dataesr/scientific-paragraphs-categorization huggingface dataset:
These software programmes are embedded in an application developed with the OpenPALM (Projet d'Assimilation par Logiciel Multimethodes) coupling system  (Duchaine et al. , 2015). All of these models cover an area of about 149 000 km 2 and contain up to 10 overlaid aquifer layers. AquiFR accounts for spatial heterogeneity by using different spatial scales. The SAFRAN meteorological analysis  (Quintana-Seguí et al. , 2008; Vidal et al. , 2010) , available over the French metropolitan area at an 8 km resolution, supplies the meteorological variables to the SURFEX (SUR-Face EXternalisée) land surface model  (Masson et al. , 2013) , which evaluates the water balance over the French metropolitan area. SAFRAN provides hourly precipitation (rainfall and snowfall), temperature, relative air humidity, wind speed and downward radiation.
The simulation of the watersheds depends on its hydrogeologic characteristics. For sedimentary basins, these two fluxes are transferred to the MARTHE  (Thiéry, 2015a)  or EauDyssée  (Saleh et al. , 2013)  groundwater models. These models simulate the transfer to the unsaturated zone, groundwater flows within and between the aquifer layers, the routing of surface runoff to and within rivers, and river-aquifer exchanges. They also account for the numerous groundwater abstractions within the river basins. The temporal resolution is daily, and the spatial resolution varies from 100 m to a maximum of 8000 m. The depth of the deepest aquifer layer can locally reach about 1000 m. It must be stressed that the hydrogeological models could have been classically fed with the SAFRAN analysis precipitation, potential evapotranspiration and temperature data using their own water balance calculation.
However, the combined use of SURFEX and SAFRAN provides a consistent set of hydro-meteorological data over an 8 km resolution grid over France, including groundwater recharge and surface runoff from SURFEX, as well as potential evapotranspiration, precipitation and temperature from SAFRAN. The use of these SURFEX 8 km resolution fluxes made the recalibration of the hydrogeological models included in the platform necessary. Karstic aquifer systems are simulated through a conceptual reservoir modelling approach using the EROS software programme  (Thiéry, 2018a). Each karstic system is represented by a lumped reservoir model solved at a daily timescale. Conceptual approaches are preferred for simulating karstic systems. Indeed, their heterogeneities make it difficult to use a physically based approach.
EROS uses the daily precipitation, snow, temperature and potential evap-otranspiration provided by SAFRAN to compute karstic spring flows. Technically, the AquiFR hydrogeological modelling platform was developed using the OpenPALM coupling system  (Buis et al. , 2005; Duchaine et al. , 2015). OpenPALM allows for the easy integration of high-performance computing applications in a flexible and scalable way. It was originally designed for oceanographic data assimilation algorithms, but its application domain extends to multiple scientific applications. In the framework of OpenPALM, applications are split into elementary components that can exchange data. The AquiFR platform is an OpenPALM application that currently gathers five components. Figure  2  shows the linkage between these components and the workflow of an AquiFR run.
In version 1. 2 of AquiFR, no feedback from groundwater to the soil of SURFEX is taken into account. Therefore, a preliminary step illustrated by Fig. 2a  is carried out in order to estimate groundwater recharge and surface runoff with SURFEX accounting for the atmospheric forcing from SAFRAN prior to an OpenPALM run. This preliminary step gives access to 60 years of daily groundwater recharge and surface runoff on a regular 8 km resolution over all the French metropolitan area. These water fluxes are then accessible by the OpenPALM application that includes the three hydrogeological modelling components, the pre-processing component and the post-processing component as shown in Fig. 2b. All of these components exchange data during the parallel execution of a single OpenPALM run. At each daily time step, a first preprocessing component retrieves both the atmospheric forcing and the SURFEX groundwater recharge and surface runoff at the beginning of the time step.
