dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology 2. 1 ORCHILEAK 2. 1. 1 Model overview ORCHILEAK  (Lauerwald et al. , 2017)  is a branch of the model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems;  Krinner et al. , 2005) , the land surface component of the Institut Pierre-Simon Laplace (IPSL) Earth system model (ESM). ORCHIDEE simulates energy, water and C fluxes between the atmosphere and the land at a global scale. This LSM is based on four sub-modules. The first one, SECHIBA, simulates the energy budget (energy, carbon and water) between the atmosphere and the biosphere, as well as hydrology, which in the default setup used here are both represented using a 30 min time step. The second sub-module, adapted from the LPJ model  (Sitch et al. , 2003) , represents the dynamics of vegetation distribution on long timescales (1 year), while the third one (STOMATE) simulates the C dynamics in vegetation and soils at a daily to sub-daily step  (Krinner et al.
, 2005). Finally, the fourth sub-module handles the routing of water that is lost via surface runoff and drainage from soils to the ocean through the global river network  (Polcher, 2003; Guimberteau et al. , 2012) , for which a daily time step is used. All processes are simulated on a horizontal model grid, the resolution of which can be adapted to that of the meteorological forcing files. In this study, simulations are run at a spatial resolution of 0. 5 •. Moreover, in the default setup, up to 13 plant function types (PFTs; bare soil, eight types of forest, two types of grassland and two types of cropland) can be distinguished for each cell, for which C budgets are simulated individually, while energy and water budgets are simulated at the grid-cell level. ORCHIDEE represents the soil C dynamics distinguishing different pools of plant litter and soil organic C over a 2 m profile.
Soil carbon module The soil carbon module of ORCHILEAK (Fig. 1 ) is derived from the CENTURY soil carbon model of  Parton et al. (1988). In the standard scheme  (Krinner et al. , 2005) , C in the soil of each model grid cell, and of each PFT, is represented by four different litter pools and three different soil organic carbon (SOC) pools with different turnover rates. The four litter pools correspond to metabolic aboveground and belowground litter, and structural aboveground and belowground litter (Fig. 1 ). The SOC is subdivided into three pools, active, slow and passive, which have different default decomposition rates that are further modified at each time step according to the evolving soil moisture and soil temperature. In the CENTURY scheme, C from the decomposed structural litter enters the active and the slow pools with the fraction allocated to each pool depending on lignin content of the litter, while the entire metabolic litter pool and the remaining part of structural litter are allocated to the active SOC pool.
, 2003; Turgeon, 2008)  and DOC concentrations in the soil. The much higher DOC production rates simulated by ORCHIDEE-SOM in its original configuration during preliminary tests over Europe led us to implement the new approach (Eqs. 4-7). While preserving the basic structure of ORCHIDEE-SOM, we thus adapted the model in a way that organic C exchange occurs mainly among the particulate litter and SOC pools, similar to the original CENTURY model, while preserving the basic structure of ORCHIDEE-SOM. The production of DOC is represented as a side product of this C exchange between the pools of litter and SOC, with production rates as used in ECOSSE. In the modified soil carbon module, we used the parameter ω (Eqs. 4-7) as a scaling factor that determines how much DOC is produced by the decomposition of litter and SOC.
2c. Finally, Fig. 2d  illustrates the actual river network derived from the Hy-droSHEDS DEM data  (Lehner et al. , 2008)  and the one corresponding to our river routing scheme at 0. 5 • resolution, highlighting that the representation of the river network is not optimal due to the coarse spatial resolution of our model. This coarse resolution limits the possibility of model validation to the downstream parts of larger river networks. It is worth noting that the mouth of the Rhine is more than 100 km too far east, which further limits model validation for that river. The forcing data applied in our study are listed in Table  1. They are the same as those used in  Lauerwald et al. (2017)  except for the meteorological forcing data and the land cover. The WFDEI meteorological forcing dataset used in this study was derived by applying the methodology originally used to create the WATCH Forcing Data (WFD) from the ERA-Interim reanalysis data  (Weedon et al.
