dataesr/scientific-paragraphs-categorization huggingface dataset:
Cloud and Magnetic Field Coevolution The large-scale B POS lines are predominantly perpendicular to the main ridge of NGC 6334  (Li et al. 2015) , which are 97 We use the already-existing edge-detection class in the Skit-image Python library (van der Walt et al. 2014). 98 skimage. filters. threshold_otsu. 99 W2, S2-a, W3, and S3 do not exhibit radial polarization in the observations. altered on substructure scales by HII regions within the cloud. We suggest that these B POS lines were initially even more coherent and perpendicular to the filament, but were then altered by feedback mechanisms, such as HII regions, or gas flows (see the filament within W6), as illustrated in a schematic view by Figure  15. Additionally, the physical properties of HII regions may reveal the initial orientation of the magnetic field prior to the HII expansion.
Stanford University  and the  Kavli Institute for   Particle Astrophysics and Cosmology (KIPAC)  Fellowship. K. P. is a Royal Society University Research Fellow, supported by Grant No. URFR1211322. D. J. is supported by the  National Research Council of Canada  and by a  Natural Sciences and Engineering Research Council Discovery  grant. Y. D. is supported by  JSPS KAKENHI  grants  25247016  and  18H01250. T. O. acknowledges a  Grant-in-Aid for Scientific Research of JSPS  ( 18K03691 ). Mo. Ta. is supported by  JSPS KAKENHI  grant Nos. 18H05442 ,  15H02063 , and  22000005. J. K. is supported by  JSPS KAKENHI  grant No. 19K14775. W. K. is supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  ( NRF-2021R1F1A1061794 ). F. P. acknowledges support from the  Spanish State Research Agency (AEI)  under grant No.
PID2019-105552RB-C43 , and support from the  Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI)  under the  European FEDER (FONDO EUROPEO DE DESARROLLO REGIONAL) de Canarias   2014-2020  grant No. PROID2021010078. C. E. acknowledges the financial support from grant  RJF/2020/000071  as a part of the  Ramanujan Fellowship  awarded by the  Science and Engineering Research Board (SERB),  Department of Science and Technology (DST), Government of India. L. F. acknowledges support from the  Ministry of Science and Technology of Taiwan , under grant Nos. MoST107-2119-M-001-031-MY3 ,  111-2811-M-005-007 , and  109-2112-M-005-003-MY3 , and from  Academia Sinica  under grant No. AS-IA-106-M03. F. K. is supported by the  Spanish program Unidad de Excelencia María de Maeztu   CEX2020-001058-M , financed by  MCIN/ AEI/10.
13039/501100011033. C. W. L. is supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education, Science and Technology ( NRF-2019R1A2C1010851 ), and by the  Korea Astronomy and Space Science Institute  grant funded by the  Korea government (MSIT)  (Project No. 2022-1-840-05 ). SPL acknowledges the grant from the  National Science and Technology Council of Taiwan   109-2112-M-007-010-MY3.
The spatio-temporal variation of surface water storage (SWS) in the Congo River basin (CRB), the second-largest watershed in the world, remains widely unknown. In this study, satellite-derived observations are combined to estimate SWS dynamics at the CRB and sub-basin scales over 1992-2015. Two methods are employed. The first one combines surface water extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS-2) dataset and the long-term satellite-derived surface water height from multi-mission radar altimetry. The second one, based on the hypsometric curve approach, combines SWE from GIEMS-2 with topographic data from four global digital elevation models (DEMs), namely the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing Satellite (ALOS), Multi-Error-Removed Improved Terrain (MERIT), and Forest And Buildings removed Copernicus DEM (FABDEM).
Papa et al. (2013)  developed a hypsometric curve approach to derive SWS variations by combining surface extent from the Global Inundation Extent from Multi-Satellite (GIEMS;  Prigent et al. , 2007)  dataset and topographic data from the global digital elevation model from the Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER). At the basin scale, they showed that the mean annual amplitude of the Amazon SWS is ∼ 1200 km 3 and contributes about half of the annual terrestrial water change as detected by Gravity and Recovery Climate Experiment (GRACE) data  (Papa and Frappart, 2021). Despite being the second-largest river system in the world, in terms of both the drainage area and discharge to the ocean, the Congo River basin (CRB)'s SWS still remains widely unknown. The CRB still hosts extensive floodplains and wetlands such as the well-known Cuvette Centrale region, which stores a large amount of freshwater, playing a crucial role in the sediment dynamics of the river and in the global carbon storage  (Datok et al.
