Well Data Processing Tool Help

Well Data Processing Tool Help

This tool is used when the number of SWLs (Static Water Levels) or observed water level measures is very large (>50,000) and it is not practical to compare the simulated heads to all observations. The tool allows converting the original data into a smaller subset, or creating an interpolated SWL / water level surface from the original data.

Raw data sampling

This option allows selecting, at random, a subset of SWL / water level measurements from the original dataset.

The user can control how many samples to include in the subset ('Select' input), which field from which to extract the data (drop-down menu next to 'Single Value', and the Random Seed (starting point for random sequence of selecting samples from the original dataset)

The user can also apply a mathematical expression based on field names, for example: DEM-SWL_depth gives the SWL, if DEM is field containing the land surface elevation and SWL_depth is the field containing the depth to SWL.

Statistical sampling

This option generates one measurement from several/many SWL or water level measurements based on statistical averaging or the statistical maximum or minimum value (choose an option from drop-down menu next to 'Aggregation method').

The statistical analysis is done based on grid prescribed the user ('Nx' is number of grid cells in the x-direction; 'Ny' is the number of grid cells in the y-direction). In other words, each cell will have one value, using the data within that cell to compute the mean/maximum/minimum value.

To include data from outside of a cell for the statistical calculation, use a non-zero 'Buffer' percentage. For example, a Buffer of 1% means data outside of the cell will be used in the calculation if that data falls within a distance equal to 1% of the cell size from the edge of the cell.

Inverse Distance

This option is similar to statistical sampling in that a single measurement is derived from several/many measurements in the original dataset, but the valued is computed using the Inverse Distance Weighting (IDW) method, which assumes the interpolating surface should be influenced the most by the nearby points and less by the more distant points. The interpolating surface is a weighted average of the scatter points and the weight assigned to each scatter point diminishes as the distance from the interpolation point to the scatter point increases.

There are two parameters that need to be set when using the Inverse Distance interpolation method: 1) the power parameter, p, in the weighting function (typically p=2), set in the ‘Power’ field nn; and the number of nearest scatter points to use for interpolation (efault is to use all point within a cell).

Like statistical sampling, a non-zero 'Buffer' can be used to include data from outside of the cell for its IDW value calculation (see above).