dataesr/scientific-paragraphs-categorization huggingface dataset:
The methodology is similar to  Marchi et al. (2010)  and  Tarolli et al. (2012) , where the duration is defined as "the time duration of the flood-generating rainfall episodes which are separated by less than 6 h of rainfall hiatus". We made this threshold explicit to reduce subjectivity. Here, the minimum duration depends subjectively on hydro-climatic settings and basin size. The reported D th is the duration of the rainfall responsible for each event flood peak, separated from other rainfall events that may have occurred before or after the main event depending on the characteristics of the largest involved catchment. In a number of cases in which the features of the flash flood response were specifically affected by wet initial soil moisture conditions, rainfall data are provided for a longer period than the storm duration.
This enables us to account for antecedent rainfall in the analyses. In general, the preliminary selection of flash floods was based on rainfall data (amount, intensity) from meteorological agencies and qualitative field recognition of flood response. This led to the exclusion of a number of low-intensity events. Post-flood reconstruction of peak discharge was carried out for events that passed this preliminary screening. Several of these events were not included in the dataset because they failed to meet the requirements in terms of flood magnitude, spatial extent and storm duration. Given these constraints, the EuroMedeFF database includes 49 highintensity flash floods: 30 events in France, 7 events each in Israel and in Italy, 3 events in Romania, and 1 event each in Germany and in Slovenia.
(2015) , who analysed the seasonality signal of atmospheric deep convection in the Mediterranean area. Table  1  reports summary information of the EuroMedeFF database. In the table, each event is labelled as an "EventID", which comprises the impacted catchment/region and the year of occurrence, e. g. ORBIEL1999 (cf. event 1 in Table  1 ). The "EventID" is used in the archive to uniquely identify the event. The table is ordered first on a country basis, followed by the date of flood peak for each country, from past to most recent events. For each of the 49 events, the table reports the river basin and the country, the date of the flood peak, the climatic region, the number of river sections for which discharge data are available (in terms of both indirect post-flood estimates and streamgauge-based data), with indications of the sections with streamgauge information, the range of basin area for the catchments closed at the studied river sections, the storm duration, the range of unit peak discharges and the indication of earlier works on the event.
In a few cases, more than one flash flood event is reported for the same river basin. We used the Budyko diagram  (Budyko, 1974)  to characterise the climatic context of the catchments included in the EuroMedeFF database (Fig. 2 ). The Budyko framework plots the evaporation index (i. e. the ratio of mean annual actual evaporation to mean annual precipitation, AET / P ) versus the aridity index (i. e. the ratio of mean annual potential evapotranspiration to mean annual precipitation PET / P ). The mean values of these variables were calculated for each river basin, so the number of points plotted in Fig. 2  is smaller than the total number of flash floods in the database. Figure  2  also reports the empirical Budyko curve (dotted curve;  Budyko, 1974) , which fits well with the upper envelope (continuous curve) of the data included in the data archive.
At least, all the data relative to one country are in the same coordinate system. The time reference for the radar data is provided as yymmddH b M b -yymmddH e M e , with H b , M b referring to the beginning and H e , M e to the end of the considered time period. The spatial data (DTM and radar) are provided in ASCII format. The coordinates for radar and DTM data as well as locations of streamgauge and IPEC sections are consistently provided in both local (country-specific) and WGS84 systems. The main advantage of WGS84 is that it avoids possible conversion problems from local coordinate systems while providing a homogeneous coordinate system throughout the database.
Radar and raingauge rainfall estimates were merged using the same procedure: a mean field bias calculated at the event accumulation scale using rain gauges located in or around the study catchment. Additional details on the procedures can be found in  Bouilloud et al. (2010) ,  Delrieu et al. (2014) ,  Marra et al. (2014) ,  Marra and Morin (2015) ,  Boudevillain et al. (2016) , and in the references therein. Earth Syst. Sci. Data, 10, 1783-1794, 2018 www. earth-syst-sci-data. net/10/1783/2018/ For French events 7, 26 and 30 in Table  1 , only rainfall data from one local rain gauge are available. These floods have been kept in the database because of the interest in including flood response data for very small basins (< 1 km 2 ) and because the small catchment size of the Valescure basin (4 km 2 ) causes the absence of radar rainfall data to be less detrimental than for floods that hit larger catchments.
Discharge estimation methods Discharge data in the EuroMedeFF database derive from both streamflow monitoring stations and post-flood indirect estimates of flow peak through IPEC. Streamflow data, permitting recording of flood hydrographs, thus enabling assessment of not only discharge, but also time response and flood runoff volume estimation, were checked for the uncertainties affecting rating curves at high-flood stages by using hydraulic models and topographic data. Discharge data from reservoir operations, water levels and use of the continuity equation, when available, were also included in the database after accurate quality control. Different methods have been used for the indirect reconstruction of flow velocity and peak discharge from flood marks, such as slope area, slope conveyance, flow-throughculvert, and lateral super-elevation in bends.
Amongst these methods, the most commonly used for the implementation of the dataset presented in this paper is the slope conveyance, which consists of the application of the Manning-Strickler equation, under assumption of uniform flow, and requires the topographic survey of cross-section geometry and flow energy gradient, computed from the elevation difference be-tween the high water marks along the channel reach surveyed  (Gaume and Borga, 2008; Lumbroso and Gaume, 2012). Although the identification of river cross sections suitable for indirect peak discharge assessment has sometimes proved not easy (flood marks can be hardly visible or obliterated by post-flood restoration works), and discharge reconstruction in cross sections that underwent major topographic changes is affected by major uncertainties  (Amponsah et al.
Discussion Overall, 680 peak discharge data are included in the archive: 32 % (219) were recorded by river gauging stations or based on data from reservoir operations, and 68 % (461) from IPEC surveys. We followed the geomorphic impact-based linear error analysis of the slope conveyance discharge determination presented in  Amponsah et al. (2016)  for the uncertainty assessment of the IPEC peak flood estimates. Table  2  reports the number of river sections for each of the climatic regions and the corresponding summary statistics of the upstream drainage area. Almost 90 % of the included discharge data are from the Mediterranean region, which is consistent with increasing collation and analysis of flash flood data in this region compared to other climatic regions in Europe (e. g. Gaume et al.
, 2009; Marchi et al. , 2010). The area of the basins included in the archive ranges from 0. 27 to 2586 km 2. Table  2  shows that flash flooding may impact larger basins in the Mediterranean, Alpine and Arid regions than those considered in the Inland Continental region. This supports earlier findings from  Gaume et al. (2009). Table  3  reports summary statistics of the upstream drainage area for the two discharge assessment methods (stream gauges and indirect methods). As expected, stream gauges correspond to larger areas, whereas post-flood surveys play major roles in documenting peak discharges for smaller drainage areas  (Borga et al. , 2008; Marchi et al. , 2010; Amponsah et al. , 2016). Nevertheless, the database also includes discharge data from a few measuring stations deployed in small research catchments.
This allows reduction of the uncertainty related to the estimation of peak discharge in very small catchments  (Braud et al. , 2014). The relationship between the unit peak discharge (i. e. peak discharge normalised by the upstream drainage area) and the upstream area was investigated for the EuroMedeFF database to identify the control exerted by catchment size on flood peaks (Fig. 3 ) and to analyse its variation among the four main climatic regions (Fig. 4a-d ). Not surprisingly, the unit peak discharges exhibit a marked dependence on watershed area. The envelope curve, representing the observed upper limit of the relationship, was empirically derived as a powerlaw function for all the floods as well as for the four different main climate regions. The envelope curve representative of all the floods is similar in shape to that reported by  Gaume et al.
Data availability The EuroMedeFF dataset is publicly available and can be downloaded from http://mistrals. sedoo. fr/?editDatsId= 1493&datsId=1493&project_name=HyMeX&q= euromedeff (last access: 2 October 2018). The dataset is also made available with the following unique DOI provided by the HyMeX database administrators: https://doi. org/10. 6096/MISTRALS-HyMeX. 1493  (Amponsah et al. , 2018).
Conclusions We presented an observational dataset that provides integrated fine-resolution data for high-intensity flash floods that occurred in Europe and in the Mediterranean region from 1991 to 2015. The dataset is based on a unique collection of rainfall and discharge data (including data from post-flood surveys) for basins ranging in size from 0. 27 to 2586 km 2. The archive provides high-resolution data enabling a number of flash flood analyses. It allows the analysis of the spacetime distribution of causative rainfall, which may be used to investigate methodologies for rainfall downscaling. The data may foster the investigation of the rainfall-runoff relationship at multiple sites within the flash flood environment. This may lead to the identification of possible thresholds in runoff generation which may be related to initial conditions, rainfall rates and accumulations, and catchment properties.
Moreover, it allows investigations to clarify the dependence existing between spatial rainfall organisation, basin morphology and runoff response. The archive may be used as a bench-  mark for the assessment of hydrological models and flash flood forecasting procedures in various hydro-climatic settings. The availability of fine-resolution rainfall data may be used to better understand how rainfall spatial and temporal variability must be considered in hydrological models for accurate prediction of flash flood response. Furthermore, the availability of multiple flash flood response data along the river network may be exploited to better understand how calibration of hydrological models may be transferred across events and sites characterised by different severity. Finally, inspection of the data included in the archive shows the relevance that indirect peak flow estimates have in flash flood analysis, particularly for small basins.
Acknowledgements. This paper contributes to the HyMeX programme (www. hymex. org,last access: 2 October 2018). Collation of hydrometeorological data and processing of data collated through post-flood surveys for the Italian events have been done in the framework of the Next-Data Project ( Italian Ministry of University  and Research and the  National Research Council of Italy -CNR). Part of the data was acquired through the  Hydrometeorological Data Resources and Technologies for Effective Flash Flood Forecasting (HYDRATE) project (European Commission, Sixth Framework Programme , contract  037024 ). Part of the data provided in this dataset was acquired during the  FloodScale  project, funded by the  French National Research Agency (ANR)  under contract no. ANR 2011 BS56 027. We also benefited from funding by the  MISTRALS/HyMeX programme  (http://www.
mistrals-home. org,last access: 2 October 2018), in particular for post-flood surveys. The rainfall reanalyses for the French events were provided by the OHM-CV observatory (observation service supported by the  Institut National des Sciences de l'Univers, section Surface et Interfaces Continentales  and the  Observatoire des Sciences de l'Univers de Grenoble). We thank  Arthur Hamburger , whose work was supported by  Labex   OSUG@2020 (Investissements d'avenir - ANR10 LABX56 ) and the SCHAPI. We also thank the  SCHAPI, EDF-DTG , for providing some of the French streamgauge discharge data, and  Meteo France  for providing French raingauge and radar input data. For Israel, raw radar data were obtained from E. M. S. (Mekorot company), raingauge data from the  Israel Meteorological Service , streamgauge data from the  Israel Hydrological Service , and post-flood estimates from reports by the  Soil Erosion Research Station at the Ministry of Agriculture and Rural Development.
Preparation of libraries was performed with the Ion Plus Fragment Library Kit (Life Technologies) and 4 samples were treated in each sequencing run using Ion Xpress Barcodes Adapters 1-16 and Ion Xpress Barcode Adapters 17-32 (Life Technologies). All libraries correspond to DNA fragments from 200 bp to 400 bp. Emulsion PCR was made with the Ion PGM™ Template OT2 400 Kit in OneTouch2, the enrichment of balls was realized in OneTouchES. Quality control was carried out with the Ion Sphere™ Quality Control Kit in Qubit 2. 0 fluorometer. Sequencing was undertaken in the 316v2 microchip with Ion PGM Hi-Q Sequencing Kit on the ION PGM sequencer. All the corresponding raw data has been posted on the ENA database with the accession number ERP104935 (PRJEB23198).
For each electrophoresis process, a QIAxcel DNA High Resolution Cartridge, an Alignment Marker 50 bp/5000 bp, a size marker 250 bp-4000 bp, and the OM 500 method were used. The electrophoresis analyses were performed using the QIAxcel Screengel Software v1. 2. 0 (Qiagen), and the DNA fingerprint gel images were analyzed with the GelJ v1_3 software, according to Heras et al. [30]. Dendrograms were generated using the following parameters: similarity method (Pearson's correlation), linkage (UPGMA) and tolerance of 2%.
Bioinformatic analysis The analysis of 16S sequencing data was performed with the Torrent Suite Software (v4. 4) and ION Reporter (v4. 4). The bioinformatics workflow corresponded to Metagenomics 16S beta. The following parameters were used: Curated MicroSEQ ® 16S Reference Library V2013. 1 (Data Bank), size minimum of reads 150 bp, percentage of alignments for identification 90%, minimal read abundance for validation 10, cut-off Gender 97% minimal alignment, cut-off species 99% minimal alignment, slash-call was defined as 0. 8%. The data was expressed as percentages of phylum and families from taxonomical consensus between the 7 hypervariable regions. 16S sequencing data were then analyzed by multivariate analysis with the SIMCA-P (V13) software (Sartorius Stedim Data Analytics AB Malmö, Sweden).
As a result this observation was excluded and the model refitted. The new PCA model is very similar to the previous and explains 82% of the X-variation with two components (Fig. 2A ). The loading plot (Fig. 2B ) revealed that the S24-7 variable, which belongs to the Bacteroidetes phylum, has a high positive loading on the first component, characterizing the treated females. Contrastingly, Lactobacillaceae and Erysipelotrichaceae variables, belonging to Firmicutes phylum, have negative loadings on the first component and characterize the group of controls (males and females) and treated males. Second, on the basis of PCA results, an OPLS-DA model on the 16S sequencing dataset of families was built with two classes of observations: treated males and controls (n = 14) and treated females (3 groups of n = 3).
MATERIALS We examined a total of 50 skeletons: 40 of Mesosaurus tenuidens, four Hovasaurus boulei, two Thadeosaurus colcanapi, and four Claudiosaurus germaini (Figure  1 ). The other data were collected from the literature (see below). The mesosaur specimens studied were all assigned to the species Mesosaurus tenuidens given that the observed phenotypic variability can be considered to reflect normal infraspecific variations. The specimens consist of well-preserved articulated individuals and partial skeletons, mostly featuring the axial skeleton (Figures  1A, D ), belonging to the following paleontological collections: Fossil Vertebrates of the Facultad de Ciencias (FC-DPV), Montevideo, Uruguay; Instituto de Geociências (section Paleontology) of the São Paulo University (GP/2E), São Paulo, Brazil; Senckenberg Institute (SMF-R), Frankfurt, Germany; Institute of Paleobiology, Polish Academy of Science (ZPAL); Paleontological Institute and Museum, University of Zurich (PIMUZ), Suitzerland; Departamento Nacional de Produção Mineral, Museu de Ciências da Terra (DNPM), Rio de Janeiro, Brazil; Muséum national d'Histoire naturelle, Madagascan Permian (MNHN-MAP), and the American Museum of Natural History (AMNH), New York, USA.
We measured the parameters developed by  McShea (1992). The metric range (R), polarization (C), irregularity (Cm), concentration (E1), and smoothness (E2), were calculated for each specimen. However they could not be compared with more recent data (e. g. ,  McShea, 1993; Buchholtz, 2001a; Felice and Angielczyk, 2014)  because the original metric was modified, customizing the equations and thus making the comparisons operose, impractical and meaningless. The range R represents the logarithm of the difference between the maximum and minimum vertebral length in each specimen. The parameter C reflects the polarization of the data. The concentration E1 is the difference between R and C. As this parameter is the subtraction of two logarithmic operations, it results in normalization independent of the size.
The comparison has a mean of zero with a three sigma deviation of FIGURE 3 | Centrum length profiles for Mesosaurus. Plots showing centrum length profiles for the Mesosaurus specimens sorted according their mean vertebral length. All patterns were adjusted so that the sacral vertebrae are fixed in position 35 and 36 in the plots. All graphs display the same scale in their y-axis (a 6 cm span of values). The dashed lines mark the approximate placement of the last cervical (12 or 13), the relative placement of the first thoracic vertebra (position 13 or 14), and of the two sacral vertebrae (positions 35 and 36), the end of the caudal lateral processes (position 44) and the assumed end of the tail (position 101). See the scaled mesosaurid representation of these limits in Figure  5. Raw data can be found in Table  S2  (Supplementary Material).
A Comparative Profile of the Analyzed Vertebral Column in Extinct and Extant Species The comparison between extant and extinct taxa is shown in Figures  10, 11. All patterns were re-scaled in their x-axis, so that the trunk of all individuals has the same length. Patterns of some species were averaged with the same method as for Mesosaurus, when more than one specimen was available. All patterns, including those obtained from data of  Felice and Angielczyk (2014) , were normalized with their mean centrum lengths so the y-axis reflects the variation in length with respect to their mean vertebral lengths.
ACKNOWLEDGMENTS We are grateful to the Academic Editor  Martín Ezcurra  and reviewers  Torsten Scheyer  and  Juan Carlos Cisneros  for their constructive contribution to this paper, and to  Ivone Cardozo  and  Marcelha Páez Landim  ( USP, São Paulo, Brazil ),  Rainer Brocke  and  Gunnar Riedel  ( Curators of the Paleontological Collection at the Natur Museum Senckenberg, Frankfurt, Germany ), for kindly help us during the revision of several of the specimens used in this study. We are also grateful to the curator  Heinz Furrer  and collection managers of the  Paläontologisches Institut und Museum der Universität Zürich (Switzerland) (PIMUZ)  for provide good quality photographs of mesosaur specimens housed in that institution. We thank  Pablo Velozo  for taking the photographs of the Madagascan taxa deposited in the fossil collection of  Muséum National d'Histoire Naturelle of Paris , and to  Piotr Bajdek  for helpful comments and suggestions on mesosaur cololites taphonomy.
2009, Heikinheimo 2015)  and for the Jimma area  (Hailu et al. 2014 (Hailu et al. , 2015b)). Expected future land use and land cover changes were quantified over a 35-year period, based on past trends for cropland expansion and deforestation rates from remote sensing analysis  (Clark and Pellikka et al. 2009, Heikinheimo 2015  for the Taita Hills; land-cover maps developed at the Regional Centre for Mapping of Resources for Development (RCMRD)/SERVIR-Eastern and Southern Africa for the Jimma area) and on expected scenario trajectories developed during the multi-stakeholder workshops (Supplementary 1a and 1b). Therefore the presented scenarios represent a potential quantification within the range of the envisaged trajectories. The spatial pattern of land use and land cover changes expected by workshops participants were simulated by composite indicators of likelihood of change built from high resolution spatial datasets, including mid-21 st century projections temperature and rainfall regime changes derived from AFRICLIM 3.
0  (Platts et al. 2015) , population distribution (Worldpop,  Lloyd et al. 2017) , elevation and slope (SRTM 90,  Jarvis et al. 2008) , rivers and roads extracted from satellite image datasets. In the final validation and feedback workshops stakeholders provided information that was used to refine the scenarios. Furthermore we elicited the participants' reflection on potential impacts on ecosystem services state (e. g. increase, decline, or stable) under the proposed alternative scenarios compared to the current situation.
Acknowledgement We are grateful to the workshops participants in the  Taita Hills and Jimma. We acknowledge the logistic and administrative support from the  College of Agriculture and Veterinary Medicine of Jimma University , the  Taita Environmental Research and Resource Arc (TERRA) , in particular  Mr Mwadime Njombe , the  International Centre of Insect Physiology and Ecology (ICIPE) , the  University of York , and the  University of Helsinki. This study was funded by the  Ministry of Foreign Affairs of Finland  through the CHIESA and AFERIA projects.
, 1994). A systematic analysis of the short and long term effects of intense wind events on the properties of the Western Mediterranean Sea is lacking. With this work we perform this analysis exploiting high resolution observational datasets for surface winds, sea surface temperature and sea surface height. The paper is structured as follows. In Section 2 we present the observational datasets and describe the statistical analysis we have performed in order to identify the intense wind events and evaluate their impact on the state of the upper ocean. In Section 3 we discuss the results of this analysis. We first describe the short term (few weeks) behavior of sea surface temperature and sea surface height anomalies just after the occurrence of the intense wind event, both locally and area averaging over a region with the typical size of the dynamical structures responsible for the intense winds.