Quadtree and Sub-Grid Sampling FAQ: Difference between revisions

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=How coarse can the base cell size be for a Quadtree model with Sub-Grid Sampling (SGS)?=
There are two answers to this question depending on your modelling objectives. For example, by doubling your base cell size, and not changing the Quadtree levels, you will be doubling the cell sizes throughout your model. Provided this doubling of cell sizes does not conflict with other objectives (remember, only one velocity is calculated per cell face, so a consequence of doubling cell size is to reduce the quality of velocity based outputs such as hazard), whether or not it's is okay to increase the base cell size simply comes down to whether results convergence can be proven. By results convergence we mean you can increase (or decrease) your cell sizes without unacceptably changing the model results. If you do see a significant change in results that is considered unacceptable, then you need to possibly make your cell sizes smaller (not larger!). A well-designed model mesh is one where you can decrease cells sizes without seeing an unacceptable change in results. SGS will greatly help with achieving results convergence and very much increase your ability to use a coarser base cell size or coarsen up parts of your model.
The second part of the answer is if you wish to coarsen up parts of your model but retain the same cell sizes in your focus area. To achieve this you can increase your base cell size to your largest cell size you wish to use, then add additional levels of nesting layers for your Quadtree mesh noting that the 2020-01 release of TUFLOW allows for up to 9 levels of nesting, so your smallest cell size can only be one-eighth of your base cell size.
 
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The main advantages of using Quadtree are shorter run times (with considerable cell count reduction compared with using a HPC grid), lower GPU RAM footprint and smaller size of output files. If you're not achieving this by using Quadtree then there is little benefit in using it. Some models with cell count reduction of only around 30% may even run slower with Quadtree than the original HPC model.
 
=Why IWhen should I not use a combination of SGS and Quadtree?=
Based on the benchmarking thus far there seems to be little reason not to use SGS with Quadtree. The one exception is if the underlying resolution of the DEM is of similar (or coarser) resolution to the 2D cells thethen there is little reason to use SGS as there is no detailed sub-cell terrain to sample. SGS was not made the default in the 2020-01 release because for some models, especially those with coarse cell sizes over highly variable terrain, you can see substantially different results due to SGS picking up a lot more detail of the terrain within a 2D cell. Using Quadtree further supports the case of usingwith SGS, as it is very likelyand by using coarser cells within your model than you would have using a HPC grid, you will achieve a much better result and good results convergence (as discussed further above) by using SGS. It's highly likely that SGS will be made the default in a future release once there has been extensive industry benchmarking demonstrating using SGS to be consistently superior to not using (which is the case thus far).
 
=The resolution of my underlying DEM is 1m and my 2D cell size is 1m. Is there any benefit in using SGS?=
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=Does using SGS increase model runtimes?=
Turning SGS on can increase run times by 20-30% for a model that is well designed with appropriate cell sizes. However, in cases where the model resolution is too coarse for the terrain, the improvement in model stability and hydraulic performance by using SGS can actually cause a reduction in run times. For example, for a direct rainfall model of the Johnstone River catchment SGS vastly reduced the choking of narrow flow paths caused by one elevation per cell face in the steeper part of the catchment, thereby reducing high depth and high velocity areas that allowed the simulation to progress at much larger timesteps reducing the simulation time from 26 hours to 4(!). Using SGS will alsomay allow you to use larger cell size(s) without adversely changing results so that runs can be performed much faster - this is of great benefit when developing or calibrating a model when you wish to have a high turnover of simulations - if you can carry out this phase of the modelling using coarser cell sizes by using SGS without greatly changing results your workflow efficiency can be greatly enhanced.
 
=I'm using SGS and my water level extent is larger than depth extent. Why?=
For map outputs, the default for SGS models is to treat whether a 2D cell is wet or dry differently for water level surfaces compared to other map outputs. For water level surfaces a cell is wet if any part of the cell is wet (based on the lowest elevation from the SGS sampling), therefore, all partially wet cells are flagged as wet and will appear wet in the XMDF and other map output. The advantages here are as a modeller you can see which cells are wet even if they are only partially wet, and there is no need to buffer the water level surface for high quality mapping produced by subtracting the DEM from the water level surface. For all other map outputs a cell is deemed wet only if the water level in the cell exceeds the SGS elevation at the 2D cell centre. This was necessary as otherwise greatly distorted depth, hazard and other outputs could occur by taking the depth at the lowest part of the cell based on the SGS sampling. There are a range of commands that allow you to adjust the default settings for map outputs using SGS. For a more detailed discussion and a description of these new commands see Section 7.5 in the 2020-01 Release Notes.<br>
Note there is a new remap function that is part of the asc_to_asc utility that can be used to post-process the result grids using a finer resolution DEM to produce high resolution mapping. See <u>[[TUFLOW_Remapping | TUFLOW Remapping]]</u>.