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_wang_and_liu algorithm is based on the computationally efficient approach of examining each cell based on its spill elevation, starting from the edge cells, and visiting cells from lowest order using a priority queue. As such, it is based on the algorithm first proposed by Wang and Liu (2006). However, it is currently not the most efficient depression-removal algorithm available in WhiteboxTools; fill_depressions and breach_depressions_least_cost are both more efficient and often produce better, lower-impact results.

If the input DEM has gaps, or missing-data holes, that contain NoData values, it is better to use the fill_missing_data 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. Importantly, the fill_depressions tool algorithm implementation assumes that there are no 'donut hole' NoData gaps within the area of valid data. Any NoData areas along the edge of the grid will simply be ignored and will remain NoData areas in the output image.

The user may optionally specify the size of the elevation increment used to solve flats (`flat_increment`), although it is best not to specify this optional value and to let the algorithm determine the most suitable value itself.

## Reference

Wang, L. and Liu, H. 2006. An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science, 20(2): 193-213.

## See Also

fill_depressions, breach_depressions_least_cost, breach_depressions_least_cost, fill_missing_data

## Function Signature

`def fill_depressions_wang_and_liu(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan')) -> Raster: ...`

## Project Links

WbW Homepage User Manual Support WbW