dataesr/scientific-paragraphs-categorization huggingface dataset:
The observational coverage used in this dataset allows the analysis of the variations of the D/H ratio in three dimensions, including the vertical, latitudinal, and seasonal effects. The analysis of the data and the retrieval of the atmospheric vertical profiles is performed using the NEMESIS (Nonlinear optimal Estimator for MultivariatE spectral analySIS) algorithm  27  , which utilises the optimal estimation framework  28. In particular, the retrieval scheme is performed in two steps. First, vertical profiles of pressure and temperature are retrieved from CO 2 absorption, along with the vertical distribution of the H O and HD  16  O, assuming an a priori isotopic composition of  18   and 18 O/ 16 O = 2. 0 x 10 -3 ), and using a spectral window suitable for the derivation of the isotopic ratios (see Fig.
On the other hand, the impact of the doubling is milder for the lower part of the detector frame. While the measured spectra show clear differences between the doubling at the slit centre and edge in diffraction order 217, these differences are milder for diffraction order 223, which is situated at the bottom part of the detector frame. The dataset assembled for this study includes spectra measured in diffraction orders 223 and 224, which are both situated at the lower part of the detector frame. Although the impact of the doubled-image is expected to be small in this portion of the detector, it is accounted for in the analysis of the data, considering an instrument line shape (ILS) consisting of a superposition of two Gaussian functions  11. In particular, the following assumptions are made to create a model to fit the ILS: 1) The full-width at half-maximum (FWHM) of the two Gaussian functions is the same, and constant in wavelength units; 2) The relative offset of the two Gaussians is constant along the selected spectral range; 3) The relative depth of the two Gaussians is linearly dependent with wavenumber.
Retrieval scheme The retrieval scheme is performed in two steps. First of all, the pressure and temperature profiles are retrieved from the CO 2 absorption in diffraction order 223, while the vertical profile of H O and HD  16  O in diffraction order 224, assuming an a priori isotopic composition of  18  O/ 16 O = 1 and D/H = 5 with respect to VSMOW, and using a spectral window suitable for the derivation of the isotopic ratios. All gaseous absorption is modelled using pre-computed look-up tables using line-by-line modelling. The absorption of CO 2 is modelled using the spectroscopic parameters listed in the 2016 edition of the HITRAN database  47. In the case of H 2 O we use spectroscopic parameters suitable for a CO 2 -rich atmosphere  [48] [49] [50] [51]. Pressure and temperature profiles are retrieved from the CO 2 absorption under the assumptions of a known CO 2 volume mixing ratio, which is obtained from the Mars Climate Database (MCD)  52  , and an atmosphere in hydrostatic equilibrium.
The initial retrieval of the vertical distribution of H 2 16 O volume mixing ratio is also performed using the maximum-intensity row, but using a spectral window between 3759. 5 and 3767 cm -1 in diffraction order 224, which encompasses strong lines of H O with those derived from simultaneous measurements by ACS NIR 31 in 1. 38 μm band revealed a bias between the retrieved water vapour abundance using both datasets when observing high water vapour densities (see Fig. S4 ). This bias is presumably related to the saturation of the water vapour absorption lines, which impact the sensitivity of the line along the line-of-sight. In order to reconcile the retrievals from both datasets, we select a smaller spectral window (3763-3765. 4 cm -1 ) including weaker absorption lines of H 2 16 O. In this case, the retrievals are in good agreement with those retrieved from ACS NIR spectra at low altitudes (see Fig.
Validation of measurements and methods From the start of the science mission, comparisons between the simultaneous observations made by the MIR and NIR channels have been used to validate the measurements and methods. Here, we validate the ACS MIR position 4 vertical profiles of temperature and water vapour volume mixing ratio against those measured by ACS NIR, which constrain the pressure and temperature profiles from the CO 2 absorption band at 1. 57 m and the water vapour mixing ratio from the absorption band at 1. 38 m 31. As shown in Fig. S6 , both analyses are found to be in good agreement, increasing the confidence in the retrieved profiles from both datasets. Unfortunately, the ACS NIR channel does not include any absorption bands of the minor water isotopologues (i. e. H 2.
Besides, when taking into account that the contribution of each data point to the annual average is proportional to the photolysis rate (see Fig. 3B ), it is clear that the annual average fractionation factor will be heavily weighted towards the perihelion season, when most of F ′ the photodissociation of water vapour molecules occurs. In order to calculate the seasonal evolution of the column-integrated photolysis rates and fractionation factor (see Fig. 3 ), these are binned in L s with a step of 10°. In order to obtain averages of the fractionation factor over latitude, we calculate a contribution index ( CI ), which is equivalent to the photolysis rate, but multiplied by a normalising factor to match its maximum with unity. The averages of the fractionation factors are then calculated using the contribution index as a weight.
Data collection The land feature related information, i. e. land cover, canopy cover, vegetation type, crown density, was collected at the plot level. At the subplot level, data for tree measurements (live and dead), down woody material, litter, and soil samples were collected. All live and dead standing trees and stumps ≥ 30 cm DBH were measured within the largest subplot; all live and dead standing trees and stumps with DBH ≥ 10 cm to < 30 cm and bamboo clumps were measured in the intermediate subplot; all live, dead saplings and stumps with DBH ≥ 2 cm to < 10 cm of two non-tree species (Nypa fruticans and Cerops decandra) and all tree seedlings for regeneration The number of subplot locations in the Sundarban zone was limited to three to ensure plot measurements to be completed within one day estimation (DBH < 2 cm) were measured in the smallest subplot.
The measurements on field plots were performed by 13 field inventory teams of seven members. Local people were engaged as much as possible in the composition of the field team or as guides. Local FD staff helped by providing information about access conditions to the plot and recruiting local people with required local knowledge. Field teams were trained using a manual developed by FAO, SilvaCarbon and staff from universities and two research organisations (FD 2017a). Field work preparation involved maps, digital field forms, equipment and other logistics. Quality control was achieved through hot and cold checks on 3% and 7% of the plots respectively by four Quality Assurance and Quality Check (QA/QC) teams composed of FD and the University Faculty members (FD 2016c). The data were collected in the field using the Open Foris Collect Mobile app (FAO 2018a) installed on tablets.
The data were collected by five enumerator teams with five members in each team which followed the manual for the socio-economic component (FD 2017b). For verification of household interviews and ensuring consistent field data collection, a quality assurance and quality control (FD 2016c) was conducted in 4% interviewed households (254 hot checks, 13 cold checks). The preparation of the national land representation system (GoB 2017d) and the land cover map involved 13 national entities. In addition, agreements were signed with other organisations for the preparation of, for example, the land representation system (Soil Resource Development Institute, Bangladesh Society of Geo-Informatics and the Centre for Environmental and Geographic Information Services), allometric models (Khulna University, Bangladesh Forest Research Institute), soil analysis and archiving (Soil Resource Development Institute, Dhaka and Khulna University), the development of the Bangladesh Forest Information System (Bangladesh Computer Council), and the tree species list (Bangladesh National Herbarium).
Allometric models A national database of allometric models was prepared to identify gaps and needs for further development  (Hossain and Siddique 2016). Among the 517 models, only 189 species-specific models belonging to 35 tree species (including several models for the same species with differences such as sample size, diameter range, author, year) were identified as valid in a country with rich biodiversity which indicates a great dearth of siteand species-specific allometric models  (Hossain 2016). Based on this review, new allometric models were developed to fill needs at both species and zone levels through extensive field tree data collection in all five zones  (Hossain and Siddique 2016). A decision tree for selecting the most appropriate allometric models was developed  (Hossain et al.
Data analysis All the estimation procedures of the BFI indicators were documented to ensure transparency, sustainability and future improvement  (Hossain et al. 2019). The data collected were archived into a mass storage for backup. MS Access and R-scripts were used for updating the database regularly with predefined queries. For data cleaning and cold checking individual plot reports were prepared as. pdf from MS Access. Data cleaning was done through multiple procedures including within the Open Foris platform, manual cleaning based on the errors identified in the plot report and identifying inconsistencies or outliers through running customized Rscripts. After data cleaning, customized R-scripts (supplementary information 1) were used for the estimation and are based on the assumptions for stratified random sampling  (Hossain et al.
