dataesr/scientific-paragraphs-categorization huggingface dataset:
5  shows the ratio of trees by country bearing at minimum one TreM. The number of TreMs recorded on a plot may vary considerably due to, for example, the given tree species composition, stand structure, stand age or management regimes (including long-time unmanaged forests). Thus, there are variations from 0. 1 to nearly 0. 7, while the overall average across all countries and plots is about 0. 4. Fig. 6  presents the distribution of TreMs by main categories. Each of the main categories is further divided into subcategories as described in  (Kraus et al. 2016). The average number of TreMs by individual host tree varies from nearly 1. 3 in the Spanish to nearly 3. 4 in Chilean plots (Fig. 7 ). For all plots in the database, two TreMs are found on average for each TreM-bearing tree. Total number or recorded trees as compared to those bearing tree-related microhabitats by country.
Ranius T, Hedin J(2001)The dispersal rate of a beetle, Osmoderma eremita, living in tree hollows. Oecologia 126: 363-370. https://doi. org/10. 1007/s004420000529 • Regnery B, Couvet D, Kubarek L, Julien JF, Kerbiriou C (2013) Tree microhabitats as indicators of bird and bat communities in Mediterranean forests. Ecological Indicators 34: 221-230. https://doi. org/10. 1016/j. ecolind. 2013. 05. 003 • Rosenzweig ML (1995) Species diversity in space and time. Cambridge University Press https://doi. org/10. 1017/CBO9780511623387 • Speight MC, Castella E, Sarthou JP, Monteil C (2020) Species accounts of European Syrphidae (Diptera). Syrph the Net, the database of European Syrphidae. Syrph the Net publications, Dublin. • Tews J, Brose U, Grimm V, Tielborger K, Wichmann MC, Schwager M, Jeltsch F (2004) Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures.
Acknowledgements This work was kindly supported by the  German Federal Ministry for Food and Agriculture -BMEL  through the projects ' Establishing a European network of demonstration sites for the integration of biodiversity conservation into forest management -Integrate+ ' ( 2013 -2016; Forst 2013-4 ), ' Integrated Forest Management Learning Architecture -Informar ' (2017 -2020; Forst 2017-1) and continues to be through the ongoing project 'Managing forests for resilience and biodiversity -bridging policy, practice,  science and education -"FoReSite'  ( 2020-2022; Forst 2020-1 ). Since 2017, the  expansion of the marteloscope plot network has taken place mainly under the auspices of the 'European Integrate Network '. Our sincere thanks goes out especially to all authors listed under the 'Spatially-explicit database of tree-related microhabitats (TreMs)' (https://doi.
org/10. 15468/ocof3v)who were, in many cases, engaged in data collection, but most importantly ensured that their data could be made available via GBIF. Without this dedicated support, such an extensive dataset would not have materialised. Our thanks also go to all data collection teams and the many bachelor and master students who established marteloscope sites in connection with their thesis topics. We are thankful to  Dr. Robert Mesibov  for his comprehensive data eveluation and for useful suggestions for improvements.
• Ranius T, Hedin J(2001)The dispersal rate of a beetle, Osmoderma eremita, living in tree hollows. Oecologia 126: 363-370. https://doi. org/10. 1007/s004420000529 • Regnery B, Couvet D, Kubarek L, Julien JF, Kerbiriou C (2013) Tree microhabitats as indicators of bird and bat communities in Mediterranean forests. Ecological Indicators 34: 221-230. https://doi. org/10. 1016/j. ecolind. 2013. 05. 003 • Rosenzweig ML (1995) Species diversity in space and time. Cambridge University Press https://doi. org/10. 1017/CBO9780511623387 • Speight MC, Castella E, Sarthou JP, Monteil C (2020) Species accounts of European Syrphidae (Diptera). Syrph the Net, the database of European Syrphidae. Syrph the Net publications, Dublin. • Tews J, Brose U, Grimm V, Tielborger K, Wichmann MC, Schwager M, Jeltsch F (2004) Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures.
