dataesr/scientific-paragraphs-categorization huggingface dataset:
Biotite The experimental dataset collected in this study of biotite dissolution includes 19 studies with 129 data points. These studies focused on biotite having compositions ranging between its annite and phlogopite endmembers; most of these reported rates were measured at temperatures near 25 • C and over the pH range of 1 to 8. 5  (Sverdrup, 1990; Acker and Bricker, 1992; Kenoyer and Bowser, 1992; Kalinowski and Schweda, 1996; Malmström et al. , 1996; Malmström and Banwart, 1997; Murphy et al. , 1998; Taylor et al. , 2000; Voinot et al. , 2013). Samson et al. (2005)  reported rates at pH up to 14. 3. Biotite dissolution rates at temperatures up to 280 • C have been reported by  Carroll et al. (2017). Other studies considered natural biotite weathering at pH from 4. 0 to 6. 0 at ambient temperature.
Moreover, the rates from  Velbel (1985)  and  White et al. (2002)  were not considered in the regression either as the surface areas in these studies are likely to be overestimated. Experimental datasets not included in the regression are marked in circle or by arrow in Fig. 7  and Appendix. Fig. 7  compares measured rates with rates calculated using Eq. (  7 ) and parameters listed in Table  2. The dissolution rate equation for biotite describes the experimentally measured rate data with an average difference of 0. 43 log units; 50% of the measured dissolution rates are within 0. 35 log units of those calculated. Previous equations describing far-from-equilibrium biotite dissolution rates have been presented by  Acker and Bricker (1992) ,  Kalinowski and Schweda (1996) ,  Malmström and Banwart (1997) ,  Palandri and Kharaka (2004), and Bray et al.
Muscovite The experimental dataset for muscovite collected in this study includes 136 data points from 8 experimental studies. Nickel (1973)  described muscovite dissolution rates measured in stirred tank reactors at 25 • C and pH 0. 2, 3. 6, 5. 6, and 10. 6. These rates were normalized to BET surface areas by  Nagy (1995). Cabrera and Talibudeen (1979)  and  Kalinowski and Schweda (1996)  investigated muscovite dissolution rates at 25 • C over a 1 to 4. 1 pH range. Lin and Clemency (1981)  measured muscovite dissolution rates at pH 4. 6 and 5. 1 and CO 2 -saturated solutions in closed system reactors. Moreover, Swoboda-Colberg and Drever (1993) compared muscovite dissolution rates at <25 • C observed in the field and measured in the laboratory conditions. Hurd et al. (1979)  reported muscovite dissolution rates at 1 • C and pH 8 in seawater.
(  7 ) and the parameters listed in Table  2  at 25 • C as a function of pH. The symbol shape and outline color indicate the source of the data, whereas the shading of the symbol indicates the experimental temperature, with black symbols being 25 • C. The shaded regions in the plots illustrate the 95% confidence limits of the corresponding regression fits at 25 • C. and ease of acquisition. Rimstidt et al. (2012)  and  Oelkers et al. (2018)  summarized and reviewed olivine dissolution rate data and compared previous rate equations describing these rates. Some datasets are available for fayalite, with less than 10 mol% MgO in this site. Regressions of the experimentally measured rates of the olivine minerals were performed assuming two parallel rate mechanisms, one dominating at acidic conditions and the second dominating at basic conditions.
According to the results of  Shirokova et al. (2012) , the presence of both live and dead bacteria increases somewhat olivine dissolution rates. There is also some experimental evidence that the presence of organic acids might affect olivine dissolution rates  (Bailey, 1974; Wogelius and Walther, 1991) , insufficient systematic data are available to quantify such effects. Hence, the results of the latter two studies were not considered in the data regression. The study of  Siegel and Pfannkuch (1984)  was not considered due to the contamination of the mineral structure, and experimental data from  Grandstaff (1986)  were not considered due to ambiguities in quantifying the surface area of their forsterite grains. Note that experimental datasets that were not considered in the regression, including data from  Grandstaff (1977) ,  Siegel and Pfannkuch (1984) , and those affected by added organic acids and presence of CO 2, are marked in Fig.