At the scale of the polygons of the land cover map 2015, zonal statistics of the 2000-2014 tree cover data were estimated. This resulted in each polygon of the land cover map containing the number of pixels with tree cover in 2000, and the number of pixels with tree cover gains and the number of pixels with tree cover loss for each year between 2000 and 2014. Based on this, tree cover percentage within each land cover class was estimated for 2000 and 2015 (assuming 2014 tree cover), and changes in percentage tree cover were also estimated. Stratum-specific mean values of carbon density were assigned by land cover classes for the preparation of the map of carbon density (Fig. 9 ). The carbon density with standard errors by stratum and land cover classes were provided in Table  S2. 2.
In addition, mobilising different technical expertise, such as forestry and remote sensing, contributes to its cost-effectiveness, e. g. reducing the cost and time related to data collection and processing. Strengthened partnerships and stakeholder engagement contributes to increasing interest in the multiple uses of the datasets for planning and monitoring. As an example, the socio-economic and biophysical information about fuelwood demand and supply were used to assess access to wood fuel supply and support land restoration activities in Cox's Bazar district. The results were also used as baseline information in support of Investment Plans such as for the World Bank Forest Investment Program. In 2018, GoB confirmed its commitment to the plan by seeking an IDA credit of USD 175 million to finance the Sustainable Forests & Livelihoods project.
Acknowledgements The authors would like to express their profound gratitude to the various national and international entities which contributed to this process and in particular to the  Bangladesh Forest Department, Government of the People's Republic of Bangladesh , for its leadership,  USAID  ( GCP/BGD/058/USA ) and  UN-REDD programme  ( UNJP/BGD/057/ UNJ ) for financial support, and  FAO  for technical assistance. The authors would like to acknowledge  Antonio di Gregorio  and  Gianluca Franceschini  for their support in preparing the national land representation system,  Oswaldo Carrillo  for data analysis and R scripts,  Remi d' Annunzio ,  Marco Piazza  and  Stefano Ricci  in deploying the Open Foris suite. The authors thank  Daniel Wachira ,  Ikbal Faruk ,  Nasrin Akter ,  Mahbubur Rahman ,  Kamrul Hossain ,  Farah Tasnim ,  Nazrin Sultana ,  Shuhala Ahasan ,  Champa Rani Saha ,  Zarin Tasnim ,  Nandini Sarker ,  Tamanna Tabassum ,  Abdul Halim  and other colleagues for their support throughout the process.
River freshwater contribution in the European Atlantic margin and its influence on the sea salinity field are analyzed. The impacts of using a new river discharge database as part of the freshwater forcing in a regional ocean model are assessed. Ocean model scenarios, based on the CMEMS (Copernicus Marine Environment Monitoring Service) operational IBI-MFC (Iberia Biscay Ireland Monitoring Forecasting Centre) model set-up, are run to test different (observed, modeled and climatological) river and coastal freshwater forcing configurations throughout 2018. The modelled salinity fields are validated, using as a reference all known available in-situ observational data sources. The IBI model application is proven to adequately simulate the regional salinity, and the scenarios showcase the effects of varying imposed river outflows.
However, available databases and services that progressively provide better estimates of coastal runoff and river discharges have started to improve ocean model freshwater coastal forcing input. Several research initiatives and established services are focused on generating at a global scale up-to-date runoff and freshwater river discharge historical datasets. Dai et al. [11]  compiled for community use a global dataset with monthly streamflow at farthest downstream stations for the world's 925 largest ocean-reaching rivers; likewise, the service currently provided by the Global Runoff Data Centre  [12] , under the auspices of the World Meteorological Organization (WMO), delivers quality controlled "historical" databases, comprising daily and monthly mean river discharges from more than 9900 in-situ stations from 159 countries.
