Scientific and Sociological Aspects of Natural Disasters (ATS 490/590) - Fall 2012
Eric Anderson (M.S. ESS candidate)
NSSTC Rm 1044
eric.anderson@nsstc.uah.edu
Identifying landslide trigger areas and exposed assets around Monte Sano, North Alabama
Overall Goals
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Pinpoint locations most prone to slope failure on Monte Sano in North Alabama
Use rainfall climatology to infer the likely timing of slope failures
Identify threatened populations, infrastructure
Specific Objectives
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Perform basic surface and hydrology analyses of high resolution digital terrain model
(DTM), including: Fill, Flow Direction, Flow Accumulation, Stream Link, Slope and
Hillshade, among other complementary tools
Predict the locations of unstable conditions as a function of slope and upslope
contributing area (simplification of SHALSTAB – shallow slope stability model –
criterion).
Consider monthly precipitation climatology to apply gain a better idea of timing of these
slope failures
Add additional layers to visually compare locations of active landslides with buildings,
roads, etc.
Concepts
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The term “mass movement” encapsulates pretty much any large volume of material that
moves downslope under the force of gravity (Abbott 2012)
Significant landslide hazards exist in the southeastern US, particularly in areas with steep
slopes along the weathering Appalachian mountains. Within Madison County, Alabama,
moderate to very high susceptibility has been identified across Monte Sano (Geological
Survey of Alabama 2011).
Slope stability models can be used to identify “triggers,” or specific points more prone to
causing landslides. The main factors needed to define unstable conditions include
sufficiently steep slope, availability of soil or other debris, and enough water to trigger
and perpetuate a flow (Montgomery and Dietrich 1994; Deitrich et al 1998)
Exercise
Identify landslide trigger cells
1. Open ArcMap 10.1
2.
and create a new empty project.
Add the layer called ms_fill. This is a hydrologically-corrected Digital Elevation
Model (DEM) from the City of Huntsville GIS Office, made using a Light Detection And
Ranging (LiDAR) sensor. Right click on this layer in the Layer Manager and select
Properties.
a. What is the projection? ________________________________
b. What is the horizontal resolution (include units)? _________________________
c. What is the area in each cell? _______________________
d. Use the
Identify tool to query some values of these cells.
3. Make a hillshade of the DEM using the Hillshade tool found in
Spatial Analyst /
Surface. Name it ms_fillsh (NOTE: be sure to save all of the outputs in the same directory as
your inputs, for this step and all other tools!). Be sure to save this and all other outputs in the
same directory as ms_fill!
4. Calculate Flow Direction (Spatial Analyst Tools / Hydrology) to determine the direction
from each cell to its steepest downslope neighbor (i.e., which direction the majority of water
would flow across the surface). Find the tool in Spatial Analyst Tools / Hydrology / Flow
Direction. Name the output flow direction raster: ms_dir:
5. Observe the output. How many values are there? _____________
If your flow direction does not look like the layer below, make sure you started with the
filled DEM.
6. Run Flow Accumulation in
Spatial Analyst / Hydrology to calculate the how many
upstream cells flow into each cell. Select ms_dir as the flow direction raster, name the output
ms_flac, keep the input weight raster blank, and change the output data type to Integer:
7. Change the symbology of the resulting flow accumulation layer by right clicking on ms_facc
and selecting Properties. Then select the Symbology tab. Under Stretch / Type: select
Histogram Equalize, and select a more colorful color ramp. Click Ok.
8. Click and drag the hillshade layer on top and change its transparency to 60% (Properties /
Display).
9. Zoom to the area shown above, and
“identify” or query the values of ms_flac. These
values correspond to the number of upstream cells that flow into the cell you are querying.
We will use these channels to determine starting points for small landslides or rock
avalanches that could turn into larger debris flows.
10. Now we will separate the cells that represent streams/small rivulets from those that do not,
directly from the flow accumulation layer. Open the Con tool in
Spatial Analyst Tools /
Conditional / Con, select ms_flac as the input conditional raster, Expression: value > 2000;
Input true: 1; Input false: 0; Output raster ms_str:
11. Your result should show a grid with two values: 0 and 1. Cells with a value of 1 represent
streams and small rivulets. The value 2000 was chosen based on the resolution of the DEM
and the density of streams/rivulets we wanted to see. For other cases, you’ll need to play
with this value to find the right threshold.
12. Next, we will analyze the slope for steepness. Open the Slope tool in
Spatial Analyst /
Surface. The input will be ms_fill, calculate slope in degrees, and nthe output ms_slope.
Explore the values of these slopes.
13. Generally, slopes of 30° and greater are considered to be prone to landslides, but not all such
slopes will experience landslides. This depends on geology and other factors.
