dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This study is supported by the  EU H2020   RINGO  project. The TCCON site at Réunion Island is operated by the  Royal Belgian Institute for Space Aeronomy  with financial support in 2014, 2015, and  2016 ,  2017  under the  EU  project  ICOS-Inwire and the ministerial decree for ICOS  ( FR/35/IC2 ) and local activities supported by  LACy/UMR8105 -Université de La Réunion. We want to thank  Francis Scolas  ( BIRA-IASB ) for maintaining the TCCON measurements at  St Denis ,  Juha Hatakka  and  Tuomas Laurila  ( FMI ) for operating Atmos. Meas. Tech. Discuss. , https://doi. org/10. 5194/amt-2019-94Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 23 April 2019 c Author(s) 2019. CC BY 4. 0 License. the in situ and AirCore measurements at Sondakylä. We would also like to thank  TCCON  network for making the data publicly available.
, 2016). The MCM was implemented in Matlab (C) based on  Wolfe and Thornton (2011). The red inset denotes a hypothetical trajectory from NO x -rich urban conditions to BVOC-rich rural conditions in Innsbruck, Austria. A left shift of the ozone forming potential curve (gray dotted line) indicates higher ozone pollution in suburbs, since the NMVOC:NO x ratio reaches optimal values to form ozone (blue box). Youngsteadt et al. , 2015). Reduced emissions due to higher CO 2 concentrations and more frequent drought stress are not likely to reverse this trend, particularly since trees are often irrigated in arid and semiarid regions or during heat wave events in order to sustain their cooling function. However, estimating the net effect of urban trees on ozone concentrations, deposition would need to be taken into account as well, which would be enhanced by more trees.
One control chamber without any organisms was used for measuring background noise signals. Concerning density measurements, four sediment samples from both areas (Site 1 and Site 2) were randomly collected at low tide using plastic cores (10 cm x 35 cm) at a distance of 10 m from each other. Sediment was sieved through a 1 mm mesh to collect and count worms. Fresh weight of H. diversicolor individuals was determined to establish biomass. An average of 4 biomass replicates was calculated as the sum of fresh weight (g FW) divided by the sediment surface sampled (m 2 ) and expressed as FWg m -2. Statistical analysis for burrowing kinetics was performed on R (R Core Team 2014) using a generalised nonlinear model with α as burrowing rate (h -1 ). This generalised non-linear model with a conditional binomial stochastic part was fitted to each data set by using Bayesian inference as described in  Buffet et al.
(2014). For all the other parameters, results are presented as mean ± standard deviation, and statistical analyses were performed using XLSTAT 2012. Normality was assessed using the Shapiro-Wilk test. To define significant differences between sites for each sampling date, normally distributed data were analyzed by one-way ANOVA (multiple comparisons of mean values) followed by Tukey's HSD post hoc method. When normality was not established, the nonparametric Kruskal-Wallis and Mann-Whitney comparison tests were used. The level of significance was established at p < 0. 05. A Spearman's correlation  (Daniel, 1990)  was computed to quantify the relationship between AChE activity and behavioural toxicity parameter values. The Spearman's rank-order correlation is a non-parametric correlation coefficient ρ, measuring strength and direction of the monotonic relationship between AChE activity and MFB.
g. , assessing the impacts of climate and management changes). The scales of these models can vary from field-scale (APSIM model  [14] ) to large scale (INCA model  [15] ) and the time step can vary from minutes, days  [16, 17] , months, years  [18, 19] , to even statics  [14]. Over the past 15 years, the most commonly used hydrological model has been the Soil and Water Assessment Tool (SWAT) based on the Scopus environmental field database containing 3282 scientific papers using SWAT  [8]. SWAT  [16]  is an open-source model with a large number of possible applications as seen in various studies  [8, 20, 21]. SWAT can be applied from local to continental scale  [9, 22]. SWAT incorporates different modules issuing from modified stand-alone models such as QUAL2E and EPIC modules-developed by Brown and Barnwell  [23]  and Sharpley and Williams  [24]  respectively to simulate water quality and crop production.
