The effect of DEM resolution on topographic wetness index calculation and visualization: An insight to the hidden danger unraveled in Bozkurt in August, 2021

Abstract

Keywords Abstract Global DEMs Quad maps DEM resolution TWI Flood Topographic Wetness Index, also known as the compound topographic index, (TWI) is a topographic indicator that calculates the potential of where water is likely to accumulate during excessive precipitation cycles resulting from abrupt atmospheric anomalies. High index values represent serious potential of water accumulation due to low slope, and the opposite for high slope. As expected from the term, slope, Digital Elevation Model (DEM) datasets play an important role in the calculation of TWI. DEMs are produced utilizing tachometry, GPS benchmarking, UAV, aerial or satellite image capture and LIDAR capabilities. However, no matter how it is generated, a DEM is as good as the actual ground sampling algorithm, on which the final resolution is based. Using six different DEM resolutions coming from three global and one national source presented in three different setting coverages, upper feeder basin of Bozkurt sub-province, Kastamonu, was analyzed emphasizing the urbanized part of the sub-province, which was devastated during the August 11 th , 2021 flood. Coarser resolution missed the overall precision while the finer resolution captured it nicely. On the flip side, finer resolution excessively fragmented the questioned area while the coarser resolution formed a unity coinciding with the destructed area recorded during the event.

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* Corresponding Author
Cite this article
*(aoaltunel@kastamonu.edu.tr) ORCID ID 000 0-0003-2597-5587
Altunel, A. O. (2023). The effect of DEM resolution on topographic wetness index
calculation and visualization: An insight to the hidden danger unraveled in Bozkurt in
August, 2021. International Journal of Engineering and Geosciences, 8(2), 165-172
International Journal of Engineering and Geosciences– 2023, 8(2), 165-172
International Journal of Engineering and Geosciences
https://dergipark.org.tr/en/pub/ijeg
e-ISSN 2548-0960
The effect of DEM resolution on topographic wetness index calculation and visualization: An
insight to the hidden danger unraveled in Bozkurt in August, 2021
Arif Oguz Altunel*1
1Kastamonu University, Faculty of Forestry, Forest Engineering, Türkiye
Keywords
Abstract
Global DEMs
Quad maps
DEM resolution
TWI
Flood
Topographic Wetness Index, also known as the compound topographic index, (TWI) is a
topographic indicator that calculates the potential of where water is likely to accumulate
during excessive precipitation cycles resulting from abrupt atmospheric anomalies. High
index values represent serious potential of water accumulation due to low slope, and the
opposite for high slope. As expected from the term, slope, Digital Elevation Model (DEM)
datasets play an important role in the calculation of TWI. DEMs are produced utilizing
tachometry, GPS benchmarking, UAV, aerial or satellite image capture and LIDAR capabilities.
However, no matter how it is generated, a DEM is as good as the actual ground sampling
algorithm, on which the final resolution is based. Using six different DEM resolutions coming
from three global and one national source presented in three different setting coverages,
upper feeder basin of Bozkurt sub-province, Kastamonu, was analyzed emphasizing the
urbanized part of the sub-province, which was devastated during the August 11th, 2021 flood.
Coarser resolution missed the overall precision while the finer resolution captured it nicely.
On the flip side, finer resolution excessively fragmented the questioned area while the coarser
resolution formed a unity coinciding with the destructed area recorded during the event.
Research Article
DOI: 10.26833/ijeg.1110560
Received: 28.04.2022
Accepted: 24.07.2022
Published: 19.10.2022
1. Introduction
Habitation on the Turkish Black-sea coast dates back
quite some time. Due to the morphological structuring of
the topography, which the mountains run parallel to the
coast line, many provinces and sub-provinces have
settled on or near the coast line, which have provided
easy access to sea, warmer climate year-round and nicer
aesthetics to be around. However, this living preference
had a catch from time to time. Each and every urbanized
location on Black-sea coast has experienced floods and
landslides varying in severity during their existence [1-
2]. The reason for the devastations has resulted from the
fact that the towns situated at and around sea-bound
discharge paths of docile looking waterways, are swept
out to sea causing immense property damage and death
along with the surging water coming fully loaded down
from the feeder basins, aka upper watersheds [3].
Alluvial fans between the feeder basins and the sea are
where the surging water is dispersed losing its
destructive energy during heavy rains before converging
with the sea. When towns are situated on them, they are
in danger to some degree because the feeder basins can
only collect and convey a certain amount of water during
such events [4]. However, when towns are situated right
at the mouth of the feeder basins, where topography is
still not tame enough for the hurdling downward water,
the damage becomes proportionately high because man
made infrastructures cannot withstand against that
much force. One such sub-province, Bozkurt, of
Kastamonu was hit by a record-breaking rain storm in
August 11th, 2021 when the majority of the town was run
down by the rushing waters coming from its feeder basin.
Along with the neighboring two other sub-provinces,
property damage and death toll were unprecedented.
In the scope of this study, a frequently used
hydrology indices, topographic wetness index, which has
been known as an indicator for determining the places
inherently keeping water accumulation potential,
through a raster surface [5-6], was evaluated. Reliably
forecasting soil moisture patterns in landscapes was a
serious challenge in the past so that many notable
occurrences cost considerable property damage and
death all across the Earth [7-10]. TWI was first developed

