This document describes a study that used ArcGIS and R software to automatically analyze catchments based on digital elevation models. The study area was the Vasas-Belvárd catchment in Hungary. Methods included using TauDEM tools in ArcGIS to extract watersheds and stream networks from a DEM and attribute tables. R was integrated to perform statistical analyses. Results included floodplain maps extracted after normalizing slope and curvature data to improve interpretation, and long profiles of elevation drop and sinuosity. The study demonstrated the interoperability of ArcGIS and R for automated catchment analysis and that normalizing land surface parameters was useful for floodplain extraction. Further research was suggested to automate mapping, fine-tune thresholds, and

1. Using ArcGIS and R for automated DEM-
based catchment analyses
Edina Józsa
University of Pécs, Faculty of Sciences,
Doctoral School of Earth Sciences
edina.j0zs4@gmail.com
Brno, 12 May 2015

2. Content
Introduction
- Background
- Aims
- Significance
Study area
- Location,
Extent
- Topography
- Piedmont
surfaces
Methods
- Materials,
Software
- LSPs
- Extract
floodplains
- Long profiles
Results
- Floodplain
maps
- Interpreted
long profiles
Conclusions
- Applicability
- Further
research

3. • The study is connected to the research of Kovács, M.
(2012) about the geomorphological settings,
anthropogenic effects in the Vasas-Belvárd catchment.
• Aim: Using automated geomorphometric analyses to
create thorough overview of a catchment, stream channel
and floodplain.
– Channel characterization
• Significance: consistent, repeatable, updatable and
quantifiable results.
Introduction

4. Study area
Vasas-Belvárd catchment ~140 km2, southern foreland
of Eastern Mecsek Mountains

5. Study area
Topography (left) and
relative relief (right) of
the study area.
The relative relief was
calculated using the results
of the topographic grain
analysis – the mean ridge-
valley bottom distance is
620 m respectively.

6. Study area
Slope gradient in degrees
(left) and slope variability
(right) over the study
area.
The slope variability was
calculated using the results
of the topographic grain
analysis – the mean ridge-
valley bottom distance is
620 m respectively.

7. Study area
Features of the drainage
pattern indicating
tectonic effects (left).
Piedmont-like surfaces
extracted using the TPI
method (right) [Józsa, E.
– Kovács, M. 2014].

8. • Input:
– contour-based digital elevation model,
10 m horizontal resolution
– Topo To Raster interpolation method
• Software:
– ArcGIS Spatial Analyst, TauDEM Tools,
Transformation (normalization) Tool of Csillik, O.
+
– R statistical analysis
Methods

9. Methods

10. • Why to use the TauDEM Tools?
– Automated watershed and stream network extraction
with script tool
– Extensive set of hydrological tools – opportunity to
expand model
– Detailed attribute table for streams (Strahler Order,
Shreve Magnitude, Average slope, Elevation drop, etc.)
*** D-Infinity flow direction (for high-resolution data)
Tool created by: Tarboton, D. G. 1997 (last update 2013)
Methods

11. • Why to use the Transformation (normalization) Tool?
– Transformation of morphometric parameters toward the
Gaussian (normal) distribution - minimize skewness of
slope gradient frequency distribution; modify kurtosis of
profile and tangent curvature
– Better visualization and interpretation of LSPs - more
coherent results
– Script tool in ArcGIS - easy-to-use, only the knowledge
of main topographic characteristics is necessary to
parameterize (homogeneity)
Tool created by: Csillik, O. 2014
Methods

12. • Why to use the R?
– Integrating GIS with statistical analysis software 
creating maps using the spatial analyst tools of GIS and
analysing data in R via the GUI of GIS software
– Python is calling R funcionality via script tool in ArcGIS
– Capability of R to handle raster and vector data –
calculations as dataframes
– Results as maps or tables, plots (exported from R)
Tool available with Apache License
Methods

13. Results
• Watercourses &
Basins
The attribute table of
stream network (created
by Stream Reach and
Watershed Tool)
includes Strahler order
(left) and average
elevation drop (right)
for stream segments.

14. Results
• Floodplain extraction – Normalization of slope gradient (Box-Cox)

15. Results
• Floodplain extraction – Normalization of slope gradient (before)

16. Results
• Floodplain extraction – Normalization of slope gradient (after)

17. Results
• Floodplain extraction – Normalization of curvature (Arctangent)

18. Results
• Floodplain extraction – Normalization of curvature (before)

19. Results
• Floodplain extraction – Normalization of curvature (after)

20. Results
• Floodplain extraction – Script tool working with R

21. Results
• Floodplain extraction – Scatter plots to analyse given segment

22. Results
• Floodplain extraction – Effect of normalization

23. Results
• Floodplain extraction
– Extracted areas

24. Results
• Long profile, elevation drop and sinuosity of Vasas-Belvárd str.

25. • Interoperability and easy scripting make ArcGIS + R
capable of providing „push-the-button” solutions for
catchment analysis
• Normalizing LSPs proved to be useful in case of
floodplain extraction
• Further research on algorithm (further automating
preparation of maps, fine tuning thresholds,
documentation) + comparison with available algorithms
• Further research on area (regulated channels, mill canals)
Conclusions

26. Thank you for your
attention!