Property:Extended model description
From CSDMS
This is a property of type Text.
V
This code is based on Cellular Automata Tree Grass Shrub Simulator (CATGraSS). It simulates spatial competition of multiple plant functional types through establishment and mortality. In the current code, tree, grass and shrubs are used. +
H
This component calculates Hack’s law parameters for drainage basins.
Hacks law is given as
L = C * A**h
Where L is the distance to the drainage divide along the channel, A is the drainage area, and C are parameters.
The HackCalculator uses a ChannelProfiler to determine the nodes on which to calculate the parameter fit. +
C
This component calculates chi indices, sensu Perron & Royden, 2013, for a Landlab landscape. +
S
This component calculates steepness indices, sensu Wobus et al. 2006, for a Landlab landscape. Follows broadly the approach used in GeomorphTools, geomorphtools.org. +
F
This component generates random numbers using the Weibull distribution
(Weibull, 1951). No particular units must be used, but it was written with
the fire recurrence units in time (yrs).
Using the Weibull Distribution assumes two things: All elements within the study area have the same fire regime. Each element must have (on average) a constant fire regime during the time span of the study.<br>
As of Sept. 2013, fires are considered instantaneous events independent of
other fire events in the time series. +
D
This component identifies depressions in a topographic surface, finds an outlet for each depression. If directed to do so (default True), and the component is able to find existing routing fields output from the 'route_flow_dn' component, it will then modify the drainage directions and accumulations already stored in the grid to route flow across these depressions. +
This component implements a depth and slope dependent linear diffusion rule in the style of Johnstone and Hilley (2014). Soil moves with a prescribed exponential vertical velocity profile. Soil flux is dictated by a diffusivity, K, and increases linearly with topographic slope. +
E
This component implements exponential weathering of bedrock on hillslopes. Uses exponential soil production function in the style of Ahnert (1976).
Consider that w_0 is the maximum soil production rate and that d* is the characteristic soil production depth. The soil production rate w is given as a function of the soil depth d,
w = w_0^(-d/d*)
The ExponentialWeatherer only calculates soil production at core nodes. +
L
This component is closely related to the FlowAccumulator, in that this is accomplished by first finding flow directions by a user-specified method and then calculating the drainage area and discharge. However, this component additionally requires the passing of a function that describes how discharge is lost or gained downstream, f(Qw, nodeID, linkID, grid). See examples at https://github.com/landlab/landlab/blob/master/landlab/components/flow_accum/lossy_flow_accumulator.py to see how this works in practice. +
F
This components finds the steepest single-path steepest descent flow directions. It is equivalent to D4 method in the special case of a raster grid in that it does not consider diagonal links between nodes. For that capability, use FlowDirectorD8. +
This is a 1DV wave-phase resolving numerical model for fluid mud transport based on mixture theory with boundary layer approximation. The model incorporates turbulence-sediment interaction, gravity-driven flow, mud rheology, bed erodibility and the dynamics of floc break-up and aggregation. +
W
This is a Java Applet that allows the user to change different parameters (such as rainfall, erodibility, tectonic uplift) and watch how the landform evolve over time under different scenarios. It is based on a Cellular Automata algorithm. Two versions are available: linear and non-linear. Details can be found in:
Luo, W., Peronja, E., Duffin, K., Stravers, A. J., 2006, Incorporating Nonlinear Rules in a Web-based Interactive Landform Simulation Model (WILSIM), Computers and Geosciences, v. 32, n. 9, p. 1512-1518 (doi: 10.1016/j.cageo.2005.12.012).
Luo, W., K.L. Duffin, E. Peronja, J.A. Stravers, and G.M. Henry, 2004, A Web-based Interactive Landform Simulation Model (WILSIM), Computers and Geosciences. v. 30, n. 3, p. 215-220. +
G
This is a Landlab wrapper for A Wickert's gFlex flexure model (Wickert et al., submitted to Geoscientific Model Development). The most up-to-date version of his code can be found at github.com/awickert/gFlex.
This Landlab wrapper will use a snapshot of that code, which YOU need to install on your own machine. A stable snapshot of gFlex is hosted on PyPI, which is the recommended version to install.
If you have pip (the Python package install tool), simply run 'pip install gFlex' from a command prompt.
Alternatively, you can download and unpack the code (from github, or with PyPI, pypi.python.org/pypi/gFlex/), then run 'python setup.py install'. +
C
This is a special case of the Regional Ocean Modeling System(ROMS). The National Ocean Service presently has an Operational Forecast System (CBOFS) for the Chesapeake Bay which generates only water levels and depth‐integrated currents. As a next generation system, a fully three‐dimensional, baroclinic Forecast System (CBOFS2) was developed, calibrated and validated; this system will produce water levels, currents, temperature and salinity. First, a two‐month tides only simulation was conducted to validate the water levels and currents and thereafter, a synoptic hindcast simulation from June 01, 2003–September 01, 2005 was conducted to validate water levels, currents, temperature and salinity. Upon comparison with observations, CBOFS2 for the most part met the target NOS water level error criteria and for current error, the criteria were met exceptionally well; the temperature and salinity errors were frequently less than 1 C and 3 PSU respectively. Hence, the predictive accuracy of CBOFS2 warranted it being accepted as a suitable three‐dimensional upgrade to CBOFS.
Please see https://csdms.colorado.edu/wiki/Model:ROMS for details. +
N
This is a time-stepping point model which uses linear finite elements to determine the vertical structure of the horizontal components of velocity and density under specified surface forcing. Both a quadratic closure scheme and the level 2.5 closure scheme of Mellor and Yamada are used in this code. +
G
This is a tool that I created to help find knickpoints based on the curvature of a landscape. It provides information about a stream including, knickpoint locations, Elevation/distance that can be used to create longitudinal profiles, XYvalues of all the cells in a stream path, etc. The tool uses built-in tools for ArcGIS 10.x (so you must run this on a machine with ArcGIS 10.x installed), but it is written in python. I used it with a 1m LiDAR DEM, so I'm not totally sure how well it will pick out knickpoints on coarser gridded DEMs. +
A
This is an Arctic-delta reduced-complexity model that can reproduce the 2-m ramp feature observed in most Arctic deltas. The model is built by first reconstructing from published descriptions of the DeltaRCM-Arctic model (Lauzon et al., GRL, 2019), which is, in turn, based on DeltaRCM by Liang et al. (Esurf, 2015). All the modifications and refinements leading to this model (ArcDelRCM.jl) are detailed in a manuscript submitted to Earth Surface Dynamics journal for publication (Chan et al., 2022: esurf-2022-25). Options are retained to run this model with the "DeltaRCM-Arctic" (reconstruction) setting. The code is written purely in Julia language. +
T
This model a 1-D numerical model of permafrost and subsidence processes.
It aims to investigate the subsurface thermal impact of thaw lakes of various depths, and to evaluate how this impact might change in a warming climate. +
M
This model accounts for the bed evolution i.e. aggradation/degradation and grain size distribution of surface material in gravel bed rivers under anthropogenic changes such as dam closure and sediment augmentation. This model is developed for an alpine gravel bed river located in SE France (Buech river). +
G
This model calculates the long profile of a river with a gravel-sand transition. The model uses two grain sizes: size Dg for gravel and size Ds for sand. The river is assumed to be in flood for the fraction of time Ifg for the gravel-bed reach and fraction Ifs for the sand-bed reach. All sediment transport is assumed to take place when the river is in flood.
Gravel transport is computed using the Parker (1979) approximation of the Einstein (1950) bedload transport relation. Sand transport is computed using the total bed material transport relation of Engelund and Hansen (1967).
In this simple model the gravel is not allowed to abrade. Both the gravel-bed and sand-bed reaches carry the same flood discharge Qbf.
Gravel is transported as bed material in, and deposits only in the gravel-bed reach. A small residual of gravel load is incorporated into the sand at the gravel-sand transition. Sand is transported as washload in the gravel-bed reach, and as bed material load in the sand-bed reach.
The model allows for depositional widths Bdgrav and Bdsand that are wider than the corresponding bankfull channel widths Bgrav and Bsand of the gravel-bed and sand-bed channels. As the channel aggrades, it is assumed to migrate and avulse to deposit sediment across the entire depositional width. For each unit of gravel deposited in the gravel-bed reach, it is assumed that Lamsg units of sand are deposited. For each unit of sand deposited on the sand-bed reach, it is assumed that Lamms units of mud are deposited.
The gravel-bed reach has sinuosity Omegag and the sand-bed reach has sinuosity Omegas.
Bed resistance is computed through the use of two specified constant Chezy resistance coefficients; Czg for the gravel-bed reach and Czs for the sand-bed reach. +
