Channel concavity (θ) is the longitudinal change in the slope of a reach. Most channel profiles are concave due to detachment-limited erosion of bedrock in headwater areas and the usual downstream relationships: increase in discharge, decrease in bed grainsize, and increase in channel width.

Concavity is quantifiable at the reach scale (reach length choice is up to you). The same concavity can exist for channels having different gradients, but the difference in gradient will produce very different channels forms.

There are several ways to represent concavity for channels. Here are 3 ways.

**METHOD A: Concavity instructions
**Example watershed metrics work by former Boise State University students:

Concavity Instructions_JWade and MBrucker 2014.docx

Watershed Metrics Poster_Lange and Vega_Spring2015_20 in x 20 in.pdf

Concavity Instructions_JWade and MBrucker 2014.docx

Watershed Metrics Poster_Lange and Vega_Spring2015_20 in x 20 in.pdf

**Concavity Index (θ): **Theta (θ) is the slope of a line regressed through a log-log plot of Channel Slope (percent rise) vs. Drainage Area (km2).

**Step #1: Extract Trunk Stream**

Use Toolbars > Draw tools to create a point graphic and place the point somewhere along the trunk stream (must be in the headwaters of the watershed).

Convert the graphic to a feature layer and save the shapefile as *Converted_Graphics*.

Next, snap the *Converted_Graphics* to the nearest pixel using Spatial Analyst Tools > Hydrology > Snap Pour Point tool…

– Input raster or feature pour point data = *Converted_Graphics*

– Input accumulation raster = *FlowAcc*

– Output raster = *Downpoint*

Isolate the trunk stream using the Cost Path tool. Imagine dropping a ball into the headwaters of the watershed and trace the balls path of least resistance along the trunk stream. Use Spatial Analyst Tools > Distance > Cost Path tool…

– Point Source = *Downpoint*

– Cost Distance = *FlowAcc*

– Cost Backlink = *FlowDir*

– Output = *Trunk_stream*

Clip *Trunk_stream* to the bounds of watershed with Raster > Raster Processing > Clip tool…

– Use clip tool using the outline of the watershed as the constraining boundary.

– Output = *WS_trunk*

**Step #2: Extract the properties of the WS_trunk raster using the filled DEM**

Spatial Analyst > Extraction > Extract by Mask tool…

– Input raster =

*DEM_Fill*ed

– Feature Mask Data =

*WS*_

*trunk*

– Output =

*Trunk_DEM*

– Note: If the unit of the DEM is in feet instead of in meters, convert to meters using Raster Calculator. Enter the formula: “DEM”/3.2808

**Step #3: Obtain slope values for each pixel along the trunk stream channel**

Spatial Analyst > Surface > Slope tool…

– Input raster = *Trunk_DEM*

– Change Output Measurement to percent rise (not degrees).

– Output = *Trunk_slope*

**Step #4: Extract flow accumulation data along the trunk stream**

Spatial Analyst > Extraction > Extract by Mask tool…

– Input raster = *FlowAcc*

– Feature Mask Data = *WS_trunk*

– Output = *Trunk_flow*

**Step #5: Convert ws_trunk into points**

Conversion Tools > From Raster > Raster to Point tool…

– Input raster = *WS_trunk*

– Output point features = *Trunk_points*

**Step #6: Create a data table to be used in Excel to create the concavity graph**

Spatial Analyst > Extraction > Sample tool…

– Input Rasters = *Trunk_flow*, *Trunk_slope*

– Input Location Data = *Trunk_points*

– Output = *Concavity_data.dbf*

– Note: Add file extension .dbf to your output so the table can be opened in MS excel.

**Step #7: Open concavity_data.dbf in an excel spreadsheet**

– Open Microsoft Excel and create a new, blank spreadsheet

– Open the

*concavity_data.dbf*file that you created using ArcMap …or…Open the attribute table and copy/paste directly into an excel spreadsheet.

– Scan your data and delete all zero values from the slope data

– The Power trendline (see chart below) cannot be computed if there are zero values in the data. Deleting these values will have a negligible impact on the graphical data.

– Note: for our dataset of >1900 points, less that 1% contained zero values.

Create a new column and name it “Drainage_Area”.

– In this step, you will convert the data (pixel count) in *Trunk_flow* into upstream drainage area in km² using the following formula:

=(F2*(Pixel Size * Pixel Size))/1,000,000

– Note: the size of the pixel may vary depending on the resolution of the original DEM (e.g., 30m DEM, 10m DEM).

Select the *Trunk_slope* and *drainage_area* columns and plot the data in a scatter plot.

– Convert the x and y axis to logarithmic scale and add a Power-type trend line.

**METHOD B.) Area-normalized Stream Concavity Index (SCI)** is based on an integral area between the channel profile (a curve) and a line connecting channel endpoints (a line). The curve and line may cross for some channels. SCI covaries with θ (Zaprowski et al., 2005). See example dataset farther down in this post.

**METHOD C.) Incision Values method.** See figure below. Two values, “big Z” and “little z”, are measures of channel incision determined from the channel profile and a line connecting its endpoints (channel head and outlet). Big Z is maximum channel incision with respect to local base level. Little z is the metric for channel incision. By convention, both Z and z are measured at the median value position for Downstream Distance (x-axis), where both axes are normalized (Elev and Dist). That is, the minimum and maximum values must be rescaled from 0 to 1.

**Concavity Instructions with MatLab_Zach Genta_2015.docx**

**Wolfram Alpha Instructions
**You’ll first find root values for integration (x limits), then you’ll integrate to find the area between line and curve.

#1: Open WolframAlpha website.

#2: Example function looks like this when plotted with Online Function Grapher –>

#3: Find the roots (limits for x) by entering the equations of your functions into the yellow bar in Wolfram. Arrange your functions in parentheses as shown below. Root values are the upper and lower limits of integration. Both are x values located where the two functions cross. The area to be found in next step is that between the GREEN line and BLUE curve.

Example function equations:

f(x) = -3.4625x+1731.3

g(x) = 0.0001x^2-0.5229x+483.41

#4: With your root values in hand (x = -29814 and x = 418), integrate to find the area between the line and the curve. Again, enter the function equations and add the root values into yellow bar, following this pattern for integrals (partially cut off):

#5: Area

Example area returned for above equation = 4.60571 x 10^8 =** 460,571
**

**Additional Notes**:**
**– Higher values of m/n indicate greater concavity. Increasing concavity or gradient will tend to increase incision.

– Changes in profile concavity are often a channel’s response to changes in base level, onset of uplift, change in rock uplift rate, an abrupt climate shift, or a combination of these factors.

– Lines take the form of Y = mX + b, or in this case the line is y=ax^k. Where m = k = the slope of the line. Where b = log a = the intercept of the line with the log y-axis (where log x = 0). So, reversing the logs, a is the y value corresponding to x = 1. By plotting in log-log space, the relationship appears as straight lines, with the power (k) and constant (a) terms corresponding to slope and y-intercept of the line on the log-y axis, respectively.

– Since θ and K_{s} are strongly correlated, a **Reference Concavity (θ _{ref})** is required in order to be able to compare different streams. θ

_{ref}is calculated from the regional mean of observed θ values for a number of channel segments. Typical reference concavities range from 0.35 to 0.65 (and possibly from ~0.2 to 1.0 in certain regions). Assuming a value of 0.5 for m/n is not uncommon in models.

– See Stream Power & Erosion equations for additional information.

** **

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