dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure module. The structure module keeps track of the structure of the canopy, e. g. bud number per vine, main-stem leaf appearance, branching and canopy height (Fig. 1 ). Main-stem leaf appearance is modelled with the appearance of a new leaf every time dD accumulation exceeds the current phyllochron (the D s interval between two successive leaves). Leaf appearance occurs between budburst and véraison with a phyllochron of 1. 64 for the first five leaves to appear, increasing to 1. 82 for the 10th leaf onwards  (Greer and Weston 2010). Branching rate was calculated as the product of three components: potential branching rate, the effect of leaf area per vine and the effect of retained bud number per metre row. Parameters were optimized based on the retained node number experiment (see detailed description of the experiment in Data sets used for model calibration and validation).
S2 ]. This light interception method is used when the field is configured by two rectangular zones. Row orientation is not accounted in the light interception calculation. If the canopy of a training system cannot be assumed to be a cube (e. g. tatura training system), new light interception methods can be added in the APSIM Next Generation source code (https://github. com/APSIMInitiative/-ApsimX/), see details in 4. 1 Canopy development and radiation interception. Several modifications were made in the calculation compared with the method described in  Gou et al. (2017)  in order to capture the vineyard/orchard canopy features. For instance, for a vertical-shootpositioned (VSP) grapevine, all the leaves were accumulated in the top part of the canopy with a bare trunk at the bottom. The original model assumed that the base of all canopies was at ground level.
Based on the data of  Lakso et al. (2008) , we set the initial fine root DM to be 60 g per vine (converted into g m -2 in the model based on population density). The annual fluctuation of the fine root DM was captured by dynamics of the relative growth rate and senescence rate. The dynamics of relative growth rate and senescence rate throughout the growing season were added based on the data presented in  Comas et al. (2005). The priority factor for root DM demand was set to the same as for leaf DM demand. The perennial structural roots were separated from the root module because they have different functions and the separation makes the parameterization easier, e. g. root length growth. Root length growth was calculated using the daily DM partitioned to fine roots and specific root length (SRL [m g -1 ]).
The methods for determining bunch number per vine in  Zhu et al. (2020)  were further generalized into the product of bunch number per shoot and shoot number per vine to account for different numbers of retained nodes per vine. Mean inflorescence number per node counted on 80 nodes per plot during the flowering assessment in the regional phenology and yield monitoring data set (see Section 2. 1) were used for model calibration. Mean daily maximum temperature (TmaxIni) and daily radiation during the inflorescence initiation were used for predicting mean bunch number per shoot for the following season. TmaxIni and mean daily maximum (TmaxFlow) and minimum temperature around the flowering period of the current season were used to predict berry number per bunch. TmaxFlow, mean daily radiation intensity and cumulated rainfall around flowering, and cumulated rainfall after flowering and before véraison were chosen to predict the potential berry fresh weight.
Furthermore, in the current model, carbohydrate effects represented by total carbohydrate supply and demand were added in the calculation of bunch number, berry number and potential berry fresh weight. The yield estimation method assumes that the potential yield components can be estimated based on the weather and carbohydrate conditions at certain critical periods, e. g. inflorescence initiation, while other periods have no effect. It has been shown that weather and carbohydrate conditions pre-budburst have strong effects on flower number  (Eltom et al. 2017) , and potentially on bunch number as well. However, our data set is insufficient to quantify such responses. The dynamic of berry fresh weight was calculated based on the development days from flowering using the beta growth function (Eq.
Model calibration methods Model parameters were determined based on data sets one to three where possible, e. g. parameters linked with phenology, leaf appearance rate and the dynamics of biomass demand of shoot and berry (Table  2 ). Data set 4 was mainly used for validation. When parameters were not measured directly or linked with whole-plant function, e. g. q str,i (priority coefficient) they were optimized through the DEoptim function in R  (Ardia et al. 2011). A link between APSIM Next Generation and R programming language was created through the APSIM Next Generation edit feature. The APSIM Next Generation edit feature enables users to edit the parameters through bash commands. An R script was developed where we can call APSIM Next Generation, pass the parameters and assess the performances of the model under new parameter values.
