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Lab 2: Slope Aspect Lab Objectives:

• to investigate potential differences between north- and south-facing slopes in the foothills of the Colorado Front Range

• to become familiar with the US Geological Survey’s online program StreamStats • to practice plotting and interpreting data in Excel and to apply some simple

statistical tests Background: Research in a variety of arid and semiarid regions indicates that north- versus south-facing slopes typically have differences in moisture retention, hillslope processes and morphology. North-facing slopes are able to retain moisture (snow and rain at the surface and in the soil) longer than south-facing slopes, particularly in winter, when north-facing slopes may be continually shaded. Greater moisture translates to differences in bedrock weathering, soil development, vegetation, surface and subsurface erosional processes and, ultimately, hillslope morphology. The photo below was taken in January 2013. In this view of the foothills, looking west from Route 287 just beyond the intersection with Shields Avenue, you can clearly see that north-facing slope (north is to the right) retain snow, whereas south-facing slopes are largely free of snow. North-facing slopes are also more likely to have coniferous trees; south-facing slopes are mostly covered in shrubs and grasses. North-facing slopes usually have thicker and better developed soils (greater clay content, more clearly differentiated soil horizons) with greater infiltration capacity. At higher elevations in the Colorado Front Range, moisture becomes less of a limiting factor (i.e., moisture is more abundant), and aspect-related differences in hillslope process and form become much less pronounced. In the foothills and lower elevations of the mountains, however, up to approximately 2300 m (7600 ft) elevation, north- and south-facing slopes have different geomorphic processes and forms.

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Tasks: 1) Access the interactive map on the Colorado StreamStats website http://water.usgs.gov/osw/streamstats/colorado.html 2) For the following pairs of points, use the ‘Zoom To’ tab and enter the latitude and longitude. The point will show up on the screen. Go to 1:10,000 scale view, and use the terrain profile tool to generate a profile across the drainage between each pair of points. Use the values on the profile to calculate the average gradient of each north- and south-facing slope. Before leaving each pair of points, see task #3. Labeau Gulch 40.6001 105.2147

40.6081 105.2167

Well Gulch 40.5753 105.1879 40.5801 105.1877

Brown Gulch 40.6102 105.2171 40.6148 105.2173

Long Gulch 40.6196 105.2242 40.6237 105.2243

Rist Canyon 40.6238 105.2235 40.6305 105.2238

Unnamed 40.6329 105.2182

40.6360 105.2185 Poudre at Greyrock 40.6918 105.2835

40.6998 105.2859 Devil Gulch 40.5936 105.2060

40.5976 105.2107 Empire Gulch 40.5927 105.1982

40.5953 105.2020 Soldier Canyon 40.5799 105.1988 40.5819 105.2070 3) For each drainage, use the ‘Watershed Delineation from a Point’ tool to calculate the upstream drainage area and mean basin slope. Values are provided in square miles for the drainage area: convert to square kilometers (1 km2 = 0.36 mi2).

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4) Plot drainage area versus mean basin slope, drainage area versus average hillslope gradient (average north and south values), and drainage area versus difference in hillslope gradient between north and south using Excel (Insert, Chart, XY Scatter). Try these plots with and without the data point from the Poudre at Greyrock, which is an example of what is known as an outlier. When plotting data, the independent variable (drainage area) should be on the x-axis. For each plot, determine the r2 value for the regression (add a trendline to your plot, right click the trendline and select format trendline, go to options, and check the box that says “display R-squared value on chart”), and determine whether this value is statistically significant using the following procedure:

212r

nrt−

−=

Where r2 is the regression coefficient, n is the sample size (10, in this case). Compare t to the test statistic in the table below, and use a cutoff value of 0.1. The degrees of freedom in the table below = (n – 2). Example: r2 = 0.7, t = 4.32. This value (4.32) is > test statistic of 1.860, so relationship is significant at 90% level. Are any of these relations significant? Why or why not?

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5) Calculate the average (AVERAGE function) and the variance (VAR function) of the hillslope gradient for two populations, the north-facing and the south-facing slope. Use a t-test to examine difference between the two populations:

( ) ( )( )

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2121

222

211

21 2

11 nnnnnn

snsn

xxt+

−+

−+−

−=

Where x1 and x2 are sample averages, s12 and s22 are variances, and n1 and n2 are the number in each sample (here, 10). The two populations have significantly different averages if t > test statistic in the table below, or if t < -[absolute value] of the test statistic. Again, use 0.1 for the level of significance. The degrees of freedom = (n1 + n2 -2): in this exercise, degrees of freedom = 18. Do the two populations differ statistically? Why or why not?