dataesr/scientific-paragraphs-categorization huggingface dataset:
This model (or an adapted version) has been applied in the Amazon, from regional (thousands of km)  [5, 24]  to medium (hundreds of km) spatial scales  [25] [26] [27]. However, relatively high quality topographic data are obviously required to produce consistent results. Indeed, hydrodynamics features in floodplains are in part controlled by small topographic features  [28, 29] , such as narrow channels that drive floodplain connectivity at low and medium water levels  [27]. To date, the best topographic dataset readily available for the Amazon is from the Shuttle Radar Topographic Mission (SRTM), described in Farr et al. [30]. Nevertheless, the SRTM includes various types of errors, the dominant sources of which are summarized in Rodriguez et al. [31]. These errors, gathered hereafter under the term of interferometric bias, make the data inappropriate for use as they stand in hydrodynamics models.
The correction methodology considers interferometric and vegetation offset corrections and aims to keep the DEM coherent with hydrologic knowledge acquired during field campaigns, such as canals of communication between the Solimões and the lake. The elevation dataset merges the corrected SRTMGL1 elevations and in situ bathymetric data and is interpolated by the ANUDEM v5. 3 algorithm  [41, 42] , which is forced by a ground truth drainage network. The methodology remains relatively simple, and independent of the model. We compare the improvement of the DEMs generated by each methodology step in terms of vertical accuracy, drainage network quality and flood extent against GCPs derived from altimetry sensors (e. g. , Envisat, ICESat) and inundation maps deduced from free available remote-sensed product imagery.
SRTMGL1 and SWBD The global 1 arc-second SRTM V3. 0 dataset (SRTMGL1) is a joint product of the National Geospatial-intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA). Data were collected during 11 days in February 2000 using dual Spaceborne Imaging Radar (SIR-C) and dual X-band Synthetic Aperture Radar (X-SAR). From these data, a near global DEM was generated. By the beginning of January 2015, the SRTMGL1 had been freely released for South America. Data can be downloaded at NASA's Earth Observing System Data and Information System (EOSDIS) website  [45]. They are referenced to the World Geodetic System 84 (WGS84) ellipsoid and Earth Gravitational Model 1996 (EGM96) geoid. The EGM96 geoidal undulations were replaced by the EGM08 ones  [46] , using programs provided by NGA  [47]  for both geoid models.
As a by-product of the water body editing, a water mask was generated: the SRTM Water Body Data (SWBD). It presents the same coverage as SRTMGL1 and is available in ESRI Shapefile format  [49]  from NASA's EOSDIS. According to Lehner et al. [50] , this product presents some inconsistencies because in some cases, water body depiction required ancillary data sources, such as Landsat 5 data that were collected much earlier than the shuttle mission.
Radar Altimetry Data Satellite altimetry for continental waters really began in the 2000s, following the work involving the Geosat satellite  [51]  and the Topex/Poseidon (T/P) mission  [52]. Many studies have now shown the great ability of radar altimetry missions for retrieving river stages  [53] [54] [55] [56] [57]. In this study, we used data from three different radar altimeters, namely ENVISAT/RA-2, T/P and ICESat/GLAS, available through the CTOH website (Centre de Topographie des Océans et de l'Hydrosphère)  [58]. The ENVISAT/RA-2 altimeter launched by the European Space Agency (ESA) collected data from 2002 to 2012. ENVISAT has a 35-day repeat period; its ground-track presents an inter-track distance of approximately 80 km at the equator. Comparison at crossovers and with in situ gauges showed that the quality of the series can be highly variable, with results ranging from 12 cm to several meters  [54, 56, 59 ].
The T/P altimeter launched by NASA and the CNES (Centre National d'Etudes Spatiales) collected data from 1992-2004. T/P has a 10-day repeat cycle. Its ground-track presents an inter-track distance of 315 km at the equator. Birkett et al. [52]  showed accuracies ranging from 10 cm to several meters (mean RMSE 1. 1 m). NASA acquired elevation data from 2003 to 2009 using the GLAS altimeter on the ICESat satellite. The ground-track repeat cycle is 183 days, yielding 15 km track spacing at the equator. The ICESat/GLAS mission was initially launched to monitor icecaps. However, many studies have addressed its capability of monitoring other land covers. ICESat/GLAS instrument demonstrates height accuracies of 3 cm over water bodies  [60] [61] [62]. Due to its high accuracy, ICESat/GLAS data can be used as GCPs  [48, 60, 61].