HSO - 3(aq) + 1 2 O 2(aq) F e 3+ , M n 2+ --------→ SO 2- 4(aq) + H + (R11) The aim of this work is to estimate the sensitivity of SO 2 conversion through these pathways in a volcanic plume to several parameters that remain poorly constrained. In Section 2, we present the data used in the current study and the modelling choices that have been made. Section 3 presents the simulation outputs and their interpretation in terms of comparison to observations and in terms of sensitivity to multiple parameters. Finally, Section 4 draws conclusions and examines new perspectives that are not covered by the present study. 2 Material and methods.
CHIMERE model The Anthropogenic emissions are generated using the HTAP 2010 inventory  (Janssens-Maenhout et al. , 2015) , boundary conditions for dust are calculated from GOCART global model  (Ginoux et al. , 2001)  and from the global model LMDZ-INCA  (Hauglustaine et al. , 2004)  for other species. The CHIMERE model has been forced using  WRFv. 3. 7. 1 (Weather Research and Forecasting Skamarock et al. , 2008) , with an update of the forcing meteorological variables every 20 minutes using the WRF-CHIMERE online simulation framework  (Briant et al. , 2017; Menut et al. , 2021). The WRF model has been run with 44 vertical levels starting from surface to 50 hPa with the same horizontal grid as the one used for CHIMERE. Large-scale meteorological fields used to force the WRF model at domain boundaries as well as for spectral nudging inside the simulation domain have been taken from the NCEP GFS dataset at 0.
Code and data availability The source code for the CHIMERE model (Mailler et al. , 2017)  is available on: https://www. lmd. polytechnique. fr/chimere/. WRF source code is available on: https://github. com/wrf-model/WRF/. Clarisse, L. (2019). Daily IASI/Metop-B ULB-LATMOS sulphur dioxide (SO 2 ) L2 product (columns and altitude) [Data set]. AERIS. https://doi. org/10. 25326/42. SO 2  (Salerno et al. , 2018)  flux measurement data are available contacting the authors. Simulation outputs are available contacting the authors.
Parsimonious distributed hydrological models with SMASH SMASH is a computational software framework dedicated to Spatially distributed Modeling and data ASsimilation for Hydrology, enabling to tackle high dimensional inverse problems with adjoint-based variational method. This modular platform contains conceptual representations and numerical approximations of dominant hydrological processes while aiming to maintain a relative parsimony. SMASH enables to work at multiple spatio-temporal resolutions, and to compare different modelling operators and structures. All are defined on a regular grid and run continuously. For each time step they take as inputs rainfall and potential evapotranspiration grids, and outputs a field of routed discharges following a drainage grid. Two model structures are tested in this work, including an upgraded parsimonious one that stems from numerous numerical experiments that consisted in testing structures of increasing complexity.
Catchment selection and hydroclimatic data Our catchment sample includes 312 catchments which is a subset from  Hashemi et al. (2021) , subset of a larger dataset of 4190 French catchments from INRAE-HYCAR research unit  (Delaigue et al. , 2020; Brigode et al. , 2020). Based on a sample of 740 catchments, catchments were removed if (i) regime is Nival and Nival-Pluvial, (ii) or if area is greater than 3000 km 2 , (iii) or if more than 20% gaps exist in discharge data on one of the two periods (p1, p2) defined after. Concerning the hydrological model input, 14 years of hourly distributed rainfall, PET and locally observed discharges, covering the period 1 August 2006 to 31 July 2020, were used: -Discharge data are collected by the French Ministry of Environment covering the period of the forcing data and have been extracted from the (Hydro) platform (http://www.
hydro. eaufrance. fr/). -Rainfall: We use rainfall data from the radar observation reanalysis ANTILOPE J+1, which merges radar and insitu gauge observations. This data is provided by Météo-France at a grid resolution of 1 km 2 exactly corresponding to rasters defining model grids. -Potential evapotranspiration (PET): The interannual temperature data is provided by the SAFRAN (Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige) reanalysis that is run by Météo France at a resolution of 8×8 [km2]  (Quintana-Seguí et al. , 2008; Vidal et al. , 2010)  reanalysis and then used to calculate the potential evapotranspiration using the Oudin formula  (Oudin et al. , 2005). PET are at the same resolution as the rainfall data. This data is specific to the continuous models (GR5H and SMASH).
