dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017a; Xu et al. , 2017a). The PM 1 and PM 2. 5 Q-ACSM mass spectra were analyzed using the Q-ACSM local toolkit (version 1. 5. 11. 2), a data analysis software written in WaveMetrics Igor Pro ™. The detailed procedures for the data analysis have been described in  Ng et al. (2011b)  and Y. Sun et al. (2012). The sensitivity of the two Q-ACSMs was calibrated using size-selected ammonium nitrate (NH 4 NO 3 ) particles (300 nm), which were 1. 09 × 10 -10 and 2. 06 × 10 -11 , respectively, for the PM 1 and the PM 2. 5 -Q-ACSM. The relative ionization efficiencies (RIEs) of ammonium and sulfate were determined as 4. 9 and 4. 7, and 0. 8 and 1. 2 for the PM 1 -Q-ACSM and PM 2. 5 -Q-ACSM, respectively. The default RIE values of 1. 1, 1. 4, and 1. 3 were used for nitrate, organics, and chloride, respectively  (Canagaratna et al.
Acknowledgements. This work was supported by the  Natural Science Foundation of China  ( D0512/91544231 ,  41575120 ) and the  National Key Research and Development Plan of China  ( 2016 YFC0200505 ). The development of the PM 2. 5 -ACSM was funded by  US EPA  grant no. EP-D-12-007  and  US DoE  grant no. DE-SC0001673. We would like to thank  Ping Chen  for his support in this campaign. Yunjiang Zhang  acknowledges a PhD scholarship from the  China Scholarship Council (CSC). Edited by: Willy Maenhaut Reviewed by: two anonymous referees.
e. from c. 200 to over 2000 mm yr À1. With some of the most drought-tolerant species currently known  (Brodribb et al. , 2010; Larter et al. , 2015) , this clade presents an ideal group of species to investigate plant ecophysiology and the development of drought tolerance from an evolutionary perspective. Our main objective was to investigate the evolution of the vascular system of all members of the Callitris clade. First, we constructed an extensive physiological dataset of xylem embolism resistance, xylem specific hydraulic conductivity and xylem anatomical traits. We then obtained species occurrence data and tested whether variation in hydraulic traits was linked to species climatic preferences and the diversification of this clade, established with a time-calibrated phylogeny based on DNA sequence data from multiple loci.
Species climate The climate experienced by Callitris species varies widely from hot, semiarid Mediterranean climate in southwestern and central Australia to wet tropical or monsoonal climates in Northern Australia, Queensland and New Caledonia (Fig. 1 ). In order to describe each species' climate, we downloaded species occurrence points from the Global Biodiversity Information Facility (GBIF;  Booth, 2014). After excluding data with possible species misidentification or GPS coordinate errors, we extracted climate information for each point using climate data from the WorldClim database  (Hijmans et al. , 2005)  using QGIS software  (Quantum, 2011). We also obtained potential evapotranspiration (P-ET) and aridity index (AI) from the CGIAR-CSI database  (Trabucco & Zomer, 2009). We then extracted the mean and median values of the distributions of each climatic variable for each species.
Magnification was 9400 for all samples except for N. pancheri, C. sulcata and C. neocaledonica, for which we used 9200 magnification to increase the total number of tracheids visible on each image. Sample preparation and photography were conducted at either the University of Bordeaux or Western Sydney University (Hawkesbury Institute for the Environment), using the same protocol. Images were manually edited to remove imperfections before automatic image analysis. Tracheids with an incomplete lumen (i. e. placed on the edge of the images) were excluded from the analysis. Overall, 80 individual samples (three to five per species) and a total of 400 images were analyzed, resulting in a total of 33 652 tracheids measured. We extracted from each image the area of each tracheid lumen, total image area and number of analyzed tracheids, and pooled the data by individual for analysis.
Phylogenetic reconstruction We built on the increased availability of molecular sequences to produce a new phylogeny of Callitris, Actinostrobus and Neocallitropsis. We took a broad view of species delimitations, i. e. treating C. columellaris, Callitris intratropica, and C. glaucophylla separately, as well as C. tuberculata, C. preissii, C. verrucosa and C. gracilis. Various treatments of these as subspecies and/or synonyms are available in the literature  (Hill, 1998; Hill & Brodribb, 1999; Farjon, 2005 Farjon, , 2010;; Eckenwalder, 2009). We  Alignments from PHLAWD were manually trimmed and checked, and we selected the most appropriate DNA substitution models using maximum likelihood implemented in MEGA  (Tamura et al. , 2011)  (see Supporting Information Table  S1  for details). To check for incongruence between the chloroplastic and nuclear datasets, we first analyzed both matrices separately using maximum likelihood with RAXML (v.
