dataesr/scientific-paragraphs-categorization huggingface dataset:
Including grain size classes (sand-silt-clay content) in the dataset, as in PCA-2, allows for more refined results but still proves inconclusive. Once again, the use of aggregated data results in a partial loss of information. Ultimately, it is PCA-3 performed on raw grain size data that produces the most interesting and informative outcomes. Although Ward's method was exclusively used in this study to identify groups of similar sediment samples, alternative partitional clustering methods such as k-means or k-medoids could also be considered, particularly when working with categorical data. The silhouette coefficient can be employed to determine the optimal number of clusters in these methods. In both cases, the objective is to minimize the error between the empirical mean of a cluster and the points within the cluster.
, 2015; Wehr et al. , 2017)  have now clarified relationships between canopy parameters and environmental variables PAR, VPD and precipitation. Long-term carbon and water flux data from flux towers in forest ecosystems (e. g. Ameriflux  (Baldocchi et al. , 2001) , Euroflux  (Valentini, 2003) , FluxNet  (Baldocchi et al. , 2005) ) and satellite datasets such as EOS/Modis worldwide  (Huete et al. , 1994) , have provided canopy level and landscape wide data. Plant 135 focused environmental manipulation studies, such as FACE, can provide data on individual parameters and processes to inform and challenge the models. At the forest scale, studies of the effects of the European drought (2018-2019) on forested landscapes have shown that recovery time for surviving trees may be several years, affecting both plant growth, stem shrinkage  (Dietrich et al.
Data are collected for all three FACE facility treatments. Here we concentrate on oak, the dominant species, with sap flow monitoring restricted to 190 two oaks in each of the nine experimental arrays totalling eighteen trees. We compare individual tree's sap flux and TWU responses under the three treatments across the leaf-on seasons for these early experimental FACE years looking at within-year and inter-year relationships. We also describe daily, monthly and seasonal changes to sap flux and TWU for mature oak over five years and discuss how these results, from our tree-centred viewpoint, will improve our understanding of future-forest water dynamics of old growth forest 195 contributing to development of more realistic ecohydrological vegetation, soil and landscape models. A1. The term 'sap flow' is used generally when referencing heat transducer methods to measure the water movement through sapwood.
Measurements overview Here we report data from five treatment seasons, July 2017 to end of October 2021. The study focuses on diurnal (i. e. daylight) responses within our experimental treatment season April to October. Sap flux and TWU datasets for the eighteen old growth oaks are calculated from half-hourly tree stem sap flow measurements derived from HPC transducers. TWU data is accumulated from sap flux, then analysed monthly within the treatment season across 205 each year. For all xylem sap flux monitored oak trees, tree identification, treatment type, array number, along with their stem circumference and average bark radius, Rb, at probeset insertion point are shown in supporting information (Table  S1 ). Fig. S1  shows the key measurement points relating to tree hydraulics in this project.
Soil and throughfall precipitation data collection Pre-treatment (2015-2017 for infrastructure arrays) and pre-project (i. e. 2017 onwards data for all Ghost arrays) on-site soil and throughfall data were used to characterise the site. Supplementary (2018 onwards) throughfall/ soil monitoring sites were added (see  Mackenzie et al. (2021) ). Shallow soil moisture and soil temperature data were 220 For plants, incident precipitation affects their function in several ways during the leaf-on season. Firstly water droplets incident on the leaves which when combined with lack of sun prevent full photosynthesis. The canopy water mostly evaporates or may drip to ground. Secondly throughfall reaching ground level may either runoff the 225 surface being lost to the soil, infiltrate (providing some necessary support for root rehydration and plant water intake or evaporate.
, 2001). With respect to our set up, we optimised the high end of this limitation by choosing suggested sensor spacings recommended by a manufacturer, Tranzflo, with extensive experience in a wide range of deciduous trees. Appendix B discusses the limitations which the time-out characteristic places on this set of HPC data and consequent results. We had limited options to extend the time-315 out period due to the multiple types of data needing to be captured by our single logger/ multiplexer arrangement in each array. Validity of high outlier values is considered within our analysis. To determine whole tree sap flux several tree characteristics were used: (a) tree stem circumference at insertion point, (b) bark thickness, from which we derived tree stem cambium radius at insertion point R (m) and subsequently heartwood radius H (m) from sensor spacing.
320 The xylem sap flow installations in target trees commenced in Jan. 2017. All no-treatment-no-infrastructure control (Ghost) oak trees provided data from August 2017 and commissioning of all 18 oak trees was completed by autumn 2018. We tested our prototype installation set-up in mid-summer 2017 to determine if we could capture the expected range of heat velocities and applied similar capture programs to all array loggers. All oak sap flow installations were successful and a total of 12,259 days of individual tree data (770,667 diurnal sap flux 325 measurements across all months) were processed for the 2017-2021 TWU analysis. Data collection problems, due to logger earthing and sap probe misconnections at manufacture, caused data loss early in the project. Resulting data gaps in the earliest installations affected four of the 36 probesets installed in four trees August 2017 until September 2019.
Data processing, visualization and analysis Manually collected data was pre-processed as. csv files for import to 'R'. Raw data from dataloggers were processed, visualised and consequently analysed using 'R' versions 3. 6. 2, 4. 0. 3 and 4. 2. 1 (R Core Team, 2020, 2021 and 2022), R Studio (RStudio Team, 2022) on Windows 10 x64 (build 1909). All figures were created using 405 R package ggplot  (Wickham, 2016). Other standard packages (e. g. lubridate) are listed in R scripts. Regressions between bark radius and water usage, and between bark radius and canopy area were calculated using the lm function. Box and whisker plots to visualise seasonal and monthly differences in sap flux and water usage between trees and treatments were generated in ggplot where standard Tukey  (McGill et al. , 1978)  percentiles (median + interquartile range) whiskers (1.
Tree size considerationstree radius Example relationships between bark radius at insertion height of sap probesets, Rb, (mm) and monthly mean TWU 470 (TWU, litres d -1 ) for all trees with both probesets working were first analysed for 2019 summer months. The best fit model for the combined 2019 three treatment examples was a simple linear fit; quadratic fits were tested and rejected. During July 2019, due to two probesets malfunctioning, Ghost tree TWU results do not include trees as large as the largest in the infrastructure arrays as explained in Methods above. Data from six, 13, 17 or 17 trees are used for years  2018, 2019, 2020 & 2021    uncertainty. In July 2020, the wettest year for mid-leaf (see Table  4  and Fig. 11  below), we surmise that either smaller trees' TWU appears enhanced due to the truncation effect (Appendix B) or that larger trees suppress TWU due to poor light levels relatively more than the smaller trees, making the slope shallower.
Usability of sap flux data from HPC probesets Although this experiment has small sample size (total 18 trees, six per treatment), we have defined a parameter 680 TWUn to enable consistent water usage comparisons between individual trees and hence treatments diurnally. We consider that this method of processing HPC sap flux results, along with the extensive and continuous dataset for all no-infrastructure Ghost control trees over more than four years, gives us high confidence in provision of this normalised data for use in forest models. We can clearly demonstrate that use of four thermocouple positions across the sapwood for each of our HPC 685 probesets has enabled us to successfully capture the position and size of point sap flux density (derived from sap velocity) and that this has given us a more reliable basis to explore the effects of tree size on both sap flux and TWU.
Our normalisation may not account for the whole but, in summer months, we find that the eCO2 and aCO2 trees demonstrate similar water usage with little evidence of a CO2 effect. Although data collected are tree specific, use of TWUn will enable formation of a good estimate of oak water usage per treatment array, extending these results 720 for use in the overall CO2 treatment experiment. By comparing mean monthly TWUn we found that TWUn is less for the trees in Ghost arrays, with aCO2 trees' normalised water usage being 20% higher across the three treatment years analysed 2019 to 2021, showing that infrastructure presence affects TWU. This result may be due to the installation or operation of FACE infrastructure, or to array-specific differences in soil moisture, slope, soil respiration, or sub-dominant tree species presence.
