dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The birth of GW astronomy opened a new era also in Cosmology. Since gravitational radiation traces the underlying large-scale structure of the Universe in a very similar fashion to what electromagnetic radiation does, it allows to study the evolution of cosmic structures using an independent and complementary tracer. In fact existing and future GW detectors, as LISA or Einstein Telescope, can provide both catalogs of resolved GW events and sky maps of the GWB. Interpreting these new observables represents a true scientific challenge because of the multidisciplinary expertise required to analyse these datasets. Measuring how GWs trace the large-scale structure of the universe requires an extensive knowledge of the properties of GW sources, of their signal generation and propagation over cosmological distances, and finally of how the signal is detected.
This study assesses the detailed water budget of the Saq-Ram Aquifer System (520 000 km 2 ) over the 2002-2019 period using satellite gravity data from the Gravity Recovery And Climate Experiment (GRACE). The three existing GRACE solutions were tested for their local compatibility to compute groundwater storage (GWS) variations in combination with the three soil moisture datasets available from the land surface models (LSMs) of the Global Land Data Assimilation System (GLDAS). Accounting for groundwater pumping, artificial recharge, and natural discharge uniformly distributed over the Saq-Ram domain, the GRACE-derived mass balance calculation for water yields a long-term estimate of the domain-averaged natural recharge of (2. 4 ± 1. 4) mm yr -1 , corresponding to (4. 4 ± 2. 6) % of the annual average rainfall (AAR).
3 ± 5. 5) % of the AAR. The present study proposes a reappraisal of the GRACEderived water budget of the combined Saq-Ram Aquifer System, accounting for groundwater withdrawal, natural drainage, and artificial recharge in order to provide a longterm estimate  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019)  of the domain-averaged natural recharge. The three existing GRACE solutions (Jet Propulsion Laboratory -JPL, Centre for Space Research -CSR, Goddard Space Flight Center -GSFC) were tested for their local compatibility to compute GWS variations by combin- ing with the three soil moisture datasets (Variable Infiltration Capacity -VIC, Community Land Surface Model -CLSM, National Oceanic and Atmospheric administration model -NOAH) available from the land surface models (LSMs) of the Global Land Data Assimilation System (GLDAS).
Gravity and soil moisture data The GRACE twin-satellite system launched in 2002 by NASA and the German Aerospace Center (DLR) monitors the Earth's gravity at a 3 • × 3 • spatial resolution on a monthly basis. Followed by GRACE-FO (GRACE Follow-On) in 2018, the mission measures gravity anomalies (i. e. gravity value of a given month minus the average value from January 2004 to December 2009), which are chiefly due to mass variations in water bodies (groundwater, soil water, and surface water). Monthly gravity anomalies, denoted as terrestrial water storage (TWS), are expressed in water height (i. e. equivalent water thickness) and can thus be used for hydrological mass balance calculations at the regional scale. After more than 1 decade of gravity field processing using spherical harmonics  (Landerer and Swenson, 2012; Swenson and Wahr, 2006) , an alternative interpretation using Mass Concentration blocks (termed Mascons) of discrete spherical caps at the Earth's surface was proposed  (Watkins et al.
, 2015). The main advantage of this new interpretation is lower geophysical signal losses inducing less post-processing treatment. Provided by different computing centres (GRACE/GRACE-FO Mascon data are available at http://grace. jpl. nasa. gov, last access: 10 November 2022), three alternative solutions of Mascon interpretations were retrieved: (i) Jet Propulsion Laboratory (JPL; 3 × 3 • squared tiles;  Watkins et al. , 2015; Wiese et al. , 2019) ; (ii) Centre for Space Research (CSR; 1 × 1 • hexagonal tiles; Save, 2020;  Save et al. , 2016) ; (iii) Goddard Space Flight Center (GSFC; 1 × 1 • squared tiles;  Loomis, 2020; Loomis et al. , 2019). The proposed scaling factors which correspond to downscaling factors  (Scanlon et al. , 2016)  were not used here. In fact, these downscaling factors are based on the mass distribution calculated by LSMs accounting for surface and subsurface water transfers  (Landerer and Swenson, 2012) , while TWS variations in such arid regions are expected to be chiefly controlled by groundwater mass variations.
Moreover, as stated by the authors, the use of such gain factors is not suitable to quantify trends. We used 187 monthly observations for the period between April 2002 and July 2020. The trend of a time series can be analysed and interpreted in terms of variation in water amounts (e. g. in mm yr -1 ) and thus net water fluxes over a domain of interest. The Global Land Data Assimilation System (GLDAS; data available at https://ldas. gsfc. nasa. gov/gldas, last access: 10 November 2022) provides soil water storage (SWS) for the first couple of metres by combining ground-based, satellite data, and hydrological surface model results  (Rodell et al. , 2004). Matching the GRACE observation dates, we retrieved the 187 monthly data from the 2. 1 versions with a 1 • × 1 • spatial resolution computed with the (i) Variable Infiltration Capacity model (VIC;  Beaudoing et al.
Methods The data are used to build a regional-scale water mass balance and estimate recharge from variations in groundwater storage ( GWS). Usually obtained by piezometric measurements, it is difficult to obtain spatially meaningful averages of GWS over large and poorly monitored regions such as the Arabian Peninsula. GRACE data provide a valuable integrated mapping of water storage over large domains. In arid areas, surface water can generally be neglected. Moreover, the Saq-Ram aquifer is devoid of any permanent surface water bodies. Thus, using GLDAS SWS estimates, the first step was to isolate the GWS anomalies from the TWS anomalies expressed in water height with the following simple decomposition of the GRACE data: TWS = GWS + SWS. (1) And thus: GWS = TWS -SWS. ( 2 ) Upon identification of a long-term trend (18 years in our case) of the GWS signal (using a fourth-order polynomial regression to filter out the seasonal signal), GWS is calculated and can be introduced in an overall groundwater balance equation to identify one unknown (e.
Discussion This study provides an estimate of the 2002-2019 domainaveraged natural recharge with associated standard deviation accounting for temporal variations in natural discharge, groundwater pumping, and irrigation return flow. The uncertainties associated with the calculation of the GWS longterm trends with the GRACE and GLDAS products have also been considered. Errors associated with GRACE measurements could not be accounted for as they are not provided with the raw Mascons data (i. e. before the application of unwanted scaling factors). However,  Blazquez et al. (2018)  investigated the uncertainty of raw GRACE data by solving a global water budget using trends in ocean mass, ice loss from Antarctica, Greenland, Arctic islands, and trends in water storage over land and glaciers. The authors estimated a 0.
, 2015; Zaidi et al. , 2015)  computed a mean vadose zone thickness of about 150 m (ranging from 15 to 300 m) for a domain including the vast majority of the Saq-Ram Aquifer System. According to  Kamai and Assouline (2018) , a vadose zone thickness of 150 m would induce a mean groundwater evaporation loss of about 0. 07 mm yr -1 , well below 1. 1 mm yr -1. This discrepancy suggests that the GRACE-GSFC solution is not suitable for this study (probably due to differences in the treatment of the raw GRACE data with the other two products) and that its results may not yield a meaningful mass balance. We therefore only interpreted the JPL and CSR solutions that yield positive water balances (Table  1 ).
3 × 10 6 km 2 ;  Gonçalvès et al. , 2022) ; and Djeffara Basin in Tunisia and Libya (0. 1×10 6 km 2 ;  Gonçalvès et al. , 2021). A clear positive offset compared to the regression is observed for the Saq-Ram Aquifer System (Fig. 5 ). One of the main differences between the Saq-Ram and the five Saharan basins is that the latter correspond to almost purely sedimentary porous domains, while the Saq-Ram presents a substantial proportion of overlying volcanic lava deposits called harrats (8 % of the domain, i. e. 40 000 km 2 represented by black areas in Fig. 5 ), over which a higher local precipitation is observed (i. e. AAR of 60 mm yr -1 using CRU data from  Harris et al. , 2020). To our knowledge, there is no study reporting recharge rates on these basaltic deposits strictly within the studied domain, but data are available for other parts of the Harrat al-Sham (Syria, Jordan, Saudi Arabia; also called Al Harrah in Saudi Arabia), which corresponds to the largest volcanic lava deposit of the Arabian Peninsula, partly extending over the Saq-Ram Aquifer System.
