Exercise 4. Wetness Index, local saturation deficits, and variable source areas
Due Date: March 31, 2012
Report: ex4_youronyen_report.doc in your personal working directory
Objective
In this exercise, you will learn how to generate estimates of the variable source area of a
watershed based on topographic information and mean soil moisture levels. The work will
include generating maps of the wetness index, the mean value of the wetness index, and the
extent of the variable source area (saturated region as a proportion of total watershed area).
You will also explore the sensitivity of these results to the m parameter, that controls the
distribution of the local saturation deficit, Si, from the mean saturation deficit, 𝑆̅.
You will be working with Coweeta using the 10m DEM data. You will be using Terrain
Analysis System (TAS).
Task. Wetness Index
Wetness index is defined as:
WI = ln(/tan )
Where, WI is wetness index,  is specific catchment area, and  is the local slope. It measures
the effects of topography on the water saturation level of soils at a location. It helps to identify
the time-varying portions of a watershed that can produce surface runoff (saturation overland
flow). The mean wetness index is λ, and the local saturation deficit is computed from
knowledge of the mean saturation deficit as:
𝑆𝑖 = 𝑆̅ + 𝑚(𝜆 − 𝑊𝐼𝑖 )
You will need to compute the wetness index map, and the mean wetness index for Morgan
Creek.
1.
Copy the directory exercise_2012/ex4 into your student space.
2. Open TAS and set the working directory, and display dem_10m.dep. REMEMBER!!!!
Before you display any image you need to choose a color palette! If you don’t, TAS has the
annoying feature of crashing….
3. Preprocess the DEM to remove pits – choose to breach pits.
4. Terrain Analysis, Compound Terrain Attributes provides a menu, including the wetness
index. Use D∞. Note that the lakes may show up as -99 (zero slopes) so you will need to
adjust the minimum in the colorbar.
5.
Using Statistical Analysis and the mask of the dem_10m_br_wat – 1 is the watershed,
background is 0), compute the mean wetness index.
6. For this watershed, previous simulation work suggests an optimal value of the parameter
m is 0.4517 (m-1) . The range of the mean saturation deficit is from 2.5~4 m, mean: 3.19 m.
Using the GIS Analysis, Raster Calculator and a range of values for the mean saturation deficit
(choose at least 5 values between 2.5~4 m create the following maps:
a.
Si 2.5
Si_4
Maps of Si
si_3.19
Si_3.0
Si_3.5
b. Maps of the saturated areas
Sat_2.5
Sat_4
Sat_3.19
Sat_3.0
Sat_3.5
c.
Maps of return flow (remember for any value with an Si <0, the return flow, seepage,
is the absolute value).
RF_2.5
RF_4
RF_3.19
RF_3
RF_3.5
Compute the variable source area (total saturated area/total catchment area) for each value of
the mean saturation deficit, and then plot the variable source area against mean saturation
deficit. To compute the total area of the watershed, multiply a derived total drainage area
image by the mask. The maximum of the resulting product is the area of the basin.
0.018
0.016857972
0.016
Variable Source Area
7.
0.014
0.012
0.01
0.01004041
0.008
Series1
0.006396113
0.006
0.004
0.0030803
0.002
0.00084289
0
0
1
2
3
4
5
Mean Saturation Deficit
The point, (3. X, 0.00084289), seems not correct. The x position seems to go further than 4 (0.5)
8. Choose another value for m and repeat to test the sensitivity of the saturation area dynamics
with this critical parameter.
M=.22585
Si_2.5
Si_4
Si_3.19
Si_3
Si_3.5
Sat_2.5
Sat_4
Sat_3.19
Sat_3
Sat_3.5
Rf_2.5
RF_4
RF_3.19
RF_3
RF_3.5
0.0016
variable source area
0.0014
0.0012
0.001
0.0008
Series1
0.0006
0.0004
0.0002
0
0
1
2
3
4
5
Mean. Sat. Deficit
9. Turn in maps of wetness index, Si, saturated areas, return flow for both values of m and the
graph of the variable source area against the mean saturation deficit.
10. Compare the saturation dynamics in terms of the expansion of the saturated area as the
saturation deficit drops, and between the two values of m.
The second time when I chose a much smaller value of m, the saturated area greatly
decreased. You could not even see it with the saturation deficit of 4 or 3.5. With
smaller numbers for the saturation deficit though regardless of the m value, the
saturated area was greatly expanded.
In both values of m, as the mean saturation deficit increases, the variable source area
decreases. This indicates that as the saturation deficit becomes greater therefore
becomes drier, the variable source area contracts/shrinks in size.
With increasing of m, the decline in variable source area by increasing saturation deficit
tends to be steeper and more dramatic. This indicates that model is highly sensitive to m
value (-4.0).
20.5/25
Work: