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A list of all pages that have property "Describe numerical limitations" with value "Presently limited to grids up to 4GB". Since there have been only a few results, also nearby values are displayed.

Showing below up to 26 results starting with #1.

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List of results

  • Model:MarshMorpho2D  + (Implicit code. Very stable. Very efficient)
  • Model:TopoToolbox  + (In general, Matlab stores all data in the main memory. Manageable grid size will depend on your available RAM. For conveniently working with grids with ~5000x5000 rows and columns, a 4Gb of RAM will likely be sufficient.)
  • Model:CASCADE  + (Limited to medium-size resolutions. Typical runs 256x256)
  • Model:RCPWAVE  + (Linear wave theory)
  • Model:CosmoLand  + (Matlab may max out memory if drainage basin is too large. Since this is a simplified landscape with wrap-around boundary conditions, one workaround is to do numerous smaller runs and add the output.)
  • Model:MARSSIM V4  + (Maximum timestep must be determined by trial.)
  • Model:STWAVE  + (Model Assumptions * Mild bottom slope and negligible wave reflection * Spatially homogeneous offshore wave conditions * Steady-state waves, currents, and winds * Linear refraction and shoaling * Depth-uniform current * Bottom friction is neglected)
  • Model:TOPMODEL  + (Model Limitations * TOPMODEL only simulatModel Limitations </br>* TOPMODEL only simulates watershed hydrology, although studies have been conducted to modify it to </br>simulate water quality dynamics. </br>* TOPMODEL can be applied most accurately to watersheds that do not suffer from excessively long dry </br>periods and have shallow homogeneous soils and moderate topography. </br>* Model results are sensitive to grid size, and grid size <=50 m is recommended.size, and grid size <=50 m is recommended.)
  • Model:Inflow  + (Model has difficulty with negative (uphill) slopes)
  • Model:BRaKE  + (Model is solved implicitly, but can become inaccurate at very large (~1000 year) timesteps. When baselevel forcing is mild and block effects are significant, slope-inversion instabilities can develop. The model catches these and will not continue running.)
  • Model:Equilibrium Calculator  + (Model limitations are related to the use of the goal seek function in excel to find the solution.)
  • Model:PyDeltaRCM  + (Model should never be numerically unstable but its behavior depends on ratios of various parameters. If the model seems to not be "doing anything", look at the parameter initialization functions in deltaRCM_tools.py)
  • Model:Marsh column model  + (Model slows down as layers are added making long runs (>2000 years) impractical.)
  • Model:Hogback  + (Model works well for resistant layer dips between 10 and 80 degrees. End members will work, but domain setup must be altered.)
  • Model:Dakotathon  + (Most Dakota analysis techniques require multiple iterations of a model to explore a requested parameter space, so an experiment created with Dakotathon can take a long time to run and produce a lot of model output.)
  • Model:CoastMorpho2D  + (Most of the heavy lifting algorithms are implicit, thus numerically stable)
  • Model:HEBEM  + (N/A)
  • Model:Non Local Means Filtering  + (N/A)
  • Model:SINUOUS  + (None identified)
  • Model:RiverMUSE  + (None known; the model requires very little computational expense.)
  • Model:GullyErosionProfiler1D  + (Numerical instabilities occur if the time step is too large.)
  • Model:CellularFanDelta  + (Numerical limitations and issues: # CurrenNumerical limitations and issues:</br># Currently the model runs with a constant timestep, which is limited by the maximum inflow. Future versions may include adaptive time-stepping.</br># As mentioned above, the model channels tend to be one or two cells wide. Future versions may address this issue with some combination of diffusive regularization or multi-scale modeling.ve regularization or multi-scale modeling.)
  • Model:Alpine3D  + (Overall, the model is very computationally intensive. It is usually ran on a grid or a cluster.)
  • Model:TopoFlow  + (Overland flow is currently modeled in a noOverland flow is currently modeled in a nonstandard way. Diffusive wave and dynamic wave routing routines need more testing. The linkage between the unsaturated zone (infiltration component) and saturated zone (subsurface flow component and water table) is not robust. component and water table) is not robust.)
  • Model:ISSM  + (Poor scaling for ice-flow models with direct solvers (improves upon use of iterative solvers, but convergence is not systematic).)
  • Model:CSt ASMITA  + (Probably more than we know but none come to mind.)
  • Model:MCPM  + (Quasi-static tide propagation. Flow neglected when water depth too small.)
  • Model:ROMS  + (ROMS has a predictior-corrector algorithm 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.ent algorithms that minimize this problem.)
  • Model:ChesROMS  + (ROMS has a predictior-corrector algorithm 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.ent algorithms that minimize this problem.)
  • Model:CBOFS2  + (ROMS has a predictior-corrector algorithm 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.ent algorithms that minimize this problem.)
  • Model:UMCESroms  + (ROMS has a predictior-corrector algorithm 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.ent algorithms that minimize this problem.)
  • Model:Caesar  + (Run times can be long (60 +days for large areas over many 100's of years). Flow model is steady state)
  • Model:Lake-Permafrost with Subsidence  + (Runs slowly - iterates implicit scheme. Some sort of matrix algebra might improve speed.)
  • Model:MARSSIM  + (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.)
  • Model:SBEACH  + (SBEACH is an empirically based model that 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.ore and after storms on the project coast.)
  • Model:PHREEQC  + (See 'Description of Input and Examples for PHREEQC Version 3 - A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations'.)
  • Model:WRF-Hydro  + (See WRF-Hydro Technical Description https://ral.ucar.edu/projects/wrf_hydro/technical-description-user-guide)
  • Model:EstuarineMorphologyEstimator  + (See article: https://doi.org/10.3390/rs10121915)
  • Model:Chi analysis tools  + (See documentation. The major limitation is computational time. This can be alleviated with sensible selection of module parameters. See documentation for guidance.)
  • Model:SPHYSICS  + (See manual)
  • Model:Nitrate Network Model  + (See related publication by J. A. Czuba.)
  • Model:River Network Bed-Material Sediment  + (See related publications by J. A. Czuba.)
  • Model:FwDET  + (See: Version 2.0: Cohen et al. (2019), TheSee:</br>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)</br> </br>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. Water Resources Association (JAWRA):1–12.)
  • Model:GRLP  + (Semi-implicit solution can decrease in accuracy for extremely long (hundreds of millions of years for typical input parameters) time steps)
  • Model:PIHM  + (Solver is efficient and accurate for very stiff systems of equations)
  • Model:FuzzyReef  + (Some of the fuzzy logic methods do not proSome 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. carbonate facies and productivity values.)
  • Model:AR2-sinuosity  + (Some parameter values result in channels that self-intersect. The code outputs both the raw centerline and a simplified centerline with self-intersections removed.)
  • Model:IceFlow  + (Some time step limitations due to the *semi* implicit nature of the code)
  • Model:Cliffs  + (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 ))
  • Model:TUGS  + (TUGS was developed with a fairly low budget, and thus, bugs may still exist. There are, however, no known numerical limitations at this point.)