Property:Extended model description
From CSDMS
This is a property of type Text.
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. +
H
This model can be used for both transport after sediment failure and for hyperpycnal transport. +
This model evolves a hogback through time. A resistant layer of
rock, which weathers slowly, overlies a softer layer of rock that weathers
quickly. Resistant rock produces "blocks" which land on the adjoining
hillslope. Boundaries incise at a specified rate. User can set hogback
layer thickness, block size, and dip, as well as relative weathering and
incision rates. Trackable metrics included are time and space-averaged
slope, block height, weathering rate, and erosion rate. Parameters that users need to specify are surrounded by many
comment signs. +
I
This model generates investigation polygons which are used to estimate and store incremental erosion and deposition volumes along the path of a debris flow at user-defined resolution. The user will need: 1) a .LAS or .TIF of topographic change, 2) a DEM of the AOI, and 3) a shapefile (polyline) of the debris flow path of interest. Each incremental volume is georeferenced and stored within a shapefile attribute associated with a catchment area and distance from outlet for analysis. +
F
This model implement the calculation of the 'Frost Number', a dimensionless ratio based on freezing and thawing degree days in the year. Specifically, the 'Air Frost Number' intents to predict the existence of permafrost at a given location based on a cosine-function approximating the annual temperature distribution. +
W
This model is a GUI implementation of a simple cellular automata dune model. The model was originally proposed by Werner (1995, Geology 23) and has seen several extensions. It can simulate basic barchan, transverse, star, and linear dunes. The model is designed to be easy to operate for researchers or students without programming skills. Also included is a tool to operate the model from ArcGIS. +
M
This model is a nonuniform, quasi-unsteady, movable bed, single channel flow model for heterogeneous size-density mixtures +
G
This model is designed to simulate longitudinal profiles with headward advancing headcuts. This model simulates gully erosion on the centennial-scale given information such as average rainfall and infiltration rates. The modeler also specifies a headcut erosion rate and or a rule for headcut retreat (either discharge-dependent or height-dependent retreat). +
R
This model simulates the interaction between suspended sediment, chlorophyll-a, and mussel population density. Discharge is the driver; it modulates suspended sediment and its interactions in the system. The model is suitable for simulating mussel densities at-a-site. It was originally developed to test the hypothesis that increased sediment loads in Minnesota Rivers are a plausible cause of observed mussel population declines. The model and results are described in detail in the following paper: https://doi.org/10.1086/684223 +
