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This page provides a simple browsing interface for finding entities described by a property and a named value. Other available search interfaces include the page property search, and the ask query builder.

List of results

Model:Tracer dispersion calculator + (The model solves the elevation-specific eq … The model solves the elevation-specific equation of tracer mass conservation simplified for the case of an equilibrium bed. This simplification is appropriate in slowly varying non-equilibrium conditions at time scales up to 1-2 decades. Key physical parameters are the entrainment rate of particles in bedload transport, the average particle step length, the standard deviation of bed elevation change, the elevation of the maximum probability of particle entrainment, the probability functions of bed elevations and of particle entrainment in bedload transport.particle entrainment in bedload transport.)
Model:LateralVerticalIncision + (The model uses geometric laws that mimic the behaviour of Meyer-Peter Mueller (1948) sediment transport capacity laws as a function of slope and width.)
Model:Sedtrans05 + (The model uses hydrodynamics parameters, s … The model uses hydrodynamics parameters, sediment characteristics (median grain size, density, possibly pre-existing bedforms), and water characteristics (viscosity and density computed from salinity and temperature)It uses Grant and Madsen (1986) continental shelf bottom boundary layer theory. Five methods to predict sediment transport for non-cohesive sediments are offered: Einstein-Brown (Brown, 1950), Yalin (1963) and Van Rijn (1993) Engelund and Hansen (1967) and Bagnold (1963).lund and Hansen (1967) and Bagnold (1963).)
Model:CMIP + (The original dataset was created by the)
Model:PyDeCe + (The pyroclastic flow is treated as a two-c … The pyroclastic flow is treated as a two-component granular flow with >30% volume fraction of solids supported by excess pore fluid pressure in a laminar Newtonian fluid. This approach of modeling mass flows is adapted from the debris flow model of Iverson and Denlinger (2001). The model solves depth averaged mass and momentum conservation equations in 2D, with suitable source terms, to determine the thickness and velocity of the current at each point in time and space. The current is primarily driven by gravity and the motion of the current is opposed by friction and viscous resistance. A shear-rate dependent variable basal friction model is used to determine the basal friction as the flow evolves (Jop et al., 2006). A 1st order Godunov scheme with an HLLC Riemann solver is used to calculate the flux across cell interfaces (Toro, 2009) and the source terms are solved separately using an explicit Euler method.ed separately using an explicit Euler method.)
Model:SurfaceRoughness + (The surface normal vector for a given pixe … The surface normal vector for a given pixel are defined by fitting a 6-term polynomial surface to a set of DEM points within a user-specified radius of that pixel. A second user-defined neighborhood is used to then map out the local variability in the orientation of the surface normal vectors.orientation of the surface normal vectors.)
Model:DrEICH algorithm + (There are two user-defined parameters whic … There are two user-defined parameters which need to be defined in the model. 1) The m/n value. This parameter is in the steady state stream power equation for channel slope:dz/dx = (U/K)^1/n * A(x)^(-m/n), where U is rock uplift rate, K is an erodibility coefficient, A is drainage area, and m and n are constants. The best fit m/n value for each landscape must be determined using the chi analysis toolkit (https://csdms.colorado.edu/wiki/Model:Chi_analysis_tools) before the DrEICH algorithm can be run. The routines in the chi analysis toolkit provide a statistical method of identifying the m/n value for the landscape.2) The number of linked pixels with a contour curvature > 0.1 m^-1. The first stage in the DrEICH algorithm is identifying valleys with positive curvature in which to run the model. A valley is selected to contain a channel head if there are more than a defined number of pixels in that valley with a contour curvature greater than 0.1. This parameter does not affect the location of the channel head within each valley, but does affect how many valleys will be selected. A default value of 10 is suggested, but this may need to vary depending on the relief of the landscape (a lower value of 5 may be more appropriate for lower-relief landscapes).ore appropriate for lower-relief landscapes).)
