Property:Extended model description
From CSDMS
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D
This tool provides a method for extracting information on the nature and spatial extent of active geomorphic processes across deltas from the geometry of islands and the channels around them using machine learning.
The method consists of a two-step ensemble unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on morphological metrics computed on remotely sensed imagery +
This tool uses chi river profile analysis to predict channel head locations across a landscape and therefore allow the extraction of river networks. It is most suitable for use with high resolution LiDAR (1m) DEMs. The model requires an input DEM in float format and will output the extracted channel heads and networks, also in float format. For detailed information about how to use this tool please refer to the documentation (http://www.geos.ed.ac.uk/~smudd/LSDTT_docs/html/channel_heads.html)
and to the associated paper (http://onlinelibrary.wiley.com/doi/10.1002/2013WR015167/full). +
R
This toolbox was constructed to help analyze changing river planforms (aerial views). Given a binary mask of a river, tools are provided to efficiently compute
- channel centerline
- banklines
- channel width (two methods)
- centerline direction
- centerline curvature
If multiple input mask images contain georeference information, a tool is provided to "stitch" the masks together--before or after analysis. Stitching can be done for both images and vectors of x,y coordinates. The mapping toolbox is required for this functionality.
If multiple masks (realizations) of the river are available, RivMAP includes tools to
- compute centerline migrated areas
- compute erosional and accretional areas
- identify cutoff areas and quantify cutoff length, chute length, and cutoff area
- generate channel belt boundaries and centerline
- measure and map changes (in width, migration areas or rates, centerline elongation, accreted/eroded areas) in space and time +
A
This workbook computes 1D bed variation in rivers due to differential sediment transport. The sediment is assumed to be uniform with size D. All sediment transport is assumed to occur in a specified fraction of time during which the river is in flood, specified by an intermittency. A Manning-Strickler formulation is used for bed resistance. A generic relation of the general form of that due to Meyer-Peter and Muller is used for sediment transport. The flow is computed using the normal flow approximation. +
This workbook computes the time evolution of a river toward steady state as it flows into a subsiding basin. The subsidence rate s is assumed to be constant in time and space. The sediment is assumed to be uniform with size D. A Manning-Strickler formulation is used for bed resistance. A generic relation of the general form of that due to Meyer-Peter and Muller is used for sediment transport. The flow is computed using the normal flow approximation. The river is assumed to have a constant width. +
G
Three dimensional simulations of the Turbidity currents using DNS of incompressible Navier-Stokes and transport equations. +
T
TopoFlow is a powerful, spatially-distributed hydrologic model with a user-friendly point-and-click interface. Its main purpose is to model many different physical processes in a watershed with the goal of accurately predicting how various hydrologic variables will evolve in time in response to climatic forcings. +
TopoPyScale uses a pragmatic approach to downscaling by minimizing complexity, reducing computational cost,
simplifying interoperability with land surface models, while retaining physical coherence and allowing the primary drivers of land surface-atmosphere interaction to be considered. +
TopoToolbox provides a set of Matlab functions that support the analysis of relief and flow pathways in digital elevation models. The major aim of TopoToolbox is to offer stable and efficient analytical GIS utilities in a non-GIS environment in order to support the simultaneous application of GIS-specific and other quantitative methods. With version 2, TopoToolbox adds various tools specifically targeted at tectonic geomorphologists such as Chiplots and slopearea plots. +
Topography is a Python library to fetch and cache NASA Shuttle Radar Topography Mission (SRTM) and JAXA Advanced Land Observing Satellite (ALOS) land elevation data using the OpenTopography REST API.
The Topography library provides access to the following global raster datasets:
* SRTM GL3 (90m)
* SRTM GL1 (30m)
* SRTM GL1 (30m, Ellipsoidal)
* ALOS World 3D (30m)
* ALOS World 3D (30m, Ellipsoidal)
* Global Bathymetry SRTM15+ V2.1
* NASADEM Global DEM
* Copernicus Global DSM 30m
* Copernicus Global DSM 90m
The library includes an API and CLI that accept the dataset type, a latitude-longitude bounding box, and the output file format. Data are downloaded from OpenTopography and cached locally. The cache is checked before downloading new data. Data from a cached file can optionally be loaded into an xarray DataArray using the experimental open_rasterio method. +
A
Traditionally the Area-Slope equation (S=cA^alpha) is extracted from a catchment area vs. slope plot. This model calculate the Area-Slope constant and coefficient (alpha) for each pixel at the catchment as a function of its downslope neighbor. +
O
Transiently evolving river-channel width as a function of streambank properties, sediment in transport, and the hydrograph.
This model is designed to compute the rates of river-channel widening and narrowing based on changing hydrological regimes. It is currently designed for rivers with cohesive banks, with a critical shear stress for particle detachment and an erosion-rate coefficient.
OTTAR contains:
* The RiverWidth class, which contains methods to evolve the width of an alluvial river.
* The FlowDepthDoubleManning class, which is used to estimate flow depth from discharge, even with an evolving river-channel geometry. +
U
Underworld2 is an open-source, particle-in-cell finite element code tuned for large-scale geodynamics simulations. The numerical algorithms allow the tracking of history information through the high-strain deformation associated with fluid flow (for example, transport of the stress tensor in a viscoelastic, convecting medium, or the advection of fine-scale damage parameters by the large-scale flow). The finite element mesh can be static or dynamic, but it is not contrained to move in lock-step with the evolving geometry of the fluid. This hybrid approach is very well suited to complex fluids which is how the solid Earth behaves on a geological timescale. +
S
Uses the Barnes et al (2014) algorithms to replace pits in a topography with flats, or optionally with very shallow gradient surfaces to allow continued draining.
This component is NOT intended for use iteratively as a model runs; rather, it is to fill in an initial topography. If you want to repeatedly fill pits as a landscape develops, you are after the LakeMapperBarnes component. If you want flow paths on your filled landscape, manually run a FlowDirector and FlowAccumulator for yourself.
The locations and depths etc. of the fills will be tracked, and properties are provided to access this information. +
W
WACCM is NCAR's atmospheric high-altitude model; CARMA is Brian Toon's aerosol microphysical sectional model. I'm studying sulfate aerosols in the UTLS region using this coupled model. +
WASH123D is an integrated multimedia, multi-processes, physics-based computational watershed model of various spatial-temporal scales. The integrated multimedia includes:
# dentric streams/rivers/canal/open channel,
# overland regime (land surface),
# subsurface media (vadose and saturated zones), and
# ponds, lakes/reservoirs (small/shallow).
It also includes control structures such as weirs, gates, culverts, pumps, levees, and storage ponds and managements such as operational rules for pumps and control structures.
The WASH123D code consisted of eight modules to deal with multiple media:
# 1-D River/Stream Networks,
# 2-D Overland Regime,
# 3-D Subsurface Media (both Vadose and Saturated Zones);
# Coupled 1-D River/Stream Network and 2-D Overland Regime,
# Coupled 2-D Overland Regime and 3-D Subsurface,
# Coupled 3-D Subsurface and 1-D River Systems;
# Coupled 3-D Subsurface Media, 2-D Overland, and 1-D River Network; and
# Coupled 0-D Shallow Water Bodies and 1-D Canal Network.
For any of the above eight modules, flow only, transport only, or coupled flow and transport simulations can be carried out using WASH123D. +
WAVI.jl is designed to make ice sheet modelling more accessible to beginners and low-level users, whilst including sufficient detail to be used for addressing cutting-edge research questions. +
C
We have developed a hybrid numerical model at a continental scale via control volume finite element (finite volume) and regular finite element methods to evaluate the stress variation, pore pressure evolution, brine migration, solute transport and heat transfer in the subsurface formations in response to ice sheet loading of multiple glacial cycles. +
Q
We present a geometric model able to track the geomorphic boundaries that delimit the fluvial plain of fluvial-deltas: the shoreline and the alluvial-bedrock transition. By assuming a fluvial profile with a quadratic form, which satisfies the overall mass balance and the boundary conditions dictated by diffusive transport, we are able to provide a solution that accounts for general base-level changes. +
R
When wind blows over snow, it self-organizes. This forms surface features, such as ripples and dunes, that alter the reflectivity and thermal conductivity of the snow.
Studying these features in the field is cold and challenging (we've tried), so we created rescal-snow to enable snow scientists to study snow features in controlled numerical experiments. We hope that this model will be useful to researchers in snow science, geomorphology, and polar climate.
Rescal-snow is able to simulate:
- Snow/sand grain erosion and deposition by wind
- Snowfall
- Time-dependent cohesion (snow sintering)
- Avalanches of loose grains
Rescal-snow is also designed for robust, reproducible science, and contains tools for high-performance computing, data management, and data analysis, including:
- Workflow tools for generating and running many simulations in parallel
- A python-based workflow that manages data and analysis at runtime
These processes, along with model input, output, performance and constraints, are discussed in detail in the project docs and readme. +