14. Create a layer with values of 1 for slopes greater than 30° and 0 for slopes 30° and less. Use
the Con tool to do this:
15. Beyond identifying cells with slopes > 30°, we can also locate blocks of continuously steep
slopes. This is important because short, steep slopes may cause small slumps that do not
result in large landslides, while larger areas of steep slopes may lead to larger landslides that
bulk up and travel much further with greater volumes. Using neighborhood functions, we
will identify cells whose neighbors in a 10x10 radius are also steep. We will not limit this to
all of the neighboring cells but instead 95% of those cells.
16. Open the Focal Statistics Tool under
Spatial Analyst Tools / Neighborhood to calculate
the SUM of the values within a 10x10 neighborhood of each input cell. Fill in the fields
accordingly, and name the output area10x10:
17. Open the Con tool to separate the cells with values > 95 from the rest. Name the output
area095:
18. This results in all steep-sloped cells that are also surrounded by steep slopes. The final step
to identify our landslide triggers is to select these steep-slope cells that intersect the streams
or rivulets we created earlier. To do this, we can “add” the two layers area095 and ms_str
together using Raster Calculator located in Spatial Analyst Tools / Map Algebra. Enter the
expression using the layers listed, paying close attention to the syntax. Name the output trig:
19. Explore the output. What do the values 0, 1 and 2 mean?
20. Finally, open the Reclassify tool in Spatial Analyst Tools / Reclass to prepare the triggers
grid for the LAHARZ software. Reclassify 0 and 1 to 0; reclassify 2 to 1, as shown below.
Name the output trigger95:
This should result in a layer with only two values (0 and 1), where the very few cells with a
value of 1 meet all of our requirements for landslide triggers.
Save your ArcMap project.
Review and discuss
These trigger points only represent potential starting points of landslides and not their runout or
inundation zones. With other software, we could delineate these inundation zones, but for this
exercise, we will go ahead and add a few pre-processed inundation zones. We will now continue
to compare these hazards with populations and infrastructure.
But first, consider:
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What were our inputs for the trigger and inundation hazard zones?
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What are the assumptions in the models we used?
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What are the uncertainties, and how do we deal with these?
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How can we improve these models?
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How can we use the rainfall climatology of Huntsville help us understand the
probability of this potential hazard manifesting in an actual slope failure?
Identify threatened populations and infrastructure
21. Add a state-wide layer of landslide susceptibility, created by the Geological Survey of
Alabama.
(inside AL_GLS folder). If the data does not show up, you may
need to redefine the source information (Properties / Source / Set Data Source…)
22. Add a point layer of active landslides in Madison County, AL.
madison_county_active_landslides_rough_hsvgis
23. Compare these with your terrain-based landslide triggers.
24. To visually compare this landslide susceptibility, add a basemap (such as Imagery with
Labels) to locate buildings, roads and other features above and below the areas defined as
highly susceptible or as an active landslide area.
a. Alternative: use ArcGIS Online to do the same (http://www.arcgis.com/home/)
b. Select “Map” at the top of the page.
c. Click Add / Add Layer from File. Navigate to your directory with the GIS files,
and choose madison_county_active_landslides_rough_hsvgis.zip.
d. Now click Add / Add Layer from Web. Select KML and enter
http://nsstc.uah.edu/~anderse/gis/al_ls3.kmz
e. Change the Basemap to locate any buildings, roads and other features potentially
in harm’s way.
i. Are there any active landslides close to major roads? Which?
ii. Search “Joshua Dr., Huntsville, AL”
iii. Search “Runnell Dr., Huntsville, AL”
Acknowledgements
The first part of this exercise was modified and reformatted from a LAHARZ tutorial written for
Mt. Hood, provided by Julia Griswold, Steve Schilling and Jon Major of the USGS. My
participation in the LAHARZ workshop in El Salvador in January of 2012 was funded by
USAID/NASA through the SERVIR program and offered me the opportunity to learn about the
software directly from the people who created it.
Digital elevation model data for Monte Sano was provided by the City of Huntsville GIS
Department through Robert Griffin.
References
Abbott, P.L., 2011. Natural Disasters. 8th Ed. McGraw-Hill. 512 pp.
Dietrich, W.E., Real de Asua, R., Coyle, J., Orr, B. and M. Trso, 1998 “A validation study of the
shallow slope stability model, SHALSTAB, in forested lands of Northern California.” UC
Berkeley, 59 pp.
Geological Survey of Alabama, 2011. Susceptibility to Landslides in Alabama. Poster by
Ebersole, S.M., Driskell, S., and A.M. Tavis.
http://gsa.state.al.us/gsa/geologichazards/Landslides.htm#LandslidesonMap
Montgomery, D.R., and W.E. Dietrich, 1994. “A physically-based model for topographic control
on shallow landsliding.” Water Resources Research 30: 1153-1171.