Property:Describe processes
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T
The Energy Balance method of estimating losses due to evaporation. +
The Green-Ampt method for modeling infiltration. +
P
The Penn State Integrated Hydrologic Model (PIHM) is a fully coupled multiprocess hydrologic model. Instead of coupling through artificial boundary conditions, major hydrological processes are fully coupled by the semi-discrete finite volume approach. For those processes whose governing equations are partial differential equations (PDE), we first discretize in space via the finite volume method. This results in a system of ordinary differential equations (ODE) representing those procesess within the control volume. Within the same control volume, combining other processes whose governing equations are ODE’s, (e.g. the snow accumulation and melt process), a local ODE system is formed for the complete dynamics of the finite volume. +
Q
The QUAL2K framework includes the following new elements:
*Software Environment and Interface. Q2K is implemented within the Microsoft Windows environment. Numerical computations are programmed in Fortran 90. Excel is used as the graphical user interface. All interface operations are programmed in the Microsoft Office macro language: Visual Basic for Applications (VBA).
*Model segmentation. Q2E segments the system into river reaches comprised of equally spaced elements. Q2K also divides the system into reaches and elements. However, in contrast to Q2E, the element size for Q2K can vary from reach to reach. In addition, multiple loadings and withdrawals can be input to any element.
*Carbonaceous BOD speciation. Q2K uses two forms of carbonaceous BOD to represent organic carbon. These forms are a slowly oxidizing form (slow CBOD) and a rapidly oxidizing form (fast CBOD).
*Anoxia. Q2K accommodates anoxia by reducing oxidation reactions to zero at low oxygen levels. In addition, denitrification is modeled as a first-order reaction that becomes pronounced at low oxygen concentrations.
*Sediment-water interactions. Sediment-water fluxes of dissolved oxygen and nutrients can be simulated internally rather than being prescribed. That is, oxygen (SOD) and nutrient fluxes are simulated as a function of settling particulate organic matter, reactions within the sediments, and the concentrations of soluble forms in the overlying waters.
*Bottom algae. The model explicitly simulates attached bottom algae. These algae have variable stoichiometry.
*Light extinction. Light extinction is calculated as a function of algae, detritus and inorganic solids.
*pH. Both alkalinity and total inorganic carbon are simulated. The river’s pH is then computed based on these two quantities.
*Pathogens. A generic pathogen is simulated. Pathogen removal is determined as a function of temperature, light, and settling.
*Reach specific kinetic parameters. Q2K allows you to specify many of the *Weirs and waterfalls. The hydraulics of weirs as well as the effect of weirs and waterfalls on gas transfer are explicitly included.
T
The Richards 1D method for modeling infiltration. +
The Smith-Parlange 3-parameter method for modeling infilteration. +
The TopoToolbox 2 is a Matlab based software for Digital Elevation Model (DEM) analysis. It uses an object oriented programming (OOP) approach to represent and work with geoferenced raster data, flow directions, stream networks and swath profiles in Matlab. TopoToolbox offers a wide range of tools to analyse DEMs, flow and stream networks, that allow for interactive and automated workflows. +
M
The bed is represented by a 2-D matrix. At this time the bed is 250 x 250. Each block (i,j) is taken to be a slab of sediment 10cm x 10cm x 1cm deep.
A second matrix represents the flow. This is the same everywhere in the domain at each time point, except for a random spatial fluctuation representing turbulence.
The user-defined flow picks up (or puts down) sediment according to a transport law. Three transport laws have been tested: Bailard (1981), Ribberink (1998) or simple rules. The simple rules are simply thresholds: (if flow greater than 70cm/sec pick up one block).
Once sand block have been picked up, they are moved forward with the flow. Generally, I have used a fraction of the distance that the water would travel (jump_frac). So, with a flow of 100cm/sec, in one second that water goes 100 cm. The sand in this case would go 50 cm (half the distance). At the extremes, the model is sensitive to this parameter, but at intermediate values, it is not.
Tested flows have consisted of combined sinusoidal flow+steady flow, purely osc, purely steady, and natural flow time series taken from current meter measurements. All flows have a random spatial fluctuation added at each time point.
