dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology of validation and analysis Two pairs of ORCHIDEE simulations using the highresolution routing (HR) are used to perform the validation of the floodplain scheme and the analysis of the impact of the floodplains on ORCHIDEE. Each pair is forced by a different atmospheric forcings -WFDEI_GPCC at 0. 5 • resolution and AmSud_GPCC at 20 km resolution -and is composed of a simulation with the floodplain scheme activated (FP) and another one without the floodplain scheme (NOFP). The use of two forcings with different resolutions allows us to assess the influence of the resolution and the forcing uncertainty of the floodplain scheme. The forcings are further described in Sect. 3. 3. The analysis is performed between 1990 and 2013, which is the period over which both forcing datasets are available.
Model description: ORCHIDEE The simulations presented in this publication are the output of offline ORCHIDEE simulations, i. e. simulations of the ORCHIDEE LSM forced by an external atmospheric dataset containing the atmospheric data required to run the model (downward longwave and shortwave radiations, precipitation, 2 m air temperature, wind speed, 2 m specific humidity, snowfall, and rainfall). Ancillary datasets provide information about vegetation cover and the soil composition. The soil properties are described by the combination of the three main soil textures: coarse, medium and fine from the USDA soil description  (Reynolds et al. , 2000). The vegetation in ORCHIDEE is described in the model's input by the potential vegetation cover (maxvegetfrac) for 12 different plant function types (PFTs) and the fraction of baresoil cover.
This bare soil can be covered by different types of non-vegetated land surfaces such as glaciers, cities, lakes or flooded areas. For each grid cell, the sum of the maxvegetfrac of the different PFTs and the bare-soil surfaces is equal to 1. In these simulations, the PFTs are constructed from the ESA-CCI database (European Space Agency Climate Change Initiative; ESA, 2017). Readers should be aware that the original ESA-CCI remote PFT classification has been post-processed to the 13 ones used in ORCHIDEE. The vegetation cover is defined by the fraction of the grid cell occupied by each PFT (vegetfrac) whose upper limit is maxvegetfrac. It is driven by the leaf area index (LAI, in m 2 m -2 ) of the PFT; if LAI ≥ 1, then vegetfrac equals maxvegetfrac, and elsewhere the fraction not covered by this vegetation type is considered by the model as bare soil.
Forcings WFDEI_GPCC is a 0. 5 • resolution atmospheric forcing dataset for land surface models  (Weedon et al. , 2014). It is derived from the ERA-Interim re-analysis processed by the WATCH Forcing Data methodology  (Dee et al. , 2011)  and has a temporal resolution of 3 h and a spatial discretisation of 0. 5 •. WFDEI_GPCC corresponds to the version of WFDEI whose precipitation has been bias-corrected by the GPCC dataset  (Schneider et al. , 2017). AmSud_GPCC is a 20 km resolution forcing based on the bias-corrected AmSud simulation, a 30-year simulation performed with the RegIPSL regional model  (Guion et al. , 2022)  from 1990 to 2019 and using ERA5 re-analysis data for the boundary conditions. The precipitation of the AmSud simulation has been bias-corrected by the GPCC monthly precipitation, adjusting the monthly precipitation total by a multiplicative factor for each grid cell to obtain the AmSud_GPCC forcing.
Flooded area Depending on the period simulated, the simulated flooded area was assessed by different estimates of the flooded area over the Pantanal. The evolution of the flooded area over the 20th century has been estimated by  Hamilton et al. (1996)  and  Hamilton (2002) , extrapolating the correlation between river height and the flooded area established over the period 1979 and 1987. Padovani (2010) performed a satellite estimate of the flooded area by applying a linear spectral mixture model to MODIS data between 2002 and 2009. Apart from  Hamilton (2002)  and  Padovani (2010) , the satellite estimate of the flooded area based on the modified normalised difference water index (mNDWI) using the normalised difference between green and shortwave infrared bands presented in  Schrapffer et al.
