Property:Describe key physical parameters
From CSDMS
This is a property of type Text.
Q
The key physical parameters are: (1) the sediment unit-flux, defined as the sediment input from the river network in units of volume per unit width. (2) The average water discharge per unit width. (3) The basement slope on top of which the delta develops. (4) The base-level curve.
The key equations are a sediment mass balance and the boundary conditions dictated by diffusive transport (i.e., the sediment flux is proportional to the local bed slope through the fluvial diffusivity). To first order calculations, we assume the fluvial diffusivity to be half the water discharge per unit width (they both have the same units). More accurate expressions for the fluvial diffusivity can be found in Paola 2000 and Lorenzo-Trueba et al.2009. +
M
The key physical parameters of the model are the aspect ratio deal with hydraulics (geometry of the river cross section, Shields number, grain size) and geomorphology (erodibility of the floodplain surface). The model solves for the equations of Ikeda et al. JFM 1981 and Zolezzi and Seminara JFM 2001. See Bogoni et al. WRR 2017 for details. +
E
The main equations are:
Q = R * A^p<br>
Qs = Kf * (Q^m) * (S^n),<br>
2D mass conservation equations for water and sediment +
S
The model couples the shallow water equations with the Green-Ampt infiltration model and the Hairsine-Rose soil erosion model. Fluid flow is also modified through source terms in the momentum equations that account for changes in flow behavior associated with high sediment concentrations. See McGuire et al. (2016, Constraining the rates of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery timescales) for a complete model description and details on the numerical solution of the governing equations. +
W
The model is abstract. Refer to accompanying paper and references therein: Barchyn TE, Hugenholtz CH. 2011. A new tool for modeling dune field evolution based on an accessible, GUI version of the Werner dune model. Geomorphology. Available from:
http://dx.doi.org/10.1016/j.geomorph.2011.09.021
Or, also, refer to original description of the model:
Werner, BT. 1995. Eolian dunes: computer simulations and attractor interpretation. Geology 23, 1107-1110. Available from:
http://dx.doi.org/10.1130/0091-7613(1995)023<1107:EDCSAA>2.3.CO;2 +
B
The model is mainly written in fortran and is based on the following characteristics:
- The finite volume approach from Tucker et al. (2001) based on the dual Delaunay-Voronoi framework is used to solve the continuity equation explicitly,
- Node ordering is perform efficiently based on the work from Braun & Willett (2013),
- A Hilbert Space-Filling Curve method algorithm (Zoltan) is used to partition the TIN-based surface into subdomains,
- Drainage network partitioning is generated through METIS library. +
T
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. +
L
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. +
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). +
C
P
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. +
S
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. +
D
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). +
G
Thermal capacities and conductivities prescribed for each subsurface layer, volumetric water content and unfrozen water coefficients. +
C
These are described in the extensive comments within the fortran program. +
M
This is the main program that calls Programs LITHFLEX2, FLDTA, ENTRAIN, ENTRAINH, SETTLE, SVELA, TURB, BEDLOAD, LOGDIST, SUSP. +
A
S
C
Too many to list here -- see Tucker et al. (2001a), the CHILD Users Guide, and other documents listed in the bibliography. +
T