Brantley and Chen (1995)  summarized the experimentally measured dissolution rates available at the time of this publication. Most of the data have been reported for acidic pH conditions. As previously mentioned for the case of amphiboles, the lack of experimentally measured data at basic pH leads to significant uncertainties in the fit of the data at these conditions. Similar to the amphiboles,  Palandri and Kharaka (2004)  proposed a two-term fit of the available data where rates are pH-independent at basic conditions, while the fits of  Knauss et al. (1993)  and  Brantley et al. (2008)  suggested that these rates continuously decrease with increasing pH. The current study adopted the former approach as it provides a better description of the available experimental data.
Clinopyroxenes The sources of experimentally measured far-from-equilibrium dissolution rates of diopside and augite dissolution are summarized in Table  7. The dataset consists of 11 studies, of which the majority focused on clinopyroxene dissolution at ambient temperature and over the pH range of 1 to 7. Some of these studies reported pyroxene dissolution rates at pH up to 12 and, or temperatures up to 90 • C  (Schott and Berner, 1985; Knauss et al. , 1993; Chen and Brantley, 1998; Golubev et al. , 2005; Stockmann et al. , 2008; Daval et al. , 2010). Several studies reported the effect of CO 2 and aqueous organic acids on clinopyroxene dissolution rates. Knauss et al. (1993)  reported diopside dissolution rates in the presence of 10 -3. 5 to 1 atm pCO 2 for pH 1-12 and temperature range 1 to 70 • C.
9  and include datasets from  Schott et al. (1981 ), Hoch et al. (1996 ), and Daval et al. (2010). The data of  Hoch et al. (1996)  were obtained from experiments on diopside containing substantial mineral impurities. The data of  Schott et al. (1981)  were obtained from experiments that did not attain a steady state. Finally, the data of  Daval et al. (2010)  were obtained at close to equilibrium conditions such that the chemical affinity term in Eq. (  7 ) could influence the measured rates. Fig. 9A -C compares the calculated versus measured far-fromequilibrium dissolution rates for diopside at 25 to 90 • C. The average difference between the calculated and measured rates is 0. 23 log units, and 50% of the experimentally measured rates are within 0. 19 log units of the corresponding calculated rates.
Orthopyroxenes The sources of experimentally measured far-from-equilibrium dissolution rates of enstatite and bronzite dissolution are summarized in Table  8. The dataset consists of 10 studies, of which the majority focused on orthopyroxene dissolution at ambient temperature and over the pH range of 1 to 7. Some of these studies reported pyroxene dissolution rates at pH up to 12 and, or temperatures of up to 168 • C  (Brady and Walther, 1989; Oelkers and Schott, 2001; Halder and Walther, 2011). Several studies reported the effect of CO 2 and aqueous organic acids on clinopyroxene dissolution rates. Bailey (1974)  and  Grandstaff (1977)  reported bronzite and enstatite dissolution rates in the presence of aqueous organic acids for the pH range 1 to 4. 1 and the temperature range 1 to 42 • C; however, they found no evidence that added organic Fig.
Quartz Experimentally measured dissolution rates of quartz have been previously compiled and reviewed in  Dove (1994) ,  Bickmore et al. (2006), and Rimstidt (2015). The experimental rate dataset in the present study includes 410 distinct rates presented in 24 publications. This dataset covers a temperature range of 25 to 430 • C and a pH range from 0 to 12. 3. Schwartzentruber et al. (1987) ,  Knauss and Wolery (1988) ,  Brady and Walther (1990), and House and Orr (1992)  investigated the effect of pH on quartz dissolution rates at 25 • C. Kamiya et al. (1974) ,  Rimstidt and Barnes (1980) ,  Bird et al. (1986) ,  Brady and Walther (1989) ,  Dove and Crerar (1990) ,  Casey et al. (1990) ,  Dove (1994 Dove ( , 1999)) ,  Hellmann (1994) ,  Tester et al. (1994) ,  Murphy et al. (1998), and Bickmore et al.
Due to the considerable effect of added (Na, K, Li)Cl, (Pb, Ca, Ba) Cl 2 , NaNO 3 , and organic ligands on quartz dissolution rates, these datasets were not considered in the regression calculations. Experimental data affected by the addition of these species are marked in Fig. 12  and Table  S1  in Appendix. As the regression of data in the present study is limited to temperatures up to 350 • C, the quartz rates reported by  Murphy and Helgeson (1989)  were also excluded from the regression calculations. Moreover, the study of  Kamiya et al. (1974)  does not provide the BET surface areas of their quartz; therefore, this dataset is not included in the regression calculation. Fig. 12A -C shows a comparison of quartz dissolution rates generated using equations and parameters of this study with corresponding experimentally measured rates.
Discussion This article provides a compilation of experimentally measured dissolution rates and regression equations describing the dissolution rates of 27 primary silicate minerals. Rates in this study were normalized to the initial mineral-fluid interfacial surface area measured by the BET technique. As such, any application of these rates to natural systems must take into account the differences between BET surface area and that of the natural system and any changes in this surface area over time. This may depend significantly on the hydrology of the system and the effect of biotic or abiotic mineral surface coatings. The rate equations generated in this study were added into the thermodynamic database carbfix. dat (Voigt et al. , 2018)  in the form of RATE blocks. This database can thus be directly used in the PHREEQC geochemical software (Table  2  and Appendix B) and can be readily adapted for other software such as TOUGHREACT.
However, aqueous solubilities for some of the minerals, and notably solid solutions including andesine, bytownite, labradorite, oligoclase, ferroactinolite, riebeckite, glaucophane, augite, and bronzite are missing in the carbfix. dat database. This has been resolved in the version of the kinetic database for PHREEQC by calculating the saturation index of these phases by assuming the ideal mixing of appropriate endmembers. Table  10  provides a summary of the temperature and pH range of the experimental data considered in the regression calculations of this study. Within the temperature and pH range listed in this table, the equations and parameters presented in this study provide rates that can be considered interpolations. It seems reasonable to use the regression equations to estimate the dissolution rates of the primary silicate minerals beyond these limits, over temperatures ranging from 0 to 300 • C and pH from 1 to 12.
Nevertheless, it should be kept in mind that the likely uncertainties in the rates will increase as one extrapolates rates beyond the limits of the experimental data. What is clear when reviewing this effort to create a consistent mineral dissolution rate database is that, despite decades of experimental studies, large uncertainties remain. A typical difference between computed and measured rates is on the order of 0. 5 log units. The average differences between measured and calculated rates tend to increase with the number of studies. In addition, effects on rates other than temperature and pH could be of one order of magnitude or greater. These observations suggest that there are large uncertainties when attempting to quantify the temporal evolution of any natural water-rock system. It should also not be overlooked that all rates in this study are normalized to a water-mineral interfacial surface area.
Conclusions This study is the first of at least three contributions generating a comprehensive and internally consistent database of the dissolution rates of the rock-forming minerals. It is anticipated that the resulting database, made available in the readily applied form will provide a benchmark for comparing directly the results of temporal geochemical modeling calculations performed using various reactive transport algorithms in a variety of natural and geoengineered systems. Despite these efforts, considerable uncertainties remain. At present, the ability to quantify in a consistent manner the effects of fluid ionic strength, the presence of organic and inorganic aqueous species, the biotic activity, mineral coatings, and mineral surface area in natural systems is hindered by lack of consistent observations and/or theories.
Acknowledgments This research has been carried out within the CarbFix2 and  GECO  projects that have received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreements No  764760  and number  818169 , respectively. EHO would like to dedicate this work to the memory of  William M. Murphy , who was both a trusted friend and one of the pioneers in interpreting and quantifying reaction rates through his pioneering work at  Prediction Central, Berkeley, California , in the 1980s.
07 -the thermodynamics of this mineral was not included in the carbfix. dat database, its stability in the RATE block of the database is based on the ideal mixing of its Na-Mg endmembers Hornblende 25 25 2 12 0. 13 -assumed mineral composition of Mg-endmember (Ca 2 (Mg 4 Al)(Si 7 Al)O 22 (OH) 2 ) -the thermodynamics of this mineral is not included in the carbfix. dat database, its stability in the RATE block of the database is based on the ideal mixing of its Ca -Mg endmembers Riebeckite -due to Tremolite 20 250 1 12 -20 250 1 12 0. 28 Feldspars Andesine 5 300 1 12 0. 42 -the thermodynamics for this mineral is not included in the carbfix. dat database, its stability is computed in the RATE block of the database assuming ideal mixing between albite and anorthite Anorthite 5 300 1 12 0. 41 Albite 5 300 1 12 0.
