dataesr/scientific-paragraphs-categorization huggingface dataset:
This selection procedure resulted in the use of trees with at least three measured tree rings dating from before 2001. The final dataset consisted of cores from 67 oaks at the B1 site and 61 oaks at the P8 site. The ring widths (in mm) were then converted into basal area increment (BAI in cm 2 ) values, to provide a better estimate of total radial growth. For each year, the basal area (BA) was calculated as BA n ¼ p r n 2 with r n the sum of ring widths from the pith to year n inclusive. Basal area increments (BAI) were then calculated as: BAI n ¼ BA n À BA nÀ1 For multi-stem trees, the sum of BA n for the different stems was calculated per year, and BAI n was then calculated as if the tree had one stem. BA values ranged from 9 to 190 cm 2 in 2012. The largest oak sampled therefore had a BA 21 times larger than that of the smallest oak.
Acknowledgments We would like to thank the  Unité expérimentale Forêt Pierroton ,  UE 0570 ,  INRA ,  69 Route d'Arcachon, 33612 CESTAS  and  H. Jactel  and  I. Van Halder , for the installation of the experimental sites,  L. Riols  and  F. Bernier  for soil characterisation and dendrometric measurements,  G. Capron  for his contribution to powdery mildew monitoring,  J. Sappa  of  Alex Edelman & Associations  for language editing, and two reviewers for their suggestions.
However, the shortcoming of BU estimates is that they may not be consistent with the well-observed global atmospheric growth rates of GHGs. Also, accurate BU assessments are hindered by our limited understanding of microbial and belowground processes and the lack of spatially-explicit, time-series datasets of drivers (e. g. , wildfire, peatland drainage, wetland extent). The magnitude of human-induced CH 4 and N 2 O emissions reported here is more uncertain than the total emissions of these gases because it contains both the uncertainty of pre-industrial emission and contemporary emission estimates (see Methods for additional discussion). This study highlights the importance of including all three major GHGs in global and regional climate impact assessments, mitigation option and climate policy development.
In contrast, most BU estimations using land ecosystem models do not consider the full set of factors responsible for CO 2 release  32, 33. The discrepancy between TD and BU estimates for Southern Asia may come from several reasons. First, the land use history data commonly used for driving terrestrial biosphere models, e. g. , HYDE  50  and GLM  51  , was reported to overestimate cropland area and cropland expansion rate in China and to under-estimate it in India compared to regional dataset  52, 53  , thus biasing BU estimates of land conversion-induced carbon fluxes. But none of BU models included in this study conducted global simulation with such regional dataset updated. Second, large uncertainties exist in estimating carbon release due to tropical deforestation  4, [54] [55] [56] [57].
Acknowledgements: This research has been supported partially by  National Aeronautics and Space Administration (NASA) Grants  ( NNX08AL73G ,  NNX14AO73G ,  NNX10AU06G ,  NNX11AD47G ,  NNG04GM39C ),  National Science Foundation (NSF) Grants  ( CNH1210360 ;  AGS 1243232 ;  AGS-1243220 ). JGC was supported by the  Australian Climate Change Science Program. ES was supported by  NOAA Climate Program Office  (award #  NA13OAR4310059 ). CRS was supported by  NASA  Grants (# NNX12AP74G , # NNX10AG01A , and # NNX11AO08A ). KRG was supported by  NSF   CAREER (AGS-0846358 ). RGP was supported by  NASA  Upper.
7 ) is perpendicular to the flow direction, but does not change the interpretation of the inverse SP/elevation gradient as long as the gradient is clear. No gradient or a normal gradient is often considered to represent rising water flow (Lenat 2007); however, when the SP profile is perpendicular to the flow direction, as here, it would be hazardous to consider section 4 of the profile as a rising flow (Fig. 7 ). As there is no thermal anomaly, and because the SP variations are around 20 mV, it is more likely that the SP variations in section 4 (Fig. 7 ) are due to internal variation in the aquifer surface geometry. MWT was used to estimate the depth of the main sources of electric potential. On average, the aquifers are around 100 m deep (Table  1 ). Results of the MWT from SP data consist of 189 depth values measured with four different wavelets between 2006 and 2009 (Table  1 ).