This IPMA campaign covers part of the WISHE and CADIZ zones for a short time (from 28 April until 30 May 2018). The salinity is calculated as the salinity of the water inside the TSG and it represents salinity of the oceanic surface layer. Therefore, these measurements are used to validate the surface salinity extracted from the different IBI model simulations performed. Daily salinity expendable bathythermograph (XBT) profiles are obtained from the Coriolis Ocean Dataset for Reanalysis for the Ireland-Biscay-Iberia region  [51]  real-time website (www. coriolis. eu. org (accessed on 8 April 2021)). These data are located on the shelf (red dots in Figure  2 ) in the BISCA area and are a part of the RECOPESCA (Réseau de mesure de l'activité de pêche spatialisé et de données environnementales à usage scientifique) Project campaigns (IFREMER; L'Institut Français de Recherche pour l'Exploitation de la Mer), which collect in-situ data in the Bay of Biscay onboard fishing vessels  [52].
Description of the New River Freshwater LAMBDA Database The CMEMS Service Evolution LAMBDA project  [27]  aimed to improve land boundary conditions by coupling watershed models to ocean regional models. The project study area simulated the territories draining to the European Atlantic front and the North Sea, to provide boundary conditions to the CMEMS IBI and NWS (North West Shelves) Monitoring and Forecasting Centers, using the MOHID (Water Modelling System) Land model  [53]  with regular grids with a 0. 05 • horizontal resolution. The MOHID Land model, part of the MOHID Modeling System (http://www. mohid. com (accessed on 8 April 2021);  [54] ), is a physically based, spatially distributed, continuous, variable time step model for the water and property cycles in inland waters that allows simulation of single and multi-catchment simulations.
Each drainage basin domain was simulated for a 11-year period (1 January 2008-1 January 2019) using ERA5 reanalysis meteorology from the Copernicus Climate Change Service (https://climate. copernicus. eu/ (accessed on 8 April 2021)). These simulations resulted in the first LAMBDA data product, used in this study, consisting of daily flow and temperature values near the river mouth for a total of 54 main European rivers. In a second phase, 70 extra discharges for minor rivers in Western Iberia and 364 extra discharges for Ireland and UK domain were extracted to obtain a more comprehensive freshwater budget in these areas. However, these extra rivers included in a second release of the LAMBDA database are not fully validated yet and the model resolution (too coarse) used may be a limitation to accurately represent flows from such minor rivers.
Hourly 3D physical forecast data, covering exclusively the continental shelf and coastal areas, are additionally delivered to foster the dynamical downscaling and subsequent implementation of coastal downstream services. The CMEMS IBI system uses a SAM2-based (second generation of Mercator Assimilation System) data assimilation scheme  [58]  to enhance its predictive skills by constraining the model in a multivariate way with a wealth of observations: Altimeter data (i. e. , along-track sea-level anomalies), in-situ temperature and salinity vertical profiles (from the CMEMS CORA-Coriolis Ocean Dataset for Reanalysis-4. 1 database) and satellitederived sea surface temperature (OSTIA-Operational Sea Surface Temperature and Sea Ice Analysis-product) are regularly assimilated to estimate periodically initial conditions for numerical forecast simulation.
With respect to in-situ observations, the NARVAL software tool makes routine comparisons of CMEMS IBI salinity model products, extracting sea surface salinity timeseries on the grid points closest to the mooring buoy locations, and salinity profiles where Argo or glider observations are available. Additionally, the IBI SSS field is validated using daily remotely sensed satellite estimations derived from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. In recent years, the SMOS products have steadily evolved to mitigate systematic biases detected in coastal areas  [63] , and today, they better portray plume patterns in major river basins  [64, 65]. However, in the IBI area, there is still room for improvement in these SMOS-derived products, especially in the operational ones and at very coastal areas where systematic biases are identified.
The operational SMOS products' limitations (not able to fully capture variability associated to minor coastal freshwater contributions) have conditioned their use in NARVAL to operationally assess IBI salinity product quality, focusing the validation only on deep open waters. It is pointed out that recent updates of SMOS products, taking benefit of new LAMBDA Project developments, has resulted in a new 9-year (2011-2019) reprocessing of global SMOS SSS daily maps. This new dataset introduces significant improvements with respect to the operational and previous SMOS versions, tending to improve salinity gradients, preserving the small-scale ones close to the coast  [66] , and minimizing latitudinal and seasonal biases  [67]. These improvements support the possibility of using this new SMOS satellite data as a salinity reference in model assessments, and the IBI-MFC has introduced it in NARVAL as an extra salinity data source for IBI delayed mode validations.