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by physically basing on a runoff model, TOPMODEL,
which ran with the assumption that the hydraulic slope
could be estimated by the topographic slope [11]. It has
lately been accepted as a powerful measure in defining
flood preparedness scenarios [12-13]. The index
specializes in rainfall runoff predictions and spatial soil
moisture and water accumulation simulations [14]. Six
different DEM datasets, three from global sources and
one displayed in three different visualization settings,
from Turkish national quad map coverage of 2010, were
used to delineate Bozkurt feeder basin to see how DEM
resolution can differ and can help specify residential
flood risk areas during heavy rain storms.
2. Materials and Methodology
2.1. Study area
Bozkurt is one of six Black Sea bound sub-provinces
of Kastamonu. It has typical Black Sea topography and
morphological characteristics (Figure 1) along with lush
deciduous and coniferous forests managed by the
Turkish forest service. Although considered as a coastal
town, its residential part extends inland along its docile
looking creek/stream, so that the neighboring sub-
province Abana sits directly on the alluvial fan of
Bozkurt’s feeder basin. Sub-province’s neighborhoods,
recreation infrastructure and commercial districts are all
situated immediately around the stream channel, which
was overwhelmed by the deluge coming from the feeder
basin on August 11th, 2021. Compared to the neighboring
ones, it has a rather small feeder basin of 11851.5 ha
whereas Catalzeytin had 31252 ha and Ayancik had
59081 ha. Considerable property damage and human
loss also occurred in these mentioned sub-provinces,
however what Bozkurt experienced was beyond
comprehension. That’s why it is singled out for analysis
in this study.
Figure 1. Location of Bozkurt sub-province and its feeder basin
2.2. Methodology
Three different-resolution global DEM datasets,
ALOS PALSAR, 12.5 m [15], AW3D30, 30 m [16] and
TanDEM-X, 90 m [17] were acquired from their
respective geo-portals. ALOS PALSAR and TanDEM-X
DEMs were respectively produced with the capabilities
of L-band and X-band Synthetic Aperture Radar (SAR)
interferometric techniques [18-19] whereas the initial
and the successive releases of AW3D30 DEM have been
produced with panchromatic optical sensor (PRISM)
captured stereo image processing [20-21]. Many studies
have surfaced questioning and validating their
effectiveness and practicality in various fields of studies
[22-26]. All three DEM datasets were positioned defining
Universal Transverse Mercator projection over World
Geodetic System, 1984 horizontal datum under Bozkurt
feeder basin polygon. 34 m Geoid height [27] was
deducted from ALOS-PALSAR DEM [28].
Besides, three more DEM datasets were produced
from vectorized and 1:25000 scaled national quad maps
[29]. First, four such maps were simultaneously tied
together to form an initial Triangulated Irregular
Network (TIN) surface. Then, a TIN to raster conversion
was carried out accepting the default resolution(s), 104
m. Second, all maps were clipped by Bozkurt feeder basin
polygon (Figure 2), the clipped parts were tied together
and a second TIN surface was generated expecting a
tighter GRID pattern. When the conversion was again
applied, the result indeed produced a better resolution,
67 m. No resampling algorithm was applied in achieving
these figures in either case.
National quad map(s) which have been produced via
photogrammetric capabilities, solely relying on stereo
air-photo capture, depict the stationary topography of a
region in a medium scale. Accepted as a reasonable scale
by many nations [30-31], they include contour lines
following the natural formation of the terrain. Contour
intervals widens in distance due to subtle elevation
differences in flat areas whereas they come tightly
together as topography becomes treacherous,
accentuating the elevation in short distances [32]. This
uneven, non-systematic elevation distribution within
quad maps is the reason behind their DEM generation
performance. Since TIN algorithm uses these contour
lines to form a surface, it changes in precision when the
area of interest shifts, even within the same map(s) as
well as in the subsequently produced raster model(s). No
one quad map depending surface model distinctness is
the same as another. This is how two initial different
raster models given above, were produced from the same
number of national quad maps (Table 1). Additionally, a