, 2014). The dataset has a 0. 5 • spatial resolution and a 3-hourly time step covering from 1978 to 2014. The land cover forcing data set, which gives the areal proportion of the 13 PFTs within each 0. 5 • grid cell, was taken from  Peng et al. (2017). Note that the soil hydrology model in ORCHIDEE, which we adopted for ORCHILEAK, was developed and calibrated to work with the soil classes used in  Reynolds et al. (1999). We thus kept this data source, while additional soil properties such as pH, and specific soil classes that we defined as "poor soils" (Histosols, Podzols) possessing lower C turnover times and DOC filtering, were taken from HWSD v1. 1. A topographic index, which in ORCHIDEE controls the flow velocity in the river network of each cell, is taken from  Vörösmarty et al. (2000).
"Floodplains", defined as the maximum areal proportion of a grid cell that can be flooded when the river exceeds its bankfull flow, and "Swamps", representing groundwater-fed wetlands in the floodplain, were adopted from the Global Lake and Wetland database  (Lehner and Döll, 2004). Depending on the areal extent of these swamps, a proportion of stream flow is simulated to feed into the soil moisture storage of the grid cell considered. Both parameters have an effect on the simulated river discharge and soil hydrology in the floodplains. For details, see  Lauerwald et al. (2017).
Spin-up Before the model can be used to simulate C dynamics over the past decades, a spin up is needed to reach an assumed steady state for the C fluxes during the pre-industrial period. This steady state is achieved by spinning up ORCHILEAK for 15 000 years. The spin up was realized by recursively looping over 4 years of climate forcing using the WFDEI forcing dataset over the 1979-1982 period (because the first year of the forcing, 1978, is incomplete) and constant land cover and atmospheric CO 2 concentration of 286 ppm  (Guimberteau et al. , 2018)  corresponding to year 1861. After the end of the spin-up, the soil C stock across the entire European continent changed by less than 1 % over 1 century of simulation, which we considered close enough to steady state.
Transient runs Using the steady-state outputs as initial condition, the first part of the transient simulation  was carried out with increasing atmospheric CO 2 concentration, changing land use and land cover and with river routing activated while still looping over 27 years  of the WFDEI forcing dataset. From 1979 onwards, the WFDEI atmospheric forcing data were applied over the entire period covered by this product with the changing land cover map and atmospheric CO 2 values applied for each year of simulation.
Model evaluation Firstly, the simulated discharges were compared to times series of daily stream flow recorded at 11 gauging stations from "The Global Runoff Data Center (GRDC), 56068 Koblenz, Germany" dataset. For comparison, both the observed and simulated discharges were aggregated at the monthly temporal resolution over the years 1980 to 2006. Note that the river network in ORCHILEAK does not always match the real river network. The selected gauging stations were assigned to the cell best representing the parts of the river network to which the sampling location corresponds. However, important correction had to be performed for the most upstream stations in the Rhine and the Elbe river network. The period 1980 to 2006 was chosen based on the GRDC data coverage. Model performance was further evaluated with respect to several variables of the terrestrial C cycle.
Of the Rhône Basin, 50 % is covered by grasslands, while in the Seine Basin, croplands reach 50 %. The Rhine has a more diverse land cover, with a substantial proportion, about 30 %, comprised of boreal (10 %) and temperate (20 %) forests, 35 % of grasslands and 25 % of croplands. See Table  S2  for further details. The soil temperature is compared to the soil temperature generated using data from the ECMWF Reanalysis v5 (ERA5) dataset  (Muñoz-Sabater et al. , 2021). The soil heterotrophic respiration (SHR) is compared against the datadriven global SHR dataset published by  Yao et al. (2021). The global SHR data set was produced using a random forest algorithm, upscaling from 455 data points from the global Soil Respiration Database (SRDB 4. 0) based on gridded fields of climatic, edaphic and productivity-related variables as predictors  (Yao et al.
, 2021). We compared the results of ORCHILEAK with the average, minimum and maximum values of SHR estimated by  Yao et al. (2021). SOC from the Harmonized World Soil Database (HWSD) was used to evaluate the simulated SOC stocks. HWSD is a global soil database that contains up-to-date information on a large range of soil properties. For instance, this dataset reports the organic carbon content in the soil as well as the soil bulk density. The bulk density in HWSD was calculated in two different ways. The first one follows the method described in  Saxton et al. (1986)  where the bulk density is related to the soil texture -an approach tending to overestimate density in high porosity soils or in OC-rich soils. The second method uses the SOTWIS database in which the bulk density is calculated as a function of soil type and depth.