(2023a)  is also used to assess the flooded area in the FP simulations. The Global Inundation Extent from Multi-Satellites version 2 (GIEMS-2;  Prigent et al. , 2020)  database is a satellite estimate of flooded areas (agricultural irrigation and wet-lands) which is principally constructed using passive microwave observations but also using visible and near-infrared reflectance data from optical satellites. GIEMS-2 is a global monthly dataset available at a 0. 25 • resolution between 1992 and 2015. As for GIEMS version 1, GIEMS-2 is largely used to validate the floodplain representation in different models  (Zhou et al. , 2021; Marthews et al. , 2022). We use different types of satellite products to have a complete view on the flooded area. Two products have been specially constructed over the Pantanal -  Hamilton (2002)  and Padovani (2010) -so they may be more appropriate due to the specificity of the Pantanal floodplains.
However, they have some limitations:  Hamilton (2002)  is based on a relationship between flooded area and river height established during a short and wet period, and, therefore, this relationship may differ under different climatic conditions. It is also only available up to 2000. Concerning  Padovani (2010)  and  Schrapffer et al. (2023a) , the limitation is the infrequent revisit of satellite (data every 6 d) and missing images due to the use of optical satellite imagery. Padovani (  2010 ) is interpolated, which helps us to have an overview of the complete time series of flooded areas, while  Schrapffer et al. (2023a)  gives us precise estimates for precise dates without any interpolations and is available up to 2013, while  Padovani (2010)  is only available up to 2010. Therefore, both datasets are complementary.
Concerning the first point, the satellite estimate of the flooded area may erroneously consider saturated soils as a water surface  (Zhou et al. , 2021; Aires et al. , 2018)   Hamilton (2002 ) until 2000 , Padovani (2010 ) between 2000 and 2010 , GIEMS-2 (Prigent et al. , 2020)  for the period 1992-2015, and the flood estimate based on MODIS MOD09A1 using the mNDWI spectral index  (Schrapffer et al. , 2023a). while other studies show that this region is not regularly flooded  (Padovani, 2010). There is a high correlation between GIEMS-2 flooded fraction and the soil moisture down to 0. 5 m depth from the surface in the NOFP simulation (see Fig. S7 ), which confirms the hypothesis of a saturated soil moisture signal in the GIEMS-2 dataset related to the precipitation during the rainy season.
Evapotranspiration The changes in the surface energy budget are dominated by modifications of the water available for evapotranspiration and enhanced by the development of vegetation. These impacts are reflected in the annual cycle of the different components of evaporation (bare soil, transpiration, floodplain evaporation and interception loss), which are shown for both atmospheric forcings in Fig. 10. Potential evaporation is lower throughout the year in the FP simulations when compared to NOFP. This is a direct consequence of the decrease in surface temperature over the floodplains which will decrease the saturated surface humidity, and the lack of adjustment of the lower atmosphere which should be more humid than assumed in the forcing datasets. This is confirmed by the fact that the potential evaporation changes between NOFP and FP follow the same spatial structure as the surface temperature (not shown).
S12 ), driven by the increase in evaporation described previously. The difference in temperature is lower for the WFDEI_GPCC forcing (around -1 • C) compared to AmSud_GPCC (with differences up to -3 • C during the flood season and up to -6 • C during the dry season). Observational temperature datasets such as CRU TS4  (Harris et al. , 2020)  do not reflect any hydrological pattern of temperature difference over the Pantanal (cf. Figs. S13  and S14 ). This is due to the scarcity of in situ observations over the region and the coarse resolution of observational datasets which interpolate these in situ observations. The distribution of the average daily temperature over the most flooded parts of the Pantanal (with a mean flood_frac > 0. 1) in both forcings is shown in Fig. 11. During the flood season (MAM, Fig.
It is difficult to correctly assess the flooded area  (Schrapffer et al. , 2023a)  principally covered by vegetation and oversaturated soils and, therefore, to correctly identify the deficiencies of the model. The representation of the flooded area is a major issue for any flooding scheme. Under the assumption that the precipitation from the GPCC dataset is correct over the Pantanal and knowing that the river discharge is close to the observation when the floodplain scheme is activated, it can be concluded that the water cycle of the catchment is correctly represented in the model. Therefore, the issues can come from (1) the fact that there exist different ways to define what a flooded area is  (Schrapffer et al. , 2023a) , (2) the conversion of the volume of water in the floodplains to flooded area and (3) missing processes such as groundwater transfers.