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resampled DEM derived from the same national quad
map(s) was also produced while converting from TIN,
specifying a 30 m cell size. A total of six DEM datasets
with different resolutions were individually subjected to
“Topographic Wetness Index” algorithm to see how DEM
resolutions would differ, and which one would
approximate the extend of the heavily damaged area
devastated in Bozkurt sub-province in August 11th, 2021.
All analyses were performed using ArcGIS 10.8.
Table 1. Specifications of the directly used and produced DEM datasets
DEM Source
Production
type
Resolution
(m)
Maker
Availability
ALOS_PALSAR
L-band SAR
12.5
JAXA
https://search.asf.alaska.edu/#/?dataset=ALOS
AW3D30
Stereo Image
23.18
JAXA
https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm
TanDEM-X
X-band SAR
80.7
DLR
https://download.geoservice.dlr.de/TDM90/
National Quad-cut
Aerial stereo
photography
67
General
Directorate of
Mapping
upon institutional request
National Quad-whole
104
Resampled nat. quad.
30
Figure 2. Start of surface generation scheme from vectorized quad maps as whole vs. cut
2.3. Topographic Wetness Index Algorithm and
calculation progress on a raster
TWI is an algorithm which is frequently addressed in
hydrology driven studies [33-34]. It can be applied on
any DEM no matter what its resolution is because DEM
resolution is the driving factor behind the successful
generation of TWI. The practicality of knowing where
water might accumulate at unexpected times, also
translate to where water might be sought after in case of
need because every water droplet accumulated during
rain surely feeds the ground water in the long run. Figure
3 shows how TWI is calculated starting from the initial
DEM input.
Through this algorithm, elevation embedded raster
DEM cells are transformed into water accumulation risk
bearing values ranging from less than 0 to 30 [13]. High
index values would indicate high potential of water
accumulation due to low slope and vice versa. To find a
better TWI producing DEM performance, a hypothetical
threshold value, which was applied to all six TWI results,
was set. Thus, flood plain acreages were calculated,
classifying the TWI results elaborating over this
threshold value of more than or equal to “10”. Total count
of raster cells with TWI values of “10 ≤” was multiplied
with the square of the respected raster cell size to
achieve the acreages within flood plain (*-fld pln)
designated blue polygon in Figure 4. TWI is a unique tool
allowing users, decision makers and planners to identify
areas which have the potential of getting adversely
affected by water accumulation or flooding during
excessive rainfall scenarios. Thus city planners can
benefit from its visual mechanism in residential site
selection and preventive vegetative or afforestation
initiatives. Six different resolution DEMs, i.e., three
obtained from open-access geo-portals and three
manufactured from national quad map coverages, were
used to produce TWIs for Bozkurt sub-province of
Kastamonu.

