SERVIR Workshop: Disaster Management and Response to Extreme Events Soil Erosion and Flood Analysis

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– EXERCISE 3 –

Soil Erosion and Flood Analysis For the following exercises, we will use various outputs from previous exercises as inputs for the following spatial calculations. As we continue to deepen the level of analysis—from basic processing to interpretation of more advanced analysis—we will overlay many types of raster information in order to visualize spatial relationships. The following analyses integrate information from various thematic areas in order to see “cause and effect” relationships. Relative Vulnerability to Erosion Analysis This quick Relative Vulnerability to Erosion Analysis is one of the indicators that comes out of the “Revised Universal Soil Loss Equation (RUSLE);” however, it does not include land cover or land use management. Since it focuses in inherent characteristics of the physical landscape without considering human alterations, it provides and indication of potential natural erosion. Consequently it shows places that erosive activities should be avoided. While there are many ways to carry out spatial calculations, we will explore the Tools within the Spatial Analyst Menu, instead of the separate tools in ArcToolbox that we have used so far. Data required:

• Elevation (DEM) • Slope • Annual precipitation (and R-factor, a.k.a. rainfall erosivity factor) • Soil (K-factor, a.k.a. soil erodibility factor)

1) Create a new project in ArcMap. Save it as: C:\ Taller_SERVIR\3_Inund_Eros\Erosion.mxd

2) Add the following four layers to the map o Elevation: C:\Taller_SERVIR\3_DEM\dem_fill o Slope: C:\Taller_SERVIR\3_DEM\slope o Precipitation: C:\Taller_SERVIR\3_Inund_Eros\pcp_anual o Soils: C:\GIS\Taller_SERVIR\3_Inund_Eros\suelo_kff

o Tip: hold Ctrl to select numerous layers at the same time.

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3) In order to be sure that all of the spatial analyses overlay correctly, verify that all of the layers are in the same spatial reference. Open the properties of each layer and select the Source tab:

o The spatial reference should be “NAD_1927_UTM_Zone_17N” for the North American Datum.

4) From now on, all of the following steps will involve layers in the same projection and spatial

reference.

5) Before starting, define the spatial analysis settings:

o Zoom to the working window (in other words, Panama) o Spatial Analyst / Options…

o Tab: “General”

o Working directory: C:\ Taller_SERVIR\3_Inund_Eros o Analysis mask: <None>

o Tab: “Extent” o Analysis extent: Same as Layer “slope” (we will run everything

within this “window” or spatial extent, which covers all of Panama but excludes areas beyond, even though some of the other layers may extend further.)

o Tab: “Cell size” o Maximum of inputs (lowest resolution, defined by the layer with the

largest cell size)

6) Examine the suelo_kff layer. This is our soil erodibility grid, which is based mainly on soil

texture.

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Soil Erodibility Erodibilidad (K- factor)

7) Calculate rainfall erosivity (R-factor) by using the DEM and annual precipitation. In Spatial

Analyst / Raster Calculador o We will use the following equation derived in Costa Rica (try to find an equation for your

area) 3786.6 + (1.5679 * 0.1 * [pcp_anual]) - (1.9809 * 0.328 * [dem_fill])

• The Raster Calculator is very picky when it comes to syntax. Note that there are spaces between each number and operational symbol.

8) Save the resulting layer (right click on its name and select Data / Make Permanent) as a grid in

the same working folder, using a name like factor_r.

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Erosividad de lluvia (factor-R)

• factor_r should appear automatically in the Table of Contents and Data View. If not, add it.

9) Multiply slope, suelo_kff, and factor_r, to estimate relative vulnerability to soil erosion.

o Like before, save the resulting layer. Como antes, guardar la capa resultante. (right click on

its name and select Data / Make Permanent) as a grid in the same working folder, using a name like vuln_s_r_k.

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o vuln_s_r_k should appear automatically in the Table of Contents and Data View. If not,

add it. 10) Review the outputs:

o What is the resolution of this dataset? o What is are the range and average values? o Change the Symbology to show a progression of colors (for example, cream-yellow-red)

Relative Vulnerability to Soil Erosion

Summary within thematic polygons

11) Add the following layer to the project: o Vector watersheds generated in the previous exercise:

C:\Taller_SERVIR\3_DEM\basin_poly.shp

12) Open Zonal Statistics in Spatial Analyst / Zonal.

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13) Apply an interesting Symbology to the results.

Average Relative Vulnerability to Soil Erosion within each watershed

Given that we have selected the average relative vulnerability within each watershed, this graphic is the first step in identifying watersheds more susceptible to erosion. Here, the redder watersheds contain higher vulnerability values, which leads us to infer that there is more sedimentation in these watersheds. Estimating floodable areas Data required:

• SRTM Digital Elevation Model (DEM) • Synthetic rivers

14) Add the following layers to the project: o SRTM DEM: C:\Taller_SERVIR\3_Inund_Eros\dem90_fill_nad27.img o Panama Boundaries C:\Taller_SERVIR\3_Inund_Eros\pn_lim_swdb_nad27.shp o Synthetic rivers: C:\GIS\Taller_SERVIR\3_Inund_Eros\rios

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15) In addition to these layers, “aspect” a characteristic that will be very useful for this analysis.