, 2021; Kitambo et al. , 2022a). Thus, in this study, we use two approaches to estimate, for the first time, the spatio-temporal variations of the CRB's SWS over the period 1992-2015. The first approach  (Frappart et al. , 2008 (Frappart et al. , , 2011 (Frappart et al. , , 2012 (Frappart et al. , , 2019) )  is based on the complementarity between the spatio-temporal dynamics of the surface water extent and satellite-derived surface water height. The second approach relies on the methodology developed by  Papa et al. (2013)  using the relationships between elevation from digital elevation models and surface water extent variations, called the hypsometric curve approach, which enables the estimation of SWS changes. Section 2 presents the study area. Section 3 describes the datasets and Sect.
4 the methodology used in this study. Section 5 is dedicated to results and validation. An assessment is performed by comparing the developed SWS with other independent datasets such as historic and contemporary river discharge and precipitation data. Section 6 presents an application case of the dataset in which the spatial distribution of   2. the major drought that occurred across the basin at the end of 2005 and in early 2006 is investigated. Section 7 presents the repository from which the SWS dataset can be accessed freely, and finally, Sect. 8 provides the conclusions and future perspectives.
Multi-satellite-derived surface water extent We used the estimates of surface water extent (SWE) derived from GIEMS-2, which provides global coverage at a monthly time step of different water bodies, including wetlands, rivers, and lakes at 0. 25 • (∼ 27 km) spatial resolution at the Equator (on an equal-area grid, i. e. each pixel covering 773 km 2 ;  Prigent et al. , 2007 Prigent et al. , , 2020)). The dataset was developed by merging observations from different sensors, as described in  Prigent et al. (2007)  and  Papa et al. (2010). The last version used in this study spans over a long-term period from 1992 to 2015. For more details about the technique, we refer the reader to  Prigent et al. (2007 Prigent et al. ( , 2020)). Several studies, such as  Prigent et al. (2007 Prigent et al. ( , 2020)) ,  Papa et al.
, 2022a). In the current study, the satellite-derived SWHs used are the ones spanning the record period 1995-2015 acquired from three satellite missions, (1) ERS-2, with observations spanning April 1995 to June 2003, (2) ENVISAT (hereafter named ENV), with observations spanning March 2002 to June 2012, and (3) the Satellite with ARgos and AL-tiKa (SARAL/Altika, hereafter named SRL), from which we use observations from February 2013 to July 2016. All three satellite missions have a 35 d repeat cycle. These datasets were made available by the Centre de Topographie des Océans et de l'Hydrosphère (CTOH, http://ctoh. legos. obs-mip. fr, last access: 17 May 2023). They come from the geophysical data records made available by space agencies. For ERS-2, land reprocessing was used  (Frappart et al.
, 2016). These datasets were processed using either the Multimission Altimetry Processing Software (MAPS,  Frappart et al. , 2015b)  or the Altimetry Time Series Software  (Frappart et al. , 2021)  to generate the time series of water levels. Therefore, the 160 generated VSs cover the entire CRB (see Fig. S1  in the Supplement) and a period of ∼ 21 years. The south-eastern portion of the basin, including the Lake Upemba and Lake Bangweulu regions, was not covered by the SWH VSs due to the simultaneous lack of data from the three aforementioned satellite missions. Over this region, missing data were replaced by the annual cycle computed using the altimetry-based water levels available during the study period.
MERIT is closer to a DTM because of the removal of tree height bias, but it is not a complete DTM  (Yamazaki et al. , 2017; Hawker et al. , 2022)  due to other artifacts such as artificial features. In this study, only FABDEM can be considered a DTM  (Hawker et al. , 2022). Therefore, in order to remove the presence of tree bias in DSMs, we subtract from them the forest canopy height from a global dataset  (Potapov et al. , 2020). For that, the global canopy height dataset, ASTER, and ALOS were all resampled to 90 m spatial resolution using the nearest-neighbour resampling method.
Global forest canopy height The global forest canopy height (available at https://glad. umd. edu/dataset/gedi/, last access: 17 May 2023) is a global dataset developed by combining the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurement and Landsat analysis-ready data time series  (Potapov et al. , 2020). GEDI is a new spaceborne lidar instrument on board the International Space Station collecting data on the vegetation structure since April 2019  (Dubayah et al. , 2020). The spatial resolution of the dataset is 30 m, providing the global forest canopy height map for the year 2019 in the WGS84 reference system. The dataset covers zones between 54 • N and 52 • S globally, and then we use it over the CRB. For more details on the dataset, we refer the reader to  Potapov et al.
Lake water storage anomaly Over the largest lakes of the CRB, time series of the monthly water storage anomaly for Lakes Bangweulu, Kivu, Mweru, Tanganyika, and Upemba (see Fig. 1  for their locations and Fig. S2  for their time series) are estimated using surface water extent and water level time series obtained from the Hy-droSat database (accessible at https://www. gis. uni-stuttgart. de/en/, last access: 17 May 2023;  Tourian et al. , 2022). After collecting the simultaneous lake water area and height measurements, the empirical relationship between lake surface water level and area is developed. This model represents the bathymetry of the lake for the part which is captured by remote-sensing observations. By assuming that the lake has a regular morphology and a pyramid shape between two consecutive measurement epochs, the lake water level area storage model is developed.
Rainfall We used precipitation estimates from Multi-Source Weighted-Ensemble Precipitation (MSWEP) Version 2. 8 (V2. 8). MSWEP is a global precipitation product with a spatial resolution of 0. 1 • at 3-hourly temporal resolution (also available at the daily scale) covering the period from 1979 to the present in near real time. MSWEP estimates are derived by optimally merging multiple precipitation data sources, such as gauge, satellite, and reanalysis estimates  (Beck et al. , 2019a). The latest MSWEP version (V2. 8) includes several changes compared to its previous version (V2. 2). Among the major updates, in addition to near-realtime (NRT) estimates, it also features new data sources that were defined based on their superior performances. https://doi. org/10. 5194/essd-15-2957-2023 Earth Syst.
Sci. Data, 15, 2957-2982, 2023 The historical MSWEP V2. 8 considers (i) one modelbased precipitation product, the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 (ERA5), (ii) two satellite-based precipitation products, the Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm and the Gridded Satellite (GridSat) data, and (iii) gauge observations from various sources: the Global Historical Climatology Network-Daily (GHCN-D), the Global Summary of the Day (GSOD) databases, and several national databases. On the other hand, MSWEP V2. 8 NRT merges (i) two model-based precipitation products, ERA5 and National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analysis and (ii) two satellite-based precipitation products, Global Satellite Mapping of Precipitation (GSMaP) and IMERG.
Total water storage anomaly from the Gravity Recovery and Climate Experiment mission GRACE is a joint NASA and German Aerospace Center (DLR) mission launched in March 2002  (Tapley et al. , 2004)  and, together with its successor GRACE Follow-On (GRACE-FO) launched in 2018  (Tapley et al. , 2019) , provides estimates of changes in water storage at the basin scale. For the analysis in this study, we used data from GRACE and GRACE-FO Mascon data available at http://grace. jpl. nasa. gov (last access: 17 May 2023;  Wiese et al. , 2016 Wiese et al. , , 2018)). The mascon data provide surface mass changes with a spatial sampling of 0. 5 • in both latitude and longitude  (Watkins et al. , 2015). From this dataset, we obtained time series of the terrestrial water storage anomaly (TWSA) over the CRB through area-weighted aggregation of those grid cells in basins.
Multi-satellite approach The multi-satellite approach (Fig. 2a ) consists of the combination of the SWE and satellite-derived SWH over inland water bodies (rivers, lakes, reservoirs, wetlands, and floodplains), generally derived from radar altimetry over a common period of availability for both datasets  (Frappart et al. , 2008 (Frappart et al. , , 2011;; Becker et al. , 2018; Papa and Frappart, 2021). Therefore, this complementarity of multi-satellite observations offers the possibility of quantifying SWS changes and water volume variations in a watershed. SWE and SWH used in this study are respectively from GIEMS-2 and the family of spaceborne radar altimeters with a 35 d repeat cycle (hereafter ERS-2, ENV, and SRL). Their common period of availability is 1995-2015. We summarize in the next sections the two-step methodology and, for more details, we refer the reader to  Frappart et al.
org/10. 5194/essd-15-2957-2023 Earth Syst. Sci. Data, 15, 2957-2982, 2023 2 consecutive months, S max is the maximum error for the flooded surface, and (δh max ) is the maximum error for the water level between 2 consecutive months. Note that the volume of SWS variations in a given basin is the sum of the contributions of the water storage contained in floodplains, wetlands, rivers, and small lakes. For larger lakes, as mentioned previously, estimates of SWS are complementarily obtained by the HydroSat database  (Tourian et al. , 2022). Therefore, the water storage analysis takes into account variations in floodplains, wetlands, rivers, and lakes.
This interannual variability is also superimposed on variations at a larger timescale, from a few years to decades, such as a large increase in the SWS anomaly in 1996-1997 followed by a slight decrease until the minimum that occurred in 2005-2006, before SWS starts to slowly increase again after the 2007 peak until 2015. Table  3. Mean annual amplitude over the CRB calculated from the multi-satellite and hypsometric curve approaches. Error statistics comparing SWS from ALOS, ASTER, and MERIT and the multi-satellite approach against SWS from FABDEM are considered here as the reference. Comparisons are done over the same period by aggregating all SWS pixels over the basin for the compared datasets. MAE stands for mean absolute error and RMSE for root-mean-squared error (km 3 ). Figure  6c  shows the CRB's SWS annual cycle (computed over the 1992-2015 and 1995-2015 periods for the hypsometric and multi-satellite approaches respectively), revealing a strong seasonal variation.
Method As observed in Fig. 6a , ASTER's SWS provides larger estimates, with a mean annual amplitude of ∼ 124 ± 25 km 3 (Table  3 ). This can be explained by the fact that, among the four DEMs (ALOS, ASTER, FABDEM, and MERIT), ASTER has a greater vertical error (i. e. 17 m; see Table  1  of  Hawker et al. , 2019)  compared to the others and, consequently, this can impact the elevation amplitude (derived at step 2 of Sect. 4. 2 used to calculate the SWS variations (step 3 of Sect. 4. 2). Table  3  also presents the statistical errors comparing FAB-DEM's SWS dataset to other estimates and reinforces the difference highlighted in SWS magnitude between different approaches and DEMs. Note that trees and urban-area biases are only removed from FABDEM, and thus it seems to be the most adequate DEM in representing hydrology processes  (Hawker et al.
In the Lualaba sub-basin, the average month of the maximum of SWS is January-February, while in the other parts, such as Lake Mweru and the eastern part of Lake Bangweulu, with its large grassy swamps and floodplains, it recorded in March-April. Conversely to the general trend in the Cuvette Centrale, the regions of Lake Tumba and Lake Mai-Ndombe reached their maximum SWS in July-August. In general, the results from the hypsometric curve (Fig. 7 , left column) and multi-satellite (Fig. 7 , right column) approaches are quite similar in terms of spatial distribution for both the magnitude and variability of the changes. Nevertheless, as expected, the multi-satellite dataset approach shows a limitation in terms of spatial distribution caused by the reduced availability of the combined long-term VSs in some regions.
In this case, the SWS change in the lake is then added to the dataset using the lake water storage anomaly computed independently (see the data description in Sect. 3. 5). Finally, for comparison purposes, Fig. S6  shows the same analysis in terms of spatial distribution and variability of SWS across the basin for the other estimates based on the hypsometric curve approach (i. e. ALOS; see Fig. S6 , left column; ASTER; see Fig. S6 , middle column; MERIT; see Fig. S6 , right column). In general terms, results are consistent between one another. The pattern of SWS in terms of the distribution and order of magnitude of the mean annual (Fig. S6a, b,  and c ), variability in terms of the SD (Fig. S6d,  e,  and f ), mean annual maximum (Fig. S6g, h,  and i ), and average month of the maximum (Fig.
Evaluation against independent datasets In order to evaluate the monthly estimates of the large-scale SWS over the CRB, we compare, at the basin and sub-basin levels, their seasonal and interannual variability against other independent hydrological variables such as precipitation data from MSWEP V2. 8 and in situ discharges. For clarity purposes, only the SWS results from the hypsometric curve approach with FABDEM and from the multi-satellite approach are displayed and discussed here. At the basin level, Fig. 8  presents the comparison of the aggregated normalized SWS anomaly variation over the entire basin against the normalized precipitation anomaly and the in situ normalized discharge anomaly at the outlet of the basin (Brazzaville/Kinshasa station; see Table  2 ). The normalized anomalies here are estimated by subtracting the mean value of the time series from individual months and by dividing the obtained series by the SD of the original time series.
