dataesr/scientific-paragraphs-categorization huggingface dataset:
The effects of dams on streamflows could then be studied with ORCHIDEE and isolated from the effect of climate factors and irrigation demands. Different from classical approaches separating the YRB into an upper, middle, and lower stream  (Tang et al. , 2008; Zhuo et al. , 2019) , here we further divide both the upper and middle streams into subcatchments based on the locations of five key gauging stations (Fig. 1 ). This approach splits regions with and without big dams (or large irrigation areas) in the upper and middle streams, which simplifies the assessment of the effects of irrigation and damming on streamflows. In this study, ORCHIDEE, with the novel crop-irrigation module  (Wang, 2016; Yin et al. , 2020)  and the new dam operation model, was applied in the YRB from 1982 to 2014 in order to (1) demonstrate whether ORCHIDEE and the dam model, with generic parameterizations, are able to improve the simulation of streamflow fluctuations and (2) attempt to separate the effect of irrigation and dams on the fluctuations of monthly streamflows.
We first describe the OR-CHIDEE model and our new dam model in Sect 2. 1. Then we present the algorithm used for estimating sub-catchment water balances in Sect. 2. 2, followed by the input and evaluation datasets, the simulation protocol, and metrics for evaluation in Sect. 2. 3 to 2. 5. Results are presented in Section 3, and limitations are discussed in Sect. 4.
Methodology 2. 1 ORCHIDEE land surface model used in this study 2. 1. 1 Irrigation and crop modules ORCHIDEE is a physical process-based land surface model that integrates the hydrological cycle, surface energy balances, the carbon cycle, and vegetation dynamics using two main modules. The SECHIBA (surface-vegetationatmosphere transfer scheme) module simulates the dynamics of the water cycle, energy fluxes, and photosynthesis at a 0. 5 h time interval, which are used by the STOMATE (Saclay Toulouse Orsay Model for the Analysis of Terrestrial Ecosystems) to estimate vegetation and soil carbon cycle at daily time step. The ORCHIDEE model used in this study is a special version with a newly developed crop and irrigation module  (Wang et al. , 2017; Wu et al. , 2016; Yin et al. , 2020). The crop module includes specific parameterizations for wheat, maize, and rice, calibrated over China using observations  (Wang, 2016; Wang et al.
Simulation protocol The 0. 5 • half-hourly GSWP3 atmospheric forcing  (Kim, 2017)  was used to drive ORCHIDEE simulations. Yin et al. (2018)  used four atmospheric forcing datasets to drive OR-CHIDEE to simulate soil moisture dynamics over China, and they found that the GSWP3 provided the best performances; hence we chose this forcing for this study. A 0. 5 • map with 15 different plant functional types (PFTs) containing crop sowing area information for the three PFTs corresponding to the modeled crop (wheat, maize, and rice) is used, based on a 1 : 1 million vegetation map and provincial-scale census data of China. Crop planting dates for wheat, maize, and rice are derived from the spatial interpolation of phenological observations from the Chinese Meteorological Administration  (Wang et al.
The effect of dams may be still underestimated because we only considered simple regulation rules based on inflows but ignored its interactions with irrigation demand downstream. Moreover, our analysis showed that several reservoirs on the Yellow River are able to influence streamflows interannually. However, such effects are not quantified due to a lack of knowledge of the regulation rules across our study period. Code and data availability. The code of ORCHIDEE can be assessed via https://forge. ipsl. jussieu. fr/orchidee/browser/branches/ ORCHIDEE-MICT  (Yin et al. , 2020). The data used in this study, and the code of the dam operation model, analysis, and plotting can be accessed via https://doi. org/10. 5281/zenodo. 3979053 (Yin, 2020). The GLEAM ET data can be downloaded from http://gleam.
eu  (Global Land Evaporation Amsterdam Model, 2021; Martens et al. , 2017). The MSWEP precipitation data and the PKU ET are available from http://gloh2o. org  (GLOH2O, 2021; Beck et al. , 2017)  and Zhenzhong Zeng  (Zeng et al. , 2014) , respectively, and can be obtained upon reasonable request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-25-1133-2021-supplement. Author contributions. ZY, CO, and PC designed this study; ZY and XW contributed to the model development; ZY, FZ, XW, XZ, YB, and YX prepared observation datasets; ZY performed model simulations and primary analysis and drafted the manuscript; all authors contributed to results interpretation, additional analysis, and manuscript revisions.
(Yin et al. , 2020)006)ial reservoirs on the YR with considerable total capacity. Data are mainly from the YR Conservancy Commission of the Ministry of Water Resources (http://www. yrcc. gov. cn, last access: 28 February 2021). The "regulation purposes" follow the style ofHanasaki et al. (2006). "H"indicateshydropower;"C"indicatesfloodcontrol;"I"indicatesirrigation;"W"indicates water supply; and "S" indicates scouring sediment. The total capacity shrink is due to sedimentation. tions. Several precipitation (P ) and evapotranspiration (ET) datasets were selected to evaluate the simulated water budgets in each sub-catchment of the YRB. The 0. 5 • 3-hourly precipitation data from GSWP3 (Global Soil Wetness Project Phase 3) used as model input are based on GPCC v6 (Global Precipitation Climatology Centre;Becker et al.
, 2013)after bias correction with observations. The MSWEP (Multi-Source Weighted-Ensemble Precipitation) is a 0. 25 • 3-hourly P product integrating numerous in situ measurements, satellite observations, and meteorological reanalysis(Beck et al. ,  2017). Three ET datasets are chosen for their potential ability to capture the effect of irrigation disturbance on ET(Yin et al. , 2020)(noted as ET obs ). GLEAM v3. 2a (Global Land Evaporation Amsterdam Model;Martens et al. , 2017)provides 0. 25 • daily ET estimations based on a two-soil layer model in which the top soil moisture is constrained by the ESA CCI (European Space Agency Climate Change Initiative) Soil Moisture observations. The FLUXCOM model(Jung et al. , 2009)upscales ET data from a global network of eddy covariance tower measurements into a global 0.
Acknowledgements. We would like to thank the editor and the two anonymous referees for their insightful comments and efforts. We gratefully acknowledge the GLEAM team, the FLUXCOM team,  Hyungjun Kim ,  Hylke Beck , and  Zenzhong Zeng  for their selfless sharing of their datasets. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-15-CE01-00L1-0L ), the  National Natural Science Foundation of China  (grant no. 41561134016 ), and the  French Space Agency  ( Centre National d'Etudes Spatiales)  through the  TOSCA-SPAWET program  in preparation for the international SWOT space mission. Feng Zhou  was supported by the  National Key Research and Development Program of China  (grant no. 2016YFD0800501 ). Review statement. This paper was edited by  Xing Yuan  and reviewed by two anonymous referees.
Particles The selected PMMA (polymethyl methacrylate) particles had a fixed density ρ p = 1190 kg•m -3 and different diameters d p ranging from 150 and 640 µm. The PMMA particles were initially sold as a mixture with a poorly sorted grain size distribution. The particles were, therefore, previously sieved to obtain six grain classes with narrow size distributions; see Figure  2. The particle diameters were evaluated using microscope imaging analyzed with a specific software (open-source software ImageJ, imagej. nih. gov). Size statistics for the six selected classes are reported in Table  2. A particle diameter distribution was defined starting from a list of diameter sizes within the selected sieve. Than, they were ranged and normalized by the total amount of the sampled diameters. The d x diameter is the diameter at which x% of particles are finer and (100-x)% of particles are coarser.
A single acquisition typically lasted between 100 and 200 s, producing between 2500 and 4000 images depending on the frame rate. A systematic background removal was applied on each image to eliminate spurious reflections and regions with non-uniform illumination. The resulting images were then binarized by applying a proper threshold based on the image intensity to ease subsequent analysis. The processed images were analyzed using the tracking software MotionStudio TM (Integrated Design Tool, Inc, Pasadena (CA), 91103-3070, USA). The tracking procedure started manually by indicating the features that the software shall track in the image sequence and setting a small area around the interested particle, namely an Interrogation Window (IW). The tracking algorithm is based on a cross-correlation of the IW between successive pairs.
e. , H/gT 2 = 0, and thus, the settling occurs as in still water. In this limit case, we decided to describe the settling velocity by the well established Dietrich formula, which we recall here for the sake of clarity. The Dietriech settling velocity w D s can be estimated as: w D s = g g d P R f (5) where R f is: R f = exp -b 0 + b 1 ln(R P ) -b 2 [ln(R P )] 2 -b 3 [ln(R P )] 3 + b 4 [ln(R P )] 4 (6) in which the coefficients b 0 -b 4 are fitted on experimental data and assume the values: 2. 89139, 0. 95296, 0. 05683, 0. 00289, and 0. 00024. Note that the original values of the coefficient were changed in order to implement the natural logarithm instead of the base-10 logarithm, as in the original form  [39]. Starting from the measured non-dimensional settling velocity defined as the ratio between the experimental net settling velocity w m s and the velocity w D s provided by Equation (  5 ), we intend to represent the dependency on the main parameters, namely the particle Reynolds number R p and the wave parameter H/gT 2 , using the following formulation: w m s w D s = 1 + aR b P H gT 2 c (7) where the coefficients a, b, and c are evaluated as the best fit on the experimental data.