Data availability 760 All data used to carry out this study are available upon request via the correspondence author (RMK); this includes both logged data and physical tree measurements/ ecological information for example. Sap flux data are available at UBIRA eData repository doi. https://doi. org/10. 25500/edata. bham. 00000972 Phenocam data available https://phenocam. nau. edu/webcam/roi/millhaft/DB_1000/ 765 9 Supplement link: the link to the supplement will be included by Copernicus, if applicable. 10 Author contribution: SEQ designed and carried out the sap flow investigation and prepared the manuscript as part of the FACE programme designed by ARM. SEQ installed sap instrumentation, programmed the data loggers, curated, visualised and analysed the sap data, manually collected, visualised and analysed woodcores and physical tree 770 data.
Our understanding of the dynamics and timing of mantle exhumation and serpentinization processes in rift-inversion orogens, crucial to assess the associated natural H2 potential  (39) , remains restricted so far, especially since these processes occur at great depth. Here, numerical geodynamic modelling provides a means to gain key insights, and various researchers have numerically modelled rift-inversion orogens (e. g. ,  17; 18; 8; 9; 31; 19; 14; 40). Still, these studies mostly focused on the overall crustal structure of rift-inversion orogen, whereas only limited and qualitative attention was dedicated to the exhumation (and serpentinization) of mantle material. In this paper we use the thermo-mechanical geodynamic code ASPECT coupled with the surface processes code FastScape to conduct numerical models of rift-inversion orogens (Figs.
IV. Model analysis We extract for each model the following three metrics over time: (I) the area of exhumed mantle, i. e. total area of mantle above the initial Moho at 35 km depth, Figs. 2, M1 ), (II) the area of actively rising exhumed mantle (i. e. the area of exhumed mantle with upward vertical velocity > 0 m s -1 ), (III) the serpentinization window (i. e. the area of mantle within the 200-350˚C temperature window in which serpentinization is most efficient)  (7). This quantification step is performed using the open source Paraview visualisation software (www. paraview. org). We furthermore adopt the general approach of Liu et al. (2023)  (23)  to calculate serpentinization capacity in kg(km yr) -1 and associated H2 generation potential in mol(km yr) -1 in geodynamic models. This approach is based on the assumption that active and sufficiently mature faults feature a high permeability, which allows for sufficient water circulation to enable high-degree serpentinization of mantle rocks.
Therefore, at each output time step, we quantify the area of mantle rocks that have accumulated a total plastic (i. e. , brittle) strain ε ≥ 0. 5 and that undergo significant active deformation (second strain rate invariant [𝜀] ≥ 2. 5⋅10 -15 ). Note that we also test different strain and strain rate thresholds to estimate the variability in the results of our analysis (see Supplementary Material)  (45). We find that reasonably different thresholds do not change our conclusions. Using the area of actively deforming mantle rocks, we subsequently adopt the temperaturedependent serpentinization reaction formulas from  Emmanuel & Berkowitz (2006)  and Liu et al. (2023)  (7, 23)  to compute serpentinization rates using Paraview's Python calculator application: "4'(5! "$ = 𝐴𝑒 (78 " (#7% " ) # (Eq.
ACKNOWLEDGMENTS We thank our colleagues at the  GFZ German Research Centre for Geosciences in Potsdam  for valuable support and discussion. This research was funded via a  GFZ Discovery Fellowship  awarded to FZ. We gratefully acknowledge the computing time granted by the  Resource Allocation Board  and provided on the supercomputer Lise and Emmy at  NHR@ZIB  and  NHR@Göttingen  as part of the NHR infrastructure. The calculations for this research were conducted with computing resources under the project  bbp00039.
I. Numerical method We base our modelling approach on previous work by  Neuharth et al. (2022)   (61) , using the thermo-mechanical geodynamic code ASPECT (Advanced Solver for Problems in Earth's ConvecTion)  (58; 55; 63; 53; 54; 47; 48) , coupled with FastScape  (50; 69, 70; 61)  for the inclusion of surface processes (Fig. M1 ). We apply a modified version of ASPECT 2. 4. 0-pre for the coupling with Fastscape. ASPECT and FastScape installation details, the custom ASPECT plugins we use, the parameter files used for each model run, the log files of each model run, and the Paraview state files used for model analysis are provided in a publicly accessible GitHub repository  (44)  (https://github. com/geozwaan/rift-inversion_orogens) that will be archived with a DOI at Zenodo as soon as the manuscript is accepted for the publication.
