[GIS] Simplified flood “model” / estimation from elevation data

demfloodqgis

The goal is to present an estimation of potential flooded area. But still keeping it simple.

I know this will not be accurate. The question is:

will it be good enough for at gestimate / rough guideline? I am aware this is a crude method.

I am using QGIS.

Output

I want to create flood area polygons of n m^3 of water. e.g. polygons showing the max. area that will be flooded if xx m^3 water is released.

Input

raster DEM
m^3 of water to be released

Process

convert DEM raster layer to polygons based on elevation (in steps on 1m because of the DEM resolution).
set the area of the polygons as an attribute
create an volume attribute (area * 1m^3) to get an estimate of how much water the area will hold.
optionally rescaling the polygons to get estimations for volumes lesser than the polygon but greater than the "inner" smaller polygon (e.g. in fractions of 10).

Best Answer

I can think of a very simple iterative solution.

Given a DEM and a 'seed' point, use the flood fill algorithm to fill pixels below an arbitrarily selected start elevation. Modify the algorithm to compute the flooded volume above each pixel and return the sum of these volumes. This is (roughly) the total flooded volume at that elevation. If the volume is less than the input volume, fill again at a higher elevation until you reach the target. You can refine the process (reduce the step elevation) so that it approaches the target volume to within some tolerance.

Polygonize the flooded region to create a polygon and associate the volume with it, as an attribute.

It seems like a bit of a Rube Goldberg solution, but flood fill is so simple and efficient that in practice, unless you're filling the Great Lakes at 2m resolution, it would take little time to get results. I used a similar strategy for this.

Finally: I've never used Surfer, but my hydrologist friends seem to use it for everything. It can probably do this easily.

Related Solutions

[GIS] map flood inundation risk QGIS

A robust but programmatic solution would be something along these lines (roughly, Pythonic pseudocode):

# D is the DEM, W is the true/false water body mask
# delta is the max height difference for flood-prone areas

F = D * W     # F is the DEM with dry land masked out (starting flood positions)
while True:
  changed = False
  for c in F: # for every cell index in raster
    if F[c]: # flood from the cells that are already flooded
      # flood all eligible neighbouring pixels
      # needs defined neighbourhood (4/8 cells around)
      for n in neighbours(c):
        # flood all dry pixels within tolerance and re-mark those too large
        # second condition prevents the highest water pixel from flooding all the area
        if (F[n] == 0 and (F[c] + delta <= D[n])) or F[c] < (F[n] - delta):
          F[n] = F[c]
          changed = True
  if not changed: # stop if no changes have been made during the traversal
    break

In the end, F marks all flood-prone pixels with values larger than 0 and can therefore be used as a mask.

I can't give a QGIS solution due to lack of experience but the implementation should be quite straightforward.

(Giving it a second thought, the algorithm will probably not work if the DEM is too coarse, making large differences in the neighbouring water body cells' heights. Were it not with the rivers, the problem would become much easier.)

[GIS] How to extend a (river water level) raster, transfering its values to the added area

Original dataset showing flood depth and polygon I've created along it:

Let's produce points on the boundary of flood plain using flood level raster called RL, raster calculator plus some tools:

Next - assign proximity areas to above points and convert those areas into corresponding flood level:

Final step using raster calculator to define flood level where it is above DEM. If you'll get to here, you'll have no trouble to do it.

Result below compares flood LEVEL in the river and polygon (I dropped original elevation inside polygon by 10 m). As one can see it picks the trend, however every single imperfection in original flood level get exaggerated in output: