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This field work links to “Part 2 - Freshwater – issues and conflicts” of the IB syllabus, following the sub-topic “Drainage basis and flooding”. This investigation will allow us to gain knowledge in a real-world geography context. The aim is to investigate how the “Baye de Clarens river”, in the Canton Of Vaud Switzerland correlates with the Bradshaw model in terms of downstream change, therefore answering the field work question : To what extent does the Baye de Clarens river correspond to the Bradshaw model in terms of downstream change ? The “Baye de Clarens” river has its source above the hamlet of Bains-De-l’Alliaz in the municipality of Blonay at an altitude of around 1205 m above sea level. The river’s torrent is approximately 8 Km long (“Wikiwand - Baye de Clarens”). Conveniently, the river is near our school allowing easy access to the sites investigated.

To focus this investigation, I have created three hypotheses about the Baye de Clarens river that I will be testing with the data collected. I have based these hypotheses on the Bradshaw model, a geographical model which depicts the changes that occur as a river flows from its source to its mouth (“River Processes”). This model gives an approximation of how a river usually changes as it progresses.

The cross-sectional area of the river will increase downstream as the volume of water will rise, increasing velocity and therefore its erosive power.

Sediments will have an increased sphericity as the river flows downstream as their irregular edges are removed by abrasion and corrosion. The farther a particle is moved, the more rounded and spherical it becomes. Load particle size will therefore decrease.

Discharge and average velocity will increase due to erosion, conjoining tributaries and surface runoff caused by weather systems and drainage systems.

The data was collected from 16 sites along the river that were identified based on accessibility and security. This was done to ensure that the investigation could be carried out safely. We calculated the distance (km) from the source to each site. The data collected at each site will allow the hypotheses to be answered.

Distance from source (Km) | Site geolocation |
---|---|

1.35 | 46.469206, 6.949506 |

1.43 | 46.468714, 6.948781 |

2.39 | 46.462967, 6.938623 |

2.473 | 46.464033, 6.938414 |

2.978 | 46.462272, 6.933030 |

3.024 | 46.462296, 6.932434 |

4.094 | 46.462986, 6.922254 |

4.125 | 46.462879, 6.921887 |

6.095 | 46.460223, 6.901364 |

6.133 | 46.460025, 6.900978 |

6.483 | 46.457932, 6.897427 |

6.548 | 46.457391, 6.898092 |

6.955 | 46.453950, 6.898485 |

7.093 | 46.452746, 6.897701 |

7.497 | 46.449432, 6.896915 |

7.757 | 46.447314, 6.895912 |

The data was recorded on an application created prior to the data collection. At every site, our GPS location would be recorded as well as our data. The data collected could then be transferred to Excel for data procession.

Apparatus -

- Dog treat
- Tape measure
- Stopwatch

To calculate the velocity of the river at each of the 16 locations, the float method was used. A dog treat would float along a predefined distance. The dog treat was thrown upstream from the starting point into the water. Once it attained the starting point the stopwatch was started and once it passed the end point the timer was stopped. The time (in seconds) was calculated for the float to attain the determined distance. A dog treat was used because it floats, its biodegradable and therefore wouldn’t have polluted the river in any way. Hence, it was then possible to calculate the water’s velocity by the following equation -

\(V=\frac{D}{T}\)

The V represents velocity, D being displacement also known as the distance travelled by the dog treat and T being the time taken for the dog treat to reach the end point. This method was repeated three times at each site allowing us to calculate average velocity leading us to more accurate results.

**Apparatus -**

- 100 cm ruler
- Tape measure

Depth and occupied width were measured to calculate the cross-sectional area at each site. To measure the occupied width, a tape measure was held on each extremity of the river bank. Depth was recorded 10 times at each site. It was measured at every tenth of the way from one side of the riverbank to the other. To explain this concisely, at the fifth site, the width was 200cm. A tenth of 200cm is 20cm, so the measurement started at 0 and then went across 20cm every time to measure the channel depth at that point. This was done until the other side of the riverbank was reached. Hence, systematic sampling was used to measure depth since our measurements were conducted with a fixed periodic interval. With the data collected, the cross-sectional area of the river could be calculated with the following equation -* Average depth* × *actual width*.

**Apparatus -**

- Rock sphericity indication chart.

At each site 10 sediments were collected from the riverbed using random sampling. Our sample size for each site was large which allowed us to obtain a range of data. During the selection we looked away to avoid bias choices created by human instincts. For each sediment, the length and the sphericity were recorded. We could then compare how the sediments size had changed from the source of the river to the mouth. The sediments at the source were angular and as we reached the mouth of the river, they became more spherical. Sphericity was measured by comparing the sediment with an indication chart.

The cross-sectional area of the river will be the first hypothesis being compared to Bradshaw’s model, secondly it will be the load particle size and the sphericity of the sediments and finally the average velocity and discharge along the river. The data collected is presented below.

