dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, another type of geocenter inverse method is to invert for degree-1 surface mass variation coefficients from various combinations of GNSS observations, OBP models, Jason ocean altimetry and GRACE data  (Blewitt and Clarke 2003; Rietbroek et al. 2014; Wu et al. 2015 Wu et al. , 2017)). To investigate the possible ill-posedness of the GPS loading inversion over sparsely sampled regions such as the ocean, we also compare the geocenter motions estimated with combined techniques in terms of annual variations (Table  2 ). We can see that for the X component, the annual estimates from different approaches correlate quite well in both amplitudes and phases. However, the Y component of the IG2 GPS-derived geocenter motion amplitude is persistently smaller than that of the results derived from other methods, while the Z component of IG2 results has higher annual amplitude.
As a whole, the annual phases predicted with the IG2 data are in line with those based on other techniques except that there is a discrepancy in the Z component, where the IG2inverted solutions and those obtained from the KALREF approaches are more than one month before those based on other techniques. Possible reasons for the above differences can be divided into two facets. On the one hand, there exist GNSS observation errors, such as the time-correlated errors in stationdisplacements, remaining draconitic errors in the reprocessed GNSS data, inaccurate or neglected intrinsic variance and correlation structures in the covariance matrices, unmodeled technologyrelated systematic errors and the truncated higher-degree terms during geocenter motion inversion, etc. Impacts of these factors on parameter estimation, for the most part, are not precisely known right now.
Comparison with the GPS geocenter estimates from IG1 Until now, IGS has performed two times of reanalysis on the full history of GPS data collected by the IGS global network: IG2 and the IG1. In this section, GPS observations from IG1 are adopted to inverse the geocenter motions. The same analyses as the IG2 are then implemented to the IG1 solutions so as to investigate the possible improvements. Considering that the IG1 data span ended in early 2010, we compare results from 2002. 04107 to 2009. 95547. Figure  10  represents the geocenter correlations of the IG1 and the SLR results together with the annual signal comparison with (left panels) and without (right panels) coestimating the translation parameters. Generally, the IG1 estimates with truncated degree 4 are the closest to the SLR results.
We can notice that the differences between IG2 SH coefficients and ECCO solutions vary with different truncated degrees. Despite the fact that the IG2 coefficient-inversed EWH with truncated degree 5 is most closely to ECCO solutions, there are still obvious differences occurring in northern and central Atlantic Ocean areas, as well as the central Indian Ocean areas. With the truncated degree grows, the differences become more and more significant, and the consistency between the two different types of solutions is getting worse and worse. Therefore, to obtain stable geocenter motions from the global GPS data, and to expand GPS to inverse the global surface mass loading displacements, further researches still need to be done to combining the OBP models, as well as other datasets  (Rietbroek et al.
Conclusions With the development of more and more global GPS products, geocenter motions inversed by GPS need to be further assessed. In this paper, we focus on thorough evaluation to the GPS-derived geocenter motions using state-of-the-art IG2-reprocessed data based on the degree-1 deformation approach. Considering that the performance of the GPS geocenter estimates is mainly related to the truncated degrees, the spherical harmonics on the inversion of the geocenter motion are truncated with degrees from 1 to 10. Then detailed comparisons have been made between our GPS-inversed geocenter motion and the SLR results. Results demonstrate that the IG2 GPS-derived geocenter estimates obtained by coestimating translational parameters agree with the SLR results in all three components, especially for the Z component.
For both annual amplitudes and phases, geocenter estimates obtained from the degree-1 deformation approach are better than results from the network shift approach using the same GPS data. They are also very consistent with the SLR RL06 results and the geocenter estimates from the SLR contribution to the ITRF2014. As a whole, the annual phases predicted with the IG2 data are in line with those based on other combination techniques. An exception is that there is a discrepancy in the annual phase estimates of the Z component. We find that solutions based on IG2 data, as well as those based on the KALREF approaches, are more than a month before than those based on other techniques. Moreover, the same IG2 GPS geocenter motion analysis is done with the IG1 solutions to investigate the possible improvements.
Despite the fact that the IG2 coefficient-inversed EWH with truncated degree 5 is most closely to ECCO solutions, there are still obvious differences occurred in northern and central Atlantic Ocean areas, as well as the central Indian Ocean areas. With the truncated degree grows, the differences become more and more significant, and the consistency between the two different types of solutions is getting worse and worse. To obtain stable geocenter motions from the global GPS data, and to expand GPS to inverse the global surface mass loading displacements, further researches still need to be done to combining the OBP models, as well as other datasets.
For the different psychological parameters, we combined mild and moderate intensities for comparison against severe intensities. Indeed, mild-to-moderate psychological disorders were easily identi ed in our patients exhibiting severe anxiety or depressive comorbidities and emotional experiences, often affected by chronic pain, but retaining severe intensity allows a more signi cant clinical difference. That approach represents an attempt to adjust for the population recruitment bias. During the same day-hospitalization, various patient characteristics, notably rheumatological, were collected from the electronic medical les using the DxCare (medical information and prescription software) program: biological in ammatory syndrome, de ned as an erythrocyte sedimentation rate ³ 30 mm/1 st h and/or C-reactive protein ³5 mg/L; ACPA-positivity, de ned as 7 U/mL by enzyme-linked immunosorbent assay (ELISA); RF >15 IU/mL by ELISA;magnetic resonance imaging (MRI) detection of grade-2 bilateral or grade-3 unilateral sacroiliitis, with active, in ammatory (edema) or chronic (erosion, bone condensation, bone bridges, fat conversion) lesions.
Statistical analyses Continuous variables were expressed as median [Q1-Q3]. Categorical variables were expressed as number (percentages), (percentages calculated were calculated excluding missing data) and were compared by Chi 2 test or Fisher's exact test, as appropriate. Missing data were not replaced. For all analyses, P<0. 05 was considered statistically signi cant. Because of the exploratory character of the comparisons, the type I error was not adjusted for multiplicity. All statistical analyses were performed with SAS release 9. 4 (SAS Institute Inc, Cary, NC).
Many maps of open water and wetlands have been developed based on three main methods: (i) compiling national and regional wetland surveys, (ii) identifying inundated areas via satellite imagery and (iii) delineating wetlands as shallow water table areas based on groundwater modeling. However, the resulting global wetland extents vary from 3 % to 21 % of the land surface area because of inconsistencies in wetland definitions and limitations in observation or modeling systems. To reconcile these differences, we propose composite wetland (CW) maps, combining two classes of wetlands: (1) regularly flooded wetlands (RFWs) obtained by overlapping selected open-water and inundation datasets; and (2) groundwater-driven wetlands (GDWs) derived from groundwater modeling (either direct or simplified using several variants of the topographic index).
Wetlands are statically defined as areas with persistent near-saturated soil surfaces because of regular flooding or shallow groundwater, disregarding most human alterations (potential wetlands). Seven CW maps were generated at 15 arcsec resolution (ca. 500 m at the Equator) using geographic information system (GIS) tools and by combining one RFW and different GDW maps. To validate this approach, these CW maps were compared with existing wetland datasets at the global and regional scales. The spatial patterns were decently captured, but the wetland extents were difficult to assess compared to the dispersion of the validation datasets. Compared with the only regional dataset encompassing both GDWs and RFWs, over France, the CW maps performed well and better than all other considered global wetland datasets.
Two CW maps, showing the best overall match with the available evaluation datasets, were eventually selected. These maps provided global wetland extents of 27. 5 and 29 million km 2 , i. e. , 21. 1 % and 21. 6 % of the global land area, which are among the highest values in the literature and are in line with recent estimates also recognizing the contribution of GDWs. This wetland class covers 15 % of the global land area compared with 9. 7 % for RFW (with an overlap of ca. 3. 4 %), including wetlands under canopy and/or cloud cover, leading to high wetland densities in the tropics and small scattered wetlands that cover less than 5 % of land but are highly important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and GDWs based globally on uniform principles, the proposed dataset might be useful for large-scale land surface modeling (hydrological, ecological and biogeochemical modeling) and environmental planning.