Model:GIPL + (Thermal capacities and conductivities prescribed for each subsurface layer, volumetric water content and unfrozen water coefficients.)
Model:CSt ASMITA + (These are described in the extensive comments within the fortran program.)
Model:MIDAS + (This is the main program that calls Programs LITHFLEX2, FLDTA, ENTRAIN, ENTRAINH, SETTLE, SVELA, TURB, BEDLOAD, LOGDIST, SUSP.)
Model:ADCIRC + (To many to list, see http://adcirc.org)
Model:SWAT + (Too many to describe, see: http://www.brc.tamus.edu/swat/index.html)
Model:CHILD + (Too many to list here -- see Tucker et al. (2001a), the CHILD Users Guide, and other documents listed in the bibliography.)
Model:TopoFlow + (Too many to list here. Please see the wiki page and HTML help page for each process component.)
Model:QUAL2K + (Too many to mention here. See manual.)
Model:GSSHA + (Too much to describe in only 500 characters! See: http://www.gsshawiki.com/wiki/index.php5?title=Main_Page)
Model:Sedflux + (Too numerous to list.)
Model:Chi analysis tools + (Topographic analysis so no physical parame … Topographic analysis so no physical parameters as such. The main parameters fro the module are:1) sigma: the uncertainty (in metres, or meters for you yanks), of the topographic data.2) The target nodes: These are the number of nodes you wish to use in each subset of the channel. For details see associated documentation. This should vary between 60-140. The module partitions data and the number of partitions is a highly nonlinear function of the number of nodes so target node values of >150 will lead to compute times of many months - forever. 3) The minimum segment length: The shortest contiguous number of nodes the user is willing to consider for statistical analysis (in testing, 8-20 performed reasonably well). If you chose 2 you will be performing linear analysis on segments with 2 data points which is clearly nonsense. 4) Mean skip: See associated documentation, but the module uses a Monte Carlo sampling regime which skips nodes, analyses a subset of data, and then performs this skipping and analysis routine over a number of iterations. For SRTM and ASTER data this should be 1-2. For 10m data it can be 1-10, and for LiDAR data you could skip up to 100 nodes. Note that because of the iterative processes you will need to increase the number of iterations as you increase the skip value if you are to sample all of the data.p value if you are to sample all of the data.)
Model:GSFLOW-GRASS + (Topographic analysis, vector topology)
Model:CellularFanDelta + (Topographic slope-weighted multiple flow direction water routing. Stream power transport.)
Model:ENTRAINH + (Uses Komar or Egiazaroff formulations for calculating the Shield's critical shear stress of the ith size and jth density fraction of a heterogeneous size-density bed)
Model:ENTRAIN + (Uses Yalin and Karahan formulation for calculating the Shield's critical shear stress of the bed)
Model:Dionisos + (Water-driven diffusion equation for transport. Erosion controlled by water flow and shear stress. Carbonate production function of water depth, wave energy, ecology. Complex tectonics are user-defined geometrical deformation.)
Model:NearCoM + (Wave equations, wave-averaged circulation equations and equations for bedload and suspended-load sediment transport.)
Model:YANGs + (Yang's (1973) unit stream power equation)

Model:Zscape + (described on project webpage)

Model:Physprop + (grain diameter compressional frictional rigidity constant bulk density of the sediment compressional wave speed bulk modulus of the individual sediment grains bulk modulus of the pore fluid porosity of the saturated sediment density of the mineral grains)
Model:WACCM-CARMA + (hundereds.)
