Exemplar for Fieldwork answer
1
State the hypotheses and aims of your investigation
For this investigation two hypotheses were formulated.
a
b
‘Mean velocity increases as hydraulic radius increases’
‘Stone roughness decreases with distance from source’
Our general aim was to investigate the changes in characteristics and morphological processes of
a local river’s channel over its whole long profile.
Amongst other concepts, we tested David Waugh’s published theory on channel efficiency and
velocity. The theory is that when the ratio of the river’s wetted perimeter to its cross sectional area
is lower, the river’s flow is less impeded by friction and hence faster.
Another was our classroom theory centring on The Bradshaw model where stone roughness, in
theory, decreases with distance from source. This should be due to attrition between the stones
and abrasion along the river’s channel reducing their size which will be more exaggerated the
longer they have been in the channel and the further down the long profile they have moved.
2a
Describe your method of data collection for 1 hypothesis
‘Mean velocity increases as hydraulic radius increases’
[We used a mixture of random and stratified sampling in order to locate our sampling sites. Overall we chose
three sites along the long profile of the River Glass and obtained 24 sets (pairs) of data. However the sampling
wasn’t as random as first hoped due to ease of accessibility and human interference.]
Firstly depth was measured using a rigid metre stick which was held perpendicular to the river bed with the thin
edge of the ruler facing upstream. Measurements were taken every 50 centimetres across the width of the river
to achieve a fair test and account for variations in channel shape. We had to ensure the flexible tape measure
was held taut between the two sides of the river bed by two people so the measurements were accurate. The
tape was arced to find the shortest distance, and once found we started to measure. A third person proceeded to
take the measurements at the edge of the wetted perimeter. They didn’t remove bedload from the river if the
ruler landed on it to keep the results as realistic as possible. A separate person recorded the results on dry land.
This meant that they could monitor from a distance the quality of data collection. These were the measurements
we needed to calculate the cross-sectional area
Wetted perimeter was measured with a heavy chain laid across the same section of river bed where the depth
and width were measured to calculate the cross-sectional area.
If they just ask for one method –
Velocity was recorded three times, at each of the four different sampling sites. We held a taunt flexible
tape measure of ten metres in the middle of the river and one metre away from either side of the bank.
We used an orange as a float because the weight meant it sunk a little below the surface so it measured
the velocity slightly under water which was more accurate. We placed the orange upstream in the river to
allow it to gain momentum before reaching the beginning of the tape measure. We then started the
stopwatch and stopped it when the orange had reached the end of the ten metres. All units were later
calculated in metres per second (m/sec).
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2b
Discuss the strengths and weaknesses of your method of data collection
A primary advantage of our method of data collection was that we gained a full range of results. Altogether we
had 24 paired samples from three stratified sampling sites. However, a weakness of our method of sampling is
that it was biased as we chose locations with easy and or safe access.
The methods were straight-forward to carry out which meant we could take a lot of samples from one day.
However it was a disadvantage that we only sampled for one day as it wasn’t representative.
Our results were however only based on one season and one time of day. Although there was simple
calculations involved, there was a slight chance that the recording was possibly inaccurate. However this could
have been the result of misunderstanding the results the recorder read out.
Another case of differing opinions was when we were testing the hypothesis, ‘stone size reduces with distance
from the source.’ As we used a picture chart, different people judged its size contrastingly.
To aid results, we allowed the float to accelerate before it reached the tape measure so this would give accurate
velocity recordings. However the float tended to drift across the river which resulted in erratic movement and an
overlap of results. We also ran out of dog-biscuits so some of the later readings were affected. (how……
)
Also the float chosen only measured surface velocity, we should really have used a velocity meter for this
measurement.
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3a
Describe one method of data presentation
Initially we prepared our data in a table using Excel enabling us to see the precise figures and identify which part
of the river our results were from. After tabulating our data by hand and on Microsoft Excel, we presented our
data in a scattergraph, as the table didn’t show visual trends or data clustering.
We ensured that the axes were of equal length with the independent variable, (velocity) on the x-axis and the
dependent variable, mean depth, on the y-axis. We then drew on the interception points to study the interrelationships between the variables.
Add more here
We used Excel to check the ‘best fit’ line we had drawn was in the right place.
