dataesr/scientific-paragraphs-categorization huggingface dataset:
SAFRAN data at CLB contain all the meteorological variables required to continuously run a land surface model at CLB without a data gap for the entire time period. SAFRAN data include 2 m wind speed, 2 m air temperature and humidity, incoming longwave and shortwave radiation and snowfall and rainfall amount at an hourly time step. Using SAFRAN data for driving a land surface model at CLB requires accounting for several limitations of this data set, in the interpretation of the results, especially for solid precipitation and wind speed and direction. Directly measuring solid precipitation at CLB has been abandoned for several years because of the strong undercatch under windy conditions (e. g. Kochendorfer et al. , 2017)  which could not be addressed adequately in situ  (Naaim-Bouvet et al.
, 2014). Instead, SAFRAN data are used for solid precipitation, which uses observations at neighbouring sites. Note, however, that the accuracy of the SAFRAN precipitation data is known to be limited at high elevation such as CLB, because of the restricted number of stations incorporated into the analysis above 2000 m (only 2 in the Grandes Rousses range) and the potential influence of wind undercatch at these stations. Gerbaux et al. (2005) ,  Dumont et al. (2012)  and  Réveillet et al. (2018)  compared SAFRAN total solid precipitation amounts on an annual scale and ob- served a winter mass balance of the Saint-Sorlin glacier (2650-3400 m) located in the Grandes Rousses 5 km away from CLB. They found an underestimation of SAFRAN winter precipitation ranging between 33 % and 42 %. At CLB, the underestimation is expected to be less since glaciers are generally preferential accumulation areas for a given elevation.
Blowing snow data Blowing snow data stem from two sources: (i) a database of blowing snow occurrence from winter 2000-2001 to winter 2015-2016 and (ii) blowing snow fluxes derived from SPC-S7 from winter 2010-2011 to winter 2015-2016. These two data sets are described below as well as a comparison of the occurrence of blowing snow with the two methods.
Empirical database of blowing snow occurrence A database of blowing snow events at CLB was established for each winter in the period 2000-2016. It consists of an extension of the database presented in  Vionnet et al. (2013). This database was initially developed to provide a full record of blowing snow occurrence that can be compared with the avalanche activity in the Grandes Rousses massif (Guyomarc  'h et al. , 2014). Blowing snow events are defined as a time period in which snow on the ground is transported in the atmosphere in saltation and in turbulent suspension. Such events may occur with concurrent snowfall. Therefore, the database makes a distinction between blowing snow events with and without concurrent snowfall. The identification of a blowing snow event in the database relies on an empirical method that requires the combination of several data sets (wind, precipitation and snow depth) and their detailed analysis by an expert.
Only events of longer than 4 h were recorded in the database to only include the main blowing events. Overall, the database contains information on the occurrence of blowing snow and the type of event (with or without snowfall) at an hourly time step. Figure  3  shows an overview of the interannual variability of blowing snow occurrence reported in the database with a winter average ranging from 6. 4 % in  2002-2003 to 19. 0 % in 2011-2012. These values correspond to the sum of blowing occurrence with and without concurrent snowfall reported on Fig. 3. Using this method, blowing snow were identified during 11. 7 % of time at Col du Lac Blanc over the period 2000-2016 and 36. 7 % of time blowing snow were classified with concurrent snowfall. These estimations are similar to those reported in  Vionnet et al.
Because of snowfall and blowing snow events the elevation of the SPC-S7 above the snow varied substantially during the winter season preventing any direct comparison over time. That is why, in the present database, the snow fluxes near the surface (that means the snow flux provided by the SPC-S7 closest to the snow surface) and the corresponding height of the sensor are provided. When available, snow fluxes at higher positions are used to derive two standardize values of the horizontal snow mass flux: (i) the flux at 1 m above the snow surface and (ii) the flux vertically integrated over 1 m (between 0. 2 and 1. 2 m over the snow surface). The computation of these fluxes is described below. According to the diffusion theory of snowdrift, it is possible to approximate the averaged drift density (kg m -3 ) as a function of height and wind velocity  (Radok, 1977; Gordon and Taylor, 2009).
Therefore the SPC-S7 data were processed as follows. First, a filter was applied to the raw data to suppress possible electronic noise: events with a particle flux smaller than 20 particle cm -2 during 10 min were discarded and set to zero. Then, an algorithm was designed and used to categorize each 10 min profile of SPC-S7 data set into six different groups (specified in the database): undetermined: only one sensor provides data making it impossible to do the vertical interpolation; -power_law: the vertical distribution function can be expressed as a power law (Eq. 2); inconsistent: data are physically inconsistent; mean: snow fluxes at different heights are not significantly different, so that the mean horizontal snow flux at 1 m is estimated by the average of the measured flux; www. earth-syst-sci-data.
net/11/57/2019/ Earth Syst. Sci. Data, 11, 57-69, 2019  -no_flux: in this case, no sensor among the SPC-S7 in the vertical profile detects more than 20 particles per cm 2 in 10 min; maintenance: during sensor maintenance, data acquisition is stopped. When possible (power law and mean), the mean horizontal snow fluxes are determined at 1 m and vertically integrated over 1 m (between 0. 2 and 1. 2 m). Figure  4  shows an overview of the interannual variability of blowing snow occurrence and intensity derived from the SPC-S7. They measured non-zero snow flux during a percentage of time ranging from 43 % in 2010-2011 to 63 % in 2013-2014 corresponding to an average blowing snow trans-port quantities between 0. 2 and 1. 2 m per linear metre during 30 days, ranging from 2547 kg in 2010-2011 to 10 331 kg in 2013-2014.
Overall, for the period 2010-2016, SPC-S7 provided a non-zero value during 50 % of time and an average quantity of snow transported between 0. 2 and 1. 2 m per linear metre and per 30 days of 6245 kg. However, SPC-S7 can detect individual snow particles and can report a positive signal (particle number larger than 20 particles cm -2 during 10 min) even during very light snowfall events with low wind. This period is considered as a blowing snow period, even if it does not significantly contribute to the total amount of transported snow. For this reason, we quantified the sensitivity of the estimations of blowing snow occurrence and amount to the threshold value used to set flux to zero. Re-Table  3. Ratio between the quantity of snow transported between 0. 2 and 1. 2 m per linear metre during the periods identified as blowing snow periods in the empirical database and the total quantity of snow observed during the same winter season.
Comparison between empirical database of blowing snow occurrence and data from SPC-S7 devices The estimation of blowing snow occurrence determined with the SPC-S7 reported on Table  2  differs from the results obtained with the empirical database (Fig. 3 ). To gain more understanding of these differences, we determined the quantity of snow transported between 0. 2 and 1. 2 m per linear metre during the periods identified as blowing snow periods in the empirical database and we compared this value with the total quantity of snow transported between 0. 2 and 1. 2 m per linear metre derived with the SPC-S7 for the same winter season. The result is expressed as a percentage in Table  3. It shows that the empirical database of blowing snow occurrence detects 55 % of the total transported snow mass measured by the SPC-S7.
This results from the non-detection of blowing snow events of low to moderate intensity with the empirical method as discussed in  Vionnet et al. (2013). This method only reports the main blowing snow events. This mainly results from assumptions made in the method: the minimal event duration is set to 4 h and only periods with wind speed greater than 6 m s -1 are included during snowfall. Therefore, the estimation of blowing snow occurrence with the empirical method (12. 0 % of the time for the period 2010-2016; Table 3) constitutes a lower bound for the estimation of blowing snow occurrence at CLB. SPC-S7 provides estimations ranging between 23 and 50 % of the time, depending on the threshold value used when filtering the SPC-S7 data as discussed in the previous section. The empirical database of blowing snow events and the SPC-S7 data are two sources of information on blowing snow occurrence and intensity at CLB.
We recommend the use of SPC-S7 data for the study of blowing snow processes and the evaluation of models at fine temporal scales, whereas the empirical database of blowing snow events can be used to evaluate reanalysis or output of regional climate models on a longer term. Compared to the SPC-S7 data, the empirical database covers a longer time period  (11 additional years: 2000-2010). It also provides continuous hourly estimations of blowing snow occurrence, whereas about 25 % of the SPC-S7 data can be considered as invalid or missing over the period 2010-2016 (Fig. 4 ).
A strong interannual variability was found in terms of snow depth, with a maximum snow depth reaching 4. 01 m in 2012-2013 and only 2. 84 m for winter 2013-2014. The mean snow depth value during the 2010-2016 period was 1. 99 m. Events with a maximum wind speed above 15 m s -1 were frequently measured at the site. These windy events were generally associated with blowing snow events if erodible snow was present at the snow surface. The intensity of these events varied greatly (note the logarithmic scale on the graphs showing blowing snow fluxes). Additional and more detailed summary plots for each year of the present data set are provided as a supplement to this article.