The same observation is made in terms of the tem-poral shift for the second peak (i. e. March-July), where the maximum of the SWS anomaly occurs in April, 1 month later for the normalized discharge anomaly, and 1 month before for the normalized precipitation anomaly respectively in May and March. Figure  9  displays the comparison at the basin level between the aggregated normalized SWS anomaly and TWSA from GRACE. Both variables show a similar interannual variability during the common period of availability of data  (i. e. 2002 to 2015) , presenting a fair correlation of r = 0. 84 (lag = 1; p value < 0. 01; Fig. 9a ). It is also worth mentioning that both datasets capture the bimodal patterns. Figure  9b  presents the deseasonalized normalized anomaly for the two variables (r = 0. 4; lag = 0; p value < 0.
e. data before the 1990s), and thus the comparison with its in situ normalized discharge anomaly time series was integrated only at the annual cycle. For the Ubangui, Sangha, and Lualaba subbasins, the maximum linear correlation coefficient is not significant between normalized SWS anomaly and normalized precipitation anomaly variations at the interannual level and their associated anomaly (Fig. 10a , b, d, e, m, and n, Table  4 ). This could be associated with the bimodal dynamics observed in the precipitation data, whereas the SWS variations do not show that behaviour. Becker et al. (2018) , using similar datasets (GIEMS-1 for SWE and VS from ENVISAT), reported the same observation between precipitation data with bimodal patterns and SWE with unimodal patterns (Fig. 4  of  Becker et al. , 2018).
8 and 10 follow alternatively wet and dry events associated with large-scale climatic phenomena for all the datasets (SWS, precipitation, and discharge). A focus on the Lualaba and Kasaï deseasonalized normalized anomalies of SWS reveals that there are two main sub-basins significantly impacted (large positive anomaly in Fig. 10k  and n ) by the major flood event triggered by the positive Indian Ocean Dipole in combination with the El Niño event that characterized the period 1997-1998. Conversely, recent studies using hydrometeorological datasets have shown that some parts of the CRB are subject to a longterm drying trend over the past decades  (Hua et al. , 2016; Ndehedehe et al. , 2018). The droughts that affected large areas of the CRB in recent years are amongst the most severe ones in the past decades  (Ndehedehe and Agutu, 2022) , including the large anomalous event of the 2005-2006 drought (Fig.
Data availability The SWS estimates from the multi-satellite approach , the hypsometric curve approach providing the surface water extent area-height relationships from the four DEMs (before and after the corrections), the surface water extent area-storage relationships, and the four SWS estimates  are publicly available for noncommercial use and are distributed via the following URL/-DOI: https://doi. org/10. 5281/zenodo. 7299823 (Kitambo et al. , 2022b).
Conclusions and perspectives In this study, we present an unprecedented dataset of the monthly SWS anomaly of wetlands, floodplains, rivers, and lakes over the entire Congo River basin during the 1992-2015 period at ∼ 0. 25 • spatial resolution. Two methods are employed, one based on a multi-satellite approach and one on a hypsometric curve approach. The multi-satellite approach consists of the combination of SWE from GIEMS-2 and satellite-derived SWH from radar altimetry (long-term series ERS-2_ENV_SRL) on the same period of availability for the two datasets, here 1995-2015. The hypsometric curve approach consists of the combination of SWE from the GIEMS-2 dataset and hypsometric curves obtained from varhttps://doi. org/10. The temporal variations of SWS satisfactorily depicted the bimodal pattern at the interannual and seasonal scales, a well-known characteristic of the hydrological regime of the Congo basin.
Furthermore, an evaluation was conducted with independent hydrological variables, precipitation from the MSWEP dataset, and in situ discharges from contemporary and historical observations, showing an overall good correspondence among all the variables. The estimates of SWS variations also enable us to depict the major anomalous events related to droughts (e. g. the exceptional drought documented in  [2005] [2006]  and floods (e. g. the exceptional flood that occurred in 1997-1998). We further map, across the basin, the spatial signature of the widespread drought that took place at the end of 2005 and the beginning of 2006, revealing the severity of this particular event for the surface freshwater store, in agreement with satellite-derived precipitation observations, although the north-east of the Cuvette Centrale and some tributaries of the Kasaï River (in the Lake Mai-Ndombe region) were less impacted.