dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation datasets Two global and two regional wetland datasets were used to assess the validity of the CW maps, and none of them were used as inputs to the composite wetland maps to ensure an independent evaluation of the strengths and weaknesses of the CW maps. 2. 5. 1 GLWD-3  (Lehner and Döll, 2004)  The GLWD, the Global Lakes And Wetlands Dataset, is based on the aggregation of regional and global land cover and wetland maps. This dataset contains three levels of information, and the most inclusive one is GLWD-3, which is in raster format. This dataset has an original 30 arcsec resolution and contains 12 classes for lakes and wetlands (maps and details are given in the Supplement, Sect. S1 and Fig. S1 ). For large zones prone to water accumulation but without solid information on existing wetlands, fractional wetland classes are defined (together they cover 4 % of the land surface area).
Global wetland potential distribution (Hu et al. , 2017) Hu et al. (  2017 ) proposed a potential wetland distribution using a precipitation topographic wetness index based on a new TI formulation in which the drainage area is multiplied by the mean annual precipitation. This formulation is based on the concept of the topography-climate wetness index  (Mérot et al. , 2003)  in which the effective precipitation was introduced as the climate factor. The new index is calculated at 1 km resolution using GTOPO30 elevation data developed by the USGS. Wetlands are categorized into "water" and "nonwater" wetlands based on regionally calibrated thresholds for each large basin of the world (level-1 drainage area of Hy-dro1k) using a sample trained adjustment model. The water classes of several land cover datasets are used to train the model for the water threshold, and the model for the nonwater wetland threshold is trained on the regularly flooded tree cover and herbaceous cover categories (additional details are available in the Supplement, Sect.
S1 and Fig. S2 ). The global coverage of the water and non-water wetland classes in Hu et al. (  2017 ) is 22. 6 % of the Earth land surface area (excluding lakes, Antarctica and the Greenland ice sheet), considering no loss due to human influence. This dataset gives the largest wetland extent within the accessible literature, with notably large water wetlands in South America and large non-water wetlands in central Asia and the North American continent. In this paper, we used the union of the water and non-water wetland classes of this dataset for further evaluations. 2. 5. 3 Amazon basin wetland map  (Hess et al. , 2015)  Hess et al. (  2015 ) used the L-band synthetic aperture radar (SAR) data from the Japanese Earth resources satellite (JRES-1) imagery scenes at a 100 m resolution to map wetlands during the period 1995-1996 for high and low water seasons.
Wetland definition The wetland definition behind the composite maps is focused on hydrological functioning, and we aim to include both seasonal and permanent wetlands as well as shallow surface water bodies (including rivers, both permanent and intermittent). Surface water bodies and wetlands are often hydrologically connected, and the transition between them is not sharp and varies seasonally. Moreover, these features are difficult to separate based on observations (either in situ or remote), and no dedicated exhaustive dataset is currently available  (Raymond et al. , 2013; Schneider et al. , 2017). Inclusion of the shallow surface water bodies (in the RFW map) is compatible with the Ramsar classification, but we depart from this approach with respect to large permanent lakes, which are excluded from all input datasets to RFW and GDW maps (Sect.
Data processing To project, resample, intersect/overlap and convert different datasets used in wetland mapping in this study, we relied on ArcMap software (Esri, ArcGIS Desktop: Release 10. 3. 1 Redlands, CA) and its different tools. All datasets were projected to a WGS84 equi-rectangular coordination system and subsequently resampled to a single resolution for facilitated fusion and comparison. The resulting raster datasets were processed with ArcMap tools available in almost any GIS software such as QGIS (Table  3 ). The final resolution of the maps is targeted to 15 arcsec (∼ 500 m at the Equator) for consistency with the available water datasets. Therefore, all datasets were resampled to 15 arcsec resolution, which is within the resolution range of state-of-the-art wetland-related datasets.
For datasets at coarser resolutions, each coarse pixel is disaggregated to 15 arcsec while retaining the same value. We used an "all-ornothing" approach; i. e. , the pixels are either fully recognized as wetland (or lake) or not at all, based on the dominant type if the input data are finer than 15 arcsec (ESA-CCI land cover and JRC surface water). Eventually, each 15 arcsec global raster contains more than 80 000 pixels along a circle of 360 • of longitude, and wetlands can exhibit notably small-scale patterns (e. g. , patchy or river-like). To facilitate visual inspection, we calculated the mean wetland densities in 3 arcmin grids for most of the maps presented in this work. The same 3 arcmin resolution (∼ 6 km at the Equator) was used in calculating the spatial correlations. For zonal wetland area distributions, the area covered by wetlands in each 1 • latitude band is displayed.
