Property:Extended model description
From CSDMS
This is a property of type Text.
M
The Model Parameter Dictionary is a tool for numerical modelers to
easily read and access model parameters from a simple formatted
input (text) file. Each parameter has a KEY, which identifies the
parameter, and a VALUE, which can be a number or a string. A
ModelParameterDictionary object reads model parameters from an input
file to a Dictionary, and provides functions for the user to look up
particular parameters by key name.
The format of the input file looks like:
PI: the text "PI" is an example of a KEY
3.1416
AVOGADROS_NUMBER: this is another
6.022e23
FAVORITE_FRUIT: yet another
mangoes
NUMBER_OF_MANGO_WALKS: this one is an integer
4
ALSO_LIKES_APPLES: this is a boolean
true
Example code that reads these parameters from a file called
"myinputs.txt":
my_param_dict = ModelParameterDictionary()
my_param_dict.read_from_file( 'myinputs.txt' )
pi = my_param_dict.read_float( 'PI' )
avogado = my_param_dict.read_float( 'AVOGADROS_NUMBER' )
fruit = my_param_dict.read_string( 'FAVORITE_FRUIT' )
nmang = my_param_dict.read_int( 'NUMBER_OF_MANGO_WALKS' )
apples_ok = my_param_dict.read_bool( 'ALSO_LIKES_APPLES' )
As in Python, hash marks (#) denote comments. The rules are that each
key must have one and only one parameter value, and each value must
appear on a separate line immediately below the key line.
Also available are functions to read input parameters from the
command line (e.g., read_float_cmdline( 'PI' ) ) +
N
The Numerical model of coastal Erosion by Waves and Transgressive Scarps (NEWTS) model is a framework to simulate the erosion of a closed-basin coastline through time by fetch-dependent erosion or uniform erosion. +
P
The Permafrost Benchmark System (PBS) wraps the command-line ILAMB benchmarking system with a customized version of the CSDMS Web Modeling Tool (WMT), and adds tools for uploading CMIP5-compatible model outputs and benchmark datasets. The PBS allows users to access and run ILAMB remotely, without having to install software or data locally; a web browser on a desktop, laptop, or tablet computer is all that’s needed. +
The PermafrostBankErosionModel is a Python-based simulation module for modeling riverbank erosion in permafrost-dominated environments, where thermal and mechanical processes interact seasonally. The model simulates river bank ablation and collapse under fluvial and thermal forcing. +
The Princeton Ocean Model (POM), a simple-to-run yet powerful ocean modeling code that is able to simulate a wide-range of problems: circulation and mixing processes in rivers, estuaries, shelf and slope, lakes, semi-enclosed seas and open and global ocean. POM is a sigma coordinate, free surface ocean model with embedded turbulence and wave sub-models, and wet-dry capability. It has been one of the first coastal ocean models freely available to users, with currently over 3000 users from 70 countries.
For more details see: http://www.ccpo.odu.edu/POMWEB/ +
S
The SFINCS model (Super-Fast INundation of CoastS) is developed to efficiently simulate compound flooding events at limited computational cost and good accuracy. SFINCS solves the SSWE and thus includes advection in the momentum equation. However, it can also run using the LIE without advection. Processes such as spatially varying friction, infiltration and precipitation are included. Moreover, SFINCS includes wind-driven shear and an absorbing-generating weakly-reflective boundary is considered which are not included in other reduced-physics models. +
The Sea Level Affecting Marshes Model (SLAMM) simulates the dominant processes involved in
wetland conversions and shoreline modifications during long-term sea level rise. Tidal marshes can
be among the most susceptible ecosystems to climate change, especially accelerated sea level rise
(SLR). +
The Sorted Bedform Model (SBM) addresses the formation mechanism for sorted bedforms present on inner continental shelf environments. +
The Spectral Element Ocean Model (SEOM) solves the hydrostatic, and alternatively the non-hydrostatic, primitive equations using a mixed spectral / finite element solution procedure. Potential advantages of the spectral element method include flexible incorporation of complex geometry and spatially dependent resolution, rapid convergence, and attractive performance on parallel computer systems. A 2D version of SEOM, which solves the shallow water equations, has been extensively tested on applications ranging from global tides to the abyssal circulation of the Eastern Mediterranean. The 3D SEOM is undergoing initial testing for later release. +
T
The TELEMAC system is a powerful integrated modeling tool for use in the field of free-surface flows. The various simulation modules use high-capacity algorithms based on the finite-element method. Space is discretised in the form of an unstructured grid of triangular elements, which means that it can be refined particularly in areas of special interest. This avoids the need for systematic use of embedded models, as is the case with the finite-difference method.It has numerous applications in both river and maritime hydraulics. +
U
The Urban Inundation-Drainage Simulator (UIDS) is a new coupled model for simulating urban flooding dynamics, developed as an open-source, MATLAB-based platform. It integrates a rainfall-runoff model with a two-dimensional overland flow model (OFM) and a one-dimensional sewer flow model (SFM). +
The Utah Energy Balance (UEB) snow model is an energy balance snowmelt model developed by David Tarboton's research group, first in 1994, and updated over the years. The model uses a lumped representation of the snowpack and keeps track of water and energy balance. The model is driven by inputs of air temperature, precipitation, wind speed, humidity and radiation at time steps sufficient to resolve the diurnal cycle (six hours or less). The model uses physically-based calculations of radiative, sensible, latent and advective heat exchanges. A force-restore approach is used to represent surface temperature, accounting for differences between snow surface temperature and average snowpack temperature without having to introduce additional state variables. Melt outflow is a function of the liquid fraction, using Darcy's law. This allows the model to account for continued outflow even when the energy balance is negative. Because of its parsimony (few state variables - but increasing with later versions) this model is suitable for application in a distributed fashion on a grid over a watershed. +
V
The VIC model is a large-scale, semi-distributed hydrologic model. As such, it shares several basic features with the other land surface models (LSMs) that are commonly coupled to global circulation models (GCMs):
The land surface is modelled as a grid of large (>1km), flat, uniform cells
Sub-grid heterogeneity (e.g. elevation, land cover) is handled via statistical distributions.
