Property:Extended model description
From CSDMS
This is a property of type Text.
G
Gridded Surface Subsurface Hydrologic Analysis (GSSHA) is a grid-based two-dimensional hydrologic model. Features include 2D overland flow, 1D stream flow, 1D infiltration, 2D groundwater, and full coupling between the groundwater, vadoze zone, streams, and overland flow. GSSHA can run in both single event and long-term modes. The fully coupled groundwater to surfacewater interaction allows GSSHA to model both Hortonian and Non-Hortonian basins.
New features of version 2.0 include support for small lakes and detention basins, wetlands, improved sediment transport, and an improved stream flow model.
GSSHA has been successfully used to predict soil moistures as well as runoff and flooding. +
W
Gridded water balance model using climate input forcings that calculate surface and subsurface runoff and ground water recharge for each grid cell. The surface and subsurface runoff is propagated horizontally along a prescribed gridded network using Musking type horizontal transport. +
T
HIS is an internet-based system for sharing hydrologic data. It is comprised of databases and servers, connected through web services, to client applications, allowing for the publication, discovery and access of data. +
H
HYPE is a semi-distributed hydrological model for water and water quality. It simulates water and nutrient concentrations in the landscape at the catchment scale. Its spatial division is related to catchments and sub-catchments, land use or land cover, soil type and elevation. Within a catchment the model will simulate different compartments; soil including shallow groundwater, rivers and lakes. It is a dynamical model forced with time series of precipitation and air temperature, typically on a daily time step. Forcing in the form of nutrient loads is not dynamical. Example includes atmospheric deposition, fertilizers and waste water. +
E
Here, we present a Python tool that includes a comprehensive set of relations that predicts the hydrodynamics, bed elevation and the patterns of channels and bars in mere seconds. Predictions are based on a combination of empirical relations derived from natural estuaries, including a novel predictor for cross-sectional depth distributions, which is dependent on the along-channel width profile. Flow velocity, an important habitat characteristic, is calculated with a new correlation between depth below high water level and peak tidal flow velocity, which was based on spatial numerical modelling. Salinity is calculated from estuarine geometry and flow conditions. The tool only requires an along-channel width profile and tidal amplitude, making it useful for quick assessments, for example of potential habitat in ecology, when only remotely-sensed imagery is available. +
H
HexWatershed is a mesh independent flow direction model for hydrologic models. It can be run at both regional and global scales. The unique feature of HexWatershed is that it supports both structured and unstructured meshes. +
S
High order two dimensional simulations of turbidity currents using DNS of incompressible Navier-Stokes and transport equations. +
T
Hillslope diffusion component in the style of Carretier et al. (2016, ESurf), and Davy and Lague (2009).
Works on regular raster-type grid (RasterModelGrid, dx=dy). To be coupled with FlowDirectorSteepest for the calculation of steepest slope at each timestep. +
Hillslope evolution using a Taylor Series expansion of the Andrews-Bucknam formulation of nonlinear hillslope flux derived following following Ganti et al., 2012. The flux is given as:
qs = KS ( 1 + (S/Sc)**2 + (S / Sc)**4 + .. + (S / Sc)**2(n - 1) )
where K is is the diffusivity, S is the slope, Sc is the critical slope, and n is the number of terms. The default behavior uses two terms to produce a flux law as described by Equation 6 of Ganti et al., (2012). +
D
Hillslope sediment flux uses a Taylor Series expansion of the Andrews-Bucknam formulation of nonlinear hillslope flux derived following following Ganti et al., 2012 with a depth dependent component inspired Johnstone and Hilley (2014). The flux :math:`q_s` is given as:
q_s = DSH^* ( 1 + (S/S_c)^2 + (S/Sc_)^4 + .. + (S/S_c)^2(n-1) ) (1.0 - exp( H / H^*)
where :math:`D` is is the diffusivity, :math:`S` is the slope, :math:`S_c` is the critical slope, :math:`n` is the number of terms, :math:`H` is the soil depth on links, and :math:`H^*` is the soil transport decay depth. The default behavior uses two terms to produce a slope dependence as described by Equation 6 of Ganti et al., (2012).This component will ignore soil thickness located at non-core nodes. +
H
HydroCNHS is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling concept, into the HydroCNHS. +
HydroPy model is a revised version of an established global hydrological model (GHM), the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the HydroPy model requires much less effort in maintenance and new processes can be easily implemented. +
HydroTrend v.3.0 is a climate-driven hydrological water balance and transport model that simulates water discharge and sediment load at a river outlet. +
Hydrological Simulation Program - FORTRAN (HSPF) is a comprehensive package
for simulation of watershed hydrology and water quality for both conventional
and toxic organic pollutants (1,2). This model can simulate the hydrologic,
and associated water quality, processes on pervious and impervious land
surfaces and in streams and well-mixed impoundments. HSPF incorporates the
watershed-scale ARM and NPS models into a basin-scale analysis framework that
includes fate and transport in one-dimensional stream channels. It is the
only comprehensive model of watershed hydrology and water quality that allows
the integrated simulation of land and soil contaminant runoff processes with
in-stream hydraulic and sediment-chemical interactions.