Mapping by data fusion To identify the RFWs, we overlapped carefully selected datasets of surface water, land cover and wetlands, namely, the ESA-CCI land cover, GIEMS-D15 inundation surface and the maximum water extent in JRC surface water. These datasets were selected to include different types of data acquisition. The idea behind the fusion approach chosen in this work is that wetlands identified by the different datasets are all valid despite their uncertainties, although none of them are exhaustive. As a result, use of multiple inundation.
Geographic analysis Overall, the RFW map covers 9. 7 % of the land surface area (12. 9 million km 2 ) including river channels, deltas, coastal wetlands and flooded lake margins (Fig. 1e ). Areal coverage of the RFWs is by definition larger than the area of wetlands in all three input datasets (Fig. 1b-d ), which were selected to be representative of different types of data acquisition (sensors and wavelengths). Therefore, they correspond to different definitions of inundated areas, and their contribution to the RFW map is fairly different. In particular, the shared fraction of the three input maps is minuscule (5 % of the total RFW land surface area coverage), and is mostly composed of the large river corridors and ponds which are detectable by satellite visible range imaging techniques in the JRC dataset.
The latter misses most understory inundations, which are better identified by the ESA-CCI dataset owing to specific vegetation classification. Finally, owing to the use of microwave sensors, GIEMS-D15 extends over larger areas since it captures both flooded areas and wet soils below most vegetation canopies except the densest ones  (Prigent et al. , 2007). In addition, the distribution of wetlands in GIEMS-D15 involves downscaling as a function of topography, and can be very different from the other datasets. Hence, 58 % of RFWs are solely sourced from GIEMS-D15, mostly in the Southeast Asian floodplains, Northeast Indian wet plains and rice paddies, and the Prairie Pothole Region (in the northern US and Canada). The ESA-CCI contribution is mainly found in the Ob River basin where wetland vegetation exists but wet soils are not easily detected by visible (JRC) or microwave (GIEMS-D15) observation.
Spatial similarity assessment A difficulty inherent in the validation of any wetland map is the vast disagreements among available datasets and estimates. In this paper, we used independent validation datasets (explained in Sect. 2. 5) that are not used in any step as input to our final products, but we made an exception for the GDW-WTD (derived from  Fan et al. , 2013) , although it is a direct input to CW-WTD, and we used the total wetland fraction of GDW-WTD (corresponding to WTD ≤ 20 cm) to define the TI thresholds behind the TI-based CW maps. This exception is considered for two reasons. Firstly, we focus here on spatial patterns, which are completely independent between TIbased CW maps and GDW-WTD because of very different GW modeling assumptions and input data. Secondly, we also focus on wetlands rather than inundated areas, and on their detection under dense vegetation; GDW-WTD is one of the very few global datasets with these properties, but it results from a different method than  Hu et al.
(2017)  and GLWD-3, so it can help to enrich the uncertainty discussion. All seven developed CW maps and the RFW map were evaluated using spatial coincidence, the Jaccard index (JI) and the spatial Pearson correlation (SPC) coefficient with respect to the validation datasets over the globe and in several regions, the latter of which are discussed in detail below. The first evaluation criterion of spatial coincidence (SC) is defined as the fraction of pixels identified as wet in a validation dataset that are also detected in the composite wetland dataset: SC = (5) area of intersected wetland pixels in validation and CW maps area of wetland pixels in CW map. SC is calculated at 15 arcsec resolution by intersecting CWs and validation datasets, and it ranges from 0 to 1, with higher values showing greater similarity between two datasets.
For  pair-wise comparisons of datasets with different wet fractions, the Jaccard index (JI) is better suited. This index is the fraction of shared wetlands in CW and the validation dataset over the size of their union: JI = (6) area of intersected wetland pixels in validation and CW maps area of wetland pixels in union of CW and validation maps. JI ranges from 0 to 1 as well, and a zero index represents the case in which the two datasets are disjoint, and a value of 1 occurs if two datasets are exactly the same. The last criterion is the SPC. SPC is independent from the wet fractions in the CWs and evaluation datasets but is sensitive to the spatial distribution pattern in pair-wise comparisons. SPC values range from 0 to 1, with higher values showing greater similarity. Although the first two criteria were applied for comparison at the original 15 arcsec resolution, SPC was calculated based on aggregated wetland densities at 0.
Global analysis With the exception of CW-WTD, which is always more similar to GDW-WTD because the latter is a component of the former, the validation criteria for the CW maps are rather small overall (between 0. 2 and 0. 6). However, the criteria are larger than the same values between the surface water and wetland datasets (less than 0. 3 in Table  5  for the SPC of the globe and Table  S1 ), showing their advantages. CW maps (especially CW-TCI maps) are more similar to GDW-WTD and Hu's map with respect to GLWD-3 because all but GLWD-3 share the GW modeling methodology. In contrast, the RFW map extends over a 60 % larger surface area than GLWD-3 and displays the highest similarity to GLWD-3, suggesting that wetlands in GLWD-3 are the regularly flooded ones. The inclusion of GDWs in the CW maps makes them depart from GLWD-3, but it markedly increases their similarity to the other two validation datasets for JI and SPC (e.
g. , SPC [RFW, GDW-WTD] = 0. 3 versus SPC [CW-TCI15, GDW-WTD] = 0. 6). As demonstrated in Fig. 4a  (and also Table  S1 ), increasing the GDW contribution from CW-TCI6. 6 to CW-TCI15, as an example, also improves the similarity criteria (except the SC for GLWD-3 and GDW-WTD), justifying the need to account for the GDWs to provide a comprehensive description of wetlands. This is clearer for the global spatial correlation values which all increase when the contribution of GDW is increased from 6. 6 % to 15 % (Table  S1 , first row block). The following section breaks down the comparative wetland representation between our maps and that of the validation datasets at the regional scale. The selected regions encompass different climates, vegetation covers and ecosystems, both within and outside important wetland areas of the world, to assure the applicability of CW maps.
These six regions are France in the temperate climate, the Amazon basin and Southeast Asia over the tropical zone, the cold boreal areas of the Hudson Bay Lowlands and the Ob River basin and the Sudd swamp in South Sudan with a semi-arid savannah climate. Over France, wetland fractions from the validation datasets are highly inconsistent (Fig. 5 ). Visible range satellite imagery (JRC surface water) shows the smallest wet fraction (1 %). The GLWD-3 and ESA-CCI maps also produce low wetland coverage, whereas GIEMS-D15, which essentially forms the RFW map, gives 12 % coverage concentrated along the coastline and over the floodplains of the northern rivers. Wetlands from GW modeling-based datasets cover even larger areas (14 % and 18 % in GDW-WTD  and Hu et al. , 2017)  and are scattered countrywide, except for the French Pyrenees and the Alps, with moderately denser wetlands along large rivers (such as the Rhine floodplain at the eastern border) and the Landes (southwestern shore).
The MPHFM map  (Berthier et al. , 2014)  can be considered as the most comprehensive validation dataset for the country because it relies on hydromorphic soil properties and was extensively validated. This map shows much larger wetland extents (23 % of France) than the above estimates because of its inclusion of both floodplains (along the Loire, Saône and Rhône rivers) and groundwater-driven wetlands, including those over the weakly permeable granites of Brittany (shown in green in Fig. 5g ). These notorious wetlands are not considered in the global validation datasets but are captured to a good extent in CW maps (Fig. 5i, j ). By combining RFWs (which overlap with 20 % of MPHFM) and GDWs, our CW maps capture many features of the MPHFM map, including the total wetland extent (23 % for MPHFM versus 22 % and 25 % for CW-WTD and CW-TCI15) and correctly capturing most of the coastal and riparian wetlands (Fig.
5 ). The larger wetland fraction in MPHFM and CW maps is consistent with the work of  Pison et al. (2018) , who found that (wetland-driven) methane emissions over France deduced from atmospheric inversion were almost a third higher than direct estimates, from anthropogenic inventories and biogeochemical models driven by global wetland datasets (e. g. , the overlap of GLWD and SWAMPS in  Saunois et al. , 2016). The added value of CW maps is demonstrated by the higher similarity criteria between CW maps such as CW-TCI15 and MPHFM (SPC = 0. 52) than between surface water maps such as GIEMS-D15 and MPHFM (SPC = 0. 43). However, it is difficult to identify the best CW map over France based on the similarity criteria against MPHFM because four of our CW maps (all shown in grey in Fig. 4b ) display nearly the same values (Table  S2 ).
Amazon basin The Amazon River basin is considered one of the richest tropical wetland ecosystems in the world  (Mitsch and Gosselink, 2000). For ease of comparison, we limited our study to the domain of  Hess et al. (2015) , which covers 5 million km 2 (Fig. 6 ). RFWs (mostly consisting of GIEMS-D15) show a pattern rather similar to that of  GLWD-3 and Hess et al. (2015) (Figs. 4c and 6d, g, h) , covering only the main drainage network of the Amazon and certain seasonally flooded wetlands and floodplains. However, certain spatial disagreements exist among these three datasets in seasonally flooded wetlands such as Llanos de Moxos (12 • 30 -17 • 30 S, 63-68 • W), the Roraima savannah and the Negro River basin (2 • N-2 • S, 60-65 • W), which are larger in RFWs and  Hess et al. (2015)  than in GLWD-3.
River channels and surrounding floodplains are better represented in CW maps, as compared with  Hess et al. (2015) , due to the inclusion of the RFW component. Similarly, CW compares well to other datasets because almost none of the river floodplains are delineated in  Hu et al. (2017) , and GDW-WTD misses the Tapajós River floodplain and portions of the downstream Amazon corridor. However, CW maps represent the wetland extent in lower density over grassland/savannahs and the Andes dry regions compared with the validation datasets.
Southeast Asian deltas The selected window over South and Southeast Asia stretches over notably wet regions, similar to the Amazon, but with severe human interference and deforestation  (Miettinen et al. , 2011; Stibig et al. , 2013). In Southeast Asia, RFWs (mostly composed of GIEMS-D15) are larger than all validation datasets (Fig. 7d-g ) because GIEMS-D15 also detects inundated areas associated with cultivation activities such as rice paddies  (Fluet-Chouinard et al. , 2015) , which are not considered in inventories and GW modeling-based estimates. Over the window, RFWs and CWs coincide with the majority of wetlands in the validation sets, particularly over the Ganges-Brahmaputra floodplain, northern Indochina and Yunnan Plateau subtropical forests (Fig. 4d ; SC between 0. 59 and 0. 91), showing the good agreement of our developed maps with respect to spatial patterns.
As a general rule in Southeast Asia, floodplains and deltas (Ganges, Brahmaputra, Irrawaddy, Mekong and Red rivers) extend over larger  areas in CW maps than in validation maps (Fig. 7 ), giving a more realistic extent than those in  Fan et al. (2013)  and  Hu et al. (2017) , considering the vast flood irrigated cultivation lands along floodplains. However, only a few small wetlands in the validation datasets are missed in RFWs (and CW maps), such as the upstream Mekong River corridor (near 20 • N-102 • E) and Irrawaddy River (near 25 • N-97 • E) in GLWD-3. The CW-WTD and CW-TCI15 maps present patterns that are highly similar to each other (Fig. 7h, i ) and to the validation datasets. However, high similarity criteria (especially SC) can be the result of the large extension of RFWs, itself overlapping almost all of the wetlands in the validation datasets.
Hudson Bay Lowlands The Hudson Bay Lowlands (HBL) are a vast flat wetland area in the low subarctic regions of North America dominated by extensive peatlands, swamps and marshes  (Mitsch and Gosselink, 2000; Packalen et al. , 2014) , where belowfreezing temperatures for most of the year reduce drainage in the soil layer  (Hamilton et al. , 1994). A systematic contrast is noted between inundation maps (Fig. 8a-c ; maximum wet fraction: 21 %) and validation datasets (Fig. 8df ; minimum wet fraction: 49 %), underlining the inability   (df ) evaluation datasets and (g-i) datasets generated in this study. The panels also give the mean areal wetland fraction of each dataset in the study area (using the mean fraction of each fractional wetland class of GLWD-3; cf. Sect. 2. 5. 1). The bounds of the study area are 48-56 • N, 76-86 • W.