g. perennial and non-perennial streams, lakes, and playas). The standard interpretation of GRACE data consists of subtracting SWS given by GLDAS models from TWS obtained from satellite Earth gravity monitoring. However, the water balance calculated by LSMs in GLDAS is restricted to the top soil (about the first 2 m, SWS T ; Fig. 6 ). Therefore, the implicit assumption behind the standard interpretation is that the soil moisture variations in the deep vadose zone ( SWS D from 2 m depth to the water table elevation) are negligible, which means that infiltration at 2 m from the top of this deep vadose zone (I ) equals the recharge outflowing at its bottom (R). For small lag times (focused recharge), or shallow aquifers, this assumption is valid both on an annual basis and for long-term averages.
7 ). The computed recharge of the Saq-Ram Aquifer System and the NWSAS  (Gonçalvès et al. , 2013; Mohamed and Gonçalvès, 2021)  is considerably different when considering different time periods of GRACE data. Compared to the NWSAS real estimate for the longterm average recharge obtained using a 14 C interpretation (i. e. (1. 6 ± 2. 3) mm yr -1 ;  Chekireb et al. , 2021) , it appears that about 15 years of GRACE data are required to obtain the long-term average recharge. By fitting the best threshold regression models (dashed lines), it is possible to estimate that the long-term natural recharge of the Saq-Ram Aquifer System is 2. 6 mm yr -1.
Conclusion In this study, we retrieved gravity data from the GRACE satellite system and soil moisture variations inferred from GLDAS models to construct a water mass balance with the aim of estimating the long-term average natural recharge over the Saq-Ram aquifer domain. As recommended by  Gonçalvès et al. (2013)  and  Scanlon et al. (2019) , the local compatibility of the three existing GRACE solutions (JPL, CSR, GSFC) with the soil moisture datasets available from the three GLDAS models (VIC, CLSM, NOAH) was tested to compute groundwater storage variations. Accounting for (15. 7 ± 1. 1) mm yr -1 of groundwater pumping, (2. 2 ± 0. 8) mm yr -1 of artificial recharge, and (0. 3 ± 0. 06) mm yr -1 of natural discharge derived from previous studies, the GRACE-derived water budget yielded a (2.
Acknowledgements. We thank  Séverine Thomas  and  Imen Khabouchi  for their logistical and technical support. The three anonymous reviewers and the editor are acknowledged for their comments, which improved the paper. Financial support. This research has been supported by the  Agence Française pour le développement d'AL-ULA (AFALULA)  and the  Royal Commission of Al-Ula (RCU)  through funding of the project " Past, present and future Water resources in Al-Ula Oasis " (WAO), which this study is part of. Review statement. This paper was edited by  Harrie-Jan Hendricks Franssen  and reviewed by three anonymous referees.
Data availability. The two major datasets used in this study are GRACE (three products: https://search. earthdata. nasa. gov/search?q=JPLCRI&fpj=GRACE-FO, NASA, 2022a; https://www2. csr. utexas. edu/grace/RL06_mascons. html, CSR, 2022; https://earth. gsfc. nasa. gov/geo/data/grace-mascons, NASA, 2022b) and GLDAS (three products: https: //search. earthdata. nasa. gov/search?fpj=GLDAS, NASA, 2022c). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-26-5757-2022-supplement.
EuroDiad version 4. 0 is a set of data tables that store information about the presences/absences and population functionality of diadromous species (lampreys and fish) populations in selected catchments in Europe, the Middle East, and North Africa from 1750 to present time. This database contains distribution and life-history trait information for twenty-eight European diadromous species and geomorphological data for each of the selected catchments, though not every species has data for every catchment and time period. EuroDiad was originally created in 20 05-20 06 (Eu-roDiad 1. 0 and 2. 0), and contained data for 196 catchments and two time periods (1851-1950 and 1951-2010). It underwent a major update in 2009-2010 (EuroDiad 3. 2) through a validation process by European fisheries experts.
mation for a new generation of species distribution models, referred to as hybrid models, which incorporate both habitat suitability and population dynamics within their framework. Secondary objectives of this update were to: (a) incorporate new catchments for which information was provided by additional experts, (b) validate existing information about the presences or absences of diadromous species and categorize their population functionality within a catchment, and (c) perform data hygiene to prepare the database for broad dissemination. Information on the life history, morphology, and phenology of four emblematic species (i. e. eel, salmon, lamprey and shad) were added in this occasion. Data for this update were validated by DiadES project partners ( www. diades. eu ) and local experts.
This update was focused on catchments located in the Atlantic Area for use in the DiadES project. Data were divided by country, and validation was performed for catchments in Ireland, the U. K. , Spain, Portugal, and France under the supervision of national organisations in fisheries and environmental management. Di-adES project partners were asked to validate geomorphological information for the catchment (location of the outlet, surface area of the drainage basin, length of the main watercourse, elevation at the headwaters), as well as the presences/absences information and population functionality categories for all species already present in EuroDiad for their country. If possible, verification was done for each of the three time periods. Partners were also asked to provide data for any other catchments for which they had access to information on fish population status.
EuroDiad 4. 0 now stores data for 350 catchments (of which 292 have population functionality records) and three time periods, though the precision of information varies and not every species has information for each time period. This validation process strengthened the usefulness of EuroDiad, which is now updated and available for use by the research community. © 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Type of data Table How data were acquired The date came from a thorough review of the primary literature on diadromous species. Data were validated twice by different groups of regional diadromous species experts. The database was initially developed in Microsoft ACCESS, and then was reformatted in PostgreSQL. Data format Raw Parameters for data collection Data extracted from the primary literature had to pertain to three major categories: (1) species distribution within selected catchments, (2) catchment-specific descriptive information, and (3) species traits in the selected catchments. ( continued on next page ) Description of data collection Catchment-specific values for the surface area of the drainage basin, length of the main watercourse, and altitude of the source were compiled from Wikipedia and online and physical Atlases.
Data source location For each entry, all the primary data sources could be found in the field "bibliography" in the "basin", "abundance" and "species traits" tables. Full references were provided in the online repository under two formats RIS and BibTeX. • This dataset also contains life-history trait information for four valuable species (Salmo salar, Alosa alosa, Petromyson marinus, Anguilla anguilla) across their distribution range. The entire life cycle was covered including the shift from continental to marine habitats. • Data were produced after an extensive scientific literature review based on digital libraries and other electronic resources, and two expert validation processes.
General dataset structure EuroDiad v. 4. 0 is a set of data tables that store information pertaining to the presences/absences, life-history traits and functionality of populations in a selection of catchments in Europe, the Middle East, and North Africa ( Fig. 1  ). Here, functionality refers to the reproductive capacity of a population, which is discussed in more detail in the next section. Throughout the data collection and updating process, values for the surface area of the drainage basin, length of the main watercourse, and altitude of the source were compiled using primary literature, specialised websites, and expert validation. Presence/absence designation was also taken from the primary literature and represented three 100-year time periods (1751-1850, 1851-1950, 1951present time).
Life-history traits were extracted from the primary literature but also from reports and personal communications of Diadfish network experts ( www. diadfish. org ). The time range covered by the data is 1905-2006. All the data sources were made available in dedicated fields named "bibliography" with full references in the online repository. This dataset was compiled and entered in 20 05-20 06 in Microsoft Access format (EuroDiad 1. 0 and 2. 0), and underwent a major update in 2009-2010 (EuroDiad 3. 2). The database was then reformatted to PostgreSQL (The PostgreSQL Global Development Group, 1996-2020), which is an open-source relational database management system. The most recent version (EuroDiad 4. 0) consists of multiple tables, all stored within one schema in PostgreSQL ( Fig. 2  ). These tables are connected using primary keys, which provide unique identifiers (UIDs) for each data record within a table.