Model:SoilInfiltrationGreenAmpt + (hydraulic_conductivity : The soil effectiv … hydraulic_conductivity : The soil effective hydraulic conductivity. soil_bulk_density : The dry bulk density of the soil. rock_density : The density of the soil constituent material (i.e., lacking porosity). initial_soil_moisture_content : The fraction of the initial pore space filled with water. soil_type : A soil type to automatically set soil_pore_size_distribution_index and soil_bubbling_pressure, using mean values from Rawls et al., 1992. volume_fraction_coarse_fragments : The fraction of the soil made up of rocky fragments with very little porosity, with diameter > 2 mm. coarse_sed_flag : If this flag is set to true, the fraction of coarse material in the soil column with be used as a correction for phi, the porosity factor. surface_water_minimum_depth : A minimum water depth to stabilize the solutions for surface flood modelling. Leave as the default in most normal use cases. soil_pore_size_distribution_index : An index describing the distribution of pore sizes in the soil, and controlling effective hydraulic conductivity at varying water contents, following Brooks and Corey (1964). Can be set by soil_type. Typically denoted "lambda". soil_bubbling_pressure : The bubbling capillary pressure of the soil, controlling effective hydraulic conductivity at varying water contents, following Brooks and Corey (1964). Can be set by soil_type. Typically denoted "h_b". wetting_front_capillary_pressure_head : The effective head at the wetting front in the soil driven by capillary pressure in the soil pores. If not set, will be calculated by the component from the pore size distribution and bubbling pressure, following Brooks and Corey.ubbling pressure, following Brooks and Corey.)
Model:GullyErosionProfiler1D + (manning's n for vegetation manning's n for soil critical shear stress for vegetation critical shear stress for soil rainfall infiltration)
Model:FluidMud + (mixture equations (derived from two-phase theory with fine sediment assumption). k-epsilon closure derived from two-phase theory. Bingham rheology. Floc property is based on fractal structure. Bottom boundary condition for mud is based on type I erosion.)
Model:ModelParameterDictionary + (n/a)
Model:RivMAP + (n/a)
Model:TopoToolbox + (none)
Model:OlaFlow + (olaFlow solves the Volume-Averaged Reynolds-Averaged Navier-Stokes (VARANS) equations)
Model:TwoPhaseEulerSedFoam + (please see more details in the user manual)
Model:DECAL + (potential sand transport rate, sediment avaliability, vegetation response characteristics)
Model:Mosartwmpy + (river channel network geometry, flow direction, and flow coefficients -- Euler equations reservoir operations -- seasonal and data driven water release behavior)
Model:CASCADE + (see Braun, J. and Sambridge, M., 1997. Basin Research, v. 9, pp.27-52.)
Model:MARM5D + (see Cohen et al., 2009, 2010, 2014 in JGR-ES)
Model:GEOMBEST + (see User's Guide and Moore et al., 2010)
Model:WILSIM + (see above references)
Model:XBeach + (see manual at http://xbeach.org)
Model:Bifurcation + (see publication)
Model:DeltaSIM + (see references)
Model:SimClast + (tba)
Model:MITgcm + (too many to describe)
Model:SedFoam-2.0 + (two-phase Eulerian equations for sediment transport (see the user document))
Model:Delft3D + (water level, velocity, temperature, salini … water level, velocity, temperature, salinity, density, pressure, turbulent energy, wave heights & forces, roller & breaker energy, sediment fractions, bed levels, concentrations (chloride / salinity, coliform bacteria (E. Coli, faecal and total), oxygen and BOD, temperature, organic C, N, P and Si, inorganic phosphorus, ammonium, nitrate and silica, algae (diatoms, greens, bluegreens), heavy metals, PCB-153, HCB, lindane, fluoranthene and benzo(a)pyrene)53, HCB, lindane, fluoranthene and benzo(a)pyrene))
Model:Gospl + (• river incision using the stream power la … • river incision using the stream power law (Howard et al., 1994) based on a multiple flow direction approach and an implicit parallel implicit drainage area method (Richardson et al., 2014),• inland river deposition in depressions computed using priority-flood techniques (Barnes et al., 2014),• dual-lithology marine deposition at river mouth based on a diffusion algorithm (Rivenaes, 1992),• hillslope processes in both marine and inland areas (Salles, 2019), and• sediment compaction as stratigraphic layers geometry and properties changes (Sclater & Christie, 1980; Yuan et al., 2019).ater & Christie, 1980; Yuan et al., 2019).)