Statistical analyses Vulnerability curve fitting was done in SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA). All remaining data manipulation and analysis were run in R v. 3. 2. 3 (R Core  Team, 2015). We performed linear regressions on raw data and log-transformed data with nearly identical results, so we present only untransformed correlations to simplify interpretation. When significant relationships appeared nonlinear, we fitted a nonlinear model (i. e. y = a9log(x) + b) with nonlinear least-squares (NLS function) in R. However, to allow comparisons across bivariate relationships, in all cases we report R 2 values from the linear correlations. Phylogenetic generalized least squares (PGLS) were run using the CAPER package v. 0. 5. 2  (Orme, 2013). PGLS accounts for the statistical nonindependence in cross-species trait correlations by adjusting the residual error structure using a variance/covariance matrix derived from the phylogeny  (Garamszegi, 2014).
We note that since MAP enters into the calculation of AI, these two variables are strongly correlated. In our dataset, we found that P 50 and tracheid diameter were strongly positively correlated, and P 50 and tracheid frequency were negatively correlated, with resistant species having numerous smaller tracheids compared with species that were more vulnerable (Fig. 4a, b ). No relationship was detected between P 50 and void-to-wood ratio or wood density (Fig. 4c, d ). In turn, K s appeared to be disconnected to some extent from tracheid and wood traits, showing only a significant relationship with tracheid frequency (Fig. 4f ). The remarkably high conductance measured for C. intratropica must be highlighted, as it is the result of a highly unusual combination of hydraulic traits: narrow D h (Fig.
used the PHLAWD pipeline (Smith et al. , 2009)  to obtain sequences from GenBank (Benson et al. , 2011)  with the aim of constructing a large multilocus dataset. We used a range of loci from plastid (rbcL, matK, trnL, psbB, petB) and nuclear DNA (ITS, needly, leafy). To complete the dataset, we used unpublished sequences (rbcL and matK) for the species C. roei, C. monticola, C. baileyi, and C. columellaris (N. Nagalingum, pers. comm. ).
Acknowledgements The authors acknowledge  Lin Kozlowski  and  Renee Smith  for their analyses of wood anatomical traits,  Anita Wesolowski  and  Vanessa Hequet  for help with conducting sampling for this study, Fr ed eric Lagane for assistance with the wood density measurements, and  Gaelle Capdeville  for assistance with embolism assessment. The authors thank  Stacey Smith  for helpful comments on a previous version of the manuscript, and three anonymous reviewers for their suggestions to improve the manuscript. Part of this study was funded by the  programme 'Investments for the Future ' (grant no. ANR-10-EQPX-16 , XYLOFOREST) from the  French National Agency  for research, and mobility grants within the  COTE Cluster of Excellence  (grant no. ANR-10-LABX-45 ).
The 440 monitored glaciers represent only 0. 25% of the nearly 200,000 glaciers inventoried on Earth and included in the Randolph Glacier Inventory (RGI) and GLIMS databases  [3, 4]. This small sample limits our understanding of the relationship between climate and glacier changes at regional to global scales, as well as our understanding of the contribution of glaciers to water resources and to the biodiversity of high-altitude watersheds. Consequently, there is a need for monitoring approaches that are complementary to field measurements to quantify the SMB of individual glaciers at a regional scale. For this purpose, satellite remote sensing has proven to be a very useful tool. The increase in the number and availability of satellite sensors over nearly four decades, as well as their improved performance, i.
This has been achieved via the MODImLab MATLAB toolbox whereby the downloading and processing of thousands of MODIS images can be efficiently batched. MODImLab also provides algorithms to compute isotropic and anisotropic albedos  [34]  for every MODIS pixels, allowing end-users to easily obtain albedo maps. Regarding the snow-map method, the data processing is partly automated. Indeed, a chain of MATLAB routines allows processing the downloaded SPOT/VGT images and the DEM in order to estimate the seasonal SMB.