In this database, all variables are reported for the topsoil (0-30 cm) and the sub-soil (30-100 cm) horizons. For comparison purposes, our simu-   (Abril et al. , 2002; Mattsson et al. , 2009). lated SOC stocks were thus integrated over the same depth intervals. We further assessed the extent to which our model can reproduce the main features in the observed soil DOC profiles. To that end, we compared our simulated DOC profile averaged over the entire European forest biome against the one established by  Camino-Serrano et al. (2014)  on the basis of a synthesis of local measurements. Although there are many studies on DOC concentrations in the soil, we selected that of  Camino-Serrano et al. (2014)  because it provides a synthesis at the pan-European scale, and is thus ideal for extracting "representative" concentration profiles over a sufficiently large domain, compatible with the regional scope of our study.
5 • or about 2 × 10 3 km 2 at the corresponding latitude), we consider the comparison against measured river DOC fluxes to be more relevant for our purposes, as rivers are good integrators of mean, largerscale catchment properties. For the riverine export fluxes, we assessed the modeled discharges and DOC concentrations separately. For evaluation of stream DOC concentrations, DOC data were extracted from the GLObal RIver CHemistry database  (GLORICH, Hartmann et al. , 2014)  for the Rhine and Elbe basins and from the "Eau de France" database for the Seine and Rhône basins. These data were complemented by river DOC concentrations reported by  Abril et al. (2002)  for 9 river mouths (Sado, Thames, Ems, Scheldt, Gironde, Douro, Loire, Elbe and Rhine), and by  Mattsson et al. (2009)  for several river basins located in Finland, Denmark, Wales and France.
NPP, SHR, soil temperature, biomass and soil organic C stocks We briefly compare simulated NPP with the gridded observation-based products GIMMS and CARDAMON (Sect. 2. 2. 2) as C fixation by the vegetation exerts an important control on DOC stocks in the soil and thus on DOC leaching. We first perform our comparison over a large domain comprising 35 to 70 • N and 10 • W to 30 • E -covering the area from Ireland to the western Black Sea (where the Danube flows into) and from the south of Spain to the north of Scandinavia. Over this area (referred to as "Europe" from here onwards), the modeled yearly averaged NPP amounts to 445 g C m -2 yr -1 , a value in remarkable agreement with both GIMMS and CARDAMOM estimates of 430 and 460 g C m -2 yr -1 , respectively. These two datasets entail an uncertainty that we assume to be similar to that reported for the MODIS dataset, i.
Code and data availability. The model code used in this study is available at DOI: https://doi. org/10. 14768/75AC2F47-4691-46AF-9B12-B1A9629CBC56 (Gommet et al. , 2021). All forcing data set are listed in Table  1. The data of observed discharges used in this study are available from the Global Runoff Data Center (GRDC) at https://www. bafg. de/GRDC/EN/Home/ homepage_node. html (BAfG, 2021). The data of the observed DOC concentrations for France are provided by eau de France, available at http://www. data. eaufrance. fr/ (eaufrance, 2021). Earth Syst. Dynam. , 13, 393-418, 2022 https://doi. org/10. 5194/esd-13-393-2022 C. Gommet et al. : Spatiotemporal patterns and drivers of terrestrial DOC leaching into the European river network 413 Supplement. The supplement related to this article is available online at: https://doi.
GAM EGPD model For this spatial EGPD model, we have X(x) ∼ EGPD(σ (x), k(x), ξ(x)), where x denotes some covariate and each of the model parameters depends on some form of x. The relationship between the model parameter (say α) and the covariate x is through an identity link: α ( x) = β 0 + K k=1 D k d=1 β kd b kd (x), (6) where β kd and b kd are, respectively, the basis coefficients and the basis functions. K is the number of smooths and D k is the dimension (number of knots) for smooth k. For the choice of spatial covariates, we use longitude, latitude, and mean daily precipitation because they give a better Akaike information criterion (AIC)  (Akaike, 1974). To fit EGPD with the GAM, we extended the functions already available in the evgam R package  (Youngman, 2020).