3b
Explain how you used it and assess it’s strengths and weaknesses
Although the scattergraph was a simple method, it was very laborious and needed precision to accurately plot
each point. However it was useful as it could be electronically checked by Microsoft Excel so although there
were cases of human error, they were altered.
The graph allowed to us to interpret trends and clustering of data.
In addition, the scattergraph was a useful method of data presentation as we could recognise residuals
(anomalies), such as when the float became trapped at site 3 (Groudle Glen), and once questioned we could
remove these to achieve precise correlation.
In contrast, although you could spot residuals, they were purely figures so we didn’t know which site this mistake
could have occurred at.
If we decide to use scattergraphs (alone) for later analysis, identifying a best fit line can be very subjective,
meaning that there may be differing interpretations of exactly where it should be. This is a fundamental
weakness. Excel would produce a scattergraph and line of best fit so we could compare this with ours.
Add more here:- (research why scattergraphs are poor or useful)
4a
Describe one technique you used to analyse your data
After we tabulated our data in a spreadsheet on Microsoft Excel, the data was then transferred onto another table
(see below) where each paired set of results from each site was ranked. Each velocity and depth result was
ranked in order from highest to lowest and then we calculated the difference between the two ranks. In addition,
to remove any negative values, we squared the difference to make it a positive result (eg -1² = 1). We then
totalled all of the squared the differences along with the paired sets of data and these totals were substituted into
the formula of Spearman’s Rank Correlation Coefficient (SRCC).
When we used SRCC for the data that was collected for the second hypothesis (‘Stone roughness decreases
with distance from source’) the resultant value left from the formula was + 0.75 which is very close to +1. We
looked at the significance table and it was a significant result. This means that there was only a 5% possibility
that the results we obtained were obtained by CHANCE.
4b
Explain why you chose this technique and assess how successful it was in your analysis
We decided to use Spearman’s rank correlation coefficient (SRCC) as
1. It is an objective method where there is only one possible answer as opposed to the line of best fit on a
scattergraph which is subjective when done manually.
2. Additionally, SRCC is universally recognised which is useful as it can be compared on a universal scale.
3. Another advantage of using SRCC is that it wasn’t too mathematically challenging as it was relatively
simple to input the values into the formula and finish with the value for ‘r.’
4. Plus, it meant we could study the connection between the two sets of data, velocity and mean depth, with
just one value ® to check.
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However, SRCC doesn’t take into account residuals which in our case affected the value of r
considerably.
A further disadvantage with this technique is that although it shows correlation, it can never prove
causation as it is purely one value left at the end. Therefore no matter how many samples are taken, we
don’t know whether or not velocity increases as mean depth increases.
Similarly as the data is ranked, actual values are lost, meaning the difference between each value can be
great or small – that is - any residuals are disguised.
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Anything extra here: -
5
Explain how your investigation helped you to gain an understanding of the topic/environment that
you were studying. Your explanation should make specific references to the results that you obtained.
Our second hypothesis tested was - ‘Stone roughness decreases with distance from source’
’
To be proven our results (r) had to be over +0.4 or -0.4 for the 24 pairs of data taken from the significance table.
For this particular hypothesis the resultant value was 0.76 which exceeded the critical value for r at the 95%
probability level and at the 99% level. Therefore it proved our classroom theory and The Bradshaw theory
correct. So, the bed load does become smoother as we progress down the long profile.
However on reflection the resultant value of appears inordinately high. This may be because of several factors.
For 24 pairs of data
95%
0.344
99%
0.4716
The other aspect of geography we were investigating was the link between the velocity found in a channel and
the hydraulic radius of the same section of channel. Although none of our results showed negative correlation, all
results were affected by time and access constraints. Consequently sampling wasn’t random enough. This
therefore led us to question our overall results.
Extra here In conclusion, although the results show some correlation, they didn’t show causation. All of our results had to be
questioned due to SRCC’s ranking system. For instance for our hypothesis ‘Mean velocity increases as
hydraulic radius increases’ the result from site 3 was just 0.16. Similarly the result at site 1 was 0.057.
However when the 3 sites were put together, the resultant value was 0.55.
Moreover if I did this investigation again, I would visit the River Glass at a different time of year and examine
seasonal variation.
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