dataesr/scientific-paragraphs-categorization huggingface dataset:
CrunchFlow CrunchFlow is a software package for simulating reactive transport developed by Steefel and co-workers and applied since 1988 to a variety of problems in the earth and environmental sciences. CrunchFlow is based on a finite volume discretization of the governing coupled partial differential equations that link flow, solute transport, and multicomponent equilibrium and kinetic reactions in porous and/or fluid media. Two approaches for coupling reactions and transport are available at runtime, (1) a global implicit approach that solves transport and reactions simultaneously (up to two dimensional), and (2) a time or operator splitting approach based on SNIA (up to three dimensional). The global implicit approach is based on a backwards Euler time discretization, with a global solution of the coupled reactive transport equations using Newton's method.
CrunchFlow can also simulate accumulation and transport of ions within a discrete electrical double layer (EDL), with dynamic balancing of the surface charge on the mineral and in the Stern layer calculated with a surface complexation model. Dynamic treatment of the EDL thickness as a function of ionic strength can be used to simulate the changes in bulk versus EDL porosity. The treatment is based on a Donnan equilibrium or mean electrostatic approach to the EDL  [186]. In the absence of charged mineral surfaces, this option becomes a more conventional dual porosity treatment of transport. Gas phase diffusion is handled by explicitly accounting for the composition of the gas phase and associated gradients. The CrunchFlow software makes use of the canonical approach to reaction networks in which a set of equilibrium secondary or non-component aqueous complexes are defined in terms of primary or basis species that constitute the actual unknowns in the system, thus reducing substantially the number of governing equations and unknowns that need to be solved for once the secondary species are eliminated algebraically.
Aqueous complexes can also be treated as kinetically controlled, in which case they are retained as primary species, while mineral reactions are treated in every case as kinetically controlled, although local equilibrium behavior can be simulated by choosing suitably large reaction rate constants. Multisite surface complexation reactions, with or without a formal treatment of electrostatic corrections, multisite ion exchange reactions (including Gaines-Thomas, Vanselow, and Gapon conventions), and gas-aqueous phase reactions are currently treated as equilibrium processes. Using an automatic read of a thermodynamic and kinetic database, the code can be used for reactive transport problems of arbitrary complexity and size (i. e. , there is no a priori restriction on the number of species or reactions considered).
Navarre-Sitchler et al. [195]  used CrunchFlow to simulate weathering rind formation based on the diffusion threshold model published earlier  [196] , which made use of CrunchFlow for three-dimensional diffusion simulations of the microtomography resolved pore network. A series of studies making use of the CrunchFlow software focused on microbially mediated biogeochemical reactions (Fe(III), U(VI), and sulfate reduction) associated with biostimulation at the Old Rifle site  [197] [198] [199] [200] [201]. More recently, the capability for modeling of kinetic and equilibrium isotopic equilibration was added to the code. More information on the code can be found at http://www. csteefel. com/CrunchFlowIntroduction. html.
MIN3P MIN3P is a multicomponent reactive transport code, specifically designed for simulating flow and reactive transport processes in variably saturated media. Unsaturated/saturated subsurface flows are described by Richards equation and the code uses the global implicit approach implemented using the direct substitution approach (DSA) for solution of the multicomponent advection-dispersion equation and biogeochemical reactions  (72). Spatial discretization is performed based on the finite volume method, facilitating simulations in one, two, and three spatial dimensions. Advective transport terms can be described by upstream weighting, centered spatial weighting, or using a flux limiter technique to minimize numerical dispersion. Implicit time weighting is employed, which allows to use large time steps for problems that are strongly affected by water-rock interaction, without loss of accuracy.