The reasons for this ambiguity are a diversity of scientific points of views as well as the complexity of classification in transitional land features and temporally varying land features under human influences  (Mialon et al. , 2005; Papa et al. , 2010; Ringeval et al. , 2011; Sterling et al. , 2013; Hu et al. , 2017; Mizuochi et al. , 2017). The first global wetland maps were developed based on a compilation of regional archives and estimates. Matthews and Fung (1987)  developed a 1 • resolution wetland map based on vegetation, soil properties and inundation fractions that covered ca. 4 % of the land. Finlayson et al. (1999)  based their estimates on surveys and the Ramsar global inventory in which wetlands cover 9. 7 % of the land area. Later, the Global Lakes and Wetlands Database (GLWD) was developed at 30 arcsec resolution (∼ 1 km at the Equator) by compiling several national and regional wetland maps with a global cover of 6.
This strategy was proven successful at the basin scale (e. g. ,  Curie et al. , 2007) , but it has been shown to be ineffective at larger scales because it is not possible to uniquely link TI values to soil saturation levels across different landforms and climates  (Marthews et al. , 2015). Hu et al. (2017)  produced a global wetland map by calibrating TI thresholds for every large basin of the world based on land cover maps, as pioneered over France due to independent TI threshold calibration in 22 hydro-ecoregions using soil type datasets  (Berthier et al. , 2014). Uniform WTD   (Lehner and Döll, 2004)  30 arcsec, ∼ 1 km Compilation of national/international maps 8. 3-10. 2 c 6. 2 %-7. 6 % GLC2000  (Bartholomé and Belward, 2005)  1 km at Equator SPOT vegetation mission satellite observations 4.
9 3. 4 % GIEMS  (Prigent et al. , 2007)  0. 25 Satellite imagery: Landsat and SRTM topography 5 3. 7 % SWAMPS  (Schroeder et al. , 2015)  25 km Modeling using multi-sensor info: SSM/I, SSM/S, QuikSCAT, ASCAT 7. 7-12. 5 d 5. 2 %-8. 5 % ESA-CCI land cover  (Herold et al. , 2015)  10 arcsec, ∼ 300 m Multi-sensor: SPOT vegetation, MERIS products 6. 1 4. 7 % GIEMS-D15  (Fluet-Chouinard et al. , 2015)  15 arcsec, ∼ 460 m Multi-sensor: SSM/I, ERS-1, AVHRR, downscaled from a 0. 25 thresholds (0 cm for inundated areas and 25 cm for wetlands) are applied in the only example (to the best of our knowledge) of direct global GW modeling for wetland delineation  (Fan and Miguez-Macho, 2011; Fan et al. , 2013). All these datasets based on GW modeling estimate the wetland fraction as being much higher than those based on inventories and satellite imagery  (Hu et al.
, 2017 : 22. 6 %, Fan et al. , 2013:  15 % of the land surface area). It must be emphasized that adjustment of wetland thresholds, both for directly mod-eled WTD and TI, always implies subjective choices and can result in over-or underestimation of wetland extents or unrealistic wetland distribution patterns. The scientific objective of the current work is to develop a comprehensive global wetland dataset based on a unique and applicable wetland definition for use in hydrological and land surface modeling. Based on the above analysis, our rationale is that inundated and groundwater-driven wetlands must both be considered to realistically capture the wet-land patterns and extents. This approach leads to a definition of wetlands as areas that are persistently saturated or nearsaturated because they are regularly subject to inundation or shallow water tables.
This definition is focused on hydrological functioning, and is not restricted to areas with typical wetland vegetation. In this context, although inundated areas and zones with shallow groundwater partially overlap and share similar environmental properties, they cannot be detected using a single method. Thus, we rely on data fusion methods, which have proven advantageous in developing high-quality products by merging properties from various datasets  (Fritz and See, 2005; Jung et al. , 2006; Schepaschenko et al. , 2011; Pérez-Hoyos et al. , 2012; Tuanmu and Jetz, 2014) , including wetland mapping  (Ozesmi and Bauer, 2002; Friedl et al. , 2010; Poulter et al. , 2017). In this framework, we tested several composite wetland (CW) maps, all constructed at 15 arcsec resolution, by merging two complementary classes of wetlands: (1) regularly flooded wetlands (RFWs), where surface water can be detected at least once a year through satellite imagery, and (2) groundwater-driven wetlands (GDWs) based on groundwater modeling.
