Geography Reference
In-Depth Information
Table 2. Categorization parameters for potential flood risk by magnitudes
Risk magnitude
Elevation class (m)
Buffered distance (m)
Very high
≤5
50
High
0-5
100
Moderate
0-5
150
Low
6-10
200
Very low
6-10
300
Figure 9. Spatial distribution of flood prone areas by magnitude in the study region.
Nonetheless, the so-generated flood risk mask (Figure 9) was overlaid on
the land cover, administrative/population density dataset to detect and estimate
vulnerable components such as urban land use and agricultural land use.
Specific attention was focus on these land uses because they are high valued
structures pertaining to flooding impact. Such land uses are economic and
infrastructural based and also involve human lives, that is the reason behind
their frequent use as a measure of quantifying flood damage and level of
seriousness.
Linking elevation data, such as SRTM DEM with high spatial resolution,
to hydrographic dataset and progressively aligning the resulting interplay with
satellite derived land use data is a methodology that is likely to yield a more
accurate result and produce a reliable flood risk map, database and modeling
procedure compared to one derived ultimately from pure hydrological
modeling. The major limitation of this method, typically rest on the accuracy
of the adopted DEM dataset, land use data and the overall competency of the
analysts. SRTM data and Landsat imagery have become paramount and most
widely used datasets among contemporary researchers.
