Property:Describe numerical limitations
From CSDMS
This is a property of type Text.
C
Probably more than we know but none come to mind. +
M
R
ROMS has a predictior-corrector algorithm that is efficient and accuarate. This class of model (terrain-following) exhibits stronger sensitivity to topography which results in pressure gradient errors. ROMS has several pressure gradient algorithms that minimize this problem. +
C
ROMS has a predictior-corrector algorithm that is efficient and accuarate. This class of model (terrain-following) exhibits stronger sensitivity to topography which results in pressure gradient errors. ROMS has several pressure gradient algorithms that minimize this problem. +
ROMS has a predictior-corrector algorithm that is efficient and accuarate. This class of model (terrain-following) exhibits stronger sensitivity to topography which results in pressure gradient errors. ROMS has several pressure gradient algorithms that minimize this problem. +
U
ROMS has a predictior-corrector algorithm that is efficient and accuarate. This class of model (terrain-following) exhibits stronger sensitivity to topography which results in pressure gradient errors. ROMS has several pressure gradient algorithms that minimize this problem. +
C
Run times can be long (60 +days for large areas over many 100's of years).
Flow model is steady state +
L
Runs slowly - iterates implicit scheme. Some sort of matrix algebra might improve speed. +
M
Runs with grid sizes greater than about 600x600 may require many days on a PC.
Model assumes fluvial streams have gradients determined by steady-state transport.
Depositional stratigraphy not modeled. +
S
SBEACH is an empirically based model that was developed for sandy beaches with uniform representative grain sized in the range of 0.2 to 0.42 mm. SBEACH should be tested or calibrated using data from beach profile surveyed before and after storms on the project coast. +
P
See 'Description of Input and Examples for PHREEQC Version 3 - A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations'. +
W
See WRF-Hydro Technical Description https://ral.ucar.edu/projects/wrf_hydro/technical-description-user-guide +
E
See article: https://doi.org/10.3390/rs10121915 +
C
See documentation. The major limitation is computational time. This can be alleviated with sensible selection of module parameters. See documentation for guidance. +
S
N
See related publication by J. A. Czuba. +
R
See related publications by J. A. Czuba. +
F
See:
Version 2.0: Cohen et al. (2019), The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences (NHESS)
Version 1.0: Cohen, S., G. R. Brakenridge, A. Kettner, B. Bates, J. Nelson, R. McDonald, Y. Huang, D. Munasinghe, and J. Zhang (2017), Estimating Floodwater Depths from Flood Inundation Maps and Topography. Journal of the American Water Resources Association (JAWRA):1–12. +
G
Semi-implicit solution can decrease in accuracy for extremely long (hundreds of millions of years for typical input parameters) time steps +
P
F
Some of the fuzzy logic methods do not produce unique results as there are a variety of method choices that best 'match' test data. For example, the user can choose a variety of aggregation methods to calculate final carbonate facies and productivity values. +
A
Some parameter values result in channels that self-intersect. The code outputs both the raw centerline and a simplified centerline with self-intersections removed. +
I
C
Subject to CFL stability condition. Sharp depth changes can cause instability even with low Courant numbers. Pre-processing with depth_ssl is recommended (see Cliffs User Manual at http://arxiv.org/abs/1410.0753 ) +
T
TUGS was developed with a fairly low budget, and thus, bugs may still exist. There are, however, no known numerical limitations at this point. +
S
O
The calculations assume the waves are wind waves with periods in the range of 0-~30 s. If the waves are much larger or produced by a different mechanism, the calculations are not likely to be accurate. +
T
The elevation specific equation of mass conservation is integrated with the Euler method. Thus, the user should carefully choose the spatial distance between computational nodes in the vertical and streamwise direction, as well as the temporal increment, to guarantee the numerical stability and mass conservation. +
C
The fluvial sediment transport equations are quasi-diffusive and typically have orders of magnitude spatial variations in rate coefficient (reflecting differences in water discharge), which makes the system of equations stiff. Small time steps are typically required, which can lead to long compute times for large meshes. +
S
The model cannot yet fully handle complex coastline geometries, such as those that cannot be represented (after rotation) by a single-valued function. +
Q
C
The model handles complex-shaped coastlines, such as cuspate-capes and spits. However, where the shoreline curvature becomes extreme (radius of curvature comparable to the cross-shore shoreface extent), as at the ends of spits, the assumptions of a locally rectilinear coordinate system break down, and sediment is conserved less rigorously locally. See Ashton and Murray (2006a) for details. +
W
M
The model might be unstable if the meander bends are too sharp and/or flow parameters are somehow borderline. +
R
The model was designed for laminar to transitional flows, up to 10 cm/s. Under these conditions, the flow velocity solution is approximate but is realistic and stable. +
L
The profiles should not be spaced too closely in order to avoid an unstable saw-tooth longitudinal profile of the river. +
C
T
This model is only tested in rectangle domains, and compared the results with idealized experiments;
Sediment bed state is affected by the initial condition (mainly due to the frictional stess closure in this model). For 2D or 3D runs, it is suggested that first run 1DV to steady or quasi-steady state, and map the 1DV results to 2DV or 3D, in this way, the initial instability of the sediment bed can be avoided. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
This model/component needs more rigorous testing. +
D
Use with high rates of SLR will require additional work on the vegetation rules; currently with both vegetation and high rates of SLR the simple water routing rules are unable to consistently route water to the shoreline. +
C
Usual Python limitations on floating point precision. ODE's are solved on scales much finer than annual, and their performance is monitored. +
N
Wave modules have an automatic grid refinement feature. Circulation modules have two kinds: CFL limited and CFL-free schemes. +
D
With larger DEMs this tool will take longer to run - it takes around half an hour to run with a 1m DEM with 23000 rows and 18000 columns. Documentation is available for guidance. +
K
analytical solution +
Z
X
D
F
W
M
R
D
n/a
Limitations on the method do occur when delta systems consist of only a few islands, then the input dataset of geometric parameters becomes too small for the machine learning methods. +
C
D
relatively mild stability and accuracy constraints (depending on application) due to implicit numerical schemes +
G
O
S
B
uses forward-Euler timestepping, therefore timestep must be small enough to insure numerical stability +