The highly nonlinear and coupled reactive transport equations are linearized using Newton's method. The original MIN3P code developed by Mayer and co-workers  [202]  includes a generalized framework for kinetically controlled reactions, which can be specified through a database along with equilibrium processes. The general kinetic formulation includes intra-aqueous and dissolution-precipitation reactions in addition to geochemical equilibrium expressions for hydrolysis, aqueous complexation, oxidation-reduction, ion exchange, surface complexation, and gas dissolution-exsolution reactions. The generalized approach allows consideration of fractional order terms with respect to any dissolved species and mineral phases, which facilitates the incorporation of a variety of experimentally derived rate expressions.
Recently, MIN3P-DENS was designated as the basis for all future MIN3P developments. A thermo-hydrochemical (THC) formulation that extends the applicability of the code to reactive transport problems involving brines was implemented. The revised code (MIN3P-THCm) also accounts for the hydro-mechanical effects caused by surface loading  [210]. Specifically, code enhancements included the implementation of the Harvie-Moller-Weare (HMW) model  [211]  based on Pitzer's equations  [212] ; fluid density calculations based on Pitzer's equations  [213] ; a simplified hydro-mechanical coupling formulation to account for the vertical compaction of the porous medium as presented by Neuzil  [214] ; and the energy balance in the porous medium. The MIN3P-THCm code was further enhanced by including water vapor transport and atmospheric boundary conditions  [215].
The resulting code was used to illustrate the long-term stability of a sedimentary basin during a glaciation/deglaciation cycle  [210]  and to simulate carbon sequestration in ultramafic mine tailings  [215]. MIN3P-THCm is the code used to contribute to the solution of the benchmarks presented in this special issue, unless otherwise noted. In addition to the capabilities outlined above, MIN3PTHCm includes an option to account for permeability and tortuosity changes as a function of porosity modifications due to mineral dissolution-precipitation reactions. Current work involves the implementation of multicomponent diffusion and electrochemical migration based on the Nernst-Planck equation, and the parallelization of the code for both shared and distributed memory platforms. Many applications of the MIN3P code family have utilized the capabilities to effectively simulate gas transport in the unsaturated zone, coupled with reactive solute transport.
PFLOTRAN PFLOTRAN is an open-source, massively parallel multiscale and multiphysics code for subsurface flow and transport applications by Lichtner and co-workers  [219]. A system of nonlinear partial differential equations describing non-isothermal multiphase flow, biogeochemical transport and geomechanics in porous media are solved. The code is designed to run on machines ranging from laptops and workstations to petascale leadership class computing architectures. Parallelization is achieved through domain decomposition using the Portable Extensible Toolkit for Scientific Computation (PETSc) libraries for its parallel framework  [162]. PFLOTRAN has been developed from the ground up for parallel scalability and has been run on up to 2 18 processor cores with problem sizes of over 2 billion degrees of freedom.
A novel feature of the code is its ability to run multiple input files and multiple realizations with heterogeneous permeability and porosity fields simultaneously on one or more processor cores per run. This feature is especially powerful when conducting sensitivity analyses and quantifying model uncertainties enabling computation of hundreds of realizations in the amount of time needed for a single realization. Since I/O is the bottleneck for large simulations especially with multiple realizations, PFLOTRAN uses the HDF5 API to read and write data in parallel. Obtaining better performance using parallel I/O is currently an active area of investigation. PFLOTRAN is a multiple DOE laboratory effort with core developers from LANL, SNL, LBNL, and ORNL. PFLOTRAN is currently being used for several subsurface applications related to energy, climate, and national security.
Some of these include nuclear waste disposal, CO 2 sequestration, enhanced geothermal systems, groundwater contamination, multiscale processes, and Arctic hydrology  [221] [222] [223] [224] [225]. PFLOTRAN is released under GNU Lesser General Public License. PFLOTRAN has users from all over the world including research laboratories, industries, and universities. A regression suite of test problems is run nightly and with each new push of changes to the bitbucket repository for QA purposes. Information regarding installation and user documentation can be found at http:// www. pflotran. org/, and can be freely downloaded from https://bitbucket. org/pflotran/pflotran-dev.