Forest Canopy Height Map Simard et al. [39]  released a global map of Forest Canopy Height (FCHM, hereafter) at 1 km spatial resolution. This map is based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua satellites and from the ICESat/GLAS. The Simard et al. [39]  FCHM was created by regressing ICESat RH100 (Relative Height) canopy height measurements with global grids of annual mean precipitation, precipitation seasonality, annual mean temperature, temperature seasonality, elevation, and percentage tree cover. It is distributed as an 890 MB GeoTIFF file at the website [63]. This product by Simard et al. [39]  presents an accuracy of 6. 1 m compared with measurements at 66 Fluxnet sites.
All the other water level gauges were leveled with the help of satellite radar altimetry level time series data, according to the method described in Santos da Silva et al. [56] , and validated by high-precision bi-frequencies Global Positioning System (GPS) stations. Finally, level time series were reported using the EGM08 geoid. According to the available altimetry data, the slope of the water surface along the reach between Manacapuru and the virtual station SVR shows a mean of 2 cm/km over the years 2006-2012. Gauge element displacements or errors in reading or reporting data are always possible. The comparison between altimetry data and in situ records made it possible to correct or complete the in situ measurements, improving confidence in the in situ water level series  [67]. In particular, as shown in Figure  2 , the water level time series at the Manacapuru gauge distributed by ANA present noisy variations during the years 1994 and 1995.
To estimate the water level in the lake at the date of acquisition of the JERS-1 images (RL1 and RL2 records are available from only 2006), we needed confident water levels from Manacapuru at the same dates. From the T/P series SV_076 and SV_063 T/P, we estimated the water level on October 10, 1995 to be 12. 20 m, against the 10. 42 m given by the Manacapuru gauge series. The same reasoning for the HW level gave a difference of 0. 2 m between the altimetry estimation and the Manacapuru gauge level. As the T/P altimetry data have a vertical accuracy of approximately 0. 5 m [59], we did not modify the recorder level provided by the ANA gauge for the HW level. Then, building a linear regression between the Manacapuru gauge and RL1 over the LW and HW periods, we estimated the water level in the floodplain at the RL1 gauge to be 13.
Bathymetric in Situ Data Most of the data were acquired during a field trip organized during June 2012, when the water level in the floodplain rose exceptionally to a high absolute level of 24. 3 m recorded at the RL1 gauge. The in situ bathymetric data were acquired using an Acoustic Doppler Profiler Current (Teledyne RD Instruments, ADCP 1200 Hz) linked to a GPS. Everywhere else, we used an echo sounder linked to a GPS station. ADCP and echo sounder data were checked against manual measurements using a ruler and showed overall good agreement. Other in situ bathymetric data were acquired in May 2008 and in August 2006. Errors in the bathymetric grid are expected in both depth and position accuracy. According to the constructor, ADCP has a vertical precision of 1 cm in 99% of the measurements, far below the errors induced by field conditions (instruments onboard).
Method Prior to analysis, all gridded products except Landsat images were resampled to the SRTMGL1 1 arc-second resolution. All data were projected into the WGS84 Universal Transverse Mercator (UTM) zone 20S. We gathered the in situ bathymetric data and ICESat points into a global dataset of 201,935 GCPs. We split the latter into two independent sets, 90 GCP and 10 GCP, whose respective sizes were 90% and 10% of the size of the global GCP dataset. The methodological steps are presented in Figure  3. First, focusing on a window including a large area around the floodplain and the river, the process begins with the correction of the interferometric bias, followed by correction of the positive offsets related to land cover and vegetation height to produce an adjusted DEM (denoted DEM_ADJ hereafter).