The SAFRAN meteorological analysis The SAFRAN meteorological analysis is a mesoscale atmospheric analysis system for surface variables. It provides meteorological forcing data over France on an 8 by 8 km grid at the hourly time step using observed data and atmospheric simulations. Originally intended for mountainous areas, it was later extended to cover France  (Quintana-Seguí et al. , 2008). SAFRAN estimates eight variables: rainfall, snowfall, incoming solar and atmospheric radiation, cloudiness, air temperature and relative humidity 2 m above ground, and wind speed at 10 m. Potential evapotranspiration can also be computed from these atmospheric variables. SAFRAN is based on climatic zones where the atmospheric variables only vary according to the topography. More than 600 homogeneous climate zones are defined over France.
The average area for each zone is about 1000 km 2 so that each one contains one surface meteorological station and at least two rain gauges. SAFRAN uses all the observations available to estimate each atmospheric variable except for radiation. For each variable, values are assigned to given altitudes using an optimal interpolation method. The analyses are computed every 6 h, and an interpolation is made to an hourly time step. Radiation fluxes are computed using a radiative transfer scheme. The daily precipitation rates are estimated using a wide range of daily rain gauges and converted to hourly data using the evolution of the relative air humidity. The vertical profiles of the atmospheric parameters are then computed in each climatic zone, and the values are spatially interpolated over the 8 km grid as a function of the altitude within each climatic zone.
The SURFEX modelling platform SURFEX is a modelling platform aimed at simulating the water and energy fluxes at the interface between the surface and the atmosphere  (Masson et al. , 2013). SURFEX is built to be coupled to forecast and climate models. It includes databases, interpolation schemes and several physical options that allow its use over different spatial and temporal scales. SURFEX gathers several physical schemes in a single platform, allowing for the simulation of the urban surfaces and the main components of the water cycle: sea and ocean, lake, vegetation, and soil. Land surface processes are taken into account using the Interaction between Soil, Biosphere, and Atmosphere  (Noilhan and Planton, 1989 ) land surface scheme. ISBA uses a short list of parameters depending on vegetation and soil types.
The EauDyssée groundwater modelling software programme The EauDyssée modelling platform gathers numerical modules representing several hydrological processes, the most important being the aquifer module based on the Simulation des Aquifères Multicouches (SAM; multilayer aquifer system) regional groundwater modelling software programme  (Ledoux et al. , 1989 ) and the river routing scheme based on the Routing Application for Parallel computatIon of Discharge (RAPID) model  (David et al. , 2011). SAM computes the evolution of the piezometric heads of multilayer aquifers using a finite difference numerical Hydrol. Earth Syst. Sci. , 24, 633-654, 2020 www. hydrol-earth-syst-sci. net/24/633/2020/ scheme to solve the groundwater diffusivity equation with a square grid discretization. Groundwater horizontal flows are 2-dimensional, and vertical flows through aquitards are taken into account.
Methodology 3. 1 The regional models implemented in the AquiFR platform AquiFR aims at covering all groundwater resources in France. Figure  3  shows the main aquifers covering France classified by geological type as defined in the French hydrogeological reference system Base de Donnée des Limites des Systèmes Aquifères (BDLISA; https://bdlisa. eaufrance. fr/, last access: 11 February 2020). The current version of AquiFR gathers 13 spatially distributed models corresponding to regional single-layer or multilayer aquifers (Table  1  and Fig. 4 ). Some regions are simulated by two spatialized models (Fig. 4 ): the Somme and the Basse-Normandie basins are covered by the MARTHE and EauDyssée models, and the chalk aquifer of the Seine basin is covered by both the EauDyssée Seine model and four EauDyssée sub-models (Marne-Loing, Marne-Oise, Seine-Eure and Seine-Oise regional models; see Fig.
, 2017)  and integrated in the near future. Groundwater withdrawals are integrated as timedependent boundary conditions in the spatially distributed models. On annual average and with respect to the total surface area of the simulated domain, it corresponds to about 16 mm yr -1 (2. 4 billion of cubic metres per year) distributed in more than 16 000 grid cells. Data on groundwater pumping are provided by the regional water agencies based on tax reporting. Pumping concerns drinking water, irrigation and industrial use. The quality of the dataset as well as its temporal extension varied for each regional model, although the later does not exceed 20 years. Further details on regional models can be found in the references listed in Table  1. To extend the pumping estimation to the 1958-2018 period, a monthly mean annual cycle is defined for the years without data.
Calibration of the hydrogeological models The original hydrogeological regional models were developed independently, most often based on stakeholder requests. The water budgets were usually computed using less physical methods and atmospheric local data (precipitation, temperature and potential evapotranspiration) that differ from the physically based approach using SURFEX and the SAFRAN analysis. As a result, in order to be consistent with the estimation of the groundwater recharge estimated by SAFRAN-SURFEX, most of the regional models were recalibrated based on new fluxes  (Habets et al. , 2017). This recalibration effort was not undertaken for the Alsace and Loire models, since both of them will be soon updated and then recalibrated. Periods of recalibration were the same as those initially used to develop and calibrate each model (see references in Table  1 ) in order to facilitate the comparison between the recalibrated models and the initial models.
For the other basins, the estimation by SURFEX of the partition of the wawww. hydrol-earth-syst-sci. net/24/633/2020/ Hydrol. Earth Syst. Sci. , 24, 633-654, 2020 ter fluxes between surface runoff and groundwater recharge was used. Overall, the performance of the models are similar with the original water balance fluxes and the ones simulated by SAFRAN-SURFEX, although locally, they may be better or otherwise degraded. For the karst system software programme EROS, the models were calibrated based on the SAFRAN atmospheric analysis by using an optimization of the statistical comparison between observed and simulated daily river flows. More information about the calibration is given in  Habets et al. (2017).
Dataset and model setup The long-term simulation was carried out over a 60-year period from 1 August 1958 to 31 July 2018 at a daily time step using the SAFRAN meteorological analyses. State variables from 1 August 2013 were chosen from a first simulation over the 1958-2018 period in order to initialize the simulation on 1 August 1958. The year 2013 was chosen as the best proxy of the year 1958 by analysing time series of long-term observed groundwater levels with data since 1958. The mean precipitations corresponding to the simulated domain of Fig. 4  and averaged over the 60-year period is equal to 743 mm yr -1. SURFEX then computes the surface water budget from the SAFRAN outputs. The mean simulated total runoff is partitioned between 163 mm yr -1 of groundwater recharge and 60. 5 mm yr -1 of surface runoff.
Thus, the groundwater abstractions represent about 25 % of the groundwater recharge. The evaluation of this simulation is made using the numerous in situ datasets available in France. Observed piezometric heads are available in the Accès aux Données sur les Eaux Souterraines (ADES) database (http://www. ades. eaufrance. fr/, last access: 11 February 2020). A total of 639 observation boreholes covering the AquiFR domain corresponding to both confined and unconfined aquifers, and with at least 10 years of continuous time series, were selected. Figure  5  shows the temporal evolution of the number of daily measurements along the 60-year period. Starting in 1958, only a few measurements are available. Starting from 1970, the number of wells increases slowly to reach about 100 in 1990. Then the number of daily measurements quickly increases to reach more than 450 in 2010.
Such transformation is necessary because of the excessive sensitivity of the E f criterion to extreme values in a river flow time series  (Legates and McCabe Jr. , 1999). Other statistical scores using less assumptions on the underlying data distribution, such as the non-parametric variant of the Kling-Gupta efficiency  (Pool et al. , 2018) , could be used to reduce the sensitivity to the extremes. For these four examples, all the E f values are greater than 0. 7. The distributed models included in the AquiFR modelling platform integrate river networks and the simulation of river flows on each river grid cell. A total of 362 gauging stations were selected to evaluate the simulated river flows. Figure  13  shows the comparison between the observed and simulated daily river discharges for four of them: the Charente River, the Somme River, the Seine River and the Loire River.
Discussion The results obtained in the 1958-2018 period demonstrates the feasibility and the utility to gather several regional models developed separately in different research institutes into a single numerical tool to provide simulations of the water resource at a daily time step at the national scale. It was shown that the AquiFR platform is able to reproduce the evolution of the observed hydrological variables, including piezometric levels and river flows, with reasonable statistical scores. Some regions are nevertheless better reproduced than others. For example, the Loire region exhibit poor bias scores in Fig. 6. Part of the error is linked to the estimation of the groundwater recharge by SURFEX. Indeed, the regional groundwater models were developed and calibrated independently using various methods and data to compute groundwater recharge.
(  2014 ) used an approach closer to AquiFR by coupling five physically different models for different water domains with different concepts, different temporal and spa-tial scales, and different national and regional databases altogether embedded in the National Hydrological Instrument. These two models are used for integrated water management and policymaking issues. The areas covered by these models are 22 500 and 41 500 km 2 respectively. According to Fig. 3 , the BDLISA database references regional sedimentary aquifer systems (in green) and alluvial aquifers (in blue) in France both covering an area of about 355 000 km 2. Reaching the complexity of a fine-tuned regional model, including the geometrical, geological and physical contexts, in a single integrated numerical tool covering such large territory would be time consuming to build, to calibrate and to evaluate.
This multi-model approach allows for promoting the share of knowledge in hydrogeology and gathering the competencies accumulated in the different research institutes involved in AquiFR in water resource modelling. Moreover, thanks to the evolutive approach of the OpenPALM coupling software programme, the platform facilitates the addition of new software and new models. Results from AquiFR show a global view of the performance of the AquiFR platform but are characterized by uncertainties. These uncertainties are mainly related to the calibration of the models and to the lack of some input data like groundwater abstractions. Indeed, the regional models have been calibrated over a shorter period compared to the longterm simulation, as stated in Sect. 3. 2. Consequently, the cal-ibration process may have not included the extreme climatic conditions encountered in the 1958-2018 period.
Other uncertainties may be related to the choices of the resolution, the databases used, the geometry of the models and more generally the representation of the physical processes in the hydrogeological software. Some regions are better monitored than others, and the global view of the performance of the AquiFR platform is certainly affected by this. Moreover, the chosen method of evaluation based on statistical scores such as E f could also be improved. Indeed, some authors report that the use of more realistic upper and lower benchmarks for each simulated basins could improve the judgement of model performances with respect to the climate and hydrogeological context of the basins  (Pappenberger et al. , 2015; Seibert et al. , 2018). At last, in order to diminish uncertainties, a long-term calibration effort using a denser observation network could be undertaken to improve the AquiFR performance.
Conclusions This study introduces the AquiFR hydrogeological modelling platform with an aim to provide French national-scale short-term-to-seasonal hydrological forecasts as well as realtime monitoring for daily water management and long-term simulations for climate impact studies. It was developed using a coupling software programme in order to gather different software and several models covering a set of French multilayer aquifers. Daily surface runoff and groundwater recharge values provided by the SURFEX land surface model using the SAFRAN meteorological analysis were used for simulating the daily evolution of groundwater levels and river flows of French regional multilayer aquifers and karstic systems in the 1958-2018 period. The results confirm the feasibility of gathering independent hydrogeological models developed in different research institutes into the same coupling platform.
All of these models were initially developed and calibrated on shorter periods with heterogeneous geological and meteorological databases. Some of these models were recalibrated against the SAFRAN-SURFEX fluxes. The evaluation of the 1958-2018 long-term simulation shows a good comparison with the observations available for the same period. It confirms the relevance of using AquiFR as a tool for long-term im-pact studies. The evaluation of the SPLI indicator also shows that AquiFR could be used in an operational context to monitor future events and be part of the Monthly Hydrological Survey, provided the necessary caution in terms of communication of model uncertainties and performance. The advantage of this platform lies in its modularity. AquiFR encourages the development of groundwater modelling where it is missing, and, more generally, it has the potential to be a valuable tool for many applications in water resource management and research studies, for instance in climate change studies and seasonal forecasts.
Acknowledgements. The authors are grateful to the research institutions that provide computer access, data, models and software for making this project feasible (BRGM, CERFACS,  Météo France ,  Mines ParisTech and Geosciences Rennes). Special thanks goes to  Claire Magand ,  Thimotée Leurent  and  Bénédicte Augeard  for supporting this project with the  French Agency for Biodiversity (AFB)  and  Nathalie Dörfliger  who supports this project with  BRGM. Finally , the authors wish also to thank  Eric Martin  and  Jean-Michel Soubeyroux  for their involvement at the beginning of this project. Financial support. This research has been supported by the  AFB  and  French Ministry of Ecological and Solidarity Transition. Review statement. This paper was edited by  Jan Seibert  and reviewed by three anonymous referees.