Data availability The database (https://doi. org/10. 17178/CRYOBSCLIM) presented and described in this article is available for download at http://doi. osug. fr/public/CRYOBSCLIM_CLB/index. html (last access: 3 December 2018). The digital elevation model of the study area (1. 5 km 2 -see Supplement) at 1 m resolution is also provided in RGF 93 Lambert 93 coordinates. Available data and the corresponding DOIs are introduced in Table  4. The data of the winters 2016-2017 and 2017-2018 and of the upcoming years will be added to the database on a yearly basis and made available to the community through the CRYOBSCLIM data portal (available at: http://data. cryobsclim. fr, last access: 3 December 2018).
Conclusions The Col du Lac Blanc is a unique high-elevation experimental site in the French Alps where meteorological and blowing snow data are automatically acquired. This site is dedicated to studies of snow-atmosphere interactions and windinduced snow transport. Sixteen years of quality controlled data have been combined and made freely accessible for the scientific community. They are composed of meteorological data from four automatic stations and a reanalysis product, a database of the main blowing snow events that occurred at the site over 16 years and 6 years of automatic measurements of blowing snow fluxes with snow particles counters SPC-S7. These high-quality data have been already used to evaluate snow redistribution models for specific blowing snow events or years  (Schön et al.
, 2015; Vionnet et al. , 2014 Vionnet et al. , , 2017 Vionnet et al. , , 2018) )  and to carry out detailed analysis of the physics of snow transport  (Naaim-Bouvet et al. , 2010 , 2011; Nishimura et al. , 2014). We anticipate that these data will be used in the future in modelling studies of snow accumulation over multiple snow seasons to better understand the links between the occurrence and intensity of blowing snow events and meteorological and snow conditions. We hope that further instrumental developments will allow us to improve the monitoring of snow-atmosphere interactions at this site for the benefit of the snow, avalanche, mountain hydrology and weather forecasting communities. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-11-57-2019-supplement.
Acknowledgements. The authors would like to thank  Fred Ousset  and  Xavier Ravanat  for their valuable help in the field,  Isabelle Gouttevin ,  Fatima Karbou  and  Hugo Merzisen  for past and future involvment. We also would like to acknowledge SATA  G. Guyomarc 'h et al. : A meteorological and blowing snow data set (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) ( Socièté d'Aménagement Touristique de l'Alpe d'Huez et des Grandes Rousses ), who provided logistical support and OSUG-DC for hosting the data. Col du  Lac Blanc  is a part of  IR OZCAR  and receives financial support from  OSUG, LabEx OSUG@2020 ,  Météo-France  and Irstea. CNRM/CEN and Irstea are part of  LabEx OSUG@2020  ( ANR10 LABX56 ). Edited by: John Pomeroy Reviewed by: three anonymous referees.
Data DOIs AWS Lac Blanc https://doi. org/10. 17178/CRYOBSCLIM. CLB. LACBLANC (Guyomarc'h et al. , 2018a) AWS Muzelle https://doi. org/10. 17178/CRYOBSCLIM. CLB. MUZELLE (Guyomarc'h et al. , 2018b) AWS Dome https://doi. org/10. 17178/CRYOBSCLIM. CLB. DOME (Guyomarc'h et al. , 2018c) AWS Col https://doi. org/10. 17178/CRYOBSCLIM. CLB. COL (Guyomarc'h et al. , 2018d) Empirical database of blowing snow occurrence https://doi. org/10. 17178/CRYOBSCLIM. CLB. BSO (Guyomarc'h et al. , 2018e) SAFRAN reanalysis https://doi. org/10. 17178/CRYOBSCLIM. CLB. SAFRAN (Guyomarc'h et al. , 2018f) Digital elevation model https://doi. org/10. 17178/CRYOBSCLIM. CLB. DEM (Guyomarc'h et al. , 2018g).
In addition the S 2-ion was estimated using a chromatographic method, converting it in sulfate ion after oxidation with H 2 O 2. Heavy metals (Pb, Cd, Cu, and Zn) were estimated by means of a polarographic method, with a Metrohm 797 VA Computrace; this system uses a multimode working Mercury electrode and an Ag/AgCl electrode as reference. Using this method, metals that are soluble in mercury such as zinc, cadmium, lead, and copper are simultaneously determinable  (Chianese et al. , 2019). Also in this case, calibration curves were calculated using certified multistandard solutions. For the grain size analysis  (Eleftheriou and McIntyre, 2008) , sediment was sieved over a series of 11 sieves with mesh size ranging from 1 cm to 63 µm. Fractions were dried in oven at 60 • C for 48 h and weighed; data were expressed as percentages of the total sediment dry weight, differencing it in three size classes: gravel (>2 mm), sand (2 mm <Ø> 0.
Data Analysis Multivariate ordination by principal component analysis (PCA) was performed on normalized environmental variables in order to determine their distribution patterns among the four sampling sites. Sinecological indices, such as number of individuals (N) per 25 dm 3 , species richness (SR), Shannon-Weaver diversity (H :  log 2 ) and Pielou's evenness (J) were calculated based on three replicate samples for each site. The quantitative (DI, percentage of individuals of a given species upon total individuals collected in the sample) and qualitative dominances (DQ, percentage of species of a given taxon upon the total of species collected in the sample) were also calculated. Differences of sinecological variable among sites were detected by permutational analysis of variance (PERMANOVA;  Anderson, 2001a) , based on Euclidean distance  (Terlizzi et al.
ACKNOWLEDGMENTS We are grateful to Soprintendenza of the  Underwater Archeological Park of Baia  (prot. 5667,  24/10/2016 ) for the authorization to sampling. We are thankful to  Dr. Guido Villani  for assistance during the underwater sampling and permission to use photos that enriched the manuscript. Thanks are also due to the diving center  Centro SUB Pozzuoli (Guglielmo Fragale ) for support field activities and  Dr. Aurélie Tasiemski  and  Dr. Céline Boidin-Wichlacz  for helping in sampling activities. We also thank the reviewers for the suggestions and comments. We are indebted to  Dr. Jeroen Ingels  ( Florida State University ) for useful discussions.
FUNDING Financial support was provided by the project "Prokaryotenematode Interaction in marine extreme envirONments: a uniquE source for ExploRation of innovative biomedical applications" (PIONEER) funded by the Total Foundation and IFREMER  (2016) (2017) (2018) (2019) , and by the project Boost Europe -ERC "Extreme marine nematoDes: moDel orgAnisms for a Journey toward the origin and Evolution of metazoan life in our changing planet" (EDDAJE) funded by the Brittany Region and IFREMER (2019).
However, overdependence on remotely sensed national scale datasets means that key facets of ecological and human importance are neglected. Human experiences of WAs are intrinsically situated, multisensory and subjective. The value of WAs from a human perspective cannot be mapped remotely, but requires in situ assessments in order to reflect the rich, multisensorial and subjective reality of how people understand and value wild places  (Ólafsdóttir et al. , 2016). Current attempts to develop complementary methods to capture human-level experience repurpose everyday technologies to support terrestrial mapping: e. g. viewshed analysis, an approach adapted from computer gaming, has been explored in order to assess ground-level vistas, rather than aerial land cover  (Carver and Washtell, 2012; Sang 2016) ; and social media networks have been coopted to enable crowdsourcing of visitor perceptions of trails in the USA  (Carver et al.
Acknowledgements This work was carried out with support from  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  642935  ( ENHANCE). Computational analysis of the sound recordings was undertaken on ARC3, part of the High Performance Computing facilities at the  University of Leeds, UK. The authors would like to thank  Scottish Natural Heritage, The Centre for Ecology and Hydrology  and the  Pyrenees National Park  for their assistance.
We consider the 15 rain gauges inside the catchment, together with the 27 stations located less than 15 km outside. This gives a total of 42 stations with 20 to 64 years of data between 1 January 1948 and 31 December 2013. In both databases, daily values are recorded every day at 06:00 UTC, corresponding to rainfall accumulation between 06:00 UTC of the previous day and 06:00 UTC of the present day. The Ardèche catchment is chosen for illustration purposes and because, despite its relatively small size, it shows strong inhomogeneity in rainfall distribution. To illustrate this, we show in Fig. 2  the averages of annual totals and annual maximum daily rainfalls for each station. Computing the ratios between the largest and lowest values in Fig. 2  gives ratios of 2. 6 for the annual totals and 3.
The lower AIC, the better the model. Then we repeatedly drop the variable that increases most the AIC -if any -, and add the variable that decreases most the AIC -if any. This stepwise method is implemented in the "step" function of R package "stats". At algorithm stop, the model may contain 1 to 10 parameters, for each ψ. Predictions θ (l) are then obtained as the back transformation of the estimated regressions. Last but not least, bivariate and trivariate thin plate splines are considered for ψ(l)  (Boer et al. , 2001; Hutchinson, 1998). These methods are implemented in function "Tps" of R package "fields". In the bivariate case, ψ(l) is modeled as ψ(l) = u(x, y) + (x, y), where u is an unknown smooth function and (x, y) are uncorrelated errors with zero means and equal variances. The function u is estimated by minimizing Q i=1 ψi -u (x i , y i ) 2 + λ +∞ -∞ +∞ -∞ ∂ 2 u(x, y) ∂x 2 2 +2 ∂ 2 u(x, y) ∂x∂y 2 + ∂ 2 u(x, y) ∂y 2 2 dxdy, ( 13 ) where λ is the so-called smoothing parameter, which controls the trade-off between smoothness of the estimated function and its fidelity to the observations.
Evaluation at regional scale in a cross-validation framework Evaluation is performed in two ways. The first one is a leaveone-out cross-validation scheme aiming to test at regional scale how the interpolated distributions are able to fit the data of the stations when these data are left out for estimating the mapping model. The second step assesses spatial stability by comparing the interpolated distributions obtained at a given station whether the data of this station are used or not in the mapping estimation. In other words, it is a comparison between leave-one-out and leave-zero-out mappings. So the two evaluations differ in that the first one compares an interpolated distribution to data, while the second step compares two interpolated distributions. First, let us consider a given parameter estimate θ (1) i,j obtained at station i based on the subsample C (1) i (for a given marginal model).
, 2005)  (Figure  1 ). El Teniente geologists and geotechnicians have observed that the latter, particularly the "weak veins" corresponding to veins and veinlets filled with a weak mineral assemblage (hardness less than 3 in the Mohs scale) and with a thickness greater than 1 mm, are the most important factors in explaining rock fragmentation, rock bursts and geomechanical instabilities in the mine  (Brzovic and Villaescusa, 2007; Brzovic, 2009). Geotechnical borehole data collected in the CMET rock between 2010 and 2016 are available for this study. The boreholes were drilled from the surface or from underground galleries and, for each of them, several intervals (with a length generally comprised between 20 and 30 m) were logged. The logging information consists of a record of the positions of the weak veins intersecting the borehole and the angles between their poles and the borehole axis.
Acknowledgements The authors acknowledge the constructive comments of two anonymous reviewers, the funding of the  Chilean Commission for Scientific and Technological Research (CONICYT) , through grants  AMTC AFB180004 PIA CONICYT  and  CONICYT / FONDECYT / REGULAR / N°1170101 , and of the  CSIRO-Chile International Center of Excellence in Mining and Mineral Processing , as well as the support of  CODELCO-El Teniente  for providing the data used in this work. L. Katherine Sánchez  is also grateful to the  International Association of Mathematical Geosciences (IAMG)  for the Computers & Geosciences Research Scholarship, to  CONICYT  for her postgraduate study scholarship.
Polymer analysis with FTIR spectroscopy Five items for each size fraction (including the >5 mm fraction) and shape category (except beads) were randomly selected at each site and analyzed to determine their plastic nature and where appropriate their polymeric composition  (Löder and Gerdts, 2015). A total of 40 fibers, 60 fragments, 60 films, 60 foams and 4 beads were examined (fibers were only present inside four size classes). MPs were analyzed with a Perkin Elmer Frontier FTIR spectrometer, and fibers, due to their small volumes, were analyzed with a microscope IR-Plan Spectra Tech coupled to a Bomem ABB FTLA FTIR spectrometer. The identification of each polymer was performed by comparison with a self-collected spectrum database including polyethylene (PE), polypropylene (PP), polyamide (PA), polyvinyl chloride (PVC), polystyrene (PS), poly(ethylene-vinyl acetate) (PEVA), poly(ethylene terephthalate) (PET), polyester (PES), polyurethane (PUT), acrylic (A) and some non plastic materials such as cotton, wool, wood, paper, calcium carbonate, talcum powder and plant fibers.