Then, DEM_ADJ is merged with the 90 GCP dataset and interpolated using the ANUDEM algorithm constrained by a drainage network to produce the corrected DEM (denoted DEM_COR hereafter). The drainage network used to force the ANUDEM algorithm was automatically extracted from SRTMGL1 and corrected by visual inspection using Landsat images, denoted GTSN hereafter. Several products were generated: DEM_COR, representing the whole of the inputs; DEM_COR_1, generated from the GTSN and DEM_ADJ (without vegetation correction); DEM_COR_2, generated without the river network; and DEM_COR_3, generated without the 90 GCP dataset. To evaluate the weight of each method step and each input during the interpolation process, these generated products, DEM 1 and SRTMGL1, were validated vertically (against the 10 GCPs dataset), horizontally (against maps of flood extent), and hydrologically (against watershed and river network assessments).
Interferometric Bias and Vegetation Bias Correction Interferometric Bias Correction We followed a similar method to Rudorff et al. [26] , which consisted of creating two elevation subsets: one built with bathymetric-and ICESat-data that overlap bare soil areas, and the other one built using the corresponding SRTM elevations. Bare soil areas were deduced from the WM. The Mann-Whitney U test was used to exclude the hypothesis of equal means between real elevations and SRTMGL1 elevations. The bias was computed as the mean difference between the two elevation subsets mentioned above and uniformly reported over the entire study site domain. The resulting intermediary adjusted DEM is denoted DEM 1 hereafter.
All other classes are strictly derived from WM. As a whole, 16 regions were identified and summarized in Table  1. All regions except the terra firma and one forest class (ID41) may be flooded. Consequently, the pixel elevations of these regions must remain lower than or equal to the HWFM water level (22. 1 m as mentioned above). Alternatively, non-flooded/flooded regions must exhibit pixel elevations ranging between the LWWM and HWWM water levels. However, a less restrictive criterion was applied to the elevations of permanently flooded regions: they must be lower than the HWWM water level as the natural criterion for these zones (to be lower than the LWWM water level) was too restraining. The adjusted DEM, DEM_ADJ, was obtained by subtracting an elevation offset from the DEM 1 pixel within each region and by turning into NODATA all pixels that do not fulfill the elevation constraints after the offset subtraction.
The elevation distributions of the DEM 1 pixels overlapped by the never flooded forest (ID41), intermittently flooded forest (ID42) and always flooded forest (ID43) regions are shown in Figure  4a-c , respectively. The pixel elevation in the ID41 region must remain higher than 22. 1 m (HWFM water level). The elevation distribution of the DEM 1 overlapped pixels is mono-modal (Figure  4a ), with a median value of 26. 7 m and with 33% of the pixels presenting an elevation lower than 22. 1 m. As it was difficult to propose a vegetation offset that made sense while minimizing the quantity of pixels that would have been turned into NODATA after correction, the vegetation offset was kept undefined, and we turned only pixels less than 22. 1 m into NODATA. The elevation distributions of the pixels overlapped by the ID42 and ID43 regions are mono-modal (Figure  4b, c ), with median values of 27.
0 m, denoted ID511, and the rest, denoted ID-512. A comparison of the spatial repartition of these pixels with a Landsat image stressed that ID511 was linked to deforested areas surrounding water bodies or linked to roads and that ID512 corresponded to forested areas. A subtraction of 14. 70 m was used in the ID512 region, assuming the SRTM C-band penetration of 60% proposed by Carabajal and Harding  [34]. However, the elevation range of the pixels located in ID511 was globally smaller and fit with the imposed elevation range. Consequently, the elevations of these pixels were kept unchanged in this zone. The correction for the influence of vegetation led to turning to NODATA classifications for 31% of the whole study zone. However, the removal process was slightly counterbalanced by the introduction of 181,741 points from the 90 GCP dataset during the interpolation process that represents 2.
DEM Elevation Interpolation The second step in the DEM correction procedure consisted of the interpolation of DEM_ADJ using the ANUDEM v5. 3  [40]  algorithm implemented through the TOPO TO RASTER command in ARCGIS v10. 1. This algorithm generates a hydrologically correct raster surface from points, in contrast to other interpolation technics such as Inverse Distance Weighting or Kriging. The streamlines were generated applying the commonly used D8 algorithm  [68] , which involved the following steps: filling sinks; generating flow direction; generating flow accumulation; generating stream network. The river network was substantially improved with the help of a water/non-water classification obtained from Landsat images to produce a "Ground Truth" Stream Network (GTSN). The point elevation set was obtained by converting the DEM_ADJ raster to points after merging it with the 90 GCP dataset.