The result of this simulation is a time history of the runoff flow rate,
sediment load, and nutrient and pesticide concentrations, along with a time
history of water quantity and quality at any point in a watershed. HSPF
simulates three sediment types (sand, silt, and clay) in addition to a single
organic chemical and transformation products of that chemical. The transfer
and reaction processes included are hydrolysis, oxidation, photolysis,
biodegradation, volatilization, and sorption. Sorption is modeled as a
first-order kinetic process in which the user must specify a desorption rate
and an equilibrium partition coefficient for each of the three solids types.
Resuspension and settling of silts and clays (cohesive solids) are defined in
terms of shear stress at the sediment water interface. The capacity of the
system to transport sand at a particular flow is calculated and resuspension
or settling is defined by the difference between the sand in suspension and
the transport capacity. Calibration of the model requires data for each of
the three solids types. Benthic exchange is modeled as sorption/desorption
and deposition/scour with surficial benthic sediments. Underlying sediment
and pore water are not modeled.
W
I am developing a GCM based on NCAR's WACCM model to studied the climate of the ancient Earth. WACCM has been linked with a microphysical model (CARMA). Some important issues to be examined are the climate of the ancient Earth in light of the faint young Sun, reducing chemistry of the early atmosphere, and the production and radiative forcing of Titan-like photochemical hazes that likely enshrouded the Earth at this time. +
C
I am developing a recent adaptation of CAM 3.0 that has been converted to Titan by Friedson et al. at JPL. I am adding the aerosol microphysics from CARMA. +
I
IDA formulates the task of determing the drainage area, given flow directions, as a system of implicit equations. This allows the use of iterative solvers, which have the advantages of being parallelizable on distributed memory systems and widely available through libraries such as PETSc.
Using the open source PETSc library (which must be downloaded and installed separately), IDA permits large landscapes to be divided among processors, reducing total runtime and memory requirements per processor.
It is possible to reduce run time with the use of an initial guess of the drainage area. This can either be provided as a file, or use a serial algorithm on each processor to correctly determine the drainage area for the cells that do not receive flow from outside the processor's domain.
The hybrid IDA method, which is enabled with the -onlycrossborder option, uses a serial algorithm to solve for local drainage on each processor, and then only uses the parallel iterative solver to incorporate flow between processor domains. This generally results in a significant reduction in total runtime.
Currently only D8 flow directions are supported. Inputs and outputs are raw binary files. +
ISSM is the result of a collaboration between the Jet Propulsion Laboratory and University of California at Irvine. Its purpose is to tackle the challenge of modeling the evolution of the polar ice caps in Greenland and Antarctica.
ISSM is open source and is funded by the NASA Cryosphere, GRACE Science Team, ICESat Research, ICESat-2 Research, NASA Sea-Level Change Team (N-SLCT), IDS (Interdisciplinary Research in Earth Science), ESI (Earth Surface and Interior), and MAP (Modeling Analysis and Prediction) programs, JPL R&TD (Research, Technology and Development) and the National Science Foundation +
IceFlow simulates ice dynamics by solving equations for internal deformation and simplified basal sliding in glacial systems. It is designed for computational efficiency by using the shallow ice approximation for driving stress, which it solves alongside basal sliding using a semi-implicit direct solver. IceFlow is integrated with GRASS GIS to automatically generate input grids from a geospatial database. +
Icepack is a Python package for simulating the flow of glaciers and ice sheets, as well as for solving glaciological data assimilation problems. The main goal for icepack is to produce a tool that researchers and students can learn to use quickly and easily, whether or not they are experts in high-performance computing. Icepack is built on the finite element modeling library firedrake, which implements the domain-specific language UFL for the specification of PDEs. +