Code and data availability. The code version and data used for this study are available at https://doi. org/10. 5281/zenodo. 7761859 (Schrapffer et al. , 2023b). It contains the ORCHIDEE code used for the simulations, the parameterisation used in each simulation and the routing file used for the simulations. The code of the pre-processing tool to generate the routing files is available in  Polcher et al. (2023)  and  Polcher et al. (2022)  (https://doi. org/10. 5281/zenodo. 7058895). The GRACE data have been extracted using Google Earth Engine. The Global Lakes and Wetlands Database is available from https://www. worldwildlife. org/pages/global-lakes-and-wetlands-database (WWF, 2004). The discharge used for Porto Murtinho (ANA ID: '67100000) is available from the Brazilian National Water Agency (https://www.
Context. Modern astronomical observations give unprecedented access to the physical properties of nearby galaxies, including spatially resolved stellar populations. However, observations can only give a present-day view of the Universe, whereas cosmological simulations give access to the past record of the processes that galaxies have experienced in their evolution. To connect the events that happened in the past with galactic properties as seen today, simulations must be taken to a common ground before being compared to observations. Therefore, a dedicated effort is needed to forward-model simulations into the observational plane. Aims. We emulate data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which is the largest integral field spectroscopic galaxy survey to date with its 10 000 nearby galaxies of all types.
For this, we use the latest hydro-cosmological simulations IllustrisTNG to generate MaNGIA (Mapping Nearby Galaxies with IllustrisTNG Astrophysics), a mock MaNGA sample of similar size that emulates observations of galaxies for stellar population analysis. Methods. We chose TNG galaxies to match the MaNGA sample selection in terms of mass, size, and redshift in order to limit the impact of selection effects. We produced MaNGA-like datacubes from all simulated galaxies, and processed them with the stellar population analysis code pyPipe3D. This allowed us to extract spatially resolved maps of star formation history, age, metallicity, mass, and kinematics, following the same procedures used as part of the official MaNGA data release. Results. This first paper presents the approach used to generate the mock sample and provides an initial exploration of its properties.
We show that the stellar populations and kinematics of the simulated MaNGIA galaxies are overall in good agreement with observations. Specific discrepancies, especially in the age and metallicity gradients in low-to intermediate-mass regimes and in the kinematics of massive galaxies, require further investigation, but are likely to uncover new physical understanding. We compare our results to other attempts to mock similar observations, all of smaller datasets. Conclusions. Our final dataset is released with this publication, consisting of 10 000 post-processed datacubes analysed with pyP-ipe3D, along with the codes developed to create it. Future work will employ modern machine learning and other analysis techniques to connect observations of nearby galaxies to their cosmological evolutionary past.
In this work, we present MaNGIA (Mapping Nearby Galaxies with IllustrisTNG Astrophysics), a new forward-modelled sample from the TNG50  (Pillepich et al. 2019 , Nelson et al. 2019)  simulation to mimic the properties of the MaNGA survey  (Bundy et al. 2015). MaNGA is currently the largest integral field spectroscopic survey with data for 10 000 galaxies and enables a statistical analysis of spatially resolved physical properties of nearby galaxies. The survey's science goals include the study of galaxy assembly histories, galaxy quenching and its link to environment, and how galaxy morphology and components form  (Bundy et al. 2015). Previous works have addressed mock MaNGA datacubes for a reduced number of simulated galaxies, following different recipes. Bottrell & Hani (2022)  emulated the instrumental effects of the survey with the RealSimIFS code and applied it to TNG50 simulations  (Pillepich et al.
2019 , Nelson et al. 2019 ), the highest-resolution hydro-dynamical simulation of the IllustrisTNG family. This TNG simulation offers the best compromise to date between resolution and number of unique galaxies in a cosmological context. We mimicked MaNGA's target selection and followed the procedures of  Ibarra-Medel et al. (2019)  to generate mock datacubes from simulated galaxies. These include producing the spectra associated with the particles in the simulation, recreating the fibre bundle observing scheme to mimic MaNGA's spectral and spatial resolution, and incorporating typical observational effects as atmospheric seeing and noise. The data products are aimed to be comparable with the MaNGA MPL-11 release 1  analysed with the pyPipe3D code 2   (Lacerda et al. 2022 , Sánchez et al. 2022) , which extracts the stellar populations and emission line properties from the integral field spectroscopy data.