Distance from source (Km) | Actual width (m) | Average depth (m) |
---|---|---|

1.35 | 0.73 | 0.115 |

1.43 | 0.75 | 0.116 |

2.39 | 0.75 | 0.152 |

2.473 | 0.6 | 0.052 |

2.978 | 2 | 0.033 |

3.024 | 0.7 | 0.052 |

4.094 | 3.6 | 0.139 |

4.125 | 1.1 | 0.102 |

6.095 | 3.3 | 0.082 |

6.133 | 2.4 | 0.094 |

6.483 | 2.8 | 0.237 |

6.548 | 2.5 | 0.221 |

6.955 | 5.13 | 0.122 |

7.093 | 3 | 0.104 |

7.497 | 5.87 | 0.165 |

7.757 | 4.3 | 0.128 |

Distance from source (Km) | Cross sectional area (M |
---|---|

1.35 | 0.07592 |

1.43 | 0.087 |

2.39 | 0.114 |

2.473 | 0.0312 |

2.978 | 0.072 |

3.024 | 0.0371 |

4.094 | 0.5112 |

4.125 | 0.1122 |

6.095 | 0.297 |

6.133 | 0.2496 |

6.483 | 0.7112 |

6.548 | 0.615 |

6.955 | 0.6618 |

7.093 | 0.312 |

7.497 | 1.0683 |

7.757 | 0.6106 |

Distance from source (Km) | Velocity 1 (ms | Velocity 2 (ms | Velocity 3 (ms | Average velocity (ms |
---|---|---|---|---|

1.35 | 00.00 | 0.00 | 0.00 | 0.000 |

1.43 | 0.02 | 0.03 | 0.02 | 0.02 |

2.39 | 0.37 | 0.26 | 0.30 | 0.31 |

2.473 | 0.2 | 0.25 | 0.25 | 0.23 |

2.978 | 0.07 | 0.11 | 0.09 | 0.09 |

3.024 | 0.25 | 0.2 | 0.22 | 0.22 |

4.094 | 0.62 | 0.71 | 0.19 | 0.51 |

4.125 | 0.28 | 0.33 | 0.31 | 0.31 |

6.095 | 0.45 | 0.35 | 0.37 | 0.39 |

6.133 | 0.6 | 0.5 | 0.6 | 0.57 |

6.483 | 0.36 | 0.32 | 0.31 | 0.33 |

6.548 | 0.7 | 0.58 | 0.88 | 0.72 |

6.955 | 0.49 | 0.53 | 0.57 | 0.53 |

7.093 | 1 | 0.67 | 0.67 | 0.78 |

7.497 | 0.74 | 0.65 | 0.69 | 0.69 |

7.757 | 0.7 | 0.5 | 0.7 | 0.63 |

Distance from source (Km) | Average length (Cm) | Average sphericity |
---|---|---|

1.35 | 4 | 2 |

1.43 | 10 | 3 |

2.39 | 4 | 3 |

2.473 | 8 | 4 |

2.978 | 3 | 4 |

3.024 | 8 | 4 |

4.094 | 5 | 3 |

4.125 | 7 | 3 |

6.095 | 5 | 5 |

6.133 | 5 | 4 |

6.483 | 3 | 4 |

6.548 | 6 | 5 |

6.955 | 5 | 5 |

7.093 | 7 | 3 |

7.497 | 5 | 4 |

7.757 | 7 | 6 |

The river’s discharge was calculated by using the following formula - *O = × A, *V being the average velocity and A being the river’s cross-sectional area.

Distance from source (Km) | Discharge (m |
---|---|

1.35 | 0 |

1.43 | 0.00174 |

2.39 | 0.03534 |

2.473 | 0.007176 |

2.978 | 0.00648 |

3.024 | 0.008162 |

4.094 | 0.260712 |

4.125 | 0.034782 |

6.095 | 0.11583 |

6.133 | 0.142272 |

6.483 | 0.234696 |

6.548 | 0.4428 |

6.955 | 0.350754 |

7.093 | 0.234 |

7.497 | 0.737127 |

7.757 | 0.384678 |

Hypothesis 1 - the cross-sectional area of the river will increase downstream The graph below created with the results from Table 3 displays that the cross-sectional area of the river generally increases downstream as each location becomes further from the source of the river and closer to the mouth. This is due to tributaries joining the river increasing the volume of the water and its velocity therefore it’s erosive power (“Changing Channel Characteristics”). The graph displays a linear trend line which has a positive gradient thus suggesting that the river does follow Bradshaw’s model.

However, our graph equally displays some anomalies as data varied and these need to be considered. At the last site we visited, 7.757 Km (site 16) from the source, the cross- sectional area dropped significantly by 0.4577m^{2} from the previous site (site 15) which had the highest cross-sectional area. A significant decrease in cross-sectional area as the source becomes further is unusual and does not correspond to Bradshaw’s model of downstream change. At areas 4.125 Km (site 8), 6.548 Km (site 12), and 7.497 Km (site 16) away from the source, the cross-sectional area peaks downwards although it should be following the positive trend. Nevertheless, Figure 15 presents that if these sites were to be removed from the investigation, they would not have an impact on the result of the trend therefore our results support the Bradshaw model.

Although the sites where anomalies were present are removed on the graph of Fig 1.8, the linear trend line still has a positive gradient.

Hypothesis 2 - Sediments will have an increased sphericity and load particle size will decrease downstream.

According to the Bradshaw model, sediment size decreases downstream due to higher rates of erosion over time and attrition. In addition, sediments become far rounder than their angular nