dataesr/scientific-paragraphs-categorization huggingface dataset:
The meteorological variables acquired at the AWS are precipitation, air temperature, incoming and reflected solar radiation, infrared surface temperature, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface), and soil temperature; all were taken at 10 min intervals. Snow depth distribution was measured during 23 field campaigns using a TLS, and daily information on the SCA was also retrieved from time-lapse photography. The data set (https://doi. org/10. 5281/zenodo. 848277) is valuable since it provides high-spatial-resolution information on the snow depth and snow cover, which is particularly useful when combined with meteorological variables to simulate snow energy and mass balance. This information has already been analyzed in various scientific studies on snow pack dynamics and its interaction with the local climatology or topographical characteristics.
s. l. )  (Alvera and Garcia-Ruiz, 2000; García-Ruiz et al. , 1986; López-Moreno et al. , 2002)  and, therefore, the study of the snowpack at high elevations in the Pyrenees is crucial for understanding and managing mountain river discharges  (López-Moreno, 2005) , especially in the scenario of global climate change  (García-Ruiz et al. , 2011). However, continuous snow observations above 2000 m a. s. l. are scarce in this mountain range since most only have information from 1600 to 2000 m a. s. l. and those that are available only cover short time spans. Therefore, well-established study areas at high elevations with continuous measurements of meteorological variables and snowpack distribution are required in the Pyrenees. This paper presents the recently acquired data set of meteorological and snowpack variables obtained from a small experimental catchment on the southern face of the Pyrenees.
Although meteorological and hydrological data are available from previous years (some variables have been measured since the late 1980s; Alvera and  Garcia-Ruiz, 2000) , we present data from the 2011/12 to 2016/17 snow seasons, as data series provide higher quality and continuity, and they also match in situ observations of snow depth and snow cover. The data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between two and six TLS surveys per snow season) and (iii) time-lapse images showing the snow-covered area (SCA) evolution. Some years of this data set have already been used to study the topographic control on snow depth distribution  (Revuelto et al.
, 2014b) , the spatial variability in snowpack at different distances  (López-Moreno et al. , 2012)  or to investigate how detailed snowpack simulation could be improved by including snow distribution information  (Revuelto et al. , 2016a, b). The paper is structured as follows: Sect. 2 describes the study area characteristics; Sect. 3 presents meteorological data acquired from the AWS with a general description of the observed climatology; Sect. 4 describes the distributed measurements on snow depth distribution from the TLS and the SCA derived from time-lapse images; Sect. 5 concludes with information for downloading the database; and finally Sect. 6 summarizes all information available and the potential application of the database.
Meteorological data The study site is equipped with an AWS located in the lower elevation of the catchment (42 • 44 33. 65 N, 0 • 25 8. 83 W, 2113 m a. s. l. ; Fig. 1 ), located in a flat open area with sparse vegetation (mountain pastures). The AWS measures wind speed and direction, atmospheric air temperature, relative humidity and air pressure, soil temperature for 0, 5, 10, and 20 cm, temperature of the surface close to the AWS (snow or soil, depending on whether snow is present or not), global and reflected solar irradiance, snow depth, and precipitation (the precipitation gauge is located at 15 m from the AWS tower) (see Fig. 2 ). Information on the main atmospheric variables has been recorded since the end of 2011 (AWS installed in November 2011). Therefore, data availability covers five complete snow seasons.
Since the station is located in the lower elevation of the catchment and despite air temperature lapse rate with elevation, the AWS records serve to describe the evolution of atmospheric variables occurring at the Izas Experimental Catchment. The data acquisition system consists of a Campbell Scientific CR3000 data logger that samples each instrument and stores data at 10 min time intervals. All data are transmitted via modem to the Pyrenean Institute of Ecology where automatic quality-control checks are applied to remove outliers. Data gaps are rare for almost all variables and, therefore, instead of gap filling with interpolation methods, only measured data are available. However, some variables had long data gaps and certain periods have been discarded from further analysis. This is the case of precipitation for the first three snow seasons, which were useless because of the length of data gaps.