The overall freshwater input, surface runoff and rivers, in the operational CMEMS IBI configuration during 2018, had a daily mean flow of 16 × 10 3 m 3 s -1 , where river discharge accounted for 67%. The river discharge flows used in the reference CMEMS IBI configuration consist of a composite of observations and climatology (hereafter referred to as FWF_REF) provided by the GRDC (Global Runoff Data Centre; http://www. bafg. de/GRDC (accessed on 8 April 2021)), the French "Banque Hydro" dataset (http://www. hydro. eaufrance. fr/ (accessed on 8 April 2021)), simulations from the Swedish Meteorological Service (SMHI) E-HYPE ((HYdrological Predictions for the Environment)) hydrological model (http://e-hypeweb. smhi. se (accessed on 8 April 2021)) and observations gathered by the Previmer project.
For further technical details on how freshwater forcing is included in the IBI operational forecast service, the reader is referred to Amo et al. [56]. Ten rivers discharge in the IBBIS area (Figure  1 ). Four of them are along the French coast (BISCA area), four in the Western Iberian shelf (WISHE area) and two in the Gulf of Cádiz (CADIZ area). Since April 2018, most of the river discharges were forced by a monthly climatology (FWF_CLM) or by an annual mean flow (in the IBBIS region, only the Mondego river), calculated from the reference forcing. The new forcing set that will be assessed in this study comes from the LAMBDA project watershed product (FWF_LAM), described above. Table  3  shows the mean daily and cumulated annual flow for each of the forcing datasets in the four areas. Mean daily flow of the LAMBDA forcing for year 2018 in the IBBIS area reaches 8910 m 3 s -1 , that is 70% more than the river input used for the IBI reference run (5223 m 3 s -1 ) and 128% more than the climatological forcing (3893 m 3 s -1 ).
The rivers prescribed as forcing in the subregions are: 1 Vilaine, Loire, Garonne, Adour. 2 Minho, Douro, Mondego, Tagus. 3 Guadiana, Guadalquivir. Figure  3  shows the daily river discharge of the reference forcing (FWF_REF, blue line), LAMBDA river database (FWF_LAM, red line), climatology (FWF_CLM, cyan dashed line) and the extra coastal runoff (FWF_CRF, grey line), in the IBBIS domain and the three subdomains (BISCA, WISHE, CADIZ). For the IBBIS region, in the first months of 2018, the variability and intensity are equivalent in both the reference and the new LAMBDA datasets, with peaks of freshwater in January and March, though peaks are not always simultaneous (Figure  3a ). From April onwards, the FWF_REF is mainly based on the climatology and shows a smooth decrease in total river debit from Spring up to August, when it reaches a minimum average daily flow of ≈2000 m 3 s -1.
LAMBDA features a minimum in summer too, associated with a reduced variability, but shows two important peaks in June and November, that do not exist in the IBI reference forcing. The difference between the two datasets is particularly important in BISCA (Figure  3b ), where the mean river discharge is 1. 8 times more important in the new forcing (Table  3 ). The impact of using new river discharge forcing in CMEMS-IBI, and more specifically on the simulated salinity fields and frontal features linked to river plumes, will be evaluated through specific IBI-like model scenarios. To assess the performance of the numerical model and identify the main sources of uncertainty linked to the river runoff forcing in the model simulations, four scenarios forced by the previously described freshwater discharge parametrization were performed over the year 2018.
To assess the performance of the numerical model and identify the main sources of uncertainty linked to the river runoff forcing in the model simulations, four scenarios forced by the previously described freshwater discharge parametrization were performed over the year 2018. Table  4  shows an overview of the four IBI model land boundary scenarios: IBI_REF is the reference simulation forced by the reference river discharge forcing, and its parameterization is identical to the one used in the operational IBI configuration, IBI_LAM differs from IBI_REF by using the LAMBDA river database forcing, IBI_NOR is like IBI_LAM, but without adding the extra coastal runoff (i. e. , the river discharge forcing is the only input of freshwater), and finally, IBI_CLM is a run forced by climatology. The latter scenario was simulated only over the first 6 months of 2018, because the reference run from April is using as forcing the same climatology.