The main assumptions underlying the composite wetland maps are detailed in Sect. 2, together with the datasets involved. Subsequently, Sect. 3 sequentially presents the construction of the RFW, GDW and CW maps, with preliminary analyses of their features and uncertainties. In Sect. 4, we compare the CW maps with several validation wetland datasets, globally and in several areas with contrasting climates and wetland fractions, to show that the combination of RFWs and GDWs provides a consistent wetland description throughout the globe. This comparison allows us to select two CW maps with better overall performances, used to discuss the role of GDWs in Sect. 5. Finally, the availability and potential applications of the composite maps are presented in Sect. 6, while Sect. 7 summarizes the advantages and limitations of the approach and gives perspectives on future developments.
Mapping strategy and requirements Based on the inclusive assumptions for wetland mapping in this study, we use GIS tools to construct several composite wetland maps as the overlap (union) of the following: one RFW map developed by overlapping three surface water and inundation datasets derived from satellite imagery in an attempt to fill the observation gaps (Sect. 3. 1); one GDW map out of seven, all derived from GW modeling (either direct or simplified based on several TI versions) and meant to sample the uncertainty of the GDW contribution (Sect. 3. 2). In this process, many layers were developed and are summarized in Table  2  and detailed in Sect. 3. The map and methods to exclude lakes from all layers are explained in Sect. 2. 2. Input datasets to RFWs and GDWs are presented in Sect.
2. 3 and 2. 4, respectively, and several independent validation datasets, global and regional, are presented in Sect. 2. 5. It should be noted that in the remainder of this paper, the wetland percentages of the land surface area always exclude lakes (Sect. 2. 2), the Caspian Sea, the Greenland ice sheet and Antarctica (unless otherwise mentioned). For this reason, these percentages and areas might be different from those shown in Table  1 , which are indicated for each original paper or data description.
Lakes To distinguish large permanent lakes and reservoirs from wetlands, we used the HydroLAKES database  (Messager et al. , 2016) , which was developed by compiling national, regional and global datasets (Fig. 1a ). This database consists of more than 1. 4 million individual polygons for lakes with a surface area of at least 10 ha, covering 1. 8 % of the land surface area. It also classifies artificial dam reservoirs which amount to 300 × 10 3 km 2  (Messager et al. , 2016). The lakes' extent in HydroLAKES is smaller than in other recent databases that account for smaller water bodies: 2. 5 % in G3WBM  (Yamazaki et al. , 2015)  for water bodies above 0. 8 ha and 3. 5 % in  GLOWABO (Verpoorter et al. , 2014)  for those above 0. 2 ha. These two datasets do not differentiate lakes from other surface water elements, and using them as a mask would lead to the exclusion of shallow inundated portions of wetlands (e.
g. , Indonesian mangroves or Ganges floodplains). It must also be noted that the small water bodies tend to be overlooked after dominant resampling to 15 arcsec resolution (Sect. 2. 6), unless they are sufficiently numerous in a pixel. Therefore, the lake mask covers 1. 7 % of the land area compared with 1. 8 % in the original Hydro-LAKES database. This map also shows that most of the lakes are located in the northern boreal zones (more than 60 % of lakes area are located north of 50 • N), in agreement with the other lake databases.
ESA-CCI land cover This dataset succeeds the GlobCover dataset based on the data from the MERIS sensor (onboard ENVISAT) collected at high resolution for surface water detection, together with the SPOT-VEGETATION time series  (Herold et al. , 2015)  to aid in distinguishing wetlands from other vegetation covers. Global land cover maps at approximately 300 m (10 arcsec) resolution deliver data for three 5-year periods  (1998-2002, 2003-2007 and 2008-2012). The extents of water bodies slightly changed between the first 5-year period and the third one (such as shrinking of the Aral Sea area by more than 55 %), but the extent of wetland classes (permanent wetlands and flooded vegetation classes) did not change significantly (the variation in wetland classes throughout these periods is less than 3 % of the total wetlands area).