The script was then coupled in the optimization function (DEoptim) for improving the model performances through parameter optimization. The criteria for optimization were set to minimize the sum of squares between observed values and predicted values when one observed variable was of concern. When there was more than one variables of concern, the criteria were set to minimize the sum of squares of normalized differences between observed and simulated values (Eq. (  11 )): Cost = m j=1 n i=1 ((X sim,j,i -X obs,j,i )/X obs,j,i ) 2 (11) where i is the sample number (or time point), n the total number of measurements, j is the variable number, m the total number of variables in concern. X sim,j,i is the simulated value for variable j and sample number (time point) i, and X obs,j,i is the observed value for variable j and sample number (time point) i.
We want to check whether RF can effectively enhance our skill to mimic observed properties and to make reliable future climate projections. This work is a part of our general efforts of climate change mitigation and adaptation in China. It focuses on the recommended targets of 1. 5°C, and 2°C global warming levels, following the Paris Agreement (UNFCCC 2015). The geographic domain of our investigation is mainland China where a reliable dataset of observed climate is available. The rest of the paper is organized as follows. Section 2 describes the data, methodology, and the three algorithms involved in our study, together with the skill metrics for evaluation. Followed in Section 3 are the main results of the methodological assessment in present-day and future climate projection. Finally, conclusions and a few discussions are provided in Section 4.
Study area and data used This work focuses on mainland China, a territory highly susceptible to climate change due to its complex topography and strongly-pronounced monsoonal characteristics  (Fu et al 2008; Piao et al 2010). A high-quality in-situ dataset (CN05. 1), including conventional surface climatic variables, is employed for the calibration of all our approaches to develop a reliable multi-model ensemble-processing strategy. The daily gridded dataset covers 1961-2014, with a spatial resolution of 0. 25° × 0. 25° over whole China. Wu and Gao (2013)  provide detailed information about this dataset. On the other hand, 24 CMIP6 models' historical simulations and future projections from Shared Socioeconomic Pathway (SSP5-8. 5) are used to construct the multi-model ensemble and to generate 1.
5, 2°C and 3°C warming projection. These models were selected on the sole criterion of data availability for our purpose of determining warming targets at 1. 5, 2°C and 3°C. All CMIP6 data were retrieved through the data portals of the Earth System Grid Federation, which can be obtained from https://esgf-node. llnl. gov/search/cmip6/. Some essential characteristics of the used models are listed in Table  S1. Only their first realization (r1i1p1f1) was used in this work.
Climate indices The present study employed six quantitative indices, including mean temperature (TAS), annual maximum (hottest daytime) temperature (TXx), annual minimum (coldest nighttime) temperature (TNn), total precipitation in wet days (PRCPTOT), annual maximum consecutive 5-day precipitation amount (RX5DAY) and annual total precipitation for events exceeding the 95th percentile (R95P, an indication of strong precipitation). These indices are useful in capturing climate change information and have been widely used to identify and monitor extreme climate  (Zhang et al 2011; Zhu, H et al 2020). They are derived from daily precipitation and temperature CMIP6 datasets following the recommendation by the Expert Team on Climate Change Detection and Indices (ETCCDI) (http://etccdi. pacificclimate.
The regression coefficients a0 and Ak, (k=1, …, 24) were fitted with data in the training period comprising 34 years from 1961 to 1994. A linear model is not always inferior to nonlinear models, depending on the nature of the problem to resolve  (Choubin et al 2016; Xu et al 2020). The practical realization of LR used in this paper was done through the function "LinearRegression" in the module "sklearn. linear_model" in python 3. 8 (https://scikitlearn. org/stable/modules/generated/sklearn. linear_model. LinearRegression). Random Forest solves regression problems by growing an ensemble of decision trees based on binary recursive partitioning  (Breiman et al 1984; Breiman 2001). Although each individual regression (done at the level of leaves or terminal nodes) is still linear, but it is operated in a reduced range among the total samples.