A non-linear surface fitting is applied, based on an iterative trust-region-reflective least squares algorithm  [42] [43] [44]. Applying this procedure on the experimental data, using Equation (  7 ), leads to the values of the parameters a = 97, b = 8/5, and c = 3/5. The fitted surface is shown in Figure  8  together with the experimental data. The goodness of the non-linear fit is quantified using several statistical metrics, in particular the Sum of Squares Due to Error (SSE), R-squared (R 2 ), and the Root Mean Squared Error (RMSE). For the present database, we obtained SSE = 0. 1798, R 2 = 0. 9108, and RMSE = 0. 0787. The small values of SSE and RMSE indicate that the model used to describe the dataset through Equation  (7)  shows a small random error component, implying that the model is robust in terms of prediction in the parameter space.
Data collection and analysis Data were collected on a spreadsheet (Microsoft Excel ® ), and measurements processed using R ™ (R i386 3. 6. 2) and RStudio ™ software. The ITS-2 and partial flanking regions of the 5. 8S and 28S rRNA nucleotide sequences were run through the Basic Local Alignment Search Tool (BLAST ™ , https:// blast. ncbi. nlm. nih. gov/ Blast. cgi) to look up the reference sequences for each GIN species. A sequence identity threshold of > 99% was chosen for the assignment of each obtained nucleotide sequence to its respective reference sequence. The sequence chromatograms were checked individually through the FinchTV viewer (Geospiza, Inc. , Seattle, WA, USA) and were subjected to multiple alignment along the reference sequences. The sequences obtained for each species in this study were deposited in the GenBank database (Additional file 1: Table  S1 ).
Results Faecal culture revealed the presence of a mixed GIN population for each of the 15 samples, consisting of larvae belonging to at least two sheathed tail groups. Based Based on the BLAST algorithm, three species were identified within the genus Trichostrongylus, i. e. Trichostrongylus colubriformis (14/24, 58. 3%), Trichostrongylus vitrinus (5/24, 20. 8%) and Trichostrongylus axei (5/24, 20. 8%), having an identity matching 100% to the ITS-2 and partial flanking regions of the 5. 8S and 28S rRNA sequences in the GenBank database with the accession numbers KY355062 and KC521364 (T. colubriformis), KC998732 and MK271675 (T. vitrinus), and AY439026 and MK936874 (T. axei). The nucleotide sequences for T. circumcincta were 100% identical to its reference sequences (JQ989274 and KF989498).
ITS-2 and partial flanking regions of the 5. 8S and 28S rRNA sequences for the genus Cooperia revealed the presence of a single species, Cooperia curticei (22/22, 100%), having a matching sequence identity of 99. 3% to the nucleotide sequences (KC998738 and KC998736) deposited in the NCBI database. The sequences for H. contortus were 100% identical to the nucleotide sequences with the accession numbers MN845169 and MF398432. For C. ovina, BLAST search yielded 100% homology to the two sequences (accession numbers: KJ420887 and KF913470). A complete sample list including microscopic and molecular identification as well as the respective accession numbers for each species (MZ323365-MZ323371) can be found in Additional file 1: Table  S1. Based on sheathed tail length, 91. 8% (90/98) of larvae were correctly assigned to their respective preliminary groups.
Nevertheless, the identities of only two of the larvae microscopically classified within group B turned out to be incorrect. In both cases, H. contortus larvae were misidentified as Cooperia spp. Revision of the morphometric data of said larvae showed these two to have sheathed tails exceeding 65 µm (the only two within the Cooperia spp. dataset), thus providing additional credibility to the above-mentioned morphometric key. The authors would like to underline that, although sheathed tail length was found to be a good differentiating trait between C. curticei and H. contortus, this is most likely not the case for all Cooperia spp. For example, Cooperia oncophora is reported to have a sheathed tail very similar in size to that of H. contortus  [13, 19, 24] , and thus definite differentiation in mixed Cooperia samples or samples where C.
larvae here most likely resulted from the unreliable differentiating morphological and morphometric traits explained above. As neither the number and shape of intestinal cells nor sheathed tail filament length could be utilized, identification of Oesophagostomum spp. was based mainly on sheathed tail length, and the nine larvae in question thus represent the larvae with the longest sheathed tail within our database. Nevertheless, no larvae with a sheathed tail exceeding 160 µm were observed. However, as Oesophagostomum spp. is rarely encountered in Sardinia  [1] , the identification of the larvae within this genus was uncertain from the start, and the larvae in question were included solely in an effort to include Oesophagostomum spp. within this research. A more sensitive diagnostic technique like deep amplicon sequencing could possibly have detected the presence of Oesophagostomum spp.
within our samples, as was the case for Cooperia spp. in a Canadian study where this particular genus of GIN is uncommon  [17]. Finally, the misidentification of Oesophagostomum spp. larvae led to the occurrence of additional mistakes within our database. More specifically, underestimation of the upper limit of the sheathed tail length of C. ovina resulted in an underestimation of the lower limit of the sheathed tail length of said species as well. Consequently, six larvae having a sheathed tail length between 80 and 100 µm were wrongly identified as C. ovina while actually being H. contortus. However, current scientific literature does report the upper limit of the sheathed tail length of H. contortus to be 78-82 µm  [12, 19, 20, 27] , clarifying these mistakes.
This verification assures our ability to employ geochemical speciation codes to handle isotopic fractionation for a large variety of elements. The validity stems from the typically small deviations of relative concentrations and small differences in behavior for different isotopes. We illustrate these developments with an application case of zinc isotope fractionation modeling during sphalerite precipitation performed with the reactive transport code Hytec. The model scripts used for the application case are provided in this study and offer the opportunity for future benchmarking of software in application to explicit modeling of stable isotope fractionation.
Implementation of isotopic fractionation then means that each isotope must be created as a primary component, with all necessary attendant equilibria described. This exercise is usually performed by duplicating entries within the thermodynamic database, so that each isotopologue is considered as a fully-fledged chemical species  (Druhan et al. , 2013; Druhan et al. , 2019; Wanner et al. , 2015) ,  (Druhan and Maher, 2014; Druhan and Maher, 2017). Kinetically controlled reactions are modeled by reactive transport codes by defining the parameters of a selected kinetic law linking two or more components. When modeling kinetic isotopic fractionation, the common strategy is consistent with equilibrium reactions and involves duplication of isotopes as unique 'species' in the database so that they may be assigned different rate constants depending on the isotopologue involved in the reaction to reproduce kinetic isotopic fractionation.
Duplication of isotopologues in the database In the following equations, E i stands for a basis species (mol. kg -  ), E for a derived species (mol. kg -  ), γ for the activity coefficient, N c is the number of basis components and ν represents the stoichiometric coefficients. We hereby present the general formalism, which is appropriate to any isotope system of interest. For the sake of clarity, we also offer an example: isotopes of sulfur in anhydrite and sulfates (where 32 S can be considered as a major isotope and 34 S as a minor isotope of sulfur). Critically, this choice is intended only for the sake of clarity and does not restrict the derivation to this context. Let us consider the following chemical reaction: ∑ Nc-1 i=0 ν i E i ⇌E, (1) and its associated equilibrium constant: K = γ⋅E ∏ i γ νi i E νi i (2) For the specific case of CaSO 4 formation, it becomes: Ca 2+ + SO 2- 4 ⇌CaSO 4(s) , ( 3 ) K CaSO 4(s) = γ CaSO 4(s) CaSO 4(s) γ Ca 2+ Ca 2+ ⋅γ SO 2- 4 SO 2- 4.
Simplification based on major isotope hypothesis Where the major isotope assumption holds true, the isotopic ratios are very small (compared to 1) and it is reasonable to assume that the thermodynamic constant K of the major isotope (K 32 in our example) equals the generally known constant for the bulk chemical species, as is often already available in geochemical databases. Physically, this leads to the assumption that there is one isotope whose abundance is predominant compared to others. This assumption, which is valid for many elements (like C, N, O, Ca, K, S, Fe, Li, …), yields a condition under which R λ << 1 and R 0 λ << 1 so that ∑ λ R λ << 1 and ∑ λ R 0 λ << 1. As a consequence,: K 0 ≃ K (10) For our CaSO 4(s) example, we can safely assume that R CaSO4(s).
Second, the event duration, key to convert the total erupted mass (TEM) in the mass eruption rate (MER) and vice versa, varies depending on the sensor used, providing information on different phases of the paroxysm (i. e. , unsteady lava fountaining, lava fountain-fed tephra plume, waning phase associated with plume and cloud expansion in the atmosphere). As a result, TEM and MER derived from different sensors also correspond to the different phases of the paroxysms. Finally, satellite retrievals for grain-size can be combined with radar data to provide a first approximation of total grain-size distribution (TGSD) in near real-time. Such a TGSD shows a promising agreement with the TGSD derived from the combination of satellite data and whole deposit grain-size distribution (WDGSD).
We compiled an isomass map from measurements of ground accumu across the entire deposit. The TEM associated with the tephra-fallout deposit ( obtained based on the most used strategies in literature, i. e. , by integrating the best fit  [40] , the power-law best fit  [41] , and the Weibull best fit functions  [42]  data versus the square root of isomass contours (Figure  2 ). The average MER derived by dividing the TEMdep obtained by the three integration methods by of the tephra plume emission based on the two most meaningful remote sen for this phase (i. e. , MWR and satellite-based data; see following sections for m 2. 1. 2. Whole Deposit Grain-Size Distribution All tephra-fallout samples were manually sieved down to 4 Φ (i. e. , 63 m obtained the mass of all individual sieved fractions, i.
Total Erupted Mass and Mass Eruption Rate We compiled an isomass map from measurements of ground accumulation (g/m 2 ) across the entire deposit. The TEM associated with the tephra-fallout deposit (TEM dep ) was obtained based on the most used strategies in literature, i. e. , by integrating the exponential best fit  [40] , the power-law best fit  [41] , and the Weibull best fit functions [42] of mass/area data versus the square root of isomass contours (Figure  2 ). The average MER dep was then derived by dividing the TEM dep obtained by the three integration methods by the duration of the tephra plume emission based on the two most meaningful remote sensing systems for this phase (i. e. , MWR and satellite-based data; see following sections for more details).
Whole Deposit Grain-Size Distribution All tephra-fallout samples were manually sieved down to 4 Φ (i. e. , 63 microns). We obtained the mass of all individual sieved fractions, i. e. , each half Φ class, using a 10 -4 g resolution weighing scale (Figure  A1 ). We determined the size distributions of all samples having a mass of ≤1 g using a BETTERSIZER morpho-grainsizer (https://www. 3pinstruments. com/analyzers/bettersizer_s3_plus/; accessed on 26 May 2021). We applied the Voronoi Tessellation method of Bonadonna and Houghton  [41]  to compute the whole deposit grain-size distribution (WDGSD) by using a dedicated Matlab application  [43].
Plume Height Satellite-based observations made each 15 min by the geostationary MSG-SEVIRI platform are widely used to retrieve volcanic plume heights  [7, 8, 49, 50]. At Etna, they are retrieved by applying the dark pixel procedure  [51]  over an area of 729 km 2 (9 × 9 pixels of 3 km resolution) centered on Etna's summit craters  [6, 7]  (Figure  1a ). Assuming that detected plumes are in thermal equilibrium with the surrounding atmosphere, the volcanic plume heights can be derived from the comparison between the darkest pixel (the pixel with the lower brightness temperature computed at 11 µm) of the selected area and the ARPA atmospheric profiles available at INGV-OE every 6 h  [8, 20, 52]. When available, we also use data obtained by MODIS onboard the NASA Terra and Aqua polar-orbiting platforms.
Plume Height Estimates As previously described, plume height can be independently determined at Etn based on at least three different remote sensing systems that are complementary in term of detection limits and space-time resolution (Visible Camera, Satellite retrievals-MOD and SEVIRI, and X-Band radar-MWR) (see Figure  3 ). Even though the determination of H T by the visual camera (ECV) was not possible after 11:20 UTC due to the presence of meteorological clouds, continuous detection during the whole 10 April 2011 paroxysm was possible with the MWR and SEVIRI (Figure  3a ). In addition, a punctual measurement obtained at 12:30 UTC using MODIS data is also available. Heights obtained from ECV images (Figure  1b ) are derived from 10:10 to 11:20 UTC  [22, 30]. Starting from 5 km a. s. l. , H T increases rapidly and starts oscillating between 8 and 9 km a.