Inputs are time series of daily or sub-daily meteorological drivers (e.g. precipitation, air temperature, wind speed).
Land-atmosphere fluxes, and the water and energy balances at the land surface, are simulated at a daily or sub-daily time step
Water can only enter a grid cell via the atmosphere
Non-channel flow between grid cells is ignored
The portions of surface and subsurface runoff that reach the local channel network within a grid cell are assumed to be >> the portions that cross grid cell boundaries into neighboring cells
Once water reaches the channel network, it is assumed to stay in the channel (it cannot flow back into the soil)
This last point has several consequences for VIC model implementation:
Grid cells are simulated independently of each other
Entire simulation is run for each grid cell separately, 1 grid cell at a time, rather than, for each time step, looping over all grid cells
Meteorological input data for each grid cell (for the entire simulation period) are read from a file specific to that grid cell
Time series of output variables for each grid cell (for the entire simulation period) are stored in files specific to that grid cell
Routing of stream flow is performed separately from the land surface simulation, using a separate model (typically the routing model of Lohmann et al., 1996 and 1998) +
W
The Water Erosion Prediction Project (WEPP) model is a process-based, distributed parameter, continuous simulation erosion prediction model for application to hillslope profiles and small watersheds. Interfaces to WEPP allow its application as a stand-alone Windows program, a GIS-system (ArcView, ArcGIS) extension, or in web-based links. WEPP has been developed since 1985 by the U.S. Department of Agriculture for use on croplands, forestlands, rangelands, and other land use types. +
The Water Table Model (WTM) simulates terrestrial water changes over the full range of relevant spatial (watershed to global) and temporal (monthly to millennial) scales. It comprises coupled components to compute dynamic lake and groundwater levels. The groundwater component solves the 2D horizontal groundwater-flow equation by using non-linear equation solvers in the C++ PETSc library. The dynamic lakes component makes use of the Fill-Spill-Merge (FSM) algorithm to move surface water into lakes, where it may evaporate or affect groundwater flow. +
The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. +
The Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro) was developed as a community-based, open source, model coupling framework designed to link multi-scale process models of the atmosphere and terrestrial hydrology to provide:
An extensible multi-scale & multi-physics land-atmosphere modeling capability for conservative, coupled and uncoupled assimilation & prediction of major water cycle components such as: precipitation, soil moisture, snow pack, ground water, streamflow, and inundation
Accurate and reliable streamflow prediction across scales (from 0-order headwater catchments to continental river basins and from minutes to seasons)
A research modeling testbed for evaluating and improving physical process and coupling representations. +
The bmi_wavewatch3 Python package provides both a command line interface and a programming interface for downloading and working with WAVEWATCH III data.
bmi_wavewatch3 provides access to the following raster data sources,
30 year wave hindcast Phase 1 https://polar.ncep.noaa.gov/waves/hindcasts/nopp-phase1.php
30 year wave hindcast Phase 2 https://polar.ncep.noaa.gov/waves/hindcasts/nopp-phase2.php
Production hindcast Singlegrid https://polar.ncep.noaa.gov/waves/hindcasts/prod-nww3.php
Production hindcast Multigrid https://polar.ncep.noaa.gov/waves/hindcasts/prod-multi_1.php
All data sources provide both global and regional grids. +
C
The carbonate production is modelled according to organism growth and survival rates moderated by habitat suitability (chiefly light, temperature, nutrient). The environmental inputs are extracted from global databases. At the seabed the model's vertical zonation allows for underground (diagenetic) processes, bed granular transport, lower stable framework, upper collapsable framework. This voxelation allows for the carbonate to be placed (accumulated) correctly within the bedding and clast fabrics. The stratigraphy and seabed elevation are built in this way. As conditions change (e.g., by shallowing) the biological communities respond in the simulation, and so too do the production rates and clast/binding arrangements. Events punctuate the record, and the organism assemblages adjust according to frequencies and severities. The population stocks are calculated by diffuse competition in a Lotke-Volterra scheme, or via cellular simulations of close-in interactions to represent competition by growth, recruitment. +
B
The code computes the formation of a hillslope profile above an active normal fault. It represents the hillslope as a set of points with vertical and horizontal (fault-perpendicular) coordinates. Points move due to a prescribed erosion rate (which may vary in time) and due to offset during earthquakes with a specified recurrence interval and slip rate.
The model is described and illustrated in the following journal article:
Tucker, G. E., S. W. McCoy, A. C. Whittaker, G. P. Roberts, S. T. Lancaster, and R. Phillips (2011), Geomorphic significance of postglacial bedrock scarps on normal-fault footwalls, J. Geophys. Res., 116, F01022, doi: http://dx.doi.org/10.1029/2010JF001861. +