Ob River basin The Ob River basin in western Siberia extends over ∼ 3 × 10 6 km 2. The annual variability of the inundated area is large (e. g. ,  Mialon et al. , 2005) , making this basin one of the largest wetland complexes in the world, which contributes to buffer peak discharge during the flooding period  (Grippa et al. , 2005). Wetland fractions in different datasets compare similarly to HBL, except for GLWD-3, which appears to underestimate the total wetland extent although the climatic and   (d-f ) evaluation datasets and (g-i) datasets generated in this study. The panels also give the mean areal wetland fraction of each dataset in the study area (using the mean fraction of each fractional wetland class of GLWD-3; cf. Sect. 2. 5. 1). The bounds of the basin are taken from the HydroBASINS layer of HydroSHEDS.
geomorphologic properties are nearly alike. Datasets recognizing the contribution of GW to wetland formation (Fig. 9e, f, h, i ) indicate consistently higher wet fractions than others. However, CW-TCI15 appears to miss wetlands south of 60 • N that are extended to the upstream Ob River basin near 50 • N in both GDW-WTD and  Hu et al. (2017) , most likely due to the permafrost effect on wetland formation. With respect to the evaluation criteria, CW-WTD often displays better performances, although CW-TCI15 shows the highest SPC. We also find that CW-TCI15 outperforms CW-TCI6. 6 for all criteria and validation dataset combinations (Table  S1 ). TCTrI-based CW maps fail to surpass others in the validation process, considering that we used the transmissivity map without the permafrost adjustment due to its imprecise representation of hydraulic conductivity in these zones (Sect.
2. 4. 4). CW-WTD tends to better capture the wetland extent and spatial pattern, with more concentrated wetlands in the downstream lowlands and northwestern regions (65 • N-65 • E) of the basin due to RFWs. Overall, considering the wetland fraction solely attributed to GDWs in CW-TCI15 and CW-WTD (13 % and 29 % of the basin area) and the difference found between the inundation and validation dataset (Fig. 9 , first and second row), it becomes clear that the uncertainty of the wetland extent and spatial pattern is rather high over boreal zones.
Sudd swamp This large wetland is located in eastern South Sudan, nearly 300 m above mean sea level, and is the largest freshwater wetland in the Nile basin  (Sutcliffe et al. , 2016). The Sudd swamp extent estimations are highly uncertain in the literature, ranging from 7. 2 to 48 × 10 3 km 2  (Mohamed et al. , 2004, and references therein). Over the selected window, the wetlands and surface water distribution is also highly disparate and varies from 1 % to 27 % for different datasets   (d-f ) evaluation datasets and (g-i) datasets generated in this study. The panels also give the mean areal wetland fraction of each dataset in the study area (using the mean fraction of each fractional wetland class of GLWD-3; cf. Sect. 2. 5. 1). The bounds of the study area are 4 • 30 -14 • N, 24 • 30 -34 • E.
(Fig. 10 ). Additionally, wetlands in  Hu et al. (2017)  are rather patchy and show sharp density changes with what seems to be periods of 0. 5 •. Because GLWD-3 appears to represent only flooded wetlands (with the same wetland fraction of RFWs and overlapping with one-third of them), and the study of  Hu et al. (2017)  contains technical issues, GDW-WTD can be considered as the only comprehensive validation dataset over the Sudd swamp. The CW datasets in Fig. 10  show high wetland densities in the central floodplain, in rather good agreement with GLWD-3, GDW-WTD and regional estimates of saturated soil (compared with visuals in  Mohamed et al. , 2004, and Mohamed and Savenije, 2014). CW-WTD compares more similarly to validation datasets, closely followed by CW-TCI15 (Fig. 4g ), but the main difference between these two CW maps is that the groundwater wetlands in CW-TCI15 are extended southwest into the southern national park (over local flat valley bottoms) but are more concentrated over the main floodplain in the SE-NW direction for CW-WTD.
The total wetland fraction is nearly equal in CW-TCI15 and CW-WTD (25 % and 27 % of the selected window area), underlining a primary role of topography and climate in wetland formation. Considering the wetland fraction in the RFW map (mostly consisting of ESA-CCI wetlands) and GDW-WTD, groundwater wetlands appear to be the dominant feature in the Sudd swamp, as is the case for CW-WTD and CW-TCI15. The added value of CW maps with respect to GDW-WTD is not substantial, but they additionally contain the seasonally flooded plains downstream of the White Nile (top right of the selected window in Fig. 10g , 12-14 • N, 32-34 • E), which are not completely captured by validation datasets due to the inclusion of RFWs.