Stability of the high quantile estimate The robustness of a model is measured by the stability of a high quantile estimated from two sub-samples. A robust model should have a similar estimate of, say, a 100-year event, when the sub-sample/calibration data are changed. The SPAN criteria  (Garavaglia et al. , 2011; Blanchet et al. , 2019)  give the measure of the stability of a chosen quantile estimated from two sub-samples. The score is computed as the absolute difference between the two quantile estimates divided by their average and is given as SPAN i,T = 2 r(1) i,T -r(2) i,T r(1) i,T + r(2) i,T , (10) where r(1) i,T and r(2) i,T are the T -year return levels estimated from F (1) i and F (2) i , respectively, at station i. The score is computed for all the N stations, and the regional score SPAN reg,T is computed as SPAN reg,T = 1 - 1 N N i=1 SPAN i,T.
Acknowledgements. The authors would like to thank the editor and two anonymous referees for their fruitful suggestions. Financial support. This research has been supported by the  Bundesamt für Energie  (grant no. SI/502150-01 ) and the  Bundesamt für Umwelt  (grant no. SI/502150-01 ), through the  Extreme floods in Switzerland project. Review statement. This paper was edited by  Thomas Kjeldsen  and reviewed by two anonymous referees.
Data availability. The French data used in this paper are maintained by MétéoFrance and can be downloaded from http:// publitheque. meteo. fr/ (MeteoFrance, 2021) and those of Switzerland from MeteoSwiss (https://gate. meteoswiss. ch/idaweb/more. do; MeteoSwiss, 2021), German Weather Service (https://opendata. dwd. de; DWD, 2021), Federal Ministry of Agriculture, Regions and Tourism, Austria (https://ehyd. gv. at/; Federal Ministry for Agriculture, 2021), and for the Italian regions (https://www. arpalombardia. it/; ARPA, 2021a, http://www. arpa. piemonte. it/; ARPA, 2021b, https://www. arpa. vda. it/fr/; Aosta Valley Environmental Protection Agency, 2021). However, not all these agencies allow sharing of the data with third parties or publishing them on the Internet. Hence, we cannot offer direct access to the data used in this study.
Fostered by the upcoming BIOMASS mission, this article explores long-term trends of P-band temporal decorrelation over a tropical forest due to a time series of 617 days acquired during the TropiScat-2 experiment. The interest in this unique time series is twofold. First, it provides consistent statistics to monitor the yearly evolution of temporal coherences according to specific time scales of the BIOMASS tomographic and interferometric phases. Second, it provides key insights to explore new processing approaches with the combination of data from different orbit directions (ascending/descending) and different mission cycles separated by about seven months according to the current acquisition plan. For the first time, this study shows that 18-day coherences (corresponding to the time interval between the first and last acquisitions of the BIOMASS tomographic processing) can vary significantly according to rainy and dry seasons (medians from 0.
This article is organized as follows. Section II recalls the experiment characteristics, presents the explored two-year database, and details the performed processing. Section III presents an overview of coherence evolution along with temporal baselines ranging from ten days to 14 months, based on the whole 617-day dataset. To better understand and dissociate the multiple sources of decorrelation, specific time intervals of high importance are then considered in the course of this article, starting in Section IV by the analysis of 18-day coherences. Section V is then dedicated to a seasonal analysis over two consecutive years through the study of coherences computed for time intervals spanning from one day and up to 90 days. Finally, we investigate more thoroughly the 7-and 12-month coherences in Section VI.
A. Overview of the Experiment and Exploited Database In order to extend the P-band time series acquired during the TropiScat (2011-2015)  [7] ,  [8]  and AfriScat (2015-2017)  [14] ,  [15]  experiments as well as to investigate further aspects of temporal monitoring of a tropical forest, a new experiment named TropiScat-2 has been conducted since March 2018 in Paracou, French Guiana. Identically as for the previous experiments  [8] , TropiScat-2 consists of a radar scatterometer formed by a network of 20 antennas constituting 17 receive-transmit antenna pairs installed on the top of a 55-m-high tower (about 20 m higher than the overall canopy). The system is fully polarimetric and designed with tomographic capabilities, not deployed throughout this article, providing measurements from all 17 antenna pairs every 15 min in the frequency bands 200-400 MHz (P-band) and 800-1000 MHz (close to L-Band).