Moreover, management practices application is a strength of SWAT through the multiple options of inputs such as fertilizer, tillage practice, irrigation, and management practices. Finally, the SWAT model is also able to integrate urban and point sources inputs. Developed by the United States Department of Agriculture (USDA), SWAT has been used around the world to simulate water quality and quantity  [8, 22] , for large scale on finer resolution such as the U. S. territory with the "Hydrologic Unit Model for the United States"  [25, 26]  or the whole Europe  [9]. Abbaspour et al. [9]  demonstrate the feasibility of applying SWAT at the continental scale in Europe with a subbasin resolution and at monthly time steps. At the continental scale, the main restrictions are the lack of harmonized observed data and the lack of modelers' knowledge on the functioning of all the watersheds to calibrate the model  [9].
Human activities affect water resources  [27, 28]  such as water used for irrigation, hydroelectric dam operations, or artificial pond feeding. A model does not initially integrate this level of complexity but the calibration can help to incorporate the human influence into the model. Hence, a dataset and tools to calibrate the model are therefore important if the model is to accurately represent water stocks and fluxes in the different compartments of the hydrological cycle. Such calibration approach has been shown to be necessary in order to enhance the management of water resources especially in the context of global change  [29] [30] [31]. This study provides suggestions on the model set up choice and the calibration and validation procedures according to the level of the Index of Hydrological Alteration (IHA)  [32]  over a large-scale-transborder-European region.
Several studies pointed out the issue of water scarcity in South-Western Europe  [2, 33, 35]. It is thus essential to have a good representation of the volume of irrigation in the model, as irrigation volume will constrain water balance, especially in climate change and cultural practices impact studies. In the present study, we suggest an additional validation method of water balance through the comparisons of spatialized water irrigated or/and crop yield with national datasets. This additional validation might be useful for studies with limited documentation and a lack of financial support. One possible approach would be exploring the potential correlation between the calibration/validation procedure and the IHA  [32]. The IHA characterizes the impact of river regulation from anthropogenic activity such as dams and irrigations on the flow regime compared to the natural regime.
Input Data A large database compilation has been gathered to study the transnational SUDOE area. Data sources, as well as their spatial and temporal resolutions, are displayed in Table  1. CC and AC are using the same model set up and have the same input data. A global 1-km resolution land surface Digital Elevation Model (DEM), derived from the United States Geological Survey (USGS), was used to described topography and quantify slopes. Such DEM has been selected as a compromise between the large-scale model setup and the computation resources available. Land cover was available from Corine Land Cover map 2012  [39]. Soil characteristics were defined from the Harmonized world soil map that encompasses the dataset of the European Soil Database. Daily precipitation and temperature originate from the MESoscale ANalysis (MESAN) climate reanalyzes spanning Europe for the 1989-2010 period  [55, 56].
Urban Sewage Urban sewage localizations and characteristics (maximum capacity, annual load) were taken from the 2010 European dataset  [60]  including information about all European cities. Every wastewater treatment plant (WWTP) with more than 50,000 equivalent inhabitants' capacity has been considered. Such as for dams, the WWTPs were aggregated by subbasin. A total of 44 point sources were integrated into the model (Figure  2 ). The annual spilled volume is deduced from the number of equivalent-inhabitants of selected WWTPs and the annual water consumption per inhabitant per country (France: 58. 04 m 3 year -1 , Portugal: 67. 46 m 3 year -1 and Spain: 47. 4 m 3 year -1 ; https://www. insee. fr, https://ine/pt, https://ine/es). In order to overcome the lack of nutrient loading from WWTPs at the European scale, a simplified version of the Zessner and Lindter  [61]  method has been used to estimate the annual nitrate loading with the available information about treatment plant.
Crop Management Interannual average of monthly irrigation volumes for the South of France and Spain were given by the Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)  [62] [63] [64] , the Compagnie d'Aménagement des Coteaux de Gascogne (CACG) and the Spanish Hydrological National Plan  [65, 66]. Crop management data were obtained from the national database of agriculture statistics from each country. The technical management and fertilizer application for each crop in each region originate from the recommendations provided by the French Chambers of Agriculture (FCA), the French and Spanish Directorate-General for Agriculture and Rural Development (DGADR) and the Spanish Confederations (SC). Crop management were applied to HRUs according to the recommendations of FCA, DGADR, and SC.
