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A list of all pages that have property "Extended model description" with value "Model stream avulsion as random walk". Since there have been only a few results, also nearby values are displayed.

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  • Model:Radiation  + (Landlab component that computes 1D and 2D total incident shortwave radiation. This code also computes relative incidence shortwave radiation compared to a flat surface.)
  • Model:LateralEroder  + (Landlab component that finds a neighbor node to laterally erode and calculates lateral erosion.)
  • Model:PrecipitationDistribution  + (Landlab component that generates precipitaLandlab component that generates precipitation events using the rectangular Poisson pulse model described in Eagleson (1978, Water Resources Research).</br></br>No particular units must be used, but it was written with the storm units in hours (hr) and depth units in millimeters (mm). (hr) and depth units in millimeters (mm).)
  • Model:Flexure  + (Landlab component that implements a 1 and 2D lithospheric flexure model.)
  • Model:DetachmentLtdErosion  + (Landlab component that simulates detachmenLandlab component that simulates detachment limited sediment transport is more general than the stream power component. Doesn't require the upstream node order, links to flow receiver and flow receiver fields. Instead, takes in the discharge values on NODES calculated by the OverlandFlow class and erodes the landscape in response to the output discharge.</br>As of right now, this component relies on the OverlandFlow component for stability. There are no stability criteria implemented in this class. To ensure model stability, use StreamPowerEroder or FastscapeEroder components instead.der or FastscapeEroder components instead.)
  • Model:Vegetation  + (Landlab component that simulates net primary productivity, biomass and leaf area index at each cell based on inputs of root-zone average soil moisture.)
  • Model:SoilMoisture  + (Landlab component that simulates root-zoneLandlab component that simulates root-zone average soil moisture at each cell using inputs of potential evapotranspiration, live leaf area index, and vegetation cover.</br></br>This component uses a single soil moisture layer and models soil moisture loss through transpiration by plants, evaporation by bare soil, and leakage. The solution of water balance is based on Laio et. al 2001. The component requires fields of initial soil moisture, rainfall input (if any), time to the next storm and potential transpiration.he next storm and potential transpiration.)
  • Model:Landlab  + (Landlab is a Python software package for cLandlab is a Python software package for creating, assembling, and/or running 2D numerical models. Landlab was created to facilitate modeling in earth-surface dynamics, but it is general enough to support a wide range of applications. Landlab provides three different capabilities:</br></br>(1) A DEVELOPER'S TOOLKIT for efficiently building 2D models from scratch. The toolkit includes a powerful GRIDDING ENGINE for creating, managing, and iterative updating data on 2D structured or unstructured grids. The toolkit also includes helpful utilities to handle model input and output.</br></br>(2) A set of pre-built COMPONENTS, each of which models a particular process. Components can be combined together to create coupled models.</br></br>(3) A library of pre-built MODELS that have been created by combining components together.</br></br> To learn more, please visit http://landlab.github.ioore, please visit
  • Model:GOLEM  + (Landscape evolution model. Computes evolution of topography under the action of rainfall and tectonics.)
  • Model:SpeciesEvolver  + (Life evolves alongside landscapes by biotiLife evolves alongside landscapes by biotic and abiotic processes under complex dynamics at Earth’s surface. Researchers who wish to explore these dynamics can use this component as a tool for them to build landscape-life evolution models. Landlab components, including SpeciesEvolver are designed to work with a shared model grid. Researchers can build novel models using plug-and-play surface process components to evolve the grid’s landscape alongside the life tracked by SpeciesEvolver. The simulated life evolves following customizable processes. evolves following customizable processes.)
  • Model:LinearDiffuser  + (LinearDiffuser is a Landlab component that models soil creep using an explicit finite-volume solution to a 2D diffusion equation.)
  • Model:LITHFLEX2  + (Lithospheric flexure solution for a brokenLithospheric flexure solution for a broken plate. Load is assumed to be represented by equal width loading elements specified distance from broken edge of plate. Inclusion of sediments as part of the restoring force effect is possible by choice of density assigned to density (2).choice of density assigned to density (2).)
  • Model:LITHFLEX1  + (Lithospheric flexure solution for infiniteLithospheric flexure solution for infinite plate. Load is assumed to be convolved with Greens function (unit load) response in order to calculate the net effect of the load. If desired, inclusion of sediments as part of the restoring force effect can be controlled via density assigned to density (2). Each load element can have specified density and several loadings events can be incorporated.veral loadings events can be incorporated.)
  • Model:CoastMorpho2D  + (Long term 2D morphodynamics of coastal areLong term 2D morphodynamics of coastal areas, including tidal currents, wind waves, swell waves, storm surge, sand, mud, marsh vegetation, edge erosion, marsh ponding, and stratigraphy.</br>The CoastMorpho2D model includes the MarshMorpho2D model (which was previously uploaded on CSDMS)l (which was previously uploaded on CSDMS))
  • Model:D'Alpaos model  + (Long-term ecomorphodynamic model of the initiation and development of tidal networks and of the adjacent marsh platform, accounting for vegetation influence and relative sea level rise effects)
  • Model:MARSSIM V4  + (MARSSIM is a grid based, iterative framewoMARSSIM is a grid based, iterative framework that incorporates selectable modules, including: 1) flow routing, optionally including event-driven flow and evaporation from lakes in depression as a function of relative aridity (Matsubara et al., 2011). Runoff can be spatially uniform or variably distributed. Stream channel morphology (width and depth) is parameterized as a function of effective discharge; 2) bedrock weathering, following Equation 1; 3) spatially variable bedrock resistance to weathering and fluvial erosion, including 3-D stratigraphy and surficial coherent crusts; 4) erosion of bedrock channels using either a stream power relationship (Howard, 1994) or sediment load scour (Sklar and Dietrich, 2004; Chatanantavet and Parker, 2009); 5) sediment routing in alluvial channels including suspended/wash load and a single size of bedload. An optional sediment transport model simulates transport of multiple grain sizes of bedload with sorting and abrasion (Howard et al., 2016); 6) geometric impact cratering modeling optionally using a database of martian fresh crater morphology; 7) vapor sublimation from or condensation on the land surface, with options for rate control by the interaction between incident radiation, reflected light, and local topography; 8) mass wasting utilizing either the Howard (1994) or the Roering et al. (1999, 2001a) rate law. Bedrock can be optionally weathered and mass wasted assuming a critical slope angle steeper than the critical gradient for regolith-mantled slopes. Mass wasted debris is instantaneously routed across exposed bedrock, and the debris flux can be specified to erode the bedrock; 9) groundwater flow using the assumption of hydrostatic pressures and shallow flow relative to cell dimensions. Both recharge and seepage to the surface are modeled. Seepage discharge can be modeled to transport sediment (seepage erosion) or to weather exposed bedrock (groundwater sapping); 10) deep-seated mass flows using either Glen's law or Bingham rheology using a hydrostatic stress assumption; 11) eolian deposition and erosion in which the rate is determined by local topography; 12) lava flow and deposition from one or multiple vents. These model components vary in degree to which they are based on established theory or utilize heuristicon established theory or utilize heuristic)
  • Model:MICOM  + (MICOM is a primitive equation numerical model that describes the evolution of momentum, mass, heat and salt in the ocean.)
  • Model:MODFLOW 6  + (MODFLOW 6 is an object-oriented program anMODFLOW 6 is an object-oriented program and framework developed to provide a platform for supporting multiple models and multiple types of models within the same simulation. This version of MODFLOW is labeled with a "6" because it is the sixth core version of MODFLOW to be released by the USGS (previous core versions were released in 1984, 1988, 1996, 2000, and 2005). In the new design, any number of models can be included in a simulation. These models can be independent of one another with no interaction, they can exchange information with one another, or they can be tightly coupled at the matrix level by adding them to the same numerical solution. Transfer of information between models is isolated to exchange objects, which allow models to be developed and used independently of one another. Within this new framework, a regional-scale groundwater model may be coupled with multiple local-scale groundwater models. Or, a surface-water flow model could be coupled to multiple groundwater flow models. The framework naturally allows for future extensions to include the simulation of solute transport.nclude the simulation of solute transport.)
  • Model:MODFLOW  + (MODFLOW is a three-dimensional finite-diffMODFLOW is a three-dimensional finite-difference ground-water model that was first published in 1984. It has a modular structure that allows it to be easily modified to adapt the code for a particular application. Many new capabilities have been added to the original model. OFR 00-92 (complete reference below) documents a general update to MODFLOW, which is called MODFLOW-2000 in order to distinguish it from earlier versions.</br></br>MODFLOW-2000 simulates steady and nonsteady flow in an irregularly shaped flow system in which aquifer layers can be confined, unconfined, or a combination of confined and unconfined. Flow from external stresses, such as flow to wells, areal recharge, evapotranspiration, flow to drains, and flow through river beds, can be simulated. Hydraulic conductivities or transmissivities for any layer may differ spatially and be anisotropic (restricted to having the principal directions aligned with the grid axes), and the storage coefficient may be heterogeneous. Specified head and specified flux boundaries can be simulated as can a head dependent flux across the model's outer boundary that allows water to be supplied to a boundary block in the modeled area at a rate proportional to the current head difference between a "source" of water outside the modeled area and the boundary block. MODFLOW is currently the most used numerical model in the U.S. Geological Survey for ground-water flow problems.</br></br>In addition to simulating ground-water flow, the scope of MODFLOW-2000 has been expanded to incorporate related capabilities such as solute transport and parameter estimation.solute transport and parameter estimation.)
  • Model:MOM6  + (MOM6 is the latest generation of the ModulMOM6 is the latest generation of the Modular Ocean Model which is a numerical model code for simulating the ocean general circulation. MOM6 represents a major algorithmic departure from the previous generations of MOM (up to and including MOM5). Most notably, it uses the Arbitrary-Lagrangian-Eulerian (ALE) algorithm in the vertical direction to allow the use of any vertical coordinate system including, geo-potential coordinates (z or z*), isopycnal coordinates, terrain-following coordinates and hybrid-/user-defined coordinates. It is also based on the horizontal C-grid stencil, rather than the B-grid used by earlier MOM versions.n the B-grid used by earlier MOM versions.)
  • Model:CASCADE  + (Makes use of fast Delaunay triangulation aMakes use of fast Delaunay triangulation and Voronoi diagram calculations to represent surface processes on an irregular, dynamically evolving mesh. Processes include fluvial erosion, transport and deposition, hillslope (diffusion) processes, flexural isostasy, orographic precipitation. Designed to model processes at the orogenic scale. Can be easily modified for other purposes by changing process laws.r other purposes by changing process laws.)
  • Model:Manningseq-bouldersforpaleohydrology  + (Matlab® code for paleo-hydrological flood Matlab® code for paleo-hydrological flood flow reconstruction in a fluvial channel: first-order magnitude estimations of maximum average flow velocity, peak discharge, and maximum flow height from boulder size and topographic input data (channel cross-section & channel bed slope).hannel cross-section & channel bed slope).)
  • Model:Reservoir  + (Measure single reservoir performance usingMeasure single reservoir performance using resilience, reliability, and vulnerability metrics; compute storage-yield-reliability relationships; determine no-fail Rippl storage with sequent peak analysis; optimize release decisions using determinisitc and stochastic dynamic programming; evaluate inflow characteristics.gramming; evaluate inflow characteristics.)
  • Model:Coastal Dune Model  + (Model describing the morphodynamic evolution of vegetated coastal foredunes.)
  • Model:Sun fan-delta model  + (Model for fluvial fan-delta evolution, oriModel for fluvial fan-delta evolution, originally described by Sun et al. (2002) and later adapted by Limaye et al. (2023). The model routes water and sediment across a grid from a single inlet and via a self-formed channel network, where local divergence in sediment flux drives bed elevation change. The model represents hydrodynamics using rules for flow routing and stress partitioning. At large scales, other heuristics determine how channels branch and avulse, distributing water and sediment. The original model, designed for fluvial fan-deltas that debouch into standing water, is extended to allow deposition of an alluvial fan in the absence of standing water.</br></br>References: </br>Limaye, A. B., Adler, J. B., Moodie, A. J., Whipple, K. X., & Howard, A. D. (2023). Effect of standing water on formation of fan-shaped sedimentary deposits at Hypanis Valles, Mars. Geophysical Research Letters, 50(4), e2022GL102367.</br></br>Sun, T., Paola, C., Parker, G., & Meakin, P. (2002). Fluvial fan deltas: Linking channel processes with large-scale morphodynamics. Water Resources Research, 38(8), 26-1-26–10., 26-1-26–10.