The temporal evolution of the snow depth is shown in Fig. 4. In addition, Fig. 4  shows the precipitation values for the period with data in the precipitation gauge (from the end of July 2014). The sensor installed is a Geonor T-200B with a wind shield (Geonor A/S, 2010), which continuously weighs the accumulated precipitation (liquid and solid). The height of the gauge orifice is 3. 25 m (2. 5 m metal pedestal plus the height of the T-200B inlet). The precipitation accumulated over a certain period was calculated by subtracting final and initial weighted values. Table  7  includes the accumulated precipitation for the whole snow year and also during the accumulation and melt periods.
TLS acquisitions of snow depth distribution During the five snow seasons presented here, three to six TLS surveys were carried out each year in the Izas Experimental Catchment. TLSs are devices using lidar technology, a remote sensing method to obtain the distance between a target area and the device. During a TLS data acquisition, the device measures the distance of some hundreds of thousands of points within the area defined by the operator, creating a cloud of data points representing the topography of the target surface. The device used in this study is a long-range TLS (RIEGL LPM-321 (Fig. 2 ), RIEGL Laser Measurements, 2010). The technical characteristics of this model include (i) light pulses of 905 nm wavelength (near infrared), appropriate for acquiring data from snow cover  (Prokop, 2008) ; (ii) a minimum angular step width of 0.
Data availability The database presented and described in this article is available for download at Zenodo  (Revuelto et al. , 2017 ; https://doi. org/10. 5281/zenodo. 848277). Meteorological data of the AWS are given in. csv format. The meteorological data set includes observations at 10 min intervals. The TLS survey snow depth distribution maps are available online (one file for each TLS acquisition). These files are in ASCII format in the UTM 30T north coordinate system with a 1 m × 1 m spatial resolution. The DEM of the study area is also provided in same coordinate system. Photographs of cloud-free days from the time-lapse camera are available in the online repository, with the correspondence of pixel ground control points to GPS coordinates. Information on the optics and chip size of the camera is also provided.
Acknowledgements. This study was funded by the research projects CGL2014-52599-P "Estudio del manto de nieve en la montaña española y su respuesta a la variabilidad y cambio climatico" (Ministry of Economy and Development, MINECO) and CLIMPY "Characterization of the evolution of climate and provision of information for adaptation in the Pyrenees" (FEDER-POCTEFA). The authors are thankful for this unique opportunity to share information through the International Network for Alpine Research Catchment Hydrology (INARCH). Jesús Revuelto is supported by a postdoctoral Fellowship from the AXA research fund 2016 (le Post-Doctorant Jesús Revuelto est bénéficiaire d'une bourse postdoctorale du Fonds AXA pour la Recherche). Cesar Azorin-Molina is supported by the Marie Skłodowska-Curie Individual Fellowship (STILLING project -703733) funded by the European Commission.
A posteriori data processing Traditional δ 18 O and δ 13 C data were acquired as part of each ∆ 47 analysis and 17 O corrections were made using the 17 O parameters from  Santrock et al. (1985). In order to account for the temperature dependence of oxygen isotope fractionation between CO 2 gas and calcite resulting from the reaction with phosphoric acid at 90ºC in common acid bath, a fractionation factor of 1. 00811 was used. This fractionation factor has been constrained based on replicate δ 18 O measurements of several calcite samples digested at both 90 °C in common acid bath (CAB) and 25 °C in sealed-vessel (SV) and comparison to the widely accepted fractionation value of 1. 01025 for digestion at 25 °C in SV  (Friedman et O'Neil, 1977). We analyzed our in-house carbonate reference material (IPGP-Carrara marble) routinely, as well as the international reference material NBS-19 for oxygen and carbon stable isotopic compositions.
(2011). Figure  4  shows that the averaged Δ 47 data of coccolith calcite are statistically indistinguishable from the Δ 47 equilibrium values, as they fall within our analytical uncertainty of ± 14 ppm (ie. , the long-term external reproducibility determined on large number of replicate Δ 47 measurements of homogeneous carbonate reference materials; represented by the shaded area in Figure  4 ). At first glance, the coccolith Δ 47 data may, however, appear to be slightly higher that their respective Δ 47 equilibrium values. Nevertheless, this slight positive Δ 47 offset from Δ 47 equilibrium values are not supported, mainly because: i/ additional errors on theoretical calculations should also be considered, as those arising from approximations in the parameters used to determine it; and most importantly ii/ coccolith data are fully consistent with Δ 47 datasets on experimental inorganic calcites (Figure  5a ;  Ghosh et al.
Comparison with previous ∆ 47 calibrations In this section, we compare our coccolith data with previously-reported ∆ 47 data in literature. To this aim, we focus on the individual data rather than on the determined ∆ 47 -T calibration equations because it has been recently highlighted that either a small size of sample population and/or a narrow range of investigated temperatures could strongly influence the slopes and intercepts of the ∆ 47 -T calibration equations  (Bonifacie et al. , 2017; Kelson et al. , 2017). This caveat has been put forward as a likely cause of discrepancies between discrete ∆ 47 -T calibration equations (as discussed in the following discussion), thus hampering rigorous comparison of the temperature estimates obtained from discrete ∆ 47 -T calibrations. To circumvent this problem, it has recently been suggested that universal ∆ 47 -T calibration based on a large dataset from different laboratories should be used to standardize ∆ 47 -derived temperature estimates determined in different studies/laboratories  (Bonifacie et al.
, 2006; Dennis et Schrag, 2010; Zaarur et al. , 2013; Defliese et al. , 2015; Kele et al. , 2015; Kluge et al. , 2015; Tripati et al. , 2015; Kelson et al. , 2017)  or for a larger dataset from biogenic or inorganic carbonates regardless of their mineralogies and chemistries (all data being reported in the CDES frame) (Figure  5b ;  Henkes et al. , 2013; Came et al. , 2014; Douglas et al. , 2014; Fernandez et al. , 2014; Wacker et al. , 2014; Spooner et al. , 2016; Wacker et al. , 2016; Winkelstern et al. , 2016; Bonifacie et al. , 2017; Kelson et al. , 2017). It is also apparent that our coccolith data are consistent, when the respective uncertainties are considered, with the two coccolith samples measured by  Tripati et al. (2010). Figure  5  also highlights that even if they originate from three different coccolith species, our laboratory-precipitated coccoliths show, for a given temperature, only a small dispersion of ∆ 47 values compared to most available data (Figure  5a  and b ).
, 2004)  and ran concurrently to unknown samples. Importantly, it is striking that the additional recent data from  Kelson et al. (2017)  on inorganic calcites corrected from the 17 O interferences with the Brand et al. (  2010 ) parameters (56 mean ∆ 47 data) are falling within the 95% confidence interval envelop of the inter-laboratory universal calibration (calculated with 103 mean ∆ 47 values and proper error propagation by  Bonifacie et al. , 2017)  and also within the uncertainties of all other data plotted in Fig. 5 , while the later datasets were likely all 17 O corrected using the  Santrock et al. (1985)  parameters. As importantly, the absolute values of the slope and intercept of the composite calibration by  Bonifacie et al. (2017)  are unchanged if these 56 new ∆ 47 data from  Kelson et al.
Since our data were obtained from extant species, we must discuss whether the mechanisms leading to vital effects may have operated in the ancient species encountered in the sedimentary archive. As discussed in more details in Section 4. 5, carbon limitation by the coccolithophores in present-day oceans and laboratory cultures is the primary factor behind the expression of the vital effects affecting d 13 C and d 18 O coccolith compositions  (Bolton and Stoll, 2013; Hermoso, 2016; Hermoso et al. , 2016a; Hermoso et al. , 2016b; McClelland et al. , 2017). Under this biogeochemical assumption, we can thus make the safe assumption that during the Meso-Cenozoic Eras, when ambient atmospheric CO 2 concentrations were much higher than present-day values  (Pagani et al. , 2005; Hönisch et al. , 2012) , the isotopic composition of ancient coccolith calcite was likely close to equilibrium conditions.