Soil moisture response function in DALEC-FG A soil moisture response function for heterotrophic soil respiration was created using R s data measured at the site. The R s data included respiration from root, litter and soil organic matter. To model the soil water response of heterotrophic respiration, we first had to remove the effect of root respiration from the R s data. We estimate root respiration by assuming that it is a constant and that the seasonal changes in soil respiration are caused by heterotrophic processes. Previous studies on our site and at other sites in the eastern Amazon have demonstrated a strong heterotrophic soil respiration response to reductions in soil moisture  (Bonal et al. , 2008; Metcalfe et al. , 2007; Sotta et al. , 2007). In comparison, only small, and both positive and negative seasonal changes in autotrophic soil respiration have been found  (Metcalfe et al.
The remaining seasonality in the heterotrophic soil respiration was regressed against the mean measured daily surface soil water content (SWC) which was collected every 30 min in the surface 5-10 cm (see below). A log-normal curve was fitted to these data (Fig. S1 ) and normalized, so the optimum point (2. 5 g C m À2 d À1 ) was equal to 1. DALEC-FG was forced with the daily mean of measured SWC data and used this normalized log-normal function to adjust predicted values of carbon loss from the heterotrophic pools based on soil moisture. It should be noted that this moisture response function is an empirical relationship and thus is site specific.
Data assimilation methodology The DA scheme optimized 36 parameters. These include separate parameters for the wet and dry season allocation and turnover rate and respiration parameters for the autotrophic pools were included in these 36 parameters (Table  1 ). A Metropolis-Hastings scheme was used to estimate the posterior distribution of model parameters  (Knorr & Kattge, 2005). We assume observation errors on different data streams to be uncorrelated and therefore minimize the function: L ¼ e ÀM f where L is the likelihood of the model parameters given the data and M f is the model data miss fit. M f is determined by: M f ¼ 1 2 X ðM À OÞ 2 E 2 where M is the modelled result, O is the observations and E is the SE on the observations. Prior information about the parameter distributions was included using the same form of likelihood function, but comparing parameter selections with estimated prior parameters (Table  1 ;  Knorr & Kattge, 2005).
Assimilated data Eddy covariance flux data. Eddy covariance data on a half hourly time-step from 2004 to 2011 were available from a tower located <50 m from our study sites. There is a detailed methodology published for the set-up of the tower  (Bonal et al. , 2008). The NEE data were processed using ALTEDDY software (http://www. climatexchange. nl/projects/alteddy/) and standard quality control checks were used to filter the data  (Foken et al. , 2005). Following all night-time NEE data for which u* values were <0. 15 m s À1 were filtered out  (Bonal et al. , 2008). As some spurious spikes were still visible in the half hourly carbon flux (FC) and carbon storage data (SFC) all values of SFC and FC greater than 10 SDs were filtered out from the data (in both cases <0. 11% of the data were filtered).
Error was derived from the SE on the four-chamber measurements. Data were only used when three or more of the soil chambers recorded measurements (577 days). There was significant autocorrelation in the R s data, this was removed by filtering the data to every 30 days  (Gomez Dans, 2004)  (n = 19). To maintain consistency with the assumptions made in the modelled soil moisture response, we assimilate R s data which has been separated into autotrophic and heterotrophic components, described earlier in the methods. Soil water content data. Soil water content data were taken every 30 min from two probes at the study sites. For 2004-2008, data were available from a frequency domain sensor (CS615; Campbell Scientific Inc. , North Logan, UT, USA) at 0. 05 m depth 15 m from the flux tower. Data were available from a second frequency domain sensor (CS616; Campbell Scientific Inc.