Quality Assessment of the Generated DEMs Vertical Accuracy The vertical accuracy assessment was estimated by comparison against the 10 GCPs dataset through 4 criteria. Setting x as the datasets consisting of the elevations of the DEM pixels that contain at least one GCP and y as the dataset consisting of the mean of the 10 GCP, we computed the following statistics: the RMSE, the MEAN, the Standard Deviation (SD) of (x-y), and the ROUGHNESS, computed as the mean of the standard deviation of a 3 ˆ3 array of elevation spots.
Vertical Accuracy Assessment The vertical accuracy assessment against the 10 GCP dataset for the different generated DEMs and the original SRTMGL1, as well as the roughness criteria, are gathered in Table  3. Clearly, DEMs generated with in situ and altimetry data present a better vertical accuracy than the others. The mean of the differences between the 10 GCP and DEM elevations decreased from ´1. 2 m for SRTMGL1 to 0. 1 m for DEM_COR. RMSE decreased from 4. 7 m for SRTMGL1 to 1. 7 m for DEM_COR. SD similarly decreased from 4. 5 m for SRTMGL1 to 1. 7 m for DEM_COR. Conversely, the descriptor values for DEM_COR_1 and DEM_COR_3, which does not include the 90 GCP dataset, remained similar to the values obtained for SRTMGL1 as RMSE and SD remain greater than 4 m. As expected, the roughness of the generated DEMs was significantly reduced, by approximately 50%, except for DEM_COR_1.
Stream Network Assessment Table  3  presents the validation relative to the river network. Regarding horizontal agreement, DEM_COR presents the best matching with the GTSN (83%), against percentages below 60% in the case of the SRTMGL1 and all other DEMs generated without the GTSN. All the extracted networks, except the one derived from DEM_COR_3, capture the connectivity between the different water bodies. However, only the networks extracted from DEM_COR and DEM_COR_1 presented both connectivity and right outlet position. In the case of DEM_COR_3, generated with the GTSN input but without GCPs, two outlets were found. This result was partly due to the hydrological incoherence introduced by the vegetation removal process, which resulted in turning 31% of the pixels into NODATA values 31% of the pixels into NODATA values without any counterbalancing by GCPs.
For the remaining DEMs, the TS values remained approximately 15% lower. The negative BIAS in all comparisons except for DEM_COR_1 showed an overestimation of the flood extent. The spatial distribution of pixels contributing to overestimation or underestimation of the flood extent is shown in Figure  6  (DEM_COR was used for this purpose). At low water levels (Figure  6a, c, d ), mis-flooded pixels were mainly located in the northwestern part of the study area, where bathymetric data indicated bottom elevation greater than 13. 5 m. Some of these pixels very likely result from the limitation of retrieving the flooding extent from a simple water surface interpolation as these flooded regions remained isolated from the water body where the water level used to interpolate the water surface is recorded (at RL1).
Conclusions Hydrodynamic models are attractive tools for quantifying river-floodplain water exchanges and for studying water circulation patterns in the floodplain, but they require relatively high quality topography to produce realistic results. To date, the best free readily available topographic information for the lower Amazon basin is the most recent SRTMGL1 release, at 1 arc-second of resolution. However, this dataset still presents inconsistent elevations. We proposed a method to remove some of the errors related to interferometric and vegetation bias, mobilizing in situ bathymetric data, altimetry data and flooding extent mapped from remote sensing. The interferometric bias was estimated to be2. 0 m. The mean total offset correction over the study area was 5. 9 m, and 7. 4 m for pixels located in the upland region.
As the SRTM elevation is located approximately 40% of the distance from the canopy top to the ground, the latter value led to a mean canopy height of 12. 3 m, which was reasonable considering the proportion of secondary forest in the region. In a second step, unbiased elevations were interpolated using the ANUDEM v5. 3 algorithm. Several DEMs were generated to control for the respective influence of using or not using a GCP dataset or a ground-truth drainage network in the interpolation process. The vegetation correction and the interpolation process made it possible to reduce the DEM roughness by almost 50%. As expected, using GCPs clearly improved the vertical accuracy: the RMSE value decreased from 4. 7 m to less than 2 m for all DEMs that included the GCP dataset. The use of GCPs also improved the flooding extent predicted by the DEM, independently of using or not using a drainage network at low water levels.