For setting the instruments and quality control of TE analyses of soil extracts, the multi-element calibration range was prepared from certified single-element solutions (Chem-Lab). Control solutions were prepared from two certified multi-element solutions (Chem-Lab and Agilent) and was inserted every ten samples. Control soil material was included from the extraction step in every sample set: two internal reference soil samples for most TE analyses, and one certified reference sediment sample (CRM016, Sigma-Aldrich) for Hg. Periodic control reports checked and validated data sets. Quantification limits expressed in mg. kg -1 soil were 4, 0. 2, 5, 10, 0. 2, 5, 10, 0. 5 and 25 for As, Cd, Cr, Cu, Hg, Ni, Pb, Se and Zn, respectively.
Statistical analysis All statistical analyses were carried out using R software version 3. 3. 1 (R Development Core Team). After normality and homoscedasticity verifications (Shapiro and Bartlett test), the different parameters were analyzed using an ANOVA to compare the different modalities of substrates. When significant (P< 0. 05) effects were found, comparisons among means were performed using the Least Significant Difference (LSD) test ("agricolae" package). A radar diagram was drawn up using SigmaPlot 14. 0 (Systat Software Inc. ).
Acknowledgments The authors thank  Mathieu Ohlmann  ( La Ferme du Bonheur ),  Maximilien Koegler  ( Upcycle ) and  Roberto Garcia  ( AgroParisTech ) for their technical assistance. Bastien Lambert ,  Chenchen Fan ,  Aureĺie Sećheret , and  Florie Gay , who contributed to this study during their training period, are also acknowledged.
Acknowledgements. The authors thank  Benjamin Chazeau  and  Baptiste Marques  for their help and insightful discussions. Financial support. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie Actions program  (grant no. 713750 ). The project has been carried out with the financial support of the  Regional Council of Provence-Alpes-Côte d'Azur  and  A*Midex  (grant no. ANR-11-IDEX-0001-02 ), funded by the  Investissements d'Avenir  project, which is further funded by the  French Government , and managed by the  French National Research Agency (ANR). This study also received funding from the  French CNRS-LEFE-CHAT  ( Programme National-Les Enveloppes Fluides et l' Environnement-Chimie Atmosphérique through the Multinitrates project ) and from the  ANR-PRCI program  (grant no.
Code and data availability. Data related to this article are available at https://doi. org/10. 7910/DVN/USWU6V (González-Sánchez  et al. , 2023b). Data related to the theoretical calculations can be requested from Miquel Huix-Rotllant (miquel. huixrotllant@univamu. fr). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-23-15135-2023-supplement.
Metran runs on a webserver to provide models explaining the variation of groundwater heads in the Dutch national database (http://www. grondwaterstandeninbeeld. nl). It can also be used offline and with different explanatory variables and time series in various formats. Obergfell et al. (2019)  proposed a recharge (R) estimate based on such a time series model which is equal to the rainfall (N) minus the reference evapotranspiration (E) multiplied by the evapotranspiration factor (f e ): R = N -(f e * E) (1) This was found to give a reasonable estimate of the long-term average recharge in an area with a phreatic aquifer without surface runoff and interflow  (Obergfell et al. , 2019). In that case, the potential recharge, recharge, and inter-aquifer leakage recharge all have the same value.
The delay of infiltrated water movement within the unsaturated zone is controlled by the second module using a two-parameter Weibull probability density function. Finally, the fluctuation of groundwater levels is simulated by the third module representing the saturated zone using an explicit numerical form of the mass balance equation. The saturated zone can be represented by several vertically arranged aquifers to capture the conceptual understanding of the site. A full description of the mathematical equations used to simulate the movement of water in these three zones is detailed by  Mackay et al. (2014a)  and  Mackay et al. (2014b). This study focuses on the amount of water infiltration into the aquifer, consequently, the recharge estimated from AquiMod is considered recharge or inter-aquifer leakage.
The national recharge estimation tool the Zooming Object Oriented Distributed Recharge Model (ZOODRM) is used in United Kingdom  (Mansour et al. , 2018). The model calculates recharge using characteristics of plant growth and water uptake, as well as information on soil characteristics and climatic conditions. The model operates on grid basis and covers the entire British Mainland. The hydrodynamic model of the chalk aquifer of the Somme basin uses a finite difference groundwater modelling approach implemented in BRGM's MARTHE code  (Thiéry, 2015b; Thiéry et al. , 2018). Groundwater is the primary water resource for water supply in the French Somme Department. Hydrodynamic modelling of the Somme basin  (Amraoui et al. , 2004 ) is used to study the impacts of climate change on groundwater recharge to the chalky aquifer, piezometric level fluctuations and streamflow to the drainage network (Amraoui, 2019).
Like MODFLOW, MARTHE is a groundwater flow simulation program that needs groundwater recharge as input. Integrated hydrological models also form the basis of the national water resources model of Denmark (based on MIKE SHE) and the Netherlands (based on MODFLOW for groundwater  (de Lange et al. , 2014) ). Common to these models is the integrated calculation of unsaturated zone, surface water and groundwater fluxes, and they can therefore estimate all three recharge types. Water fluxes in the unsaturated zone are calculated based on e. g. , Richard's equation (MARTHE) or a 2-layer solver (MIKE SHE). The saturated zone fluxes are calculated by finite-difference solving Darcy equations for 3D groundwater flow. The national application of the models is well documented in previous publications for the  Netherlands (de Lange et al.
, 2014) , and Denmark  (Højberg et al. , 2013; Stisen et al. , 2019). Also included is a local high-resolution version of the Dutch national model run for the catchment of DeRaam (TACTIC, 2021a). The last tool used for comparison of recharge is a machine learning aided approach developed in the TACTIC project for the calculation of potential recharge (TYPE1) at a pan-European scale  (Martinsen et al. , 2022). The machine-learning algorithm uses gridded national model estimates of infiltration coefficients as training data, applying a range of explanatory variables to establish the governing variables for infiltration across Europe. The resulting infiltration coefficient map is then multiplied onto a European net precipitation map generated from the E-Obs precipitation dataset at 0. 1 • resolution and satellite data for evapotranspiration  (Cornes et al.
Three calibration scenarios were carried out using the entire dataset and 70% and 50% of the period, while the rest of the dataset was reserved for validation. Table  3  shows the mean recharge in the period 1978-2015 and the goodness of fit in the simulated groundwater levels obtained for different calibration scenarios. Both models provide similar mean recharge values and acceptable results in groundwater level forecast. In this case study, most AquiMod models provide a slightly better simulation of groundwater level than GARD ÉNIA, although the estimated recharge provided by GARD ÉNIA is more similar to the sitespecific model. Table  4. Fig. S1  of the electronic supplementary material (ESM) shows the observed and simulated groundwater levels obtained with the different calibrated models in the period 1978-2015.
Comparing recharge estimations to Pan-European estimate Fig. 6D  also includes potential recharge values obtained from the Pan-Eu recharge map  (Martinsen et al. , 2022). Two limitations should be noted in this comparison. First, the Pan-Eu recharge map is created using the national hydrological model's infiltration coefficient combined with other spatial variables in the machine learning algorithm. These are the same national models (applied in Spain, France, Ireland, Denmark and the Netherlands) for which a subset of estimated recharge values was used for comparison to the recharge estimate in the previous section. Thus, these two datasets for comparisons are not entirely independent. Secondly, the values from the recharge map are of potential recharge, i. e. , infiltration recharge leaving the soil zone.
g. , river influence in Serbia. In other cases, the model seems to be very sensitive to gaps in the dataset, and the quality of the input/calibration data. This meant in some cases that obtaining a calibrated model was only possible when using part of the dataset, e. g. , in Italy. Obviously, the lack of a snow module means that the model is less appropriate to apply in snow dominated areas, especially for projections into future climate. It is the only model of the suite tested that allows the modeller to directly incorporate (hydro)geological understanding of the area, and to investigate inter-aquifer leakage to deeper aquifers. The model was also found to have good predictive capability regarding groundwater levels, as shown in the case of Spain. The deviations and correlations to local and national models were acceptable for AquiMod, however despite the inclusion of a physical description of the subsurface, AquiMod did not always assure the simulation of meaningful recharge estimates in spite of good calibration performance for groundwater levels.