Numerical experiments design In order to test the proposed grid based hydrological model S6, with the variational calibration algorithm, the following experimental design is proposed on the large dataset presented above. The predictive performances of calibrated-models are evaluated on data not used in calibration. -The models considered are (i) the S3 and S6 structures in SMASH (Section 2. 2) with the variational calibration algorithm (Section 2. 3) with parameter bounds from Table  2 , and (ii) the lumped GR5H model with its calibration algorithm, used as reference given some of its operators are applied at cell scale in SMASH. Recall SMASH S3 is equivalent to the structure in Jay-Allemand et al. (  2020 ) with slightly simplified routing -found not sensitive with the considered modelinversion settings.
This analysis relies on an automatic hydrograph segmentation algorithm  (Huynh and Garambois, 2022)  applied to simulated and observed discharge time series over the whole dataset. -A spatio-temporal validation procedure, aiming to assess the capabilities at ungauged locations of downstream calibrated SMASH, consists in applying it at 77 upstream gauges not seen in calibration. Local calibrations are also performed at those upstream gauges to assess the loss of efficiency. -A global parametric sensitivity analysis over the whole catchment sample is performed to better understand model response surface. A classical regional sensitivity analysis method is used  Saltelli et al. (2008). In temporal validation, S6-D also outperforms S3-D with median N SE of 0. 8 (resp. 0. 79) compared to 0. 74 (resp.
0. 7) in temporal validation on period p2 (resp. p1) as shown on the right panel of Fig. 4  (resp. B1). A relatively narrower interquantile range is obtained with S6-D both in calibration and validation, with better performances than the 5 parameters GR5H model only shown here as a reference -recall benchmark is not the scope of this work. This will be further discussed after. Those good N SE performances show an improved capability of the proposed 6 parameters SMASH structure, which was expected with this increased model complexity compared to a 3 parameters model, to fit observed streamflows in calibration and a good temporal robustness on this dataset. Using distributed calibration increases the model performances in terms of N SE for calibration period and even for validation period, with higher performances and temporal robustness for S6-D.
Model parameters analysis The parameter sets calibrated for SMASH S6, over the large sample of French catchments, are summarized for spatially uniform setup S6-U in terms of average and standard deviation over the dataset in Table  4  and   First concerning spatially uniform calibrations S6-U, it is interesting to see similar trends, in terms of parameter means and standard deviations for each regime and between regimes over the two periods. For each regime, parameter values are rather stable between the two periods with higher differences for Mediterranean catchments, especially for second transfer parameter c tl and water exchange parameter ml; a lower negative ml being obtained on p1. This can arise from (i) hydrometric data errors (both forcings and discharge, especially for high intensity events), (ii) modeling errors, (iii) hydrological information contained in calibration data (e.
g. analysis in last section Section 4. 1 and scatter plot in Fig. 7 ). The variability of calibrated parameters over the dataset has some "physical" coherence: Median routing parameter c r is smallest, that is more rapid routing, on fastly responding Mediterranean catchments; median second (slow) transfer parameter c tl and its dispersion are highest on Uniform catchments. For those Uniform catchments, highest mean values and dispersion of reservoir capacities c p , c tr , c tl and routing parameter c r (whose high value induces a slower emptying of the reservoir) are found for both periods in calibration. Reaching such values and dispersion may testify of the relative difficulty that a parsimonious hydrological model structure has to reproduce such hydrological behaviours including complex multi-frequency and non linear hydrogeological processes  (Schuite et al.
(2021)  focusing on flood signatures in several French karstic areas. Regarding spatially distributed calibration S6-D, spatial averages of each calibrated parameter field is computed for each catchment and period, next statistics are computed over the dataset for each parameter and presented in Table  5. Note that these simple spatial statistics are computed on catchments of various surfaces. Analogously to uniform calibrations one can observe similar trends over the catchment set, in terms of calibrated parameter means and standard deviations, between the two periods, per regime and between regimes. In terms of within catchment spatial variability, interestingly, the lower ones are those of the routing parameter c r and of the interception capacity c i. This indicates those parameters are relatively well constrained in the current model-optimization setup, which corroborates this finding for routing parameter with the present optimization algorithm and a 3 parameters version on one catchment in Jay-Allemand et al.