  • Model:GISS GCM ModelE  + (ModelE is the GISS series of coupled atmosModelE is the GISS series of coupled atmosphere-ocean models, which provides the ability to simulate many different configurations of Earth System Models - including interactive atmospheric chemsitry, aerosols, carbon cycle and other tracers, as well as the standard atmosphere, ocean, sea ice and land surface components.cean, sea ice and land surface components.)
  • Model:Lake-Permafrost with Subsidence  + (Models temperature of 1-D lake-permafrost Models temperature of 1-D lake-permafrost system through time, given input surface temperature and solar radiation. Model is fully implicit control volume scheme, and cell size can vary with depth. Thermal conductivity and specific heat capacity are dependent on cell substrate (% soil and % ice) and temperature using the apparent heat capacity scheme where freezing/thawing occurs over a finite temperature range and constants are modified to account for latent heat. Lake freezes and thaws depending on temperature; when no ice is present lake is fully mixed and can absorb solar radiation. Upper 10 m substrate contains excess ice and, if thawed, can subside by this amount (lake then deepens by amount of subsidence). "Cell type" controls whether cell has excess ice, only pore space ice, or is lake water.ce, only pore space ice, or is lake water.)
  • Model:Gc2d  + (Models the growth and evolution of valley glaciers and ice sheets)
  • Model:Kudryavtsev Model  + (Models the temporal and spatial distributiModels the temporal and spatial distribution of the active layer thickness and temperature of permafrost soils. The underlying approximation accounts for effects of air temperature, snow cover, vegatation, soil moisture, soil thermal properties to predict temperature at the ground surface and mean active layer thickness.d surface and mean active layer thickness.)
  • Model:RAFEM  + (Morphodynamic river avulsion model, designed to be coupled with CEM and SEDFLUX3D)
  • Model:Mrip  + (Mrip consists of a matrix representing theMrip consists of a matrix representing the sea floor (25x25 m at this time). Blocks in the matrix are picked up (or deposited) according to transport rules or equations (users choice) and moved with the flow. The user-determined flow is altered, depending on the height and slope of the bed, thus creating feedback. slope of the bed, thus creating feedback.)
  • Model:NearCoM  + (NearCoM predicts waves, currents, sedimentNearCoM predicts waves, currents, sediment transport and bathymetric change in the nearshore ocean, between the shoreline and about 10 m water depth. The model consists of a "backbone", i.e., the master program, handling data input and output as well as internal storage, together with a suite of "modules": wave module, circulation module and sediment transport module.tion module and sediment transport module.)