On the basis of their position along the SP profiles, the depth values were grouped into 26 depths  (8 in 2006, 9 in both 2007, 2008 and 11 in 2009). Using only depths found with at least three wavelets that are spatially well located over the four years (along the profile), we characterize six datasets along the crater rim, which are named for their relative position from the crater: North, NE, East, SE, South and SW (Fig. 8 ). No obvious change in depth occurs from 2006 to 2009, within the uncertainty of the MWT analyses (Fig. 8a-f  ).
Acknowledgements The authors are grateful to  S. Rusponi  and  H. Brune  from  EPFL  for stimulating discussions,  J. Dreiser ,  C. Piamonteze  and  F. Nolting  from the  Swiss Light Source  on the X-Treme beamline and  E. Otero  and  P. Ohresser  from the french Synchrotron SOLEIL on the DEIMOS beamline for their respective investment in FeRh and FeCo XMCD measurements. Support is acknowledged from both  GDR CNRS 3182  and COST-STSM-MP0903 on Nanoalloys. All cluster samples were prepared in the PLYRA platform while SQUID measurements were performed at the  CML platform at Lyon.
Here, we used a single-layer model that was based on the assumption that all concentrations of electrons are located in an infinitely thin spherical shell at 350 km height; locations of the fluctuations are related to ionosphere piercing points. In our case study of the 2015 St. Patrick's Day storm, we used ground-based GPS stations located in the mid-and high latitudes, with ~2500 and ~180 in the Northern and Southern Hemispheres, respectively. All raw GPS data were resampled to 30 s resolution, with a cutoff elevation angle of 30°. Detailed description of the ROT/ROTI calculation using groundbased GPS data and results for the March 2015 storm is presented in  Cherniak et al. (2015). Further, we used the results obtained from the multisite GPS database processing to calculate daily polar maps based on ground-based GPS ROTI values.
These maps represent a spatial evolution of the high-latitude irregularities and their linkage with the Earth's magnetosphere (due to the strong connections between the Earth's magnetic field and the ionosphere). The ROTI mapping technique is described in detail in  Cherniak et al. (2014). ROTI behavior is represented as a function of magnetic local time (MLT) and corrected magnetic latitude (MLAT) for a specific day. We used the corrected geomagnetic (CGM) coordinates with the Definite/International Geomagnetic Reference Field (DGRF/IGRF) models. Such a polar map represents a daily 00-24 MLT time frame and a grid size of 8 min MLT by 2° MLAT, with the latter covering 50°-90°. ROTI data were binned and averaged in each MLAT-MLT bin; the map resolution is 20 bins along the MLAT axis and 180 bins along the MLT axis.
LEO GPS measurements Data from a zenith-looking antenna of a space-borne GPS receiver are used mainly for precise orbit determination (POD) purposes, but here we used them in an implementation of the ROTI mapping approach. This allowed us to reveal differences in the spatial distribution of ionospheric irregularities detected on ground and from altitudes above the ionospheric F2 layer peak. As a primary data source, we used POD GPS measurements from three satellite missions: Swarm, TerraSAR-X and GRACE. All these satellites operate in a close range of 450-550 km in a near polar orbit. The polar orbit is highly important for auroral ionosphere research as it is a region where sparse experimental data were available till now. The Swarm mission consists of three identical satellites; two of them fly in tandem separated by 1°-1.
In addition to the main gravimetric payload, GRACE has a GPS receiver onboard that uses up to 12 channels for POD purposes. The satellite has a circular polar orbit with inclination of 89°. Orbit altitude in March 2015 was about 430 km. TerraSAR-X is a German synthetic aperture radar (SAR) Earth observation satellite, which was launched in June 2007. It has a sun-synchronous dawn-dusk polar orbit with an inclination of 97. 5°. Orbit altitude in March 2015 was about 515 km. The satellite has a dual-frequency GPS receiver with two zenith antennas for POD and delivers data with 10 s resolution. POD GPS measurements from these LEO missions are available with different temporal resolutions: 1 s for Swarm and 10 s for GRACE and TerraSAR-X. To harmonize the GPS data from the different missions, we degraded the Swarm data to a 10-s sampling rate.