Mitogenome assembly and feature analyses The OSCA mitogenome was assembled in two steps as follows. First, the scaffolds of a previously published O. scapulalis genome draft  [1]  , OSCA v1. 2 (BioProject PRJNA390510), were searched for putative mitochondrion sequences by using blastn v2. 2. 28  [7]  against the mitochondrial genome sequence of the closely related Asian corn borer Ostrinia furnacalis (Guenée) (NCBI accession number NC_003368). Genome scaffolds with high quality mitochondrion hits were selected and assembled by LGC with CAP3 v2013  [8]  into a mitogenome sequence of 14,864 bp. In a second step, this draft mitogenome was refined using MITObim v1. 9  [9]  (using the default k-mer size of 31 bp and the -pair parameter) and the Trimmomatic-cleaned paired-end Illumina reads. Gene identification as well as tRNA structure prediction were carried out on the MITOS web server v2  [10]  , using as reference set 'RefSeq 81 Metazoa' and the genetic code '5' for invertebrates.
Nucleic base contents were determined with the wordcount program of the EMBOSS toolkit v6. 6. 0. 0  [11]. AT/GC skewness indices describing the genomic compositional asymmetry of a mitogenome or specific sequence region were calculated as: AT skew = [A-T] / [A + T] and GC skew = [G-C] / [G + C], where each base letter represents the respective base count. The circular map of the OSCA mitogenome was generated with the web tool OGDRAW v1. 3. 1  [12].
Phylogenetic analyses In order to perform comparative analyses of O. scapulalis with other Lepidoptera the nucleotide sequences of the 13 PCGs and 2 rRNA genes of 63 mitogenomes -comprising 59 Lepidoptera species -( Table  3  ) were downloaded from the NCBI 'nucleotide' database ( https: //www. ncbi. nlm. nih. gov/nuccore , May 2020). Our taxon sampling was focused on the superfamily Pyraloidea, encompassing 34 species belonging to the Crambidae (including O. scapulalis ) and 13 species belonging to the Pyralidae. Regarding Ostrinia spp. , we included in total nine mitogenomes belonging to five Ostrinia species. As outgroups we used 12 representative species of the lepidopteran superfamilies Bombycoidea, Geometroidea, Noctuoidea and Papilionoidea; based on the comprehensive study of Wahlberg et al.
[13]  , we used the three Papilionoidea representatives to root our trees. The PCG and rRNA nucleotide sequences of all species were grouped into 15 gene-specific sequence sets. Each gene set was in separate globally aligned with the program MAFFT v7. 471  [14]  by applying the Needleman-Wunsch algorithm (mafft-ginsi for PCGs and mafft-qinsi for rRNA sequences) with a maximum of 10 0 0 refinement iterations (maxiterate 10 0 0), leaving the other parameters set to their default value. Subsequently, for each species all gene-specific alignments were concatenated following the same gene order using inhouse Python and Perl scripts. Phylogenetic trees were generated with the PHYML web server v3. 0  [15]  and a local installation of MrBayes v3. 2. 7a  [17]. PHYML was run using the Smart Model Selection  [16]  option with the Akaike Information Criterion and the tree searching method BIONJ being set and by applying 10 0 0 bootstraps.
Concerning the MrBayes analysis, a separate partition was set for each gene. For each partition the evolutionary model was set to the GTR substitution model (nst = 6) with gamma-distributed rate variation (rates = invgamma) across sites and a proportion of invariable sites (GTR + I + ). The standard nucleotide substitution model (nucmodel = 4by4) was chosen and priors settings were left as by default. Trees were built using 10 0,0 0 0 samples (ngen = 1,0 0 0,0 0 0, samplefreq = 10). Further parameters were: printfreq = 100, diagnfreq = 10 0 0, nchains = 4, savebrlens = yes, starttree = random, startparams = reset, outgroup pamac (corresponding to Papilio machaon ), sump burnin = 25,0 0 0, sumt burnin = 25,0 0 0. The phylogenetic tree was visualized using the program FigTree v1. 4. 4  [18].