The harvest is set at 1. 2 heat units. The harvest heat unit is superior to 1 because this study considers that there is always a time lag between the optimized harvest time and the true harvest  [47]. In that case, between 1 and 1. 2 heat units, the plant consumes energy without producing more. The model used to calibrate CC and AC has the same setup. For both calibrations, the irrigation was set up as "auto-irrigation". For CC and AC calibration we enter a volume at each auto-application and a maximum volume. The exact volume to apply is determined by the model according to the parametrization (plant water demand or soil water content). However, for the AC, crop management routing, simulated irrigation volume, crop yield, and biomass were calibrated and validated with national statistic datasets (Table  1 ) whereas, for CC, these model outputs were not calibrated.
The Load Estimator model (LOADEST)  [78]  was used for each physicochemical station due to the lack of nitrate and sediment data in order to generate monthly nitrate and sediment load and extend time series, as commonly used to calibrate and validate SWAT outputs  [79] [80] [81]. This software generates water quality parameters through regressions between streamflow and water quality  [78]. A statistically-based optimization procedure identifies the most accurate of the eleven regressions. The Spearman's rank correlation coefficient (ρ,  [82] ), the non-parametric test measuring the dependence between two variables, is used to compare LOADEST simulation with observations. LOADEST simulations give sediment loads (ρ = 0. 94 and p-value < 0. 001) and nitrate loads (ρ = 0. 99 and p-value < 0. 001) close to observations (Figures  S2  and S3  in Supplementary Materials), even if the LOADEST model slightly underestimates the sediment load (slope = 0.
71). LOADEST provides us interpolated water quality data that were used to calibrate and validate the SWAT model on a monthly scale in addition to the observed data. The R 2 statistic is used to evaluate the agreement of the nitrate and sediment load simulation between LOADEST and SWAT models for calibration  (2000) (2001) (2002) (2003) (2004) (2005)  and validation periods (2006-2010).
The Additional Calibration (AC) From the CC procedure, an additional calibration (AC) we made by adding two calibrations steps: (1) the calibration of the "natural streamflow"-the streamflow that should occur without any anthropogenic pressures and (2) the calibration of the anthropogenic pressures (Fertilization application, crop yield, and irrigation volume) in order to improve the evapotranspiration and the uptake of plants. The validation step is the same procedure as CC using SWAT-CUP. Data used to validate AC were the "natural streamflow", the crop management data and the one used during CC procedure. As a reminder, hydrology, sediments and nitrate loads were calibrated  (2000) (2001) (2002) (2003) (2004) (2005)  and validated  (2006) (2007) (2008) (2009) (2010). To ensure the correct hydrological performance of the SWAT projects (Figure  2 ) calibrated with AC methodology, discharge outputs were compared with two different sources in annual and monthly time steps.
The first comparison was done with the observed discharge data at the subsystem scale (administrative entities, Figure  1B ). The second one was performed with the discharge values simulated with the "Integrated System for Rainfall-Runoff Modeling" model, commonly named SIMPA  [83]. The AC "natural" streamflow simulated by SWAT (the simulation where all human pressures were withdrawn) was calibrated by comparing it with SIMPA simulations, which simulated natural flows. SIMPA is a rainfall-runoff model, developed by the Center for Hydrographic Studies of the Spanish Ministry of Environment (CEDEX) to analyze the spatial and temporal hydrological variables and to simulate hydrological processes without anthropogenic pressures. In Spain, SIMPA model is a reference for public and private stakeholders  [84, 85].
To compare our SWAT model with SIMPA, the CC and AC calibrations were run after deleting all anthropogenic pressures (agriculture, urban inputs and dam management). Inter-annual and monthly "natural" water volumes were compared with SIMPA outputs in the 7 SUDOE main watersheds (Figure  2 ). At each calibration step, hydrology, sediments, nitrate loads, and dam and crop managements were re-verified and re-calibrated if necessary. Dam management, fertilization application, crop yield and irrigation volume were calibrated from 2000 to 2005 and validated from 2006 to 2010. Simulated irrigation volumes were compared to the annual national values. In Spain, where data were available, a comparison of irrigated volume at the regional scale has also been conducted. Moreover, a comparison between simulated crop yields, fertilizer amount applied and national statistics was made on an annual basis.
Climate Change and Agricultural Practices Changes In order to investigate the sensitivity of the model to climate change, the variability of the simulated outputs has been tested for a precipitation decrease of 20% over the period 1980-2010 based on the global precipitation change between 1971 and 2000 and the +2 • C period of Vautard et al. study  [86]. CC and AC calibrations with observed precipitation have been compared to outputs where the precipitations have been decreased between 2000 and 2010. Focus has been placed on water yield (mm), fertilization (kg ha -1 ), biomass (t ha -1 ), yield (t ha -1 ), and irrigation volume (Hm 3 •year -1 ) and a percentage of variation has been calculated for the outputs simulated by the reduced precipitation with respect to the normal precipitation dataset.
[77]. For validation period, the range of NSE, R 2 , PBIAS and KGE are respectively (-0. 48; 0. 80), (0. 39; 0. 95), (-41. 51; 3. 83), and (0. 1; 0. 79) (Table  3 ). The results are not satisfactory in terms of extreme values, however, the trends were adjusted appropriately. The global behavior of the multiple watersheds model is satisfactory to good, especially during low flow periods (Figure  4 ) considering the complexity of a multi-watershed model. NSE indicator is poor in some watersheds that are subjected to extreme values (e. g. , Duero in 2010, Guadiana in 2001 and 2010) or highly anthropized (e. g. , Jucar) (Figure  4 ). There are two major reasons for the poor NSE performances in some watershed: (1) reanalysis of MESAN as input climate datasets and (2) level of human stressors. NSE performances at Adour, Duero, Jucar, and Minho could be related to high uncertainties on the MESAN reanalyzes over the Pyrenees and over the South-Western coasts of Europe such as Aquitaine, Basque Country, Tajo, Duero, and Jucar regions  [56].
However, no data from these hydrological components (lateral flow, evapotranspiration, sediment yield) with a sufficient temporal and spatial resolution can allow the validation of one or the other calibration process regarding the distribution of water fluxes inside the water cycle. Additional validation was conducted with the comparisons of water irrigated and crop yields simulations and national datasets values. The total irrigated volume of AC (20,400 Hm 3 ) is close to observed values (21,100 Hm 3 ) (R 2 = 0. 87, p-value < 0. 001) compared to the CC simulations with the observed values (R 2 = 0. 37, p-value < 0. 001).
However, no data from these hydrological components (lateral flow, evapotranspiration, sediment yield) with a sufficient temporal and spatial resolution can allow the validation of one or the other calibration process regarding the distribution of water fluxes inside the water cycle. Additional validation was conducted with the comparisons of water irrigated and crop yields simulations and national datasets values. The total irrigated volume of AC (20,400 Hm 3 ) is close to observed values (21,100 Hm 3 ) (R 2 = 0. 87, p-value < 0. 001) compared to the CC simulations with the observed values (R 2 = 0. 37, p-value < 0. 001) which overestimates irrigated volumes by 36% (27,800 Hm 3 ). Irrigation volume has a substantial impact on water repartition over the watershed and, consequently, on the water balance between compartments (surface runoff (-114%; 28%) and evapotranspiration (-51%; 4%)).
The Jucar and Segura watersheds, the most important ones in the South East of Spain, characterized by a warm and dry climate  [93]  with low flow support based on water imported from other watersheds, thus highly anthropized. The SIMPA model (the reference in this study), which modeled upstream streamflow with low pressures, seems to support water volumes almost stable throughout the year whereas SWAT simulation shows a severe decrease of water during low flow (Figure  8 ). Overall, most of the watersheds have the same trend between models hence decreasing the water balance uncertainties. Model comparison, with models that had attained a level of familiarity and trust by stakeholders, is efficient when dataset availability is limited and show promise to evaluate scenarios and decision-making  [94, 95].
Nitrate Loads The interpolation of nitrate concentration with LOADEST gives satisfactory results (Supplementary Materials Figures  S2  and S3 ) according to  Kim et al. [96]  standards. LOADEST tends to overestimate extreme values as largely  [97] , but this work is focusing on the impact of global change on water resources and more precisely on the SUDOE water scarcity issues which mostly occur during low flow periods  [33]. Other studies already compared the nitrate loads simulated by LOADEST and SWAT models  [97]  and showed that the LOADEST software is useful when you have a discrete and large range of values under different hydrological conditions. The comparison of nitrate loads between SWAT versions and LOADEST at the main river outlets shows an overall good agreement with an R 2 of 0.