Another way to describe aspect is slope direction. It is not the same as flow direction, because it uses values between -1 and 360, 0 – 360 representing directions (translatable to N, S, E, W, NW, NE, SW, SE, and any direction between), and -1 meaning “no direction.” In one word, how could you better say “no direction?” ______________________ How would this concept be useful?

16) Run Aspect within Spatial Analyst / Surface.

• Is there anything remarkable or useful in this layer? Is there a certain value in this layer that should be directly comparable to another certain value from another raster (or various rasters) that we have already generated?

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• After thinking about this, turn the aspect layer off, but keep it in the Table of Contents to use later.

17) Use the 90m DEM to extract low elevation areas, specifically coastal cells of 2m or less. Open

the tool Extract by Attributes in Spatial Analyst / Extraction.

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18) Open the properties of dem90_bajo2 and change the Symbology so that all of the values 0, 1

and 2 are dark red. In order to do this all at once, highlight all of the values, right click, and select “Group Values.” Then, double click on the group and assign a dark red to the values.

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• In the image above, the small red pixels represent the low elevation cells in the 90m DEM, and the large blue pixels are the same values using a 1km DEM. Note the great difference in spatial resolution. We’ll definitely use the higher resolution result. (The 1km DEM should be more applicable for global studies). The layer dem90_bajo2 will serve as the coastal flood potential analysis. What are some problems with this result?

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19) We need to select the cells that are only coastal or that touch other coastal cells. To start, make a layer that represents the ocean. We have already created a water layer using ASTER’s 3rd spectral band 3, but there is another way to do this through the use of a DEM.

Open Reclassify in Spatial Analyst / Reclass. Reclassify the DEM so that all of the empty cells (NoData) have a value (1), and that all of the cells with values (the land) have NoData.

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20) Now we should have a layer with cells that represent

the ocean, but we’re looking for coastal cells only. Using this layer, how could we obtain coastal cells only?

21) Open Expand in Spatial Analyst / Generalization. This tools expands the spatial extent of

a raster given certain conditions. In this case, we will expand the “ocean” by one cell (“Number of cells”). “Zone values” allows us to define which cells of a certain value or values we want to be expanded. In this case, the ocean has a value of 1, so we’ll only have to include that in the table.

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22) Zoom in to compare the results.

23) Now we can extract the coastal cells. Since mar_y_costa has one line of cells in common with

the DEM dem90_fill_nad27, we can use Extract by Mask in Spatial Analyst / Extraction to “cut” the DEM. The mask in this case will be mar_y_costa:

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mar_y_costa

mar_y_costa

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Are there other ways we could create a raster layer of the coast? ____________________________________

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In any case, congratulations. Now you have an elevation layer with coastal cells only.

24) After obtaining these coastal cells, select those cells of 2m or less in elevation. Run Extract by Attributes en Spatial Analyst / Extraction.

• Note that this only includes coastal cells of

0, 1 or 2 meters in elevation. It is no longer a continuous line.

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25) In order to extend this analysis inland, return to the Expand tool, using the low elevation coastal cells (costa_dem_012) and expanding it by 1 cell (90m). Name the output costabaja_x1, which shows that it’s the 1st expansion inland of low elevation coastal cells (celdas costeras bajas).

How would we select other cells susceptible to coastal inundation? The resulting layer of the Expand tool (costabaja_x1) doesn’t include elevation (DEM) values any more, but only the same values as costa_dem_012 expanded 1 cell.

26) We can repeat this process, Expand by Expand, in order to define each cell as susceptible to

coastal flooding or not. Now that we have repeated the same series of Tools over ando ver, you should be able to use ModelBuilder to automate this process. Now we will practice another way to estimate floodable areas by using Aspect.

27) Create a layer of flat areas, whose source / input you should already be able to identify. The

expression “value < 0” will include all negative values, which represent flat terrain.

28) Instead of keeping values between -1 and 1, reclassify the layer aspect_plano so that it only has one value.

-1 - -1

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29) Convert the results (aspecto_plano_) to a shapefile so that it is selectable by location, polygon

by polygon. Leave Generalize lines unselected.

30) Turn on the synthetic river layer (rios), which was generated using the same methodology we saw earlier, only using the 90m DEM.

31) Convert these synthetic rivers (rios) from raster to vector, so that they may also be selectable by

their location and individual line. Leave Generalize lines unselected.

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32) In the Select Menu, open Select by

Location, and prepare “Select features from aspect_plano_poli that intersect the features in this layer: stream_poli.”

33) After having selected these

polygons, right click on aspect_plano_poli in the Table of Contents, select Data / Export Data. This will save all of those selected polygons (highlighted in cyan) as a separate vector file.

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34) Save the results in the same working directory with a descriptive name, such as rio_area_inundable.shp, since they are floodable areas around rivers.

35) Review the results. How could you improve this analysis?