In order to fix the location of the ROTI results, we used piercing points through a thin layer at a fixed altitude of 600 km. For LEO ROTI mapping, we used the same grid as for the ground-based GPS data: corrected geomagnetic coordinates, grid cells 8 min MLT by 2° MLAT and 00-24 h daily data accumulation. Likewise, the LEO ROTI value of a cell was calculated by averaging all values covered by the cell area. Joint processing of the GPS POD measurements from the missions allows us to increase effectively the spatial data coverage in both Northern and Southern Hemispheres. Figure  1  illustrates how GPS data derived from all five satellites can cover an area of interest, mid-and high latitude, in both hemispheres. All processed lines of sight between LEO and GPS satellites during one full day of March 17, 2015, were recalculated to the MLAT-MLT domain, and MLAT for 40°-90° is accounted.
When the main phase of the storm developed, a series of substorms occurred starting at ~06 UT. These substorms resulted in development of enhanced auroral activity and increased visible discrete aurora. As a result, persistent strong ionospheric irregularities are registered from ground and above LEO orbit. The daily ROTI map corresponding to the most disturbed day of March 17, 2015 (Fig. 3b ), shows dramatic changes in the pattern of high-latitude ionospheric plasma irregularities. Daily ROTI maps derived from the ground-based GPS data depict a significant increase in ROTI intensity. An oval-like structure of irregularities expands equatorward to ~50° MLAT. A very specific signature of the irregularity pattern is clearly recognized, where intense irregularities are oriented in the day-night direction across the polar cap.
Hourly ROTI maps and in situ Swarm data Hourly ROTI maps derived from ground-based GPS data were analyzed together with the Swarm in situ electron density (Ne), measured onboard by Langmuir Probe instrument, for the storm's main phase. The results provide qualitative and quantitative characteristics of the ionospheric irregularities detected from ground level and at the altitude of the Swarm satellites. This allows us to examine a possible origin of the irregularities and to study events at ~500 km altitude when Swarm satellites cross polar regions. Figure  4  shows hourly ROTI maps derived from groundbased GPS data in geographic projections for selected time intervals of  March 17, 2015: 04-06, 15-17, 18-20, and 22-24  UT. On each polar map, tracks of Swarm A and Swarm B satellites that passed over during each 2-h interval are superimposed.
In the polar region, several characteristic Ne spikes can clearly be recognized from the low density environment, which can be associated with large-scale polar patches (e. g. ,  Tsunoda 1988). One of the most pronounced Ne increases was detected by Swarm B at 15. 8 UT for the latitude range of ~70°-74° S; peak Ne reaches 5. 2 × 10 5 el/cm 3 from the density floor below 1. 0 × 10 5 el/cm 3. However, even more extreme enhancements in Swarm Ne data are found for the next time interval of 18-20 UT. Swarm A and Swarm B satellites both crossed enhanced plasma density structures with peaks of 1. 33 × 10 6 el/cm 3 and 1. 67 × 10 6 el/ cm 3 , respectively. There are several important points to.
They identified the presence of GDI and obtained GDI growth times using along-track velocities for a test case of December 29, 2013. The maximum magnitude of electron density inside the polar patches was less than 1. 4 × 10 5 el/cm 3. Goodwin et al. (2015)  reported on polar patch observation in Swarm Ne data with a top magnitude of ~6. 0 × 10 4 el/ cm 3. For the case of the March 2015 storm, we revealed polar patches in the Southern Hemisphere with peak Swarm Ne measurements at least 10 times higher than was observed previously in Swarm data.
Conclusion Here, we present results of an analysis of GPS measurements, provided by more than 2700 ground-based GPS stations and five LEO satellites, during the March 2015 severe geomagnetic storm. Our main conclusions are: 1. The combined analysis of the rate of TEC index derived from GPS data onboard several LEO satellites (Swarm A, B and C, GRACE and TerraSAR-X) together with Swarm in situ plasma probe data has allowed us to detect topside ionospheric irregularities and to compare them with features of the ionospheric storm's development. 2. The occurrence of strong ionospheric irregularities in the topside ionosphere during the storm's main phase was associated with SED formation at mid-latitudes and further evolution of the SED plume to the polar tongue of ionization. 3. Extreme plasma density enhancements associated with TOI and polar patches were found in the polar cap region of the Southern Hemisphere.