This study combines hydrological and agronomic calibration approaches in order to improve the model estimation thanks to multiple datasets with different scales and time resolutions. In this study, a suggestion is made: Calibrating the model with different data sources, easily available coming from national open-access sources such as national statistics department or water agency reports and samplings. The multiple and diversified databases used showed that the water balance and human activities were better represented in AC calibration. The multi-watershed model is complex. Sometimes a trade-off of one parameter needs to be found because watershed responses to the parameter could be opposite. The need to cross data for calibrating and validating models is underlined. This study should not be reduced to the SWAT model only but it could be extrapolated to every hydro-agronomic model.
Outputs from calibrated models come along with model uncertainties and assumptions. It is up to the analysts to quantify model prediction uncertainty and calibrate with their own field expertise of the watershed. In order to establish this methodology, running a soft calibration first and then launching an optimization procedure is advised. The soft calibration is a manual calibration based on watershed knowledge (characteristics, specificities). The optimization procedure is allowed to refine the parameters calibrated during the soft calibration, as a statistical calibration, for example, SWAT-CUP for SWAT  [69, 71]. This paper provides some tools and feedbacks on the SWAT model set up in order (1) to understand the importance of datasets choices in the calibration and validation steps, (2) to help future users in the large scale set up and (3) to choose the calibration/validation procedure.
Supplementary Materials: The following are available online at http://www. mdpi. com/2073-4441/12/1/115/s1, Figure  S1 : Comparison between observed nitrogen loads (coming from the UWWTP databases) and calculated one with the Zessner and Lindtner (2005) method, Figure  S2 : Sediment load regression between observation and LOADEST simulation at daily time step in kg d -1 , Figure  S3 : Nitrate load regression between observation and LOADEST simulation at daily time step in kg d -1 , Table  S1 : Parameters used to calibrate water quantity and quality in the SUDOE territory. The parameters to calibrate irrigated volume and crop yield were used only for the AC calibration, Table  S1 : Parameters sensitivity ranking for streamflow model outputs for each part of SUDOE territory for CC and AC models, Table  S2 : Sensitivity analysis for CC streamflow models outputs, Table  S3 : Sensitivity analysis for AC streamflow models outputs.
The Pitzer and electrolyte-NRTL models are employed to represent the nonideality of complex and concentrated solutions and offer a database which covers a diverse range of single electrolytes systems with the richest interactions parameters  (Messnaoui and Bounahmidi, 2005). The study of the thermodynamic properties of phosphoric acid has been the aim of several articles  (Christensen and Thomsen, 2003 Haghtalab and Nosrati, 1998 Jiang, 1996 Pitzer and Silvester, 1976 Rumpf and Maurer, 1994). For this type of complex electrolytes, various models have been developed to estimate the excess Gibbs energy and the corresponding adjustable parameters were defined from vaporliquid equilibrium experimental data and derived properties values (Gibbs free energy, enthalpy…). In this context,  Haghtalab and Nosrati (1998)  used the NRTL-NRF model to calculate the mean ionic activity coefficient and pH for various concentrations of H 3 PO 4 (Table  5 ).
It should be emphasized that the osmotic coefficients are evaluated on the basis of the total dissociation of H 3 PO 4 into PO 4 3-and H +. This must be indicated because the experimental values of the osmotic coefficients may have different definitions in the case of partial dissociation. From Fig. 7 , it can be noticed that the calculated values of the osmotic coefficient by  Cherif et al. (2000a)  and by  Jiang (1996)  (for the entire studied molality range from 0 to 9 mol. kg -  ) are in agreement with the experimental data  (Elmore et al. , 1946 Platford, 1975). Hence, for both mechanisms, the electrolyte-NRTL model  (Messnaoui and Bounahmidi, 2005)  showed high performance in estimating the osmotic coefficient for aqueous solutions of H 3 PO 4 from low molalities up to 36 mol. kg -1.