MaNGA MaNGA  (Bundy et al. 2015)  is the integral field spectroscopic (IFS) survey of the Sloan Digital Sky Survey IV (SDSS IV;  Blanton et al. 2017). MaNGA targets were observed with the Sloan 2. 5 m aperture telescope at APO  (Gunn et al. 2006). To obtain the spatial-spectral cubes, MaNGA used integral field units (IFUs) formed by arrays of fibres distributed in a hexagonal pattern  (Drory et al. 2015). The fibres are connected to two twin multi-object fibre spectrographs covering the 340-1030 nm wavelength range with a spectral resolution R ∼ 2000  (Smee et al. 2013). MaNGA data was released in ten public releases, completed in DR17  (Abdurro'uf et al. 2022). The MaNGA survey consists of over 10 4 galaxies to have a statistical sample of galaxies that covers a variety of environments and star formation activity.
We use the preexisting catalogues from  Rodriguez-Gomez et al. (2019)  which collect photometric parameters of TNG50 galaxy mocks produced using the Skirt radiative transfer code  (Baes et al. 2011 , Baes & Camps 2015). As these catalogues are only available for a limited number of TNG snapshots, we aim to relate the radii directly calculated from the simulations (i. e. the stellar half-mass radii) with the photometric radii derived from the mocks to later extend this relation to all the snapshots with z ≤ 0. 15. In particular, we used the catalogue corresponding to snapshot 95 (z = 0. 048) as it is the one with the available redshift that is nearest to MaNGA's average redshift. Galaxies in this snapshot are divided in five stellar mass bins to later perform a linear fit per bin between the simulation and observation-like sizes.
Matching algorithm Our simulated sample was built by finding a TNG50 analogue to every MaNGA galaxy, matching redshift, stellar mass, and R e with a similar approach to  Duckworth et al. (2020). We used the DRPall catalogue v3. 1. 1 corresponding to SDSS Data Release 17. In this catalogue 10 233 observed galaxies have a welldefined mass, R e , and redshift, of which 10 094 are unique. We considered all the galaxies with M * > 10 8. 5 M in this subsample for the matching of the sample (10 070 in total). As the number of simulated galaxies is limited, we made two assumptions that allowed us to complete the sample. First, galaxies have modified their properties enough across redshift such that they can be considered as different galaxies if they are in different snapshots. In the local Universe, merger and accretion events, bursts of stellar feedback, or SMBH feedback due to gas infall or gas brought in by merging galaxies can induce changes on timescales of  150-200 Myr (Sotillo-Ramos et al.
Construction of a mock dataset Once the subset of TNG50 galaxies is selected (Sect. 2), a mock MaNGA-like datacube is constructed for each of the simulated galaxies in this sample. We describe the steps for generating mock datacubes below. We first define the FOV of the observations. This limits the particles and cells from the simulation that will contribute to form the spectra in the final datacube. Later, instrumental effects are included to emulate MaNGA observations. The procedure is based on  Ibarra-Medel et al. (2019)  and further details can be found there. A scheme of the steps followed is shown in Fig. 3.
From particles to light The mock spectra are generated by including the stellar emission associated with the simulated stellar particles and the absorption produced by the presence of dust in the line of sight. Gas emission lines produced in Hii regions are simulated by associating star-forming gas cells to Cloudy templates  (Ferland et al. 1998 (Ferland et al. , 2017;;  see Appendix A3 in Ibarra-Medel et al. 2019 for implementation details 3  ). However, because the interstellar medium model behind IllustrisTNG  (Springel & Hernquist 2003)  does not allow us to rely on the temperature and other thermodynamical properties of the star-forming gas, we remove the emission line contribution from the spectra for our analysis. As pyPipe3D performs two spectral fits to extract emission lines before fitting the simple stellar population (SSP) template, the analysis of our spectra should be comparable to that performed on observations.
