CNRS program.
The metamorphic rocks of the highest grade granulites and surrounding migmatites are located in the center of the Ursuya massif, where they formed an anatectic dome  (Boissonnas et al. , 1974; Vielzeuf, 1984). The massif is interpreted as a part of the Variscan intermediate crust where metamorphism of the deepest facies took place at around 6 ± 0. 5 kbar and 775 ± 50°C  (Vielzeuf, 1984). The U-Pb age dataset obtained by  Hart et al. (2016)  and  Vacherat et al. (2017)  from gneisses of the Ursuya massif indicates that granulites and paragneisses crystallized and were deformed between 295 and 274 Ma. The Itxassou pluton intruded the southern part of the metamorphic series during this time, at 276. 8 ± 1. 9 Ma  (Vacherat et al. , 2017). Such late Permian plutonism is well known in the Basque massifs; for instance, the Aya pluton was emplaced within the Cinco-Villas massif at 267.
Acknowledgments This work is part of the  Orogen  project, cofunded by  Total  and  BRGM , and the  RGF  project funded by the  BRGM. BC  acknowledges funding from  Labex Voltaire  ( ANR-10-LABX-100-01 ), based at Orléans University and BRGM. We thank  Orogen project managers Sylvain Calassou (Total ),  Emmanuel Masini  (Total),  Olivier Vidal (CNRS)  and  Isabelle Thinon  (BRGM). Many thanks to  Michel de Saint Blanquat  for fruitful discussion about the geological background of the study area. The manuscript was greatly improved by reviews from  Céline Ducassou  and  Jean Van den Driessche , whom we thank for their helpful comments. Journal Pre-proof J o u r n a l P r e -p r o o f.
, 2016). Nowadays, in situ and satellite observations are commonly used to calibrate, validate and even correct models outputs or/and parameters  (Lettenmaier et al. , 2015). Estimating the total water storage variations within watershed remains a difficult task, as hydrological models are often validated or calibrated only for some variables (more frequently against in situ river discharge, sometimes using satellite estimates of snow extent or, less frequently, against water table in situ measurements) but not for all variables and storage compartments. The use of both GRACE data and hydrological modeling at watershed scale can help 1) to cross validate a combination of hydrology model outputs and 2) to identify modeled compartment(s) where the most important variations are occurring. However, due to GRACE coarse spatial resolution, many previous studies using GRACE data considered watershed with drainage area much higher than 100,000 km 2 (e.
g. Wouters et al. , 2014). It is not sure that GRACE data could be used for medium-size river basins, like the Garonne basin located in South West of France (50,000 km 2 drainage area). The present study investigates the potential of GRACE data for this kind of basin. Especially, it focuses on the following two questions: How to estimate and validate total water storage variations over the medium-size Garonne basin using GRACE data and hydrological model outputs at monthly scale? In which (hydrology) compartments those monthly variations are the most important? They are addressed in the following sections, which present the study domain (section 2. 1), GRACE products (section 2. 2) and hydrological models inputs and outputs (sections 2. 3) used, the methodology applied to tackle these two questions (section 2.
Watershed flows are impacted by these weather conditions, especially flows in the central part of the basin are generated by precipitation over the Massif Central and the Pyrenees mountains. The average annual precipitation is around 900 mm. y -1 over the basin (according to https://donneespubliques. meteofrance. fr). Upstream Garonne and Ariege rivers discharges during spring are highly influenced by snowmelt from the Pyrenees Mountains  (Caballero et al. , 2007). At the outlet of the basin (Tonneins), according to the national gauge measurements database (Banque Hydro, http://www. hydro. eaufrance. fr/), the average discharge between 1913 and 2018 is equal to 598 m 3. s -. The highest discharge on record reached 5700 m 3. s -  (March 5th, 1930) and the lowest, 37. 5 m 3. s -   (August 4th, 1989).
, 2014). Therefore, gaps in GRACE time series are quite important since then. There had also been some few missing measurements before 2011, especially in the early times of the mission. Fig. 1. Map of the Garonne basin up to Tonneins (black polygon) and river network (blue lines, from IGN BD Carthage database, available at www. sandre. eaufrance. fr). Vertical and horizontal lines correspond to a 1°x1°longitude/latitude grid (used by most GRACE products) and yellow lines correspond to pixels intersecting the Garonne. Background image is the NASA MODIS "Blue Marble Next Generation" image  (Stöckli et al. , 2005)  (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ).