Use of Other Images Regarding the ELA method, the application presented here used high-resolution satellite images (i. e. , SPOT, Landsat, ASTER). However, as mentioned earlier, the method of mapping the end-of-summer snow line can be based on different sources, from very high resolution aerial photographs (e. g. ,  [15, 16]  and, more recently,  [22] ) to medium resolution satellite images like MODIS data (e. g. ,  [24, 57, 58] ) when the studied ice bodies are wide (i. e. , hundreds to thousands of km 2 ). Adaptation of the albedo method to other sensors (currently only MODIS) is also needed. There is a good prospect for sustainable observations of glacier surface by upcoming satellite programs such as Sentinel-3 and JPSS constellations, both embarking instruments capable of providing high temporal revisit frequency at the regional scale similar to MODIS.
Image processing currently done for MODIS to retrieve horizontal equivalent reflectances and retrieve surface albedo of snow and ice needs to be adapted to the new spectral resolution. Further adaptation of the albedo retrieval to Landsat-8 and Sentinel-2 data is also needed to research the potential of resolving the spatial distribution of SMB, as well as mitigating the spectral mixture affecting albedo retrieval from currently coarse pixels. The combination of observations from Landsat-8 and Sentinel-2 may achieve sufficient redundancy to resolve the albedo cycle, albeit at a higher cost in computing resources. Applying the snow-map method with images from other optical sensors is possible as far as the temporal resolution of image acquisition makes possible the calculation of seasonal snow maps.
Such an exercise has already been initiated with observations from the PROBA-V satellite, launched in May 2013 in order to continue the SPOT/VGT mission (which ended in May 2014). Note that a similar exercise is still needed with MODIS and Sentinel-3 data. Regarding PROBA-V, an inter-calibration of the NDSI computed from the PROBA-V 1-km resolution images and from the SPOT/VGT images has been achieved by Drolon  [59]  over the acquisition overlap period of the two sensors (October 2013-May 2014). Consistency has been found between winter SMB estimations from the two sensors, in 2013-2014. A "near real-time" seasonal SMB monitoring is thus conceivable with PROBA-V or any other daily resolution optical imagery for a large sample of Alpine glaciers, if an inter-calibration of the satellite data can be achieved.
Regarding the snow-map method, a generic relationship showed good performance for winter SMB, even on non-monitored glaciers (without calibrated relationships established between Z and SMB). Indeed, for a dataset of 15 glaciers of the Alps, the winter generic relationship showed a satisfying RMSE (490 mm w. e. year -1 ). The winter generic model can thus be applied at the scale of the European Alps to sufficiently estimate winter SMB of glaciers for which no in situ data are available. The application of this generic model to other mountain ranges requires further developments.
Acknowledgments: The authors are grateful to the spatial agencies  CNES ,  ESA ,  NASA  and  VITO , for providing free access (or with a reduced cost) to the satellites images. The  French Service National d'Observation GLACIOCLIM  (http://glacioclim. osug. fr/)and the  World Glacier Monitoring Service  (http://wgms. ch/)are acknowledged for the collection and distribution of in situ glaciological data used to calibrate/validate the remote-sensing based method. Authors from the  University of Grenoble Alpes  acknowledge the support of  LabEx OSUG@2020  (Investissements d'avenir- ANR10 LABX56 ) and of  EquipEx GEOSUD (Investissements d'Avenir   ANR-10-EQPX-20 ). P. Sirguey was supported by a  University of Otago   Research Grant  ( ORG12-10864401RFW ) and support of the  Grenoble Institute of Technology (Grenoble-INP)  and the  University Grenoble-Alpes (UGA).
Acknowledgements This study was supported by the projects  PAPIIT IA104715  ( DGAPA ,  Universidad Nacional Autónoma de México) , the  Grup de Recerca Reconegut per la Generalitat de Catalunya   2014 SGR 251  " Geologia Sedimentària " and CGL 2015-69805-P (MINECO,  FEDER, EU). We appreciate the help of  Dr. Jaume GALLEMÍ  and  Dr. Vicent VI-CEDO  for providing access to the ammonites of the Marmellar area that are housed in the  Museu de Geologia de Barcelona, Catalonia, Spain. We appreciate the assistance and access to facilities to review the ammonites of the Marmellar area by  Dr. Sebastián CALZADA , director of the  Museo Geológico del Seminario de Barcelona, Catalonia, Spain. We are also very grateful to  Robert W. SCOTT  for linguistic corrections.