Once bedforms are generated, feedback rules are employed by altering the flow over the bedform. Once a bedform gets tall, the flow over the top accelerates, increasing transport at that location. In the steep lee of a bedform, a shadow zone forms where flow goes to ~zero, therefore transport goes to zero. These mechanisms destroy or build bedforms. +
P
The code models the evolution of a diffusive interface and the instabilities that arises when a less viscous fluid pushes a more viscous one in a confined rectangular geometry. +
T
The diffusive wave method for flow routing in the channels of a D8-based river network. +
The dynamic wave method for flow routing in the channels of a D8-based river network. +
C
The effects of individual storm events and SLR on shoreface evolution; dune dynamics, including dune growth, erosion, and migration; overwash deposition by individual storms; large-scale coastline evolution arising from alongshore sediment transport processes; and human management activities. +
The four primary components of our multi-physics code include geomechanical, hydrologic, solute transport and heat transfer modules. The geomechanical module calculates displacement of an elastic lithosphere disturbed by an ice sheet load. Transient geomechanical deformation is represented by one-dimensional (lateral) viscous asthenosphere flow. Our geomechanical module is partially coupled to the hydrologic module by providing the rate of change in the mean normal stress. Mean normal stress change rate is included as a source term in the groundwater flow equation driving flow. Flow is also influenced by changes in the top specified hydraulic head boundary condition. We implement two-way coupling between fluid flow, solute transport and heat transfer module via density and viscosity equations of state.
Three additional modules in our multi-physics code calculate changes to the upper hydraulic and thermal boundary conditions or alter the hydraulic transport properties (permeability) due to hydrogeomechanical failure. These include ice sheet evolution, permafrost, and failure analysis modules. Ice sheet thickness determines both the vertical load in the geomechanical module as well as the hydraulic head boundary condition at the land surface in the hydrologic module. In this study we adopted a simple parabolic polynomial equation to represent the idealized geometry of an ice sheet’s cross section in the ice sheet evolution module. We solved for permafrost
formation at and below the land surface using a suite of one-dimensional heat transfer models. We allowed for grid growth within the permafrost module to account for changes in ice sheet thickness. A failure analysis module was used to modify permeability due to hydromechanical failure. We adopted the effective Coulomb’s Failure Stress change criterion from Ge et al.(2009) to assess regions of failure during glaciations. +
M
The ground-water flow equation is solved using the finite-difference approximation. The flow region is subdivided into blocks in which the medium properties are assumed to be uniform. In plan view the blocks are made from a grid of mutually perpendicular lines that may be variably spaced. Model layers can have varying thickness. A flow equation is written for each block, called a cell. Several solvers are provided for solving the resulting matrix problem; the user can choose the best solver for the particular problem. Flow-rate and cumulative-volume balances from each type of inflow and outflow are computed for each time step. +
W
The integrated multi-processes include:
# hydrological cycles (evaporation, evapotranspiration, infiltration, and recharges);
# fluid flow (surface runoff in land surface, hydraulics and yydrodynamics in river/stream/canal networks;
# interflow in vadose zones, and groundwater flow in saturated zones);
# salinity transport and thermal transport (in surface waters and groundwater);
# sediment transport (in surface waters);
# water quality transport (any number of reactive constituents);
# biogeochemical cycles (nitrogen, phosphorous, carbon, oxygen, etc.); and
# biota kinetics (algae, phyotoplankton, zooplakton, caliform, bacteria, plants, etc.). +
C
The key processes are 1) topographically-driven overland flow and 2) bedload transport by this flow. Through these processes the model self-organizes channels which incise, back-fill, and avulse. Processes are similar to alluvial fans. There are no marine processes besides bedload dumping. +
T
The kinematic wave method for flow routing in the channels of a D8-based river network. +
R
The main source code calls sub-modules that simulate the following processes:
- Vegetation community colonization as a function of local water depth. Colonization is deterministic over some ranges and stochastic in others.
- Solution of flow field in two dimensions using a cellular automata algorithm (see Larsen and Harvey, 2010, Geomorphology, and Larsen and Harvey, 2010 in press, American Naturalist). The flow field is only simulated during high-flow events that entrain sediment.
- Sediment transport by flow according to an advection-dispersion equation. Within each high-flow pulse, steady conditions are assumed.
- Evolution of the topography through sediment transport, peat accretion (which is based on Larsen et al., Ecological Monographs, 2007), diffusive erosion of topographic gradients, vegetative propagation, and below-ground biomass expansion.
- Adjustment of water levels and high-flow discharge to satisfy a water balance and compensate for the growth of vegetation patches. +
F
The mean annual temperature of the warmest and coldest months at a given location gives a first-order estimate of distribution of permafrost. +
K
The model calculates changes in elevation and vegetation growth for a hypothetical salt marsh. In each cell, elevation change is calculated as the difference between accretion and erosion. Accretion rates are a function of inundation depth, vegetation growth, and suspended sediment concentration. Water routed according to Rinaldo et al. (1999) scheme. Erosion rates calculated according to excess sheer stress. Channels widen according to a diffusion-like routine where downslope transport is inversely proportional to vegetation. Vegetation grows according to Morris et al. (2002) where biomass is proportional to inundation depth up until an optimum depth. Episodic vegetation disturbance is simulated by removing vegetation from randomly selected cells (Kirwan et al., 2008). Another version of the model treats wave erosion in a simplistic manner (Kirwan and Murray, 2008). +