The MaStar_sLOG template consists of a set of synthetic spectra generated with the Galaxev code  (Bruzual & Charlot 2003 , Yan & Wang 2010)  based on MaNGA's stellar library, MaStar  (Yan et al. 2019). This library has spectra of a variety x 10,000 simulated galaxies The stellar particles and gas cells are projected on the sky and randomly shifted to mimic the atmospheric seeing (FWHM=1. 43"). The stellar particles' and gas cells' information is saved for a given galaxy. Output: particle file. The values per fiber are recombined to form the maps with the intrinsic stellar particles' information. Output: intrinsic and assigned 2D maps. For control, the intrinsic values of the stellar particles in the FOV of the fibers are saved as an extension of the RSS file. Gaussian noise is added per fiber spectrum.
Therefore, for the intrinsic maps we can ensure that almost all the galaxies have the minimum number of particles per fibre to recover the kinematics of the simulated galaxies. The fibres with fewer than nine particles in their FOV are mostly located at the outskirts of the galaxies, while all the fibres that cover the central regions of the galaxies have a good representation of the velocity dispersion. Additionally, the outskirt effects are minimized in the recovered maps due to the segmentation scheme of pyPipe3D as the spectral fitting code recombines the spaxels to achieve a target S/N. Therefore, the kinematics at the galaxies' outskirts are calculated from a combination of spaxels (which are also a combination of individual fibre spectra), providing a better statistic for the kinematic extraction.
Even though the dust model used in this work is rather simple, it is not less complex than the reverse analysis that pyPipe3D performs. While the dust modelling in the two cases is similar, recovering the extinction during the spectral analysis is not as trivial a task as simply inverting the problem. This is because the attenuation effect is implemented per stellar particle spectrum before shifting and integrating the contribution of all the stellar particles in the fibre during the mocking process, while pyPipe3D recovers one extinction value per observed spectrum. Another layer of complexity in the inversion problem is that the spectral fitting code is limited to derive properties from the observable light, and therefore cannot retrieve the information of the obscured light beyond the optical depth (see Sect.
Otherwise, the weights are defined as ω p,i = exp - r p,i 2 2 σ 2 rec /ω T , (6) where σ rec = 0. 7  (Law et al. 2016 ) and ω T is a normalizing factor to keep the total flux constant. The mock datacubes have the format detailed in  Sánchez et al. (2022)  to be analysed by pyPipe3D  (Lacerda et al. 2022) , namely a FITS file comprising three extensions: the flux datacube in units of 10 -16 erg s -1 Å cm -2 per spaxel, a second datacube with the flux error, and a final extension comprising a datacube with a mask for bad pixels, all with a linear fixed step in wavelength. The intrinsic and assigned values of the galaxies that were saved per fibre for control (Sect. 3. 2) are now spatially recombined in the same way as the mock datacubes. This results in a set of 2D maps with the instrinsic and assigned stellar properties of the simulated galaxies with the same spatial resolution as the datacubes.
From spectra to stellar population maps The post-processing of the mock datacubes is performed with pyPipe3D  (Lacerda et al. 2022) , the Python implementation of the Pipe3D code  (Sánchez et al. 2016a,b). The goal of this programme is to disentangle the contributions to the emission produced by the different stellar populations and extract the emission line information from galaxy spectra. For this purpose, it is necessary to separate the kinematic and dust extinction effects in the observed spectra and remove emission lines before finding the best-fitting SSPs. Therefore, the first step this software performs on given galaxy spectra is a non-linear fit to determine the extinction (A * V ) and the stellar kinematic (σ * and z) parameters. A parametric fit is then used to approximate strong emission lines.
Results We evaluate the performance of the complete pipeline by comparing the stellar population and kinematic maps produced during the mocking process (TNG50 intrinsic, assigned, and recovered maps; see Table  2 ), and then compare the simulated sample with the observed one (TNG50-recovered and MaNGArecovered, see Table  2 ). While the maps of the simulated galaxies were obtained as described in Sect. 3, those of the observed galaxies correspond to the latest pyPipe3D analysis release for 10 000 MaNGA galaxies 5   (Sánchez et al. 2022). In both cases the fitting code and the SSP template used for the analysis are the same. The maps considered in this work are the V-band reconstructed image, the luminosity-weighted age and metallicity, LOS velocity, and dispersion. First, we study the effect of discretizing in age and metallicity when associating stellar particles to a spectrum in the MaStar_sLOG template.