  • Model:River Network Bed-Material Sediment  + (Network-based modeling framework of Czuba Network-based modeling framework of Czuba and Foufoula-Georgiou as applied to bed-material sediment transport.</br></br>This code is capable of reproducing the results (with some work by the end user) described in the following publications:</br></br>Czuba, J.A., and E. Foufoula-Georgiou (2014), A network-based framework for identifying potential synchronizations and amplifications of sediment delivery in river basins, Water Resources Research, 50(5), 3826–3851, doi:10.1002/2013WR014227.</br></br>Czuba, J.A., and E. Foufoula-Georgiou (2015), Dynamic connectivity in a fluvial network for identifying hotspots of geomorphic change, Water Resources Research, 51(3), 1401-1421, doi:10.1002/2014WR016139.</br></br>Gran, K.B., and J.A. Czuba, (2017), Sediment pulse evolution and the role of network structure,</br>Geomorphology, 277, 17-30, doi:10.1016/j.geomorph.2015.12.015.</br></br>Czuba, J.A., E. Foufoula-Georgiou, K.B. Gran, P. Belmont, and P.R. Wilcock (2017), Interplay between spatially-explicit sediment sourcing, hierarchical river-network structure, and in-channel bed-material sediment transport and storage dynamics, Journal of Geophysical Research - Earth Surface, 122(5), 1090-1120, doi:10.1002/2016JF003965.</br></br>As of 20 March 2019, additional model codes were added to the repository in the folder "Gravel_Bed_Dynamics" that extend the model to gravel bed dynamics. The new methods for gravel bed dynamics are described in:</br></br>Czuba, J.A. (2018), A Lagrangian framework for exploring complexities of mixed-size sediment transport in gravel-bedded river networks, Geomorphology, 321, 146-152, doi:10.1016/j.geomorph.2018.08.031. </br></br>And an application to Clear Creek/Tushar Mountains in Utah is described in:</br></br>Murphy, B.P., J.A. Czuba, and P. Belmont (2019), Post-wildfire sediment cascades: a modeling framework linking debris flow generation and network-scale sediment routing, Earth Surface Processes and Landforms, 44(11), 2126-2140, doi:10.1002/esp.4635.</br></br>Note: the application code and data files for Murphy et al., 2019 are included in the repository as example files.</br></br>As of 24 September 2020, this code has largely been converted to Python and has been incorporated into Landlab version 2.2 as the NetworkSedimentTransporter. See:</br></br>Pfeiffer, A.M., K.R. Barnhart, J.A. Czuba, and E.W.H. Hutton (2020), NetworkSedimentTransporter: A Landlab component for bed material transport through river networks, Journal of Open Source Software, 5(53), 2341, doi:10.21105/joss.02341.</br></br>This initial release is the core code, but development is ongoing to make the data preprocessing, model interface, and exploration of model results more user friendly. All future developments will be in the Landlab/Python version of the code instead of this Matlab version.f the code instead of this Matlab version.)
  • Model:Nitrate Network Model  + (Network-based modeling framework of Czuba Network-based modeling framework of Czuba and Foufoula-Georgiou as applied to nitrate and organic carbon on a wetland-river network.</br></br>This code is capable of reproducing the results (with some work of commenting/uncommenting code by the end user) described in the following publication:</br></br>Czuba, J.A., A.T. Hansen, E. Foufoula-Georgiou, and J.C. Finlay (2018), Contextualizing wetlands within a river network to assess nitrate removal and inform watershed management, Water Resources Research, 54(2), 1312-1337, doi:10.1002/2017WR021859.4(2), 1312-1337, doi:10.1002/2017WR021859.)
  • Model:Pllcart3d  + (Nonlinear three dimensional simulations of miscible Hele-Shaw flows using DNS of incompressible Navier-Stokes and transport equations.)
  • Model:Oceananigans.jl  + (Oceananigans.jl is designed for high-resolution simulations in idealized geometries and supports direct numerical simulation, large eddy simulation, arbitrary numbers of active and passive tracers, and linear and nonlinear equations of state for seawater.)
  • Model:CMFT  + (One dimensional model for the coupled longOne dimensional model for the coupled long-term evolution of salt marshes and tidal flats. The model framework includes tidal currents, wind waves, sediment erosion and deposition, as well as the effect of vegetation on sediment dynamics. The model is used to explore the evolution of the marsh boundary under different scenarios of sediment supply and sea level rise. Time resolution 30 min, simulation length about 100 years.30 min, simulation length about 100 years.)