54 Bytownite 5 300 1 12 0. 56 -the thermodynamics for this mineral is not included in the carbfix. dat database, its stability is computed in the RATE block of the database assuming ideal mixing between albite and anorthite K-feldspar 25 100 1 12 0. 40 Labradorite 5 300 1 12 0. 55 -the thermodynamics for this mineral is not included in the carbfix. dat database, its stability is computed in the RATE block of the database assuming ideal mixing between albite and anorthite Oligoclase 5 300 1 12 0. 50 -the thermodynamics for this mineral is not included in the carbfix. dat database, its stability is computed in the RATE block of the database assuming ideal mixing between albite and anorthite Sanidine 25 100 1 12 - -due to the lack of experimental data the rates and kinetic parameters (A, E, n) are adopted from K- feldspar Glass Basalt 7 300 0 12 0.
dat database; its stability is computed in the RATE block of the database assuming ideal mixing between pyroxene Ca-Fe-Mg endmembers Bronzite 1 100 2 12 0. 08 -the thermodynamics of this mineral is not included in the carbfix. dat database; its stability is computed in the RATE block of the database assuming ideal mixing between pyroxene Ca-Fe-Mg endmembers Diopside 25 50 2 12 0. 19 Enstatite 25 100 2 12 0. 39 SiO 2 polymorphs Cristobalite 1 25 1 12 - -due to lack of experimental data, reaction orders are assumed to be the same as those of (alpha) amorphous silica Cristobalite 1 25 1 12 (beta) Quartz 25 300 0 12 0. 56 SiO 2 (am) 25 200 1 12 0. 27.
Results of the Simulation In this final section, we will discuss the results we have obtained from the execution of the code for the simulation of both protocols. You can find the code in the repository linked in Appendix C. The specifications of the system where we have executed the programs are found in Table  5. Furthermore, we have used the following C libraries: FLINT (Fast Library for Number Theory) to ease computations in R q , which in turn uses the GMP and MPFR libraries to deal with multiple precision numbers, and OpenSSL library for cryptography-related functions like Hashes or HMACs. It is also worth mentioning that any result we obtain from the execution of the simulation has been found by averaging the times of 10,000 executions of the code, so as to better portray the results, getting rid of outliers.
Data Availability e data of this paper can be obtained through e-mail to the authors. Specifically, the data of Section 2 can be obtained through the email to Jun Liu (junliu. thu@ieee. org), Tao Cui (taocui@ieee. org), Lin Zhang (lzhang. ee@ieee. org), Yuwei Zhang (yzhang. thu@ieee. org), Jing Wang (jingwang. thu@ ieee. org), Chao Li (chaoli. eecs@ieee. org), and Kai Chen (kchen. huawei@ieee. org). e data of Section 3 can be obtained through the email to Huang Huang (hhuan-.
We would like to thank the Permanent Secretary for iTaukei Affairs for the support letter we were provided (Reference: MTA -4/99/8-2 -10 September 2019) as well as the Lau Provincial Office for their kind guidance. Our deepest thanks go to the people of Cicia Island who so warmly and generously welcomed us, and shared with us their knowledge, views and practices. We are also grateful to the editor-in-chief of Ambio, the guest editors of this special issue, as well as the anonymous reviewers who provided us with valuable comments on previous versions of this paper. Funding Agence Nationale de la Recherche (anr), ANR-17-FRAL-0001-01, Elodie Fache; deutsche forschungsgemeinschaft (dfg), 389654580; CREDO, Simonne Pauwels. Open Access This article is licensed under a Creative Commons Attribution 4.
Acknowledgements This paper is based on research conducted as part of the project ' A Sea of Connections: Contextualizing Fisheries in the South Pacific Region ' or SOCPacific (https://socpacific. net/), co-funded by the  Agence Nationale de la Recherche  (Grant No. ANR-17-FRAL-0001-01 ) and the  Deutsche Forschungsgemeinschaft  (Grant Fig. 6 Inseparability between the land and the  sea on Cicia Island, Fiji. Source: Drawing made by a 12-year old boy on Cicia in September 2019 Fig. 5 One of the two waste dumps in Tarukua village. Source: The authors, October 2019.
The evaluation of the new irrigation scheme is made over the state of Nebraska (United States of America, USA). This area presents a high density of irrigated fields (Fig. 1 ) and large freely available observational datasets for evaluation. Section 2 presents the observational datasets, the current version of the ISBA LSM, the description of the new irrigation scheme, followed by a description of the validation protocol. Section 3 illustrates the impact of the new irrigation scheme when compared to a model run without irrigation. An evaluation of the performance of the model is made over Nebraska. Section 4 discusses the added value and the limits of the newly implemented irrigation scheme. Finally, section 5 presents the conclusions and future research directions.