Conclusions This article has presented the first evaluation over a large sample of Variational Data Assimilation (VDA) applied to the 410 spatially distributed calibration of a newly taylored parsimonious hydrological model. This improved parsimonious grid GRbased model has been implemented in SMASH platform and its adjoint generated as requeted by VDA algorithm. The spatially distributed calibration of this single model structure over a large sample of French catchments spanning different hydrological regimes results in high performances. From the obtained results and analysis at annual, seasonal and floods timescales and in spatio-temporal validation, as well as from parametric sensitivity analysis, the following conclusions can be raised: -Best modeling performances are obtained for Oceanic and Mediterranean basins with this 6 parameters model structure whereas it performs less well over Uniform basins where influence of groundwater processes can be significant -multifrequency hydrogeological processes with stream aquifer coupling (e.
-Coupling to hydraulic models of the DassFlow variational data assimilation platform  (Monnier et al. , 2016 ) also recently wrapped in python, adapted to use satellite datasets either with 1D network model  (Brisset et al. , 2018    (See in Fig. 8  for period p2). * k = T t=0 [Q k (t) -Q * k (t)] 2 T t=0 Q * k (t) -Q * k (t) 2 (A20) -KGE j * k = (r -1) 2 + (α -1) 2 + (β -1) 2 r = Cov[Q k , Q * k ] Var[Q k ] Var[Q * k ] α = Q k Q * k β = Var[Q k ] Var[Q * k ] ( B3 Model performances at upstream un-calibrated gauges.
Descloitres et al. [21]  recently adapted the Lee's model to analyze the influence of immersion depth and fractional coverage on the surface reflectance. Since the semi-analytical Lee's model can be inverted, it will be used here to retrieve the Sargassum aggregation parameters, especially Sargassum depth and fractional coverage, from the surface reflectance derived from Sentinel-3/OLCI observations. The paper is organized as follows: the study area in the Caribbean Sea, the Sentinel-3/OLCI satellite data and the methodology used to estimate in-water and surface Sargassum aggregation properties are described Section 2. The performances of the methodology and the application to Sentinel-3/OLCI data are outlined Section 3. The implementation of a neural network approach for reducing the computational time for processing a large OLCI dataset is presented in Section 4.
Satellite Data The Sentinel-3/OLCI sensor is used in this study for the detection and quantification of Sargassum over the study area. OLCI sensor acquires data for 21 bands spectral resolution at a spatial resolution of 300 m and a temporal revisit of two days with the two-satellite constellations  [22]. Its Signal-to-Noise Ratio (SNR) ranges from 2188 to 152 from 400 nm to 1020 nm bands, respectively, thus allowing the acquisition of high radiometric quality data over dark waters such as those encountered in the open ocean. The dataset used in this study consists of seven images that were acquired on the following dates: 8 July 2017, 13:55 (UTC); 27 May 2018, 14:21; 14 June 2020 14:02; 14 September 2020 14:17; 28 December 2020 13:55; and finally, 2 May 2021, 14:15. Those scenes were selected because they exhibit significant occurrences of Sargassum and also because they were acquired at various seasons and for different years.
Performances of the SRT Inversion Retrieval Process Using Synthetic Data A synthetic dataset was generated to evaluate the performances of the SRT model inversion for retrieving particularly FC and z. For this purpose, the forward SRT model is used to simulate the surface reflectances for clear waters (i. e. , Chl = 0. 3 mg m -3 , NAP = 1 g m -3 , CDOM = 0. 01 m -1 ), with a depth z varying between 0 m and 5 m, and a fractional coverage varying between 0 and 1. The atmospheric effects are added to simulate the top of the atmosphere radiance using MODTRAN  [33]. The OLCI instrumental features (i. e. , spectral resolution and SNR) are then applied. In addition, an error representing the imperfect correction of atmospheric effects is taken into account. The minimization of the cost function is used to retrieve the water constituents, namely, Chl', NAP', CDOM', FC', and z'.