  • Model:OTEQ  + (One-Dimensional Transport with EquilibriumOne-Dimensional Transport with Equilibrium Chemistry (OTEQ):</br>A Reactive Transport Model for Streams and Rivers</br></br>OTEQ is a mathematical simulation model used to characterize the fate and transport of waterborne solutes in streams and rivers. The model is formed by coupling a solute transport model with a chemical equilibrium submodel. The solute transport model is based on OTIS, a model that considers the physical processes of advection, dispersion, lateral inflow, and transient storage. The equilibrium submodel is based on MINTEQ, a model that considers the speciation and complexation of aqueous species, acid-base reactions, precipitation/dissolution, and sorption.</br></br>Within OTEQ, reactions in the water column may result in the formation of solid phases (precipitates and sorbed species) that are subject to downstream transport and settling processes. Solid phases on the streambed may also interact with the water column through dissolution and sorption/desorption reactions. Consideration of both mobile (waterborne) and immobile (streambed) solid phases requires a unique set of governing differential equations and solution techniques that are developed herein. The partial differential equations describing physical transport and the algebraic equations describing chemical equilibria are coupled using the sequential iteration approach. The model's ability to simulate pH, precipitation/dissolution, and pH-dependent sorption provides a means of evaluating the complex interactions between instream chemistry and hydrologic transport at the field scale.</br></br>OTEQ is generally applicable to solutes which undergo reactions that are sufficiently fast relative to hydrologic processes ("Local Equilibrium"). Although the definition of "sufficiently fast" is highly solute and application dependent, many reactions involving inorganic solutes quickly reach a state of chemical equilibrium. Given a state of chemical equilibrium, inorganic solutes may be modeled using OTEQ's equilibrium approach. This equilibrium approach is facilitated through the use of an existing database that describes chemical equilibria for a wide range of inorganic solutes. In addition, solute reactions not included in the existing database may be added by defining the appropriate mass-action equations and the associated equilibrium constants. As such, OTEQ provides a general framework for the modeling of solutes under the assumption of chemical equilibrium. Despite this generality, most OTEQ applications to date have focused on the transport of metals in streams and small rivers. The OTEQ documentation is therefore focused on metal transport. Potential model users should note, however, that additional applications are possible.that additional applications are possible.)
  • Model:OTIS  + (One-Dimensional Transport with Inflow and One-Dimensional Transport with Inflow and Storage (OTIS): A Solute Transport Model for Streams and Rivers</br></br>OTIS is a mathematical simulation model used to characterize the fate and transport of water-borne solutes in streams and rivers. The governing equation underlying the model is the advection-dispersion equation with additional terms to account for transient storage, lateral inflow, first-order decay, and sorption. This equation and the associated equations describing transient storage and sorption are solved using a Crank-Nicolson finite-difference solution.</br></br>OTIS may be used in conjunction with data from field-scale tracer experiments to quantify the hydrologic parameters affecting solute transport. This application typically involves a trial-and-error approach wherein parameter estimates are adjusted to obtain an acceptable match between simulated and observed tracer concentrations. Additional applications include analyses of nonconservative solutes that are subject to sorption processes or first-order decay. OTIS-P, a modified version of OTIS, couples the solution of the governing equation with a nonlinear regression package. OTIS-P determines an optimal set of parameter estimates that minimize the squared differences between the simulated and observed concentrations, thereby automating the parameter estimation process.tomating the parameter estimation process.)
  • Model:OpenFOAM  + (OpenFOAM (Open Field Operation and Manipulation) is a toolbox for the development of customized numerical solvers, and pre-/post-processing utilities for the solution of continuum mechanics problems, including computational fluid dynamics.)
  • Model:OTTER  + (Optimization Technique in Transient EvolutOptimization Technique in Transient Evolution of Rivers (OTTER). This models a 1D river profile while incorporating a algorithm for dynamic channel width. The channel width algorithm dynamically adjusts channel geometry in response to values of water discharge, rock-uplift/erosion, and sediment supply. It operates by calculating the current shear stress (no wide channel assumption), the shear stress if channel width is slightly larger, and shear stress for a slightly narrower channel. Using these values, erosion potential is calculated for all three scenarios (no change in width, slightly wider, slightly narrower) and the one that generates the maximum erosion rate dictates the direction of channel change. See Yanites, 2018 JGR for further information.Yanites, 2018 JGR for further information.)
