Geographical Skills for Topic 2A: Landscape Systems, Processes and Change; Glaciated Landscapes and Change

Skill1 Graphical analysis

of reconstructed climate versus landform evidence for past glacial/interglacial periods.

What links can be drawn from the three graphs?

2 Comparison of past and present distribution of glaciated landscapes using global and regional maps.

Where are glaciated regions found today?

What evidence is there of previous ice ages?

What knock on effects were there of glacial periods?

What techniques can be used to interpret ice flow during past glacials (such as those shown in the figure above, left)?

3 Use of numerical data to calculate simple mass balance and equilibrium line position; use of GIS to identify main features of glacier types and assess glacier health.

Mass balance (b) is the product of accumulation (c) plus ablation (a).

How has GIS been used in the remote sensing image (left) to identify the different parts of the glacier?

4 Use of measures of central tendency to compare rates of glacier movement.

Central tendency: the tendency for the values of a random variable to cluster round its mean, mode, or median.

5 Cirque orientation analysis using large-scale maps (OS maps); calculating Spearman’s rank correlations of height of basin, size of basin and orientation and commenting on the significance of the correlation.

Spearman’s Rank Correlation Test

Spearman’s Rank Correlation is a statistical test to test whether there is a significant relationship between two sets of data.The Spearman’s Rank Correlation test can only be used if there are at least 10 (ideally at least 15-15) pairs of data.There are 3 steps to take when using the Spearman’s Rank Correlation Test. You are going to complete a Spearman’s Rank Correlation test following the instructions to see if there is a relationship between the area of a corrie and the height of a corrie’s backwall using fieldwork measurements for 17 corries in an upland area in north Wales. Step 1. State the null hypothesisThere is no significant relationship between ___________ and _____________

Step 2. Calculate the Spearman’s Rank Correlation Coefficient

To do this:1) First rank the data from 1-17 (1 being highest, 17 lowest), this has already been

done for you for corrie lengths.

2) Calculate D and D2

Corrie backwall height (m) Corrie length D D2

Data Rank Data Ran

1025 230 5 -1 1

1850 180 8.5 6.5 42.25

1780 80 16 169

1320 255 3.5

440 200 6.5

700 170 12

1600 400 2

895 180 8.5

640 110 15

625 173 11

890 160 13

810 130 14

1020 255 3.5

580 60 17

885 175 10

800 200 6.5

2110 450 1

SUM

3) Calculate ∑D2 i.e. the sum of the D2 column This = ________

4) Calculate rs using the equation above. Rs = 1- ((6 x ________) / ( ___ x (____2 – 1))Rs = ___________

Step 3. Test the significance of the resultCompare the value of rs that you have calculated against the critical value for rs at a confidence level of 95% / significance value of p = 0.05.

If rs is equal to or above the critical value (p=0.05) then REJECT the null hypothesis. There is a SIGNIFICANT relationship between the 2 variables.

A positive sign for rs indicates a significant positive relationship and a negative sign indicates a significant negative relationship.

If rs (ignoring any sign) is less than the critical value, ACCEPT the null hypothesis. There is NO SIGNIFICANT relationship between the 2 variables.

Step 3. Test the significance of the resultThe critical value at p=0.05 significance level for 17 pairs of measurements is 0.488How does your Rs result compare to the critical value of 0.488?

What does this tell you?

6 Till fabric analysis using rose diagrams.

Imbrication is when sediments have their long axes (a-axis) aligned in one direction, usually indicating deposition from flowing water (fluvioglacial deposits). Glacial sediment is held in suspension within the ice therefore has a random orientation (not imbricated). All sediment has three axes, a, b and c. these correspond to the length (a), width (b) and height (c) of a sediment particle.

Till fabric analysis rose diagramsRose diagrams can be used to present data on clast orientation. The imbrication of 50 random sediment samples were recorded at two different sites in a lowland glaciated region. The raw data has been sorted into 15 degree intervals as shown in the table.

Clast orientation Frequency data Site A Frequency data Site B0-15 0 4

16-30 0 531-45 0 346-60 4 361-75 9 676-90 12 2

91-105 10 5106-120 8 4121-135 5 5136-150 2 3151-165 0 4166-180 0 6

Plot each set of frequency data on the right hand side of the graph in two different colours. Since each orientation will have two values 180 degrees apart, insert a mirror image of the area itself across the 180 degree line. Use the example below as a guide.

Using the results on your rose diagram suggest the origin of the sediment found at each site.

7 Use of British Geological Society (BGS) glacial drift maps, Ordnance Survey (OS) maps, GIS and fieldwork results to reconstruct past ice extent and ice flow direction.

Use the large map provided (produced by BRITICE project) to identify the key features of past ice extent and flow directions (list a min of 5 features).

8 Use of student t-test to analyse changes in sediment size and shape in outwash plains; central tendency analysis of both glacial and fluvioglacial deposits (comparison of size, shape and degree of sorting of clasts).

The table below shows sizes of sediment measured along their long axes found at two different sites.

n Site 1 Variance

Variance squared Site 2 Varianc

eVariance squared

1 1 -28.3 800.89 32 -0.5 0.252 23 23 529 30 30 9003 12 12 144 22 22 4844 6 6 36 41 41 16815 48 48 2304 39 39 15216 64 64 4096 26 26 6767 13 13 169 50 50 25008 90 90 8100 38 38 14449 32 32 1024 26 26 676

10 4 4 16 21 21 441

mean 29.3 Sum v21721.88

9 32.51032.32

5standard deviation

41.49565

32.12981

The statistics shown can then be put into the formula for the t test which give a value for the degree of difference in the two means for each site. What else can you tell from these measures of central tendency, especially standard deviation?

Graphs like those below can also be sued to present this data and see the difference in data spread. Y axis= length of long axis, x = sample no.

1 2 3 4 5 6 7 8 9 100

102030405060708090

100

Site 1

Site 1 Average

1 2 3 4 5 6 7 8 9 100

102030405060708090

100

Site 2

Average Site 2

What might the limitations be of this data and the analytical techniques?