Acknowledgements We thank  Pierre Agrinier  for help for the statistical treatment of our data and  Fabrice Minoletti  for discussions on the paleoceanographic implications of our study. We also thank  Claire Rollion-Bard ,  François Guyot  and  Emmanuelle Pucéat  for discussions. The coccolithophore species used in this study were kindly provided by the  Roscoff Culture Collection  and we are grateful to  Ian Probert  for providing access to these strains. MH acknowledges funding by the  Natural Environment Research Council (NERC)  under grant agreement  NE/H015523/1. MB thanks the  Emergences Ville de Paris Program , which funded the installation of the clumped isotope laboratory at IPGP and the  CNRS-INSU SYSTER program , which provided additional financial support. This publication arises from research funded by the  John Fell Oxford University Press (OUP) Research Fund  to MH and MB under a joint  CNRS/Oxford collaborative scheme (Project Ref :  112/871 ).
In order to measure the mass flux of atmospheric insoluble deposition and to constrain regional models of dust simulation, a network of automatic deposition collectors (CARAGA) has been installed throughout the western Mediterranean Basin. Weekly samples of the insoluble fraction of total atmospheric deposition were collected concurrently on filters at five sites including four on western Mediterranean islands (Frioul and Corsica, France; Mallorca, Spain; and Lampedusa, Italy)  and one in the southern French Alps (Le Casset), and a weighing and ignition protocol was applied in order to quantify their mineral fraction. Atmospheric deposition is both a strong source of nutrients and metals for marine ecosystems in this area. However, there are few data on trace-metal deposition in the literature, since their deposition measurement is difficult to perform.
(2016) , and a Saharan origin was identified for 107 selected samples by using satellite data and air mass trajectories. The selected samples account for 84, 78 and 73 % in mass of the total deposition in Lampedusa (37 samples), Mallorca (21 samples) and Corsica (11 samples), and it contributes for around 50 % in Frioul (21 samples) and Le Casset (17 samples). A complete description of the treatment and selection of samples can be found in  Vincent et al. (2016).
Whether or not reservoirs contain water throughout the dry season is critical to avoiding late season crop failure in seasonally-arid agricultural landscapes. Locations, volumes, and temporal dynamics, particularly of small (<1 Mm 3 ) reservoirs are poorly documented globally, thus making it difficult to identify geographic and intra-annual gaps in reservoir water availability. Yet, small reservoirs are the most vulnerable to drying out and often service the poorest of farmers. Using the transboundary Volta River Basin (~413,000 sq km) in West Africa as a case study, we present a novel method to map reservoirs and quantify the uncertainty of Landsat derived reservoir area estimates, which can be readily applied anywhere in the globe. We applied our method to compare the accuracy of reservoir areas that are derived from the Global Surface Water Monthly Water History (GSW) dataset to those that are derived when surface water is classified on Landsat 8 OLI imagery using the Normalised Difference Water Index (NDWI), Modified NDWI with band 6 (MNDWI1), and Modified NDWI with band 7 (MNDWI2).
Data gaps and uncertainties make GSW derived reservoir extents unsuitable for monitoring reservoirs that are smaller than 5. 1 ha (holding ~49,759 m 3 ), which constitute 674 (56%) reservoirs in the Volta basin, or monitoring seasonal fluctuations of most small reservoirs, limiting its utility for agricultural planning. This study is one of the first to test the utility and limitations of the newly available GSW dataset and provides guidance on the conditions under which this, and other Landsat-based surface water maps, can be reliably used to monitor reservoir resources.
Sediment or vegetation in water-particularly common in West Africa  [16] -alters the spectral signature of water, while cloud and dust particles in the atmosphere obscure or distort information about where land is water-covered  [28]. Researchers have successfully mapped water body locations and extents from Landsat imagery for several decades through the use of spectral indices  [23, 24, 29]. Spectral indices are created by calculating the difference, ratio, or normalised difference of two multispectral image bands and identifying the threshold that enhances the reflectance of wavelengths for objects of interest, such as water  [28]. The most effective index or threshold identified to map water is rarely the same between studies  [29]. More recently, Pekel J. -F. et al. [16]  used multi-spectral analysis to classify water and non-water over all of the images in the Landsat database from 1984 to 2015, resulting in pixel-level measures of water occurrence at monthly time steps over the entire globe.