M. and 6:00 P. M. , which correspond to the local acquisition times of BIOMASS ascending and descending passes. More specifically, the data used to represent these two acquisition times are, respectively, considered between 5:30 A. M. and 6:30 A. M. and then between 5:30 P. M. and 6:30 P. M. in order to get more representative samples of what happens around these two particular times of the day. In addition to the P-band time series, we also rely in this article on rainfall and wind measurements, acquired every 30 min from the flux tower. These in situ data will be used for a cross analysis with the P-band temporal coherences to help in their interpretation.
It is worth noting that i m and i M are fixed throughout the study in such a way as to consider range distances comprised within horizontal distances of 45 and 60 m from the base of the tower as shown in Fig. 1 , resulting in a limited range of incidence angle with respect to a virtual horizontal ground of 40. 6 • -48. 8 •. In the follow-up, we will focus on studying the modulus of the complex temporal coherence, which we will refer to as coherence for simplification. In the course of this article, temporal coherences are mainly computed and statistically analyzed for a given temporal baseline t. In this case, all temporal coherences of which the two acquisition dates t 1 and t 2 are separated by a time interval of t (i. e. , |t 2t 1 | = t) are considered among the whole or a selected dataset.
V. SEASONALITY EFFECTS ON TEMPORAL DECORRELATION OVER TWO CONSECUTIVE YEARS FOR INTERVALS OF UP TO 90 DAYS In view of the importance of seasonality effects highlighted above as well as in previous works with temporal baselines of 30 days  [9] , the purpose of this section is to capitalize on the present database in order to investigate the longest possible temporal baselines within the same season, particularly the major dry and rainy seasons of 2018 and 2019.
ACKNOWLEDGMENT The authors would like to thank the  French National Space Agency (CNES)  for supporting activities in the framework of the  TOSCA Research Program. They would like to thank the  European Space Agency (ESA) , the  European Space Research and Technology Centre (ESTEC) , and the  European Space Research Institute (ESRIN)  teams for following our research activity as well as people at  Eco Fog  for their contribution to the success of the Paracou research station. They would also like to thank the reviewers for their relevant comments and overall interest.
Data analysis To assess the nursery value of each coastal site, criteria inspired from  Beck et al. (2001)  and adapted to the scope of the study were used. As a rst criterion, the number of species of juveniles was assessed and the Shannon diversity index was calculated. Secondly, the density (number of individuals per 100 m²) of juveniles was assessed, with habitats possessing more juveniles corresponding to a more favourable nursery habitat. The third criterion used was the proportion of juveniles, which indireclty informs on the presence of adults that can outcompete juveniles for shelter and food and may hence lead to less favourable conditions for their survival and growth. As a fourth criterion, the presence of predators (taken as an indirect proxy of juvenile survival) was assessed through the density of piscivorous adult sh.
Acknowledgments would like to thank the staff of ' Polynésienne des Eaux'  and the  Commune of Bora Bora  for their help. This work has received several grants:  Fondation de France  ( 2019-08602 ),  Ministère de l'Economie verte et du domaine -Délégation à la recherche de Polynésie  française (contrat  N3622   MED-EPHE ),  O ce Français de la Biodiversité  ( AFB/2019/385 -OFB. 20. 0888 ), Polynésienne des Eaux ,  ANR-19-CE34-0006 - Manini ,  ANR-19-CE14-0010-SENSO.
Funding This work was suppoted by  Fondation de France  ( 2019-08602 ),  Ministère de l'Economie verte et du domaine -Délégation à la recherche de Polynésie  française (contrat  N3622   MED-EPHE ),  O ce Français de la Biodiversité  ( AFB/2019/385 -OFB. 20. 0888 ), Polynésienne des Eaux ,  ANR-19-CE34-0006 - Manini ,  ANR-19-CE14-0010-SENSO. Con ict of interest.
Tracking algorithm We track the storms using a widely used feature tracking package called TempestExtremes  (Zarzycki and Ullrich 2017, Ullrich et al 2021). Candidates are identified as local minima in the sea level pressure (SLP). Candidates with SLP increasing by 150 Pa over a 5 • radius around them are selected (closed contour criteria). Individual candidates are then stitched together into tracks based on further criteria (e. g. minimum lifetime, maximum distance between each point, minimum total distance). The same parameters were used for all RCMs (supplementary material).