Queries can be written to extract specific information from one or multiple tables, and a "view" can be created that permanently combines information from multiple tables. When new data is added to a table, the information in this view is updated along with any other tables whose columns are included in the updated information. Data were divided into multiple tables ( Fig. 2  ) which were grouped by intended use. These included a table for catchment information, distribution records, a table for species-specific life history traits, and one listing the upstream limit of captures within each catchment. In many instances, field values are coded as a numeric index rather which was associated with an explicit wording in a separate table. In the different "views", the numeric index with its explicit description is given so that the user does not have to refer back to the associated tables.
Catchment-specific information Literature-derived information associated with each catchment was initially stored in the tables "basin" included the geographic location (ecoregion, country, latitude and longitude of the outlet), as well as surface area of the drainage basin, length of the main watercourse, and altitude of the source ( Table  1  ). Recently, more precise information on the catchment sea outlets and surface areas were derived in accordance with two mapping products, i. e. (1) the Catchment Characterisation Model (CCM2) database  [1]  ( https://ccm. jrc. ec. europa. eu/ ), and (2) HydroSHEDS  [2]  ( https://www. hydrosheds. org/ ), and stored in "basin_outlet" ( Table  2  ). For the majority of catchments, the point used for longitude and latitude of the outlet was defined as being aligned with the coastline, rather than further upstream at the head of tide ( Table  2  ).
However, several catchments consisted of two large rivers that merged upstream of what would be considered the outlet using this definition. In these instances, where conjoined rivers were considered as separate catchments, the point of confluence was defined as the outlet for both. Ecoregion was determined according to the Marine Ecoregions of the World [3] ( Table  3  ), which classifies coastal and continental shelf waters. A catchment's ecoregion was defined as the marine region where its outlet was located. References for each catchment were also recorded in the database under "bibliography", as well as the person in charge of updating the record in the event of future questions ("administrator").
Digital libraries and electronic resources Data collection and updates were produced following a careful literature review. The main digital libraries were searched for references on diadromous species across time, e. g. Gallica ( https://gallica. bnf. fr ), The digital library of the Real Jardin Botanico of Madrid ( https: //bibdigital. rjb. csic. es ), World Digital Library ( https://www. wdl. org ), Biodiversity Heritage Library ( https://www. biodiversitylibrary. org/ ), JSTOR ( https://www. jstor. org/ ), Google Book ( https: //books. google. fr/ ). Searches were made by crossing a species Latin or vernacular name with a catchment name. The request was made in various languages as species and basin names might change across countries. The same request was made on various platforms until all the time periods for a given catchment-species couple were filled with information on presence/absence to a minimum.
Updates to the dataset spatial and temporal extent This dataset was enriched through a series of data updates and external validations. The successive versions were used in several published manuscripts and grey literature ( Table  10  ). Three main updates were performed on the dataset between 2005 and 2020 to increase the spatial and temporal extent and improve the accuracy of the data. The first update involved a validation of the original dataset in 20 05-20 06 (EuroDiad 2. 0) and solicited the help of six diadromous species experts across six different countries ( Table  11  ). Data was divided by country, or broader geographical entity (e. g. all the catchments flowing into the Azov sea), and sent to 20 diadromous species experts across Europe; six answered. Participants were asked to validate the presence/absence information and population functionality categories for all species present in EuroDiad for the catchments included in their list and for the two time periods.
The second update to EuroDiad 3. 2, which added data but did not include an external validation process, occurred in 2009-2010. It was performed with the help of a dedicated staff on this task over four months. The third update to EuroDiad, performed in 2019-2020, added additional catchments and involved a data validation process with 49 participants ( Table  12  ) leading to the last version. EuroDiad 2. 0 included 196 catchments: 35 large catchments ( > 50 0 0 0 km 2 ), 47 mediumsized catchments ( > 10 0 0 0 km 2 ) as identified in the same list, and 114 small-sized catchments ( < 10 0 0 0 km 2 ) that were chosen to avoid any obvious geographical bias. The large catchments together drain two thirds of the continent according to the list of major European river catchments provided by the European Environment Agency ( www.