The correction method improved the agreement between the flooding extent derived from the DEMs and from remote sensed products at low and high water levels (+10% and +27%, respectively), whereas accuracy at a medium water level was difficult to evaluate using the Landsat product. At medium and high water levels, the use of a ground truth drainage network as input to the interpolation algorithm permitted the algorithm to achieve relatively reasonable results regarding flooding extent and watershed hydrological characteristics, even with DEMs that did not include GCPs. This result is promising from the perspective of the application of the method, at least for hydrological studies at larger scale as radar altimetry can likely furnish a sufficient GCP dataset to estimate the interferometric bias.
Acknowledgments: This research was conducted in the framework of the  CNPq  ( Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil)  and  IRD (Institut de Recherche pour le Developpement)  n  490634/2013-3 , and  LMI OCE  ( Laboratoire Mixte International Observatoire des Changements Environementaux ). It was supported by three research programs:  HIDRAS (CNPq) ,  Dinâmica Fluvial do Sistema Solimões/Amazonas.
Acknowledgments We would like to thank the following people for invaluable help and excellent technical support with this work; The Mouse production team,  James Bussell  and the  Research Support Facility, Joanna Bottomley , the  Sanger Mouse Genetics Project (MGP) , Sebastian Gerety  and  Katharina Boroviak  ( Wellcome Trust Sanger Institute, UK )  Julia Fernandez ,  Maria Jesus del Hierro ,  Marta Castrillo  and  Isabel Martin-Dorado  ( CNB-CSIC, Spain )  Matteo Parmigiani  and  Fabrizio Buonaventura  ( CNR-Italy )  Susan Marschall ,  Stefanie Dunst ,  Markus Romberger  and  Alexander Wolf  ( HMGU-Germany ) and  Belner Amelie ,  Seghrouchni Emmanuelle  and  Chaumes Cyrielle  ( TAAM-CDTA-Orleans ). This work was supported by The  Wellcome Trust  under Grant Number  WT098051  and The  European Commission Infrafrontier -I3 EC  FP7 Capacities Specific Programme  Grant Number  312325.
The analysis of the LHC data at the Compact Muon Solenoid (CMS) experiment requires the production of a large number of simulated events. During the RunI of LHC (2010)(2011)(2012), CMS has produced over 12 Billion simulated events, organized in approximately sixty different campaigns each emulating specific detector conditions and LHC running conditions (pile up). In order to aggregate the information needed for the configuration and prioritization of the events production, assure the book-keeping of all the processing requests placed by the physics analysis groups, and to interface with the CMS production infrastructure, the webbased service Monte Carlo Management (McM) has been developed and put in production in 2013. McM is based on recent server infrastructure technology (CherryPy + AngularJS) and relies on a CouchDB database back-end.
The High Level Trigger (HLT) computer farm further decreases the event rate from around 100 kHz to around 0. 5 kHz, before data storage. The analysis of the LHC data at CMS experiment requires the production of a large number of simulated events. During the RunI of LHC (2010-2012), CMS has produced over 12 Billion simulated events, organized in approximately sixty different campaigns each emulating specific detector and LHC running conditions, such as multiple interactions per bunch crossing (pileup), beam-spot shape and position, etc  [4]. Such vast scale production of simulated events has continued during the first LHC long shutdown (LS1): at first in preparation of the updated offline data simulation and reconstruction software, and later, from the autumn of 2014, to produce simulated the events which will be used in the analysis of the LHC RunII data.
Monte Carlo Management: Roles and McM Platform The roots of the CMS collaboration rest on working teams, the so called level two groups, each charged with a set of goals and deliverables. Detector (e. g. the electromagnetic calorimeter ECAL detector performance group), physics object (muon physics object group) or analyses (exotica analysis group) teams are three examples of level two groups we will deal with in this paper. Any such group needs simulated datasets, either to qualify the performance they are responsible for or to produce published results. Several universities and research laboratories participating to CMS share part of their computing facilities with the collaboration, which uses them either for official data processing or to serve user jobs for analysis  [3]. The production of simulated events employed by CMS for public results is carried out using the shared storage and processing resources available at the regional Tier-1 and Tier-2 centres around the world.