ensemble" (Pedregosa et al. 2012 ) was used (https://scikitlearn. org/stable/modules/generated/sklearn. ensemble. RandomForestRegressor). For the training procedure, we have data covering 34 years, from 1961 to 1994, and 928 spatial points. The total number of samples into our RF training is thus 34×928=31552. Each of the 24 climate models is treated as a feature in our RF implementation. After RF is trained, it is used in the testing period from 1995 to 2014 to validate its performance. Similarly, it is used to make the future projection under the specific warming thresholds. The "Bayesian Optimization" was used to find the best hyperparameters implemented in the RF algorithm (Shahriari et al 2016) (http://rmcantin. github. io/bayesopt/html/bopttheory. html). It has a higher efficiency than other methods, such as "Grid Search" or "Randomized Search".
Acknowledgements Annie Millet's research has been conducted within the  FP2M federation  ( CNRS FR 2036 ). Marta Sanz-Solé  was supported by the grant  MTM2015-65092-P  and the research project MdM-2014-0445 from the  Ministerio de Ciencia, Innovación y Universidades (Spain). Annie Millet  wants to thank the  University of Barcelona  for the hospitality during her stay. The authors gratefully acknowledge the support and excellent conditions provided by the Research in Pairs programme of the Mathematisches Forschunginstitut Oberwolfach in June 2018.
In recent years, significant advances have been achieved in the development of RT codes due to the increase of capability in coupling transport simulators with geochemical reactive codes. For instance, modules have been developed that couple Phreeqc, a widely used geochemical package  [15, 16] , to other simulators  [17, 18]. Most of the RT codes mentioned above, neglect the coulombic interactions between the charged particles, as they are based on the classical Fick's law. However, it is well known that coulombic interactions processes cannot be simulated with Fick's law that assumes a constant diffusion coefficient for each dissolved species. This classical view assumes that the movements of each dissolved species in a system are merely based on concentration gradients. However, in a multicomponent electrolyte solution, each individual dissolved species migrates differently according to its ionic properties such as charge, ionic size, or ionic mobility  [19].
Despite the common use of the NC assumption in models dealing with reactive transport and electro-diffusion, the validity of this assumption is questionable. Thus, the main goal of this work is to evaluate the validity of the NC assumption by comparing it to a full model based on the NPP equations. To the best of our knowledge, this topic has never been investigated in the past. Thus, we first present a comprehensive formulation of the NPP equations. We develop a (1D/2D) finite element model to solve these equations, using the mathematical module of COMSOL Multiphysics. With the same module, we also develop a (1D/2D) finite element model based on the NC assumption. We validate the developed models based on common benchmarks and we compare them in different configurations of RT to evaluate the validity of the NC assumption.
Numerical Models Two numerical models (1D and 2D) have been developed based on the NPP equations and the NC assumption, respectively. The model based on the NPP equations is denoted NPP-model, while the model based on the NC assumption is called NC-model. Both models have been developed using the framework of the finite elements COMSOL multiphysics. COMSOL is a software that can be used to simulate problems involving multi-physical processes. It has two options to implement a model. In the first option, called the 'wizard model', a model can be developed by selecting physics available in COMSOL. In the second option, called "blank model", a model can be developed by specifying the governing mathematical equation. In this case, COMSOL can be seen as an advanced finite element library for solving coupled partial differential equations.
Electro-diffusion physics is not included in COMSOL. The implementation of the NPP and NC-models, with the 'wizard model' option, is not an easy task. The NC model can be developed using the physics of the module 'Transport of Diluted Species in Porous media-tds', by assuming a variable molecular diffusion, which is a function of the concentrations. The NPP model can be developed by coupling physics from the modules 'Transport of Diluted Species in Porous media-tds' and 'Electric Currents-ec'. However, coupling these modules in COMSOL does not allow for considering the electro-diffusion processes. To achieve that, the electrodiffusion term should be defined as a source term in the mass transport equation. However, this implementation with the 'wizard model' can only work for examples dealing with Dirichlet boundary conditions.
