This tool can be used to fill all of the depressions in a digital elevation model (DEM) and to remove the flat areas. This is a common pre-processing step required by many flow-path analysis tools to ensure continuous flow from each grid cell to an outlet located along the grid edge. The Fill Depressions algorithm is based on the computationally efficient approach of inundating a landscape and then iteratively removing excess water that was first proposed by Planchon and Darboux (2001). This algorithm is not as efficient as the algorithm proposed by Wang and Liu (2006), which is implemented in the Fill Depressions tool but it is capable of working on very large DEMs, whereas the Wang and Liu algorithm implemented in Whitebox is likely to experience a memory overflow error.
If the input DEM has gaps, or missing-data holes, that contain NoData values, it is better to use the Fill Missing Data Holes tool to repair these gaps. This tool will interpolate values across the gaps and produce a more natural-looking surface than the flat areas that are produced by depression filling.
The following is an example of a Python script that uses this tool:
wd = pluginHost.getWorkingDirectory()
inputFile = wd + "DEM.dep"
outputFile = wd + "output.dep"
flatIncrement = "0.001"
args = [inputFile, outputFile, flatIncrement]
pluginHost.runPlugin("FillDepressionsPandD", args, False)
This is a Groovy script also using this tool:
def wd = pluginHost.getWorkingDirectory()
def inputFile = wd + "DEM.dep"
def outputFile = wd + "output.dep"
def flatIncrement = "0.001"
String[] args = [inputFile, outputFile, flatIncrement]
pluginHost.runPlugin("FillDepressionsPandD", args, false)