GIEMS-D15 (Fluet-Chouinard et al. , 2015) Prigent et al. (  2007 ) used multi-sensor satellite data, including passive and active microwave measurements, together with visible and near-infrared reflectance to map the monthly mean inundated fractions at 0. 25 • resolution for a 12-year period  (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004). This dataset (GIEMS) gives the minimum and maximum extent of the inundated area (including wetlands, rivers, small lakes and irrigated rice). Fluet-Chouinard et al. (2015)  used the GLC2000 land cover map  (Bartholomé and Belward, 2005)  to train a downscaling model for GIEMS at 15 arcsec resolution based on the HydroSHEDS digital elevation model  (Lehner et al. , 2008)  and developed three static datasets for mean annual minimum, mean annual maximum and long-term maximum extent of the inundated areas (covering 3.
The decay factor is also adjusted for the permafrost region using an additional thermic factor (smaller transmissivity in permafrost areas). The modeled WTD was compared to observations available to the authors (more than 1 million observations, with 80 % of them located in North America). The resulting dataset suggests vast areas with a shallow water table over the tropics, along the coastal zones and in boreal areas of North America and Asia (almost 15 % of the land area for WTD ≤ 20 cm).
Topography, however, is often not sufficient for wetland identification because climate and subsurface characteristics also control water availability and vertical drainage. Using the original TI formulation in Eq. (1), high index zones might coincide with flat arid areas, or inversely, low index values might occur at wetland zones with small upstream drainage areas over a shallow impervious layer. Several studies have focused on improving the topographic wetness index for wet-land delineation by including other environmental factors or modifying the formulation of the wetness index  (Rodhe and Seibert, 1999; Mérot et al. , 2003; Manfreda et al. , 2011). Therefore, we used the global TI dataset of  Marthews et al. (2015)  to supply the original TI, and also as a base map to derive two other variants of the index.
The first variant index is the TCI (topography-climate wetness index, inspired by  Mérot et al. , 2003) : TCI = ln a • P e tan(β) = TI + ln(P e ), (2) where P e is the mean annual effective precipitation (in meters). The effective precipitation is first defined at the monthly time step as the monthly precipitation P m,y (in meters) for month m and year y that is not evaporated or transpired using the monthly potential evapotranspiration EP m,y (in meters) as a proxy for total evapotranspiration: P e m,y = max(0, P m,y -EP m,y ). ( 3 ) P e is subsequently calculated as the sum of the 12 pluriannual means of monthly effective precipitation. The required climatic variables are taken from the CRU monthly meteorological datasets (Sect. 2. 2. 3) for 1980-2016 to represent the contemporary period.
CRU climate variables To assess the impact of climate on wetlands, we used the Climatic Research Unit (CRU) monthly meteorological datasets. These datasets cover all land area from the beginning of the twentieth century  (Harris et al. , 2014). CRU climate time series are gridded to 0. 5 • resolution based on more than 4000 individual weather station records. To include a climate factor in the TI formulations, the time series of selected climate variables (i. e. , precipitation and potential evapotranspiration based on the Penman-Monteith equation) are extracted for the contemporary period. 2. 4. 4 GLHYMPS  (Gleeson et al. , 2014)  GLHYMPS is a global permeability and porosity map based on high-resolution lithology  (Hartmann and Moosdorf, 2012). The permeability dataset and its derived hydraulic conductivity (K s ) estimates are given in vector format, with an average polygon size of approximately 100 km 2.
www. earth-syst-sci-data. net/11/189/2019/ Earth Syst. Sci. Data, 11, 189-220, 2019  As noted by the developers of GLHYMPS  (Gleeson et al. , 2011 (Gleeson et al. , , 2014)) , "lithology maps represent the shallow subsurface (on the order of 100 m)", and thus hydraulic conductivity estimates are valid for the first 100 m of the subsurface layer. Thus, we estimated transmissivity as the integral of this constant K s over these 100 m and used it to check whether use of the available transmissivity datasets in TI formulations can improve global wetland identification. It should be noted that the hydraulic conductivity dataset has two versions: with and without the permafrost effect. To consider the permafrost effect,  Gleeson et al. (2014)  used maps of the permafrost zonation index (PZI) from  Gruber (2012)  and homogenously assigned a rather low hydraulic conductivity (K s = 10 -13 m s -1 ) for areas with PZI > 0.