In the case of the Neumann boundary condition (i. e. , a constant total mass flux), the electro diffusive flux cannot be counted in the total mass flux as it is calculated as a source term. Thus, due to this limitation, the NPP-model has been developed with the mathematic modules in COMSOL ('blank model'). For mathematical consistency, we also developed the NC-model in the same way. For the NPP model, we choose the 'General PDE form-g' from the PDE interfaces in mathematic modules to implement the mass conservation equation. We define all terms separately, with the total species concentrations as dependent variables. The 'General PDE form-g' is coupled with the 'Poisson's equation-poeq' module in the classical PDEs group of mathematic modules. In the 'poeq' module, the electric potential is defined as the dependent variable, and it appears directly in the mass flux defined in the mass balance equation.
The CH 4 monochromatic absorption of the Pacific Northwest National Laboratory (PNNL) database  [53] , in ppm -1 •m -1 , is shown in blue in Figure  1. This quasi-monochromatic absorption is then convolved with the PRISMA spectral response function (red line). In this spectral range, the PRISMA satellite has a spectral sampling distance of approximately 9 nm and a spectral resolution (Full Width of Half Maximum) between 9 and 15 nm  [54]. The hyperspectral camera's spatial resolution is approximately 30 m (ground sampling distance) and an image covers an area of 30 km × 30 km. SNR specifications of PRISMA are described in  Labate [35]  and listed in Table  1. Using the Homogeneous Regions Division And Spectral De-Correlation (HRDSDC) method, Cogliati  [55]  estimated the PRISMA pixel SNR at each wavelength in the spectral dimension (by using the adjacent wavelengths) and scene heterogeneity (by using neighboring pixels in the same homogenous region).
Wind Information Using discrete (in time or in space) gas concentration measurements, wind speed and direction are required to estimate the gas flow rate (see Section 3. 4). In this paper, European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts are used. ECMWF wind data was calculated at 00:00 or 12:00. The forecasts were then used to estimate the 10 m wind components for each hour with a grid of 0. 125 • × 0. 125 • , i. e. , 13 km × 13 km. The Korpezhe site is located between four ECMWF grid points. The wind direction is expressed in a meteorological data frame. Angles of 0 • , 90 • , 180 • and 270 • correspond to wind blowing from the north, east, south and west, respectively. Due to the grid and time of ECMWF data, spatial and temporal interpolations had to be applied. Spatially, the Korpezhe values were obtained by distance-weighted linear interpolation.
LM versus ISBR-OEM LM is the most common plume concentration quantification method. However, transmission linearization results in an underestimation of the retrieved concentration, especially for high concentration values. In order to illustrate this effect, we used a reflectance image using a land surface reflectance database and standard atmospheric parameters to create a radiance image (using the formula in Section 3. 1) with a synthetic plume of known concentration (see Figure  A3  in Appendix C) added to an atmospheric signal. The background reference signal corresponds to a standard atmosphere of MODTRAN code (mid-latitude summer atmospheric profiles and concentrations)  [65]. This image is then processed by LM and ISBR-OEM. The results are plotted in Figure  3 ; the true and retrieved ISBR-OEM concentrations are shown as a function of the retrieved LM concentration.
Overview of the Concentration Map Here, only ISBR-OEM results are provided and discussed. Of the eleven images in the dataset, nine show a plume corresponding to source A (Figure  2 ), while the plume corresponding to source D is only seen in two images. Source B and C plumes are seen only once. Independently of the sources and dates, thirteen plumes were detected. Focusing on the plume head, the plume from source A on the 2020/07/21 image, used as an illustration in the previous sections, shows the highest concentration with a maximum pixel of approximately 20,000 ppm•m. The maximum concentration values for each detected plume are listed in Table  3. To summarize, three plumes are marked for source A. Sources B and D each have one plume that appears significant, while the source C plume is less intense.
Nitrate and acetate were quantified by ionic chromatography (Dionex IC3000-SP-EG-DC system equipped with an AS50 autosampler and a conductimetric detector) according to the NF EN ISO 10304-1 (2009) method. Nitrite was analysed by colorimetry according to the NF ISO 15923-1 (2014) method. N 2 O was analysed by gas chromatography (Varian CP-3800 GC equipped with a gas injection valve and an electron capture detector). For statistical analysis, boxplots were calculated using R4. 0. 1 and RStudio (n = 2). Data were analysed by the non-parametric Kruskal-Wallis test (p > 0. 05).