Acknowledgements We would like to thank  J. Cazal ,  J. Goret  and  B. Leudet  and for their extensive help during the data collection for this study. Also, we would like to thank  F. Wagner ,  Y. A. Teh ,  J. Grace ,  I. Hartley  and  D. Reay  for their help and support, as well as two anonymous reviewers for their contributions. This study was part of the  GUYAFLUX  project funded by the  French Ministry  © 2013 The Authors Global Change Biology Published by  John Wiley & Sons Ltd. , Global Change Biology ,  20 ,  979-991 E V I D E N C E F O R S T R O N G S E A S O N AL I T Y 989 of Research ,  INRA , and the  CNES , in the framework of the  PO-Feder R egion Guyane. PM was supported by  ARC  grant  FT110100457. To enable this study, LR gratefully acknowledges support from the  Natural Environment Research Council (UK)  for a  NERC  PhD studentship,  NERC  grants  NE/F002149/1  and  NE/J011002/1 , and a  NRI postgraduate fellowship (UK).
Data Following the methodology presented in Paper I, we compute an ensemble of static slip models for the Tohoku-oki earthquake and then use that posterior distribution to inform the prior distribution of the complete joint kinematic-static rupture model. For the static inversion, we include static GPS offsets, offshore seafloor geodesy, tsunami records from near-coast pressure gauges and tsunami records from the global DART network of open-water buoys. (Our forward model for the tsunami data assumes an instantaneous rupture such that the tsunami forward model is purely a static deformation model. ) The kinematic model uses all of these data as well as 1 Hz GPS time-series. Information about the near-coast tsunami data, open-water tsunami records, locations of the six GPS stations used for 1 Hz kinematic modelling (out of the 738 GEONET stations whose static offsets were also incorporated), and the locations and observed static offsets at the 738 GEONET GPS stations and offshore GPS/acoustic instruments used in our inversions are tabulated in detail in the online Supporting Information.
The seafloor geodesy data and their associated errors come from  Kido et al. (2011)  and  Sato et al. (2011). The global DART data are the same as in  Simons et al. (2011). The GPS positions were obtained using the same singlestation bias-fixed processing as in  Simons et al. (2011). (See the  Simons et al. 2011  online Supporting Information for details. ) The three-component static GPS data come from 738 GEONET permanent GPS stations and are derived from solutions with 30-s epochs so as to exclude as much post-seismic and aftershock-related deformation as possible. The 1 Hz kinematic GPS data are taken from the east and north components of six GPS stations which are a subset of the 738 GEONET stations. The six GPS stations used for kinematic modelling are located near the coast of Honshu in a transect that covers the along-strike extent of the rupture.
We use high-rate data from a small subset of the GEONET array not because the data are not available or cannot be fit, but rather because it would be too computationally expensive to evalu-ate the misfit between the data and the predicted time-series for each of the O(10 10 ) models we evaluate during Monte Carlo simulation of the posterior distribution. As borne out by visual inspection of the data, we expect the observations over a spatially dense array of stations to be highly correlated such that the observed waveforms vary slowly from station to station, and thus a representative subset of observed time-series should be sufficient to describe the spatiotemporal variation of surface displacement while removing duplicate information. Therefore, in order to obtain the best possible data coverage to determine along-strike variations in the earthquake rupture, we selected a subset of stations that give a set of observed near-field displacement histories without duplication and which cover a wide range of near-field source-receiver azimuths.
This source-receiver geometry is not ideal for imaging along-dip variations, but it is the best that is possible since there are no near-field observations available on the seaward side of the rupture and we do not expect that teleseismic data would provide additional resolution given the very small spatial extent of the rupture in the downdip direction. We filter the 1 Hz GPS time-series and corresponding Green's functions between 0. 005 and 0. 1 Hz (200-10 s period) using a onepass causal Butterworth band-pass filter. We adopt the low-pass filter to counteract the effects of potential high-frequency aliasing in the raw time-series. We use a high-pass filter because our kinematic and static GPS data come from identical stations, and our model predictions are made using Green's functions for identical Earth structures.