Experiment design For the purpose of investigating the effect of dust on WAM circulation dynamics, two sets of experiments were performed over the West African domain (see Figure  1 ) one without the dust (referred to hereafter as CTRL) and other with the dust (referred to hereafter as DUST). The domain (Figure  1 ) is centred on 15 ∘ N and 3 ∘ E with 295 × 197 grid points with a horizontal grid spacing of 30 km and 18 sigma vertical levels. Both experiments covered a period of 7 years, spanning from January 2007 to December 2013. For forcing data, the prescribed sea surface temperature (SST) are from the National Oceanic and Atmospheric Administration Optimum Interpolation weekly 1 ×1 ∘ grid dataset  (Reynolds et al. , 2002) , while the atmospheric initial conditions and 6-h lateral boundary conditions (LBCs) necessary to run RegCM4 are from the European Center for Medium range Weather Forecast (ECMWF) reanalysis ERA-Interim  (Dee et al.
, 2011) , available at the horizontal resolution of 1. 5 ∘ over 32 pressure levels. Some basic assessments were carried out for the whole monsoon season (June-July-August-September; JJAS). Simulated precipitation from the CTRL experiment is compared against observation datasets from the Climatic Research Unit (CRU TS 3. 2. 1) of the University of East Anglia  (Harris et al. , 2014) , the CMAP [CPC Merged Analysis of Precipitation;  (Xie and Arkin, 1997) ] and the Tropical Rainfall Measuring Mission [TRMM;  (Huffman et al. , 2007) ], while near surface temperature is evaluated against the station-based observation from CRU.
Multi-angle imaging spectro radiometer (MISR) MISR on the Earth Observation Science (EOS) Terra platform has been operational since late February 2000. The  instrument observes the earth's surface using nine different view zenith angles, ranging from 70 ∘ both in forward and aftward direction along the spacecraft track, in four spectral bands (446, 558, 672 and 866 nm) and with a cross track ground spatial resolution of 275 m-1. 1 km and a swath width of 400 km. Monthly observation data from Terra MISR at the spatial resolution of 0. 5 ∘ × 0. 5 ∘ , were extracted over the AERONET stations. These datasets are available at http://giovanni. gsfc. nasa. gov/aerostat/.
AERosol Robotic NETwork (AERONET) AERONET is an ensemble network of Cimel Sunphotometers. This observation network of instruments provides aerosols seasonal cycle and are widely used for validation of aerosols modelling studies and satellite observations because the measurement characteristics are well understood and documented  (Dubovik et al. , 2000). The direct solar radiation measurements are made every 15 min in eight spectral channels at 340, 380, 440, 500, 675, 870, 940 and 1020 nm (nominal wavelengths). The Cimel Sunphotometer is a solar powered, hardy, robotically pointed sun and sky spectral radiometer. AERONET provides a long term, continuous and public accessible data in several stations widely distributed across the world. The dataset contains AOD, microphysical and radiative properties for aerosols characterization, validation of satellite observations and other aerosols databases synergy.
Three qualities of AOD data are available which are level 1. 0 (unscreened data), level 1. 5 (cloud-screened data) and level 2. 0 (cloud-screened and quality-assured data). Also, AERONET data have been used to characterize aerosol distribution and seasonality over West Africa  (Ogunjobi et al. , 2012; Oluleye et al. , 2012). For simulated AOD validation, the level 2. 0 data across West African stations between 2008 and 2013 are used when these data are available. AERONET data are available from http:// aeronet. gsfc. nasa. gov/.
MODerate resolution Imaging Spectroradiometer (MODIS) MODIS measures irradiances in 36 spectral bands from 0. 4 μm-14. 5 μm with spectral resolution of 250 m (bands 1-2), 500 m (bands 3-7) and 1000 m (bands 8-36)  (De Meij and Lelieveld, 2011). MODIS is on board the polar Terra spacecraft and was launched at the end of 1999 but data acquisition became effective in February 2000. MODIS-Terra level 2 deep blue AOD at 550 nm over the stations within the study area were used for monthly mean AOD validation. 3) and Banizoumbou (0. 38) with RMSE of 0. 17 and 0. 18, respectively. However, better correlations of 0. 65 and 0. 75 with lower RMSE of 0. 08 and 0. 075 were found for Dakar and Cape Verde respectively. This suggests that the model performs well in reproducing dust outflow over tropical Atlantic Ocean.
Acknowledgements N'Datchoh was supported by the  West African Science Service Center on Climate Change  and  Adapted Land Use (WASCAL) Programme. This paper is an extension of the first authors PhD work funded by  WASCAL programme. Also , the authors gratefully acknowledge ICTP through STEP programme for their support and computational facilities. Last but not least, the authors thank the two anonymous reviewers for their constructive comments, which improved the quality of the paper significantly. The research leading to this publication is co-funded by  WASCAL programme (West African Science Service Center on Climate Change and Adapted Land Use)  and the  NERC/DFID  " Future Climate for Africa " programme under the  AMMA-2050  project, grant number  NE/M019969/1. This paper is a contribution of AMMA-2050 and WASCAL to CORDEX-AFRICA initiative.
This study presents a methodology to detect and monitor surface water with Sentinel-1 Synthetic Aperture Radar (SAR) data within Cambodia and the Vietnamese Mekong Delta. It is based on a neural network classification trained on Landsat-8 optical data. Sensitivity tests are carried out to optimize the performance of the classification and assess the retrieval accuracy. Predicted SAR surface water maps are compared to reference Landsat-8 surface water maps, showing a true positive water detection of ∼90% at 30 m spatial resolution. Predicted SAR surface water maps are also compared to floodability maps derived from high spatial resolution topography data. Results show high consistency between the two independent maps with 98% of SAR-derived surface water located in areas with a high probability of inundation.
Finally, all available Sentinel-1 SAR observations over the Mekong Delta in 2015 are processed and the derived surface water maps are compared to corresponding MODIS/Terra-derived surface water maps at 500 m spatial resolution. Temporal correlation between these two products is very high (99%) with very close water surface extents during the dry season when cloud contamination is low. This study highlights the applicability of the Sentinel-1 SAR data for surface water monitoring, especially in a tropical region where cloud cover can be very high during the rainy seasons.
With the advantage of higher temporal resolution than previous SAR instruments, Sentinel-1 has the ability to monitor the seasonal cycle of water extent every six days over Europe and the boreal region, and with slightly reduced temporal sampling elsewhere. In this study, we propose a methodology using Sentinel-1A SAR observation for monitoring water surface extent within Cambodia and the Mekong Delta for the year 2015. It is based on a Neural Network (NN) algorithm, trained on visible Landsat-8 images (30 m spatial resolution). At the time of this study, the temporal resolution of Sentinel-1 over the Delta was 12 days: it reduced to 6 days after the launch of the Sentinel-1B in April 2016. The Sentinel-1 SAR data and the ancillary observations are described in Section 2, including the pre-processing steps.
Sentinel-1 SAR Data Sentinel-1 is a satellite project funded by the European Union and carried out by the European Space Agency. It is a two satellite constellation working at C-band (5. 405 GHz). The major objective of the satellites is the observations and monitoring of land and ocean surfaces day and night, under all weather conditions  [20]. The satellite operates in four exclusive imaging modes with different spatial resolutions (the highest being 5 m) and swaths (up to 400 km). The first Sentinel-1A satellite of the pair was launched on 3 April 2014, while the second Sentinel-1B satellite was launched on 22 April 2016. The Sentinel-1 satellites fly along a sun-synchronous, near-polar circular orbit at an altitude of ∼693 km. Incidence angle varies between 29 • and 46 •. The two satellites provide a re-visiting time of 6 days (it was 12 days before the launch of the Sentinel-1B satellite).
Sentinel-1 satellites have dual polarization capabilities (HH, VV, HH + HV and VV + VH), giving final users the ability to access a large variety of applications, including the monitoring of surface water. SAR images from Sentinel-1 satellites are freely downloaded from the sentinel scientific data hub  [21]. In this project, 20 m resolution (10 m pixel spacing) Level-1 Ground Range Detected (GRD) Sentinel-1 images are used, from the Interferometric WideSwath (IW) mode. These images have been detected and projected to ground range using an Earth ellipsoid model provided by ESA. Over the Mekong Delta, there are two polarizations available: the VH and VV polarizations. Some pre-processing steps have to be carried out using the free Sentinel Application Platform (SNAP) software developed by ESA, before moving to the analysis steps (see Figure  1 ).
These pre-processing steps are described in the "SAR Basics with the Sentinel-1 Toolbox in SNAP tutorial"  [22]. First, multi-looking processing is applied to each single Sentinel-1 image (both polarizations) to convert to 30 m spatial resolution (to match with Landsat-8 images). Applying multi-looking at the beginning of the chain reduces the processing time for the next steps since the size of the image is several times smaller than the original one. Second, the image is calibrated to convert values of the raw image from digital number to radar backscatter coefficient (σ 0 ). Third, the Refined Lee filter is applied to reduce the speckle noise and to smooth the radar backscatter coefficient data because this filter maintains details of the standing water boundary  [23]. Other filters (Lee, Lee Sigma or Median, for example) were tested, and results showed little differences in terms of water detection.