Retrieval of the Bio-Optical Parameters from OLCI Derived Surface Reflectances Using the SRT Inversion Method Here, the SRT inversion method is applied to OLCI derived surface reflectances data acquired on 8 July 2017, in the study area as described Section 2. 2 (white square in Figure  2 ). First, the results of the retrievals are shown for two types of pixels. One type of pixels contains only clear oceanic waters, i. e. , Sargassum-free pixel, while the other type of pixel contains a mixing of seawater and Sargassum (i. e. , Sargassum contaminated pixel). This last type of pixel was identified using the MCI index to ensure the presence of Sargassum. Table  2  summarizes the values of the retrieved parameters for a pixel of each type. Chl is estimated around 0. 4 mg m -3 for both pixels; such a value matches with the range provided by the OC-CCI database near Dominica Island (i.
to the OLCI data acquired on 8 July, 2017, over the study area. Note that the data for which FC is lower than 0. 1% and/or the depth is higher than 4. 9 m were filtered out. About 80% of the retrieved depths are located between 1 m and the surface. The remaining 20% left is then retrieved between 1 and 4. 9 m. The greater depths (z > 1 m) seem to be retrieved on the edges of the aggregations while the shallow depths (z < 1 m) are observed in the center of the aggregations (Figure  8b ). This could be explained by the fact that the abundance of Sargassum is lower on the edges of the aggregations, and thus the Figure  8. Spatial distribution of (a) FC (%) and (b) z retrieved by the SRT inversion method applied to the OLCI data acquired on 8 July, 2017, over the study area. Note that the data for which FC is lower than 0.
Neural networks can detect the presence of Sargassum and/or assess their abundance per pixels. However, they are not able yet to consider the Sargassum immersion depth. Here, the neural network was trained using a simulated dataset because in situ data of the immersion depth of the Sargassum aggregation are not available. The forward SRT model was used to create such a training dataset which links the bio-optical parameters to their associated surface reflectances. A noise is added to the simulated surface reflectances using the same method as described Section 3. 1 to obtain OLCI-like surface reflectances. The artificial neural network is trained to estimate the five bio-optical parameters (Chl, NAP, CDOM, FC and z) from the simulated OLCI-like surface reflectances (12 spectral bands). It is then able to perform the inversion of the SRT model for the OLCI surface derived reflectances image, pixel by pixel.
The artificial neural network is based on a multi-layer perceptron model (MLP) which enables to approximate non-linear functions to estimate continuous outputs  [36]. The neural network was built using 4 hidden layers of 50 neurons fully connected (Figure  10a ). The training phase, the test phase, and the validation phase were applied using, respectively, 70%, 15%, and 15% of the dataset. The training phase duration is 12 h when using 100,000 samples composed by the set of reflectances and associated water constituents. It should be highlighted that the training phase is performed only once. Then, the neural network can be applied to any OLCI scene within a few minutes, thus significantly decreasing the computational time. The metric used to evaluate the learning performances for each phase (training, test, and validation) is the mean squared error (MSE) which is expressed as follows (Equation (  9 )): MSE = 1 N ∑ (C reference -C estimation ) 2 ( 9 ) where N is the number of evaluated parameters for each phase.
10b ), the neural network proposed here does not show any overfitting issues. The error on the FC component (Figure  10c ) mostly ranges between -3% and +3%, which is satisfactory (i. e. , lower than 1%). The error on z (Figure  10d ) is also satisfactory since the histogram, which is centered on the value 0, rapidly decreases beyond +0. 5 m, and below -1 m. The performances of the neural network are also evaluated using a different and larger dataset than the one used during the learning process (the approach has been described in Section 3. 1). The RMSE values are 0. 3 mg m -3 for Chl, 0. 21 g m -3 for NAP, 0. 006 m -1 for CDOM, 3. 1% for FC, and 0. 54 m for z. These RMSE values are close to those obtained previously when using the SRT inversion approach (Table  1 ). Remote Sens. 2022, 14, x FOR PEER REVIEW 14 of 21 3.
The value of K dw at 400 nm is 0. 0209 m -1 , thus theoretically allowing observing a depth layer up to The retrieval of FC and z values from the neural network and from δMCI (only FC for this latter index) has also been applied to other scenes of the dataset (Section 2. 2). Table  3  summarizes the fractional coverage retrieved by both δMCI and neural network as well as the proportion of the coverage retrieved by the neural network between 2 m and 5 m depth. The coverage estimated using the neural network is higher by a factor of 2 than that derived using δMCI approach for all the scenes except for the scene acquired on 2 May 2021, for which the coverage is fairly similar for both methods. The relative increase in the coverage retrieval ranges from 0. 7% to 68. 6% for the scenes considered here when using the neural network in comparison to the current δMCI approach.