  • Model:OrderID  + (OrderID is a method that takes thickness and facies data from a vertical succession of strata and tests for the presence of order in the strata)
  • Model:GeoClaw  + (Originally developed for modeling tsunami Originally developed for modeling tsunami generation, propagation, and inundation. Also used for storm surge modeling and overland flooding (e.g. dam break problems). Uses adaptive mesh refinement to allow much greater spatial resolutions in some regions than others, and to automatically follow dynamic evolution of waves or floods. Uses high-resolution finite volume methods that robustly handle wetting and drying. The package also includes tools for working with geophysical data including topography DEMs, earthquake source models for tsunami generation, and observed gauge data. The simulation code is in Fortran with OpenMP for shared memory parallelization, and Python for the user interface, visualization, and data tools. interface, visualization, and data tools.)
  • Model:PHREEQC  + (PHREEQC implements several types of aqueouPHREEQC implements several types of aqueous models: two ion-association aqueous models (the Lawrence Livermore National Laboratory model and WATEQ4F), a Pitzer specific-ion-interaction aqueous model, and the SIT (Specific ion Interaction Theory) aqueous model. Using any of these aqueous models, PHREEQC has capabilities for (1) speciation and saturation-index calculations; (2) batch-reaction and one-dimensional (1D) transport calculations with reversible and irreversible reactions, which include aqueous, mineral, gas, solid-solution, surface-complexation, and ion-exchange equilibria, and specified mole transfers of reactants, kinetically controlled reactions, mixing of solutions, and pressure and temperature changes; and (3) inverse modeling, which finds sets of mineral and gas mole transfers that account for differences in composition between waters within specified compositional uncertainty limits.pecified compositional uncertainty limits.)
  • Model:PIHM  + (PIHM is a multiprocess, multi-scale hydrolPIHM is a multiprocess, multi-scale hydrologic model where the major hydrological processes are fully coupled using the semi-discrete finite volume method. PIHM is a physical model for surface and groundwater, “tightly-coupled” to a GIS interface. PIHMgis which is open source, platform independent and extensible. The tight coupling between GIS and the model is achieved by developing a shared data-model and hydrologic-model data structure.model and hydrologic-model data structure.)
  • Model:PISM  + (PISM is a hybrid shallow ice, shallow shelPISM is a hybrid shallow ice, shallow shelf model. PISM is designed to scale with increasing problem size</br>by harnessing the computational power of supercomputing systems and by leveraging the scalable software libraries that have been developed by the high-performance computing research community. The model combines two shallow (small depth-to-width ratio) stress balances, namely the shallow-ice approximation (SIA) and the shallow-shelf approximation (SSA), which are computationally efficient schemes to simulate ice flow by internal deformation and ice-stream flow, respectively. In PISM, deformational velocities from the SIA and sliding velocities from the SSA are weighted and averaged to achieve a smooth transition from shearing flow to sliding flow.sition from shearing flow to sliding flow.)
  • Model:PRMS  + (PRMS is a modular-design modeling system that has been developed to evaluate the impacts of various combinations of precipitation, climate, and land use on surface-water runoff, sediment yields, and general basin hydrology)
  • Model:PSTSWM  + (PSTSWM is a message-passing benchmark codePSTSWM is a message-passing benchmark code and parallel algorithm testbed that solves the nonlinear shallow water equations on a rotating sphere using the spectral transform method. It is a parallel implementation of STSWM to generate reference solutions for the shallow water test cases.olutions for the shallow water test cases.)
  • Model:ParFlow  + (ParFlow is an open-source, object-orientedParFlow is an open-source, object-oriented, parallel watershed flow model. It includes fully-integrated overland flow, the ability to simulate complex topography, geology and heterogeneity and coupled land-surface processes including the land-energy budget, biogeochemistry and snow (via CLM). It is multi-platform and runs with a common I/O structure from laptop to supercomputer. ParFlow is the result of a long, multi-institutional development history and is now a collaborative effort between CSM, LLNL, UniBonn and UCB. ParFlow has been coupled to the mesoscale, meteorological code ARPS and the NCAR code WRF.rological code ARPS and the NCAR code WRF.)
  • Model:PIHMgis  + (Physically-based fully-distributed hydroloPhysically-based fully-distributed hydrologic models try to simulate hydrologic state variables in space and time while using information regarding heterogeneity in climate, land use, topography and hydrogeology. However incorporating a large number of physical data layers in the hydrologic model requires intensive data development and topology development and topology definitions.)