They were registered in Swarm Ne data between 15 and 24 UT on March 17, 2015. Maximal Ne densities up to 1. 3-1. 6 × 10 6 el/cm 3 (that is 10-15 times higher than the surrounding density level) were observed in the polar region at 18-19 UT. 4. Degradation of GPS performance onboard Swarm satellites was observed when satellites crossed the southern polar cap region, with extreme plasma density enhancements. The number of tracked GPS satellites suddenly decreased from the maximal 8 to 0-4. 5. Analysis of in situ plasma densities measured by the three Swarm satellites demonstrates that during the 2015 St. Patrick's Day storm all polar cap events with extremely enhanced plasma densities that exceeded 10 6 el/cm 3 were observed in the Southern Hemisphere. Moreover, polar patches and TOI structures with lesser densities prevailed also in the Southern Hemisphere and were compared to the Northern Hemisphere, particularly North America, where SED/TOI structures have already been reported (e.
Acknowledgements We acknowledge use of the raw GPS data provided by  IGS  (ftp://cddis. gsfc. nasa. gov),UNAVCO (ftp://data-out. unavco. org),EUREF (ftp://rgpdata. ign. fr),  Natural Resources Canada  (webapp. geod. nrcan. gc. ca) networks. We acknowledge the  European Space Agency (ESA)  for providing the Swarm data (http://www. earth. esa. int/swarm) and the Information Systems and Data.
Similarly, in the context of the world's largest rivers, these approaches, taken alone, do not provide the full picture of bank and near-bank bed changes over suitably large spatial scales. For example, on the Mekong River, much of the bank remains submerged, even at low flow. In the present study, we present coincident above-and belowwater surface river bank survey data collected with an integrated MBES and MLS system, along a ~2 km long reach of the Mekong River (Figure  1(A) ). The flow conditions, and associated suspended sediment transport rates, during the surveys were characterised using acoustic Doppler current profiling (aDcp) (Figure  3.
Quality positioning services quality integrated navigation system (QINSy) hydrographic survey software was used to control the MLS and MBES and to correct the data using the position (RTK dGPS) and motion (IMU) data. QINSy allows direct control of third party equipment through an extensive library of drivers which can be configured by the user for the specific nature of the survey, including inputting the exact sensor location on the vessel relative to the IMU. Simultaneous with the topographic survey, detailed 3D flow velocity fields were obtained using two acoustic Doppler current profilers (aDcp; Figure  3(A) ) deployed from a second vessel. Depending on the flow conditions encountered at the time of the survey, we deployed either a RDI Teledyne 600 kHz (deeper water) or a RDI Teledyne 1200 kHz (shallower water) unit.
Data processing The MBES data require routine post-survey calibration and correction for angular offsets and the application of sound velocity corrections, which were all undertaken using CARIS-HIPS software. Although both the MBES and MLS topographic data are corrected for motion and position in real time, this is relative to a local base station at the time of survey. Raw GPS observations for the base station positions were logged in the field, allowing accurate (to within 0. 02 m) absolute base station positions to be post-processed using NOAAs online positioning user service (OPUS; http://www. ngs. noaa. gov/OPUS/). The offsets of the post-processed base station positions were applied to the MLS and MBES datasets as linear transforms at this stage in the processing procedure. Each of the four corrected data sets was exported from CARIS into the open source point cloud processing software CloudCompare  (EDF R&D, 2015).
CloudCompare was used to undertake the manual clean up and quality assurance of the data, including removal of redundant data points such as bank top vegetation, people, buildings and boats for the MLS data, and fish and acoustic noise for the MBES data, using the segmentation tools. The survey method employed ensures as much bank coverage as possible (Figure  3(A) ), but as noted above a small swath of bank immediately below the water surface remains unsurveyed (Figure  3(B) ). The vertical variations in bank morphology within the data gaps are moderated over the scales of consideration herein (mean vertical data gap of 4. 5 m, maximum of 8 m) as they tend to fall in the vertical part of the bank face. Therefore, to enable calculations of volumetric losses to be made, the missing sections of the point clouds were interpolated using Poisson surface reconstruction  (Kazhdan et al.
, 2006)  to create complete banks (Figure  3 (C)). The Poisson surface reconstruction routine, implemented as a plugin within CloudCompare, is a method of interpolating point clouds to surfaces that makes use of the orientation (normals) of the points, making it particularly robust for complex point cloud data. We note that the interpolated surfaces were then resampled at a comparable resolution to the original data (~0. 1 m spacing), resulting in complete coverages of the bank, with the exception of Field Survey 1 (FS1) that only included MBES data collected some distance from the bank due to stormy weather at the time of survey. The FS1 data set revealed only two small areas of bank toe, and as such is excluded from subsequent analysis. Finally, the point clouds were differenced using the multiscale model to model cloud comparison (M3C2) algorithm of  Lague et al.
2 L Rutner (horizontal Van Dorn) sampler. These point samples were obtained simultaneously with aDcp data collection at a variety of discharges and locations throughout the alluvial reach of the Mekong River, such that we were able to produce calibration curves for each of the aDcp units. The range of suspended sediment concentrations covered by the sampling procedure was 5. 7 to 530. 8 mg L À1. The resultant calibration curves display a good fit (R 2 values are 0. 83 and 0. 67, for the 600 kHz unit and the 1200 kHz unit, respectively; P < 0. 05 for both curves; Figure  4 ). Using these calibrations, it is possible to estimate suspended sediment concentrations across the entire water column using the corrected acoustic backscatter values returned from the aDcp unit deployed. The associated velocity measurements from the aDcp were then used to convert these concentrations into a sediment flux at each cell, and these were then integrated across each cross-section to determine the instantaneous suspended sediment load (kg s À1 ) for that cross-section.
Estimating bank erosion using remotely sensed data In order to (1) provide a long-term context for the site-specific bank retreat recorded herein and (2) allow comparisons to be drawn between the volumetric bank erosion calculations herein and more conventional approaches, we performed an analysis of bank top shift at the study site using remotely sensed images. For the long-term Lower Mekong River bank erosion rates, digitised 1959 aerial photos of the ~250 km reach between Sambor and Phnom Penh (Figure  1 ) were used in conjunction with Landsat imagery from 2013. Note that 2013 was used as it provided the most complete contiguous coverage of the Mekong at a single point in time. To facilitate comparisons with the survey data reported herein, the Landsat imagery used was collected within no more than 3 days before or after our field surveys.
Specifically, although MLS-and MBES-derived estimates of volumetric bank erosion sometimes fall within the upper and lower error bounds for the Landsat-derived estimates (Figure  5 (B)), there are instances where this is clearly not the case, such as between 1750 and 1950 m along the bank in the July to November 2014 epoch (Figure  5 (B), lower panel). Furthermore, the MLS and MBES surveys reveal no statistically significant bank accretion at any locations in the two change epochs. In contrast, the Landsat analysis for July to November 2014 shows a number of locations which are flagged as accret-  *Data at the study site was not collected during FS1 due to safety concerns, and thus was estimated using an adjustment of data collected from a site 15 km upstream by comparing discharge and SS rates for FS2, FS3 and FS4.
The site upstream consistently has 12% lower discharge rates across the data and so the data for FS1 was estimated by adding 12% to the upstream values. ing  (at 0-100, 200-250, 625-650 and 1850-1900 m along the bank). The site at 1850-1900 m is at a location that the combined MLS and MBES method identifies as experiencing some of the highest erosion (nearly 10 000 m 3 ) in the epoch. The relatively coarse pixel resolution of 30 m for the Landsat and 3 m for the 1959 aerial imagery is clearly the dominant factor driving the large levels of error and uncertainty (annualised errors of 21. 2 m; Table  II ) when compared with the MLS and MBES derived data. However, bank heights clearly play an important role in controlling the rates of volumetric erosion, with the highest MLS-and MBES-derived rates occurring where the bank heights are greatest.
In conjunction with the hydrograph (Figure  2 (C)), it is evident that discharge (as recorded at Kratie) for two of the four field surveys, FS2 and FS3, fell approximately on the mean annual hydrograph. Conversely, FS1 exhibits a far greater than average discharge and during FS4 a below average discharge was recorded. The suspended sediment transport rates for the surveys are, necessarily, just snapshots of conditions over the short time period of data collection. Therefore, to make a direct comparison with the volumetric bank erosion recorded during this study, a simple power-law rating curve for suspended sediment transport rate (Q s ) as a function of observed (aDcp) discharge (Q) was produced: Q s ¼ 0:00002 Q 1:9123 R 2 ¼ 0:92; P ¼ 0:015 À Á : (3) Using this rating curve and integrating suspended sediment transport over the hydrograph at Kratie (60 km upstream) for each of the epochs of change defined above, produces a total suspended sediment yield of 18 AE 9 Mt and 77 AE 36 Mt for the dry and wet seasons, respectively.