Their Global Surface Water Monthly Water History (GSW) dataset represents an attempt to establish a globally applicable method for water detection from Landsat imagery. Uncertainties in GSW and other surface water maps are generally reported in terms of pixel-level classification accuracy  [30]  rather than the impact on practical applications. However, the level of uncertainty that is acceptable will depend on the end-use, and knowledge of this uncertainty may help to ensure its effective use in policy  [31]. For example, water managers that unknowingly use incorrect information on where a small reservoir exists or when reservoirs run dry may make decisions on water allocation that have serious consequences for the agricultural sector and farmers who rely on reservoirs for their livelihoods.
Intra-annual surface water maps derived from Landsat satellite imagery, and particularly the globally available GSW dataset, may be useful for monitoring spatial and seasonal dynamics of small reservoirs in agricultural landscapes opening up opportunities for improved global reservoir data, but their accuracy for this end-use is currently untested. The primary objective of this paper was to compare a range of methods for rapid and low-cost monitoring of reservoirs, and to establish levels of accuracy in reservoir characterization using these methods, as reservoir properties and environmental conditions vary. For this purpose, we developed a new method for mapping reservoir extents rapidly across large spatio-temporal extents using free, globally available datasets and tools. We compare the effect of accuracy, which is the level of uncertainty in reservoir surface area and equivalent volume estimates, on information about temporal and spatial reservoir water availability to highlight where improved accuracies may be important for agricultural applications.
Using the Volta Basin in West Africa as a case study, we focus on three specific questions: (i) What is the accuracy of reservoir areal extents digitized manually from high resolution Google Earth imagery compared to those derived computationally from GSW or from commonly used spectral water indices applied to Landsat 8 OLI imagery? (ii) How does the accuracy of Landsat-based reservoir area estimates vary with environmental factors? (iii) What information on reservoir size and seasonality can be reliably determined from the GSW and what cannot? Our study represents the first attempt to test the limits of the GSW dataset for monitoring reservoirs of varied size and across a range of environmental conditions in a West African context. Establishing reliable end-uses for the GSW is particularly important given that this dataset is global, publically available, and easy to use.
The global applicability of our approach makes it useful to a wide range of stakeholders interested in surface water resources or low-cost environmental monitoring. We structure the remainder of the paper as follows. Section 2. 1 presents the study site. Sections 2. 2 and 2. 3 describe the input remote sensing datasets and processing techniques that were used to identify reservoirs and prepare surface water maps. Section 2. 4 explains how reservoir extents were extracted, and Section 2. 5 describes the validation data used to determine the accuracy of these reservoir extents. Sections 2. 6 and 2. 7 describes the comparison of accuracies and analysis of covariance with environmental factors, while Section 2. 8 explains how we used the GSW-derived reservoir extent data to illustrate potential policy-relevant applications.
Reservoir Locations We made use of the most recent imagery hosted in Google Earth in September 2015 and a 100 km × 100 km grid to systematically, manually identify, and map all of the visible engineered reservoirs across the Volta basin, generating a georeferenced point dataset of existing reservoir locations (Figure  1 ). For each of the 1200 identified reservoirs, we placed a point inside the reservoir boundary near the dam wall and where reservoir water was consistently present in months that the reservoir contained water, as shown on imagery within Google Earth's historical imagery collection.
Landsat-Derived Surface Water Maps We used Google Earth Engine (GEE) to source, generate, and analyse surface water maps that were derived from Landsat imagery. GEE is an online coding environment enabling relatively rapid, server-based analysis of large spatial datasets  [39]. GEE provides access to the EC JRC/Google Monthly Water History V1. 0 dataset  [16] , which contains maps of Global Surface Water created from decadal analysis of Landsat 4, 5, and 7 imagery and contains 30 m × 30 m pixel-level measures of water presence-absence on a monthly time step from March 1984 to October 2015. We worked in GEE to create additional monthly surface water maps from three spectral indices applied to Landsat 8 OLI imagery, for use in subsequent analyses. We sourced 1291 Landsat 8 OLI images that were acquired over the Volta basin (Landsat paths 192 to 197; rows 050 to 056) between 1 May 2013 and 31 October 2015, corresponding to the earliest complete month of data available in GEE and the temporal limit of GSW data.