Acknowledgments The  French Loire-Bretagne Water Agency (AELB)  together with the BRGM financially supported this study through the  POLDIF Research  project led by  N. Baran. The authors thanks  Florian Koch ,  Benjamin Maurice  and  Mickael Charron  for their precious asssitance in the field,  Marine Gremont  for her synthesis of the measures for recovering water quality. All colleagues from the  BRGM Labs  involved in the data production are thanked for their valuable work. Jean-Michel Schroetter  is warmly thanked for the many fruitful discussions on the local geological settings and how they can govern water circulations and water quality. E. Petelet-Giraud  addresses special thanks to Gilles L. and Christophe G. for their fruitful comments on fig. 8. We are grateful to  Dr. H. M. Kluijver  for proofreading and editing the English text.
Small ponds and large lakes are considered at the catchment scale using similar procedures and equations in the Soil and Water Assessment Tool (SWAT) model  [24, 73]  (Figure  2 ). In addition, the ability of buffering objects to dissipate pollutants is addressed without any validation steps. This is mainly due to the discrepancy between the availability of consistent datasets and the modelling requirements, which increases the uncertainties of pesticide transfer predictions at the catchment scale. In this context, we propose an integrated modelling framework in the five steps described in Figure  1  and Tables  1  and 2. The framework covers four nested scales, from the SWI of ponds up to the river basin (Figure  2 ). 1 Based on Stokes-Darcy-Brinkman conceptualization  [74] ; 2 Reactive transport (e.
g. ,  [43] ); 3 Multi-compartment models  [72] ; 4 Adapting the behavior of pesticide and ponds compartments in SWAT  [75] , with empirical relationships (0D modelling) derived from the results of the 2D models at the pond scale. The 0D modelling attributes a dissipation rate to each pesticide, depending on the measurable variables and the ponds' characteristics and connectivity; 5  [76] [77] [78]. 6 [75]; 7 following the approach developed by  [79] ; 8 A detailed mass balance can be performed from the existing databases to identify the optimal formalisms and provide a diagnosis with existing data from upstream catchment and ponds. Datasets are obtained from the well-referred experimental sites of the PESTIPOND project (Figure  S1  and Table  S1  of the Supplementary Materials). The PESTIPOND experimental sites belong to the RI-OZCAR (Research Infrastructure-Critical Zone Observatories: Research and Application  [80] ), the RECOTOX network (Research initiative in toxicology/ecotoxicology  [81] ), the RI-RZA (LTSER Zones Ateliers network  [82] ), and/or the European RI-eLTER (European Long-Term Ecosystem Research).
Such an approach has been recently developed for phosphorus mitigation in constructed wetlands  [90]. A sub-daily time step should be used to represent accurately hydraulic dynamics and pesticide distribution in pond compartments during high-flow events. This requires data of inlet pesticide concentrations acquired at high temporal resolution, which is generally missing, to feed into the pond model. In addition, pesticide dissipation and pesticide export from the ponds should be examined under both low-flow conditions between hydroperiods (i. e. , the portion of year the system holds ponded water), and high flow conditions during transient hydroperiod.
Step 4: Adapting Agro-Hydrological Models to Integrate the Role of Ponds The role of "off-fields" buffer objects, such as grass strips, vegetated ditches, riparian zones and ponds or dams, in mitigating runoff, erosion and associated nutrients and pesticides fluxes is only partly accounted for in current agro-hydrological models  [24, 97, 98]. This is because (i) the calibration and the evaluation of their buffering effect remains delicate, (ii) consistent datasets are generally missing and (iii) current agro-hydrological models require adaptation. Comprehensive catchment models, such as SWAT  [99] , cover a wide range of processes for representing relevant hydrological and pesticide related processes  [100]. Among 36 widely used pesticide models  [101] , SWAT may be particularly well-suited for modelling pesticide reactive transport under a wide variety of conditions  [100].