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Figure 3. Sequence of turning a DEM into calculated TWI surface
3. Results and Discussion
DEM resolution is important for topographic
visualization if intricate work is required. As of today, the
highest precision is acquired through LIDAR [35-37]
although there are other possibilities, i.e., UAV, air-borne
or space-borne image capture derived site specific or
large-scale DEM productions, varying in degree of
accuracy [29, 38-39]. However, highest precision is not
always the sought after or guaranteed requirement
obtained from a DEM [40-41]. In an attempt to find
suitable flood inundation maps for places where high
resolution DEM datasets were not available, Saksena and
Merwade [42] unearthed the fact that coarser resolution
DEM datasets can provide reliable and improved maps.
Quad map extracted inserts in Figure 4 appeared to
display better areal distributions compared to much
higher resolution ALSO-PALSAR and AW3D30 DEM
derived fragmented looking ones. Jeon et al. [43] stated
that channel slope became steeper as DEM resolution
increased. Although this was a conclusion derived from a
simulation, it might very well be one of the underlying
factors in real-time feeder basin dynamics of Bozkurt
sub-province (Figure 4) because given its small size,
11852 ha, the precipitation falling inside feeder basin
swiftly descended into the city and wreak havoc
especially on flood plain designated area (blue polygon).
Hancock [44] mentioned that hillslope and hydrological
details would be lost when larger than 10 m DEM
resolutions are used. Similarly, this study also confirmed
that topographical detail would be lost from the low
resolution onward simply because minute slope
differences are aggregated into larger surfaces as the
DEM resolution is increased, however this situation
obviously did not tarnish the fact that quad map derived
inserts in Figure 4 produced comparably better
inundation scenarios compared to Global DEM derived
results [45].
Although the analyses were performed on the
Bozkurt feeder basin’s entirety, the emphasize was given
to the residential part of the sub-province because the
damage mostly concentrated in this part (Figure 4). As
the DEM resolution increased, the practicality of the
visualization started losing its appeal. ALOS-PALSAR
DEM calculated TWI which comparatively provided the
highest resolution in this study, indicated almost a no-
development area right within the present-day Bozkurt
residential extent concentrating around the channel
apparent from the water indicating blue-patchy looking
pixels in the respectable insert in Figure 4. Nevertheless,
this high resolution captured so much detail that it lost
the complete picture, thus the insert showed only partial
risk as if the rest was secure to settle within the flood
plain. The east and west wings of the area were not as
heavily risky as the rest of the extent because those were
the suburbs erected on steeper topography where no
water accumulated during the flooding, whereas the rest
was completely inundated as national quad derived DEM
calculated TWIs also indicated nicely. AW3D30 and
TanDEM-X DEM calculated TWIs produced similar
results in visualization as well as in calculation despite
the varying resolutions. However, their concentrations
were mostly limited to established channel width. Dixon
and Earls [46] stated that no matter how the GRID
spacing was in a DEM, even if one is resampled to match
another one with a specified GRID spacing, they all
behave differently in hydrological studies. Bozkurt
channel as experienced in many parts of the country, was
in a so-called rehabilitated state, directing the water to a
not-wide-enough waterway which was completely
overwhelmed during the flood. Almost entire residential
parts of the sub-province turned into a flood-plain, which
was also apparent through the calculations done via
national quad maps. Both of the quad map generated
DEMs’, 67 m and 104 m, visual appeal was better than the
intentionally produced 30 m resampled DEM. Sorensen
and Seibert [47] stated that calculated designated
upstream area got smaller on average for high resolution
DEMs with varying information content. Although
resampled anticipating a better performance, the
resulting high resolution did not necessarily produce as
good of a performance as the coarser resolution ones.

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Figure 4. DEM resolution-based Bozkurt residential area inundation scenarios

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Calculated TWI value-wise, all tested DEM datasets
produced enough warning that a considerable part of the
present-day Bozkurt sub-province residential extend
will always be under flood risk unless a complete
relocation of the people and infrastructure is undertaken.
Besides, a comparison calculated to detect which of the
tested DEMs generated approximate acreage to the
heavily damaged part of the sub-province, indicated as
flood plain, 60.7 ha, in Figure 4, produced the respective
figures, ALOS PALSAR DEM, 13.5 ha; AW3D30 DEM, 17.2
ha; high resolution quad map derived DEM, 39.2 ha;
TanDEM-X, 33.8 ha, low resolution quad map derived
DEM, 46.7 ha and resampled national quad map derived
DEM, 41.6 ha. Excessive detail captured with high
resolution DEMs caused the results to produce rather
less acreages compared to those captured with low
resolution DEMs. Despite varying figures, it was obvious
that quad map derived DEM variants, especially the ones
generated without resampling algorithm, produced the
overall best results in terms of the questioned objectives.
Whether fragmented resulting from high DEM
resolution, or aggregated due to low DEM resolution,
TWI is a practical and valuable hydrological index for
urban development strategies when new sites are
planned for residential expansions or flood or landslide
mitigation works.
4. Conclusion
Digital Elevation Models have become an
indispensable part of the engineering works, today.
Along with the technological advancements, GRID
spacing is brought to unprecedented small sizes.
Geoscience capabilities are also improved considerably.
When one knows what to do using the data and the
instrument, the results are invaluable on many matters.
This simple example was applied to a feeder basin which
produced record breaking flood to the town erected in
lower elevations. Topographic Wetness Index algorithms
calculated over different resolution DEMs showed that
the threat was simply sleeping as if nothing would ever
have happened. Threats must be taken seriously. Thanks
to geoscience capabilities, nothing should be left
unturned when lives and country’s assets are at stake.
Investments tied to the development phases of the cities
and the well-being of the people choosing to live in them
should not be taken lightly. Technology and know-how
are available for the ones willing to accept and
incorporate them in their planning. Thus, each and every
attempt must be considered to make the living easier,
more enjoyable and safer for everyone. Türkiye is not a
third World country where capacity build-up is essential
before the actual initiative, because financial capabilities,
know-how, and trained personal are all present. More
derivatives must be integrated into final decisions if they
will have long term critical impacts on people’s well-
being. This way prosperity will thrive and ambitions will
be achieved.
Acknowledgement
I thank JAXA and DLR for making the dissemination of
such datasets freely accessible. I also thank Turkish
General Directorate of Mapping for supplying the quad
maps upon request.
Conflicts of interest
The authors declare no conflicts of interest.
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