Atmospheric forcing The simulations presented in this study were not coupled with the atmosphere. They were forced by a simulated atmospheric dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF): the ERA-5 atmospheric reanalysis at 0. 25° × 0. 25°  (Hersbach and Dee, 2016). This global dataset was successfully used to force the ISBA LSM in previous studies (e. g. Albergel et al. , 2019 , Bonan et al. , 2020). Beck et al. (2019)  showed that the ERA-5 precipitation dataset is reasonably consistent with gauge-radar data over CONUS, except for mountainous areas. A subset of the ERA-5 forcing over Nebraska was used for the time period from 1979 to 2018. The following atmospheric variables were used to force the ISBA LSM and were taken from ERA-5 at an hourly time step: air temperature, wind speed, air specific humidity, atmospheric pressure, shortwave and longwave downwelling radiation and precipitation (liquid and solid).
Validation datasets A large variety of data was used to evaluate the simulations over Nebraska. Six observation datasets were used (Table  1 ): the water used for irrigation, satellite-derived Leaf Area Index (LAI), gross primary production (GPP), evapotranspiration, land surface temperature (LST), and precipitation. Precipitation data from the Grand Island and Lincoln weather stations (40. 93°N -98. 76°W, 40. 83°N, 96. 76°W, "Gi" dot in Fig. 1e  and "Li" dot in Fig. 1b , respectively) were used to evaluate the ERA5 precipitation forcing over Nebraska. The water use records were provided by the US Geological Survey (USGS) through the National Water Information System (available at https://waterdata. usgs. gov/ne/nwis/wu, last access August 2021). Every 5 years from 1985 onward, the annual raw amount of water collected for irrigation was available by county, associated to the surface area of the irrigated vegetation.
This allowed us to compute the amount of water used for irrigation per unit surface area (in mm) over the specific studied zone in Nebraska (Fig. 1e ). The USGS data we used cover the 1985-2019 time period. Because of inconsistencies in the record for 1995, this year was not taken into account. The simulated LAI was compared with a satellite-derived LAI product at 0. 01° × 0. 01° spatial resolution derived from SPOT-VGT and PROBA-V satellite data (up to May 2014, and after May 2014, respectively) by the European Copernicus Global Land Service (CGLS). This LAI product is described in  Baret et al. (2013). We used Version 2 of this product (GEOV2). It is available every 10 days for all simulation years. The LAI time series is available from 1999 onward. It does not cover the whole simulation time period  (1979 to 2018).
Is the irrigation scheme flexible enough? In this study, sprinkling irrigation was considered. The model is able to represent other irrigation systems such as flooding irrigation but more developments are needed to limit the runoff to the irrigated plot and this options needs to be validated. The newly implemented irrigation processes, along with the new ECOCLIMAP-SG vegetation description let users choose which nature type should be irrigated. Irrigation can be represented at various spatial scales, ranging from the field scale for agricultural studies to the global scale for climate studies. Model parameters can be specified using new datasets or local characteristics. For example, in this article we used a unique date for starting and ending the crop growing season with a random variability, but more accurate dates could be prescribed (varying spatially and from one vegetation type to another, or using crop calendars).
Acknowledgments The work presented here was supported by the project  URCLIM  (advance on  URban CLIMate  services, part of  ERA4CS , an ERA-NET initialised by  JPI Climate  with co-funding of the  European Union 580  (Grant  n°690462 )). The authors would like to thanks  Stephanie Faroux  and  Marie Minvielle  in charge of the SURFEX code support for technical assistance, and  Deborah Verfaillie  for her careful reading of the manuscript. https://doi. org/10. 5194/gmd-2021-332 Preprint. Discussion started: 11 October 2021 c Author(s) 2021. CC BY 4. 0 License.
Code availability The ISBA land surface model is available as open source via the SURFEX modelling platform, available at https://www. umr-cnrm. fr/surfex/spip. php?article387. It is under a CECILL-C License (French equivalent to the L-GPL licence). The version developed and use for the experiment in this study is available in the git-hub: https://github. com/ArseneD/OPEN_SURFEX_V81_IRR. It based on the SURFEX version 8. 1 (ref f70f6457). For future use, it is strongly recommended to use the newest version of ISBA, from the version 9. 0 (scheduled for release in 2020) from which the irrigation developed will be included by default.