, 2016). The mascons solutions have been computed by CSR (over a 0. 5°x0. 5°grid, version 1;  Save et al. , 2016) , NASA Goddard Space Flight Center (GSFC, 1°x1°grid, version 2. 2;  Luthcke et al. , 2013; Loomis and Luthcke, 2014)  and JPL (0. 5°x0. 5°grid, RL06 M. MSCNv01 dataset with Coastal Resolution Improvement Filtering;  Watkins et al. , 2015)  processing centers, called hereafter 'CSR mascon', 'GSFC mascon' and 'JPL mascon', respectively. Time invariant scaling factors are recommended and provided only for the JPL mascon solution. Documentation for this solution also clearly mentioned that "the native resolution is the size of a single mascon which are 3 degrees in size" (https://grace. jpl. nasa. gov/data/get-data/ jpl_global_mascons/), despite the solution is provided on a 0. 5°x0.
Climate forcing dataset In this study, climate dataset used to force the hydrological models comes from the Système d'Analyse Fournissant des Renseignements A la Neige (SAFRAN;  Durand et al. , 1993; Quintana-Seguí et al. , 2008)  analysis system. Raimonet et al. (2017)  have shown that SAFRAN currently provides the best estimate of near-surface variables over France, compared to other existing datasets. Especially, reconstruction of the liquid and solid precipitation fields benefits from the high-density network of Meteo-France local measurements. Moreover,  Grusson et al. (2017a)  showed that using SAFRAN dataset in a hydrological modeling of the Garonne basin led to better performances. SAFRAN performs an analysis of near-surface variables by optimal interpolation (OI) on homogeneous climatic areas, i.
e. without strong horizontal climatic gradient. The OI method combines upper-air atmospheric analysis and in situ observations to calculate the best estimate of total daily precipitation, and 6 -hly 2-meter air temperature and humidity, 10-meter wind speed, downward solar and infrared radiation. These analyzed variables are then interpolated in space, on an 8km-mesh regular grid to better take into account topography, and in time, at an hourly time step. More detailed description of SAFRAN analyses can be found in  Durand et al. (1993)  and  Quintana-Seguí et al. (2008). SAFRAN analyses are computed from 1958 onwards  (Vidal et al. , 2010). Fig. 2  shows SAFRAN 2000-2014 mean annual total precipitation in South West of France. The two hydrological models used in this study, which were forced with SAFRAN datasets, are described in the following sections.
Concerning the input data, apart from the SAFRAN climate dataset, SIM needs parameters that define the Garonne basin in terms of natural ecosystems, orography and soil texture. For that purpose, the ECOCLIMAP database  (Faroux et al. , 2013)  is used to represent land cover at a 1-km horizontal resolution. Among the hundreds of ecosystems available in ECOCLIMAP, vegetation in the model is represented by only 12 plant functional types (PFTs) combining several ecosystems. The orography is derived from the Shuttle Radar Topography Mission (SRTM) at 90-meters  (Farr et al. , 2007) , upscaled at 1-km resolution to be consistent with the ecosystems representation. Soil texture at 1-km resolution comes from the Harmonized World Soil Database  (Nachtergaele et al. , 2012). It is used to define model parameters that depend on clay or sand concentration, like the field capacity or wilting point soil moisture contents, which are commonly used to define thresholds between which plants evapotranspirate at potential rate or are stressed due to a lack of water.
This parameter was setup uniformly for the Garonne basin in the SIM operational set up. Although no specific calibration was made in the current study, whether for the land surface model (ISBA) or the hydrogeological model (MODCOU), the parameters of this latter that were calibrated over the 1974-1982 period  (Boukerma, 1987)  were not modified. Table  1  sums up SIM input parameters and forcing sources, and validation datasets used for this study.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM;  Tachikawa et al. , 2011)  from NASA and the Ministry of Economy, Trade and Industry (METI) of Japan, available on 90 m global grid, was chosen to delineate the watershed and compute the river system. Land uses are from the CORINE Land Cover 2012  (Büttner et al. , 2014)  map with a 100 m resolution and the soil data originate from the European Soil Database (EEA, 2007) with a 1 km resolution map. Soil properties have been adjusted by expertise. The SWAT model setup includes 52 different types of soil over the watershed. Each soil consists in 1 to 4 layers according each type of soil, each layer having a depth of 10 to 1400 cm and its own layer characteristics. From these land use, soil databases and watershed topography, 22 land use, 13 soil and 5 slope classes were defined in SWAT.