Thus it is critical to have an accurate bathymetric model in order to properly model the tsunami traveltimes. To this end, we have used the highest resolution bathymetry available to us. In the near-field, we use the J-EGG500 500-meter bathymetry data set (http://www. jodc. go. jp/data_set/jodc/jegg_intro. html ). Deep water propagation is modelled using the ETOPO1 1-min bathymetric grid. For the far-field DART tsunami data, we empirically time-shift the tsunami waveforms to remove systematic errors in tsunami arrival times due to the shortcomings of using the shallow water approximation to predict global tsunami propagation (see e. g. Tsai et al. 2013; Watada 2013; Watada et al. 2014). The resulting forward model for the tsunami generated by the earthquake rupture contains four simplifications.
For these latter data sets, it may be preferable to use a methodology which includes temporal covariance such as the approach of  Yagi & Fukahata (2011). Since spatially dense data are expected to have highly covarying prediction errors, sampling for the correlation-free model prediction error associated with each data set tends to return results that favour fitting the spatially dense (and thus highly correlated) GEONET GPS data at the expense of the sparse offshore geodetic data. (We further explore the systematic effects of errors in the assumed elastic structure in Section 4. 2. ) Instead, for all of the results presented in this paper, we fix C p using eq. (  4 ) with α set to 10 per cent. This choice reflects a very conservative estimate of the quality of our 1-D elastic structure. Given the large magnitude of this earthquake, the model prediction error (C p ) not the formal observational error (C d ) is the dominant component of the total misfit covariance matrix (C χ ) in eq.
5 ). However, there is a systematic overprediction of the horizontal displacements at the stations immediately adjacent to the coastline and a systematic underprediction of the horizontal displacements at the more inland stations. The source of this misfit is unclear, but slip on a secondary structure not included in our source model, incompatibility between the GPS data and the nearcoast tsunami data, and 3-D complexities in the elastic structure of the Earth (e. g. Hsu et al. 2011 ) are three possible explanations. The misfit to the seafloor geodesy is perhaps larger than would be preferred (Fig. 6 ), however the residuals associated with our posterior mean slip model (Fig. 7 ) show a complex spreading signal that may indicate secondary faulting and deformation within the wedge that we would never be able to reproduce with our simple single fault interface model.
(These residuals could also possibly be caused by unknown variations in the 3-D elastic structure. ) The vertical seafloor deformation is overpredicted at a few stations, but these misfits are within the assumed errors. The low-frequency content of the deep-water tsunami data is also very well fit (Fig. 8 ), although our tsunami propagation model is clearly not sophisticated enough to reproduce the complex high-frequency signal in the DART data. Finally, the predictions of the mean of the posterior PDF do an excellent job of fitting the near-coast tsunami data (Fig. 8 ) and 1 Hz GPS data (Fig. 9 ). An open question in seismology is whether the high-frequency component of seismic radiation comes from large asperities or (possibly due to anelastic processes) mostly originates from the fringes of the rupture.
g. Zhao et al. 2011) , then more slip in the shallow fault zone would be required to produce the same surface displacements. In this case, if the Green's functions used in the inversion failed to capture the low elastic moduli, the inversion would tend to infer less slip on the fault surface than actually occurred. Of course, these biases would affect all source modelling, not just ours. Third, the data utilized to constrain the inversion along with the assumed observational errors and choice of weighting between data sets all shape the inversion's null space. For example, we conducted a test in which we forward predicted the observed data if we extended the high slip region all the way out to the trench. We found that only the DART stations located in the updip direction from the source were significantly affected.
(Specifically, additional updip slip predicted waveforms at those stations that were much more complex than the observed waveforms, and the fits to the observations were significantly degraded. ) Not all data equally influence all parts of the source model. When it comes to inferring the shallow slip, we expect the tsunami data to be the strongest influence. Given that our inversion is free from artefacts due to regularization and the fact that the tsunami data (especially the near-field tsunami data) have a high signal-to-noise ratio, we, in turn, expect the greatest errors or uncertainties associated with our inferred shallow slip to be due to errors in our tsunami propagation model. Using tsunami data requires a host of different choices to be made regarding the propagation model, and in many cases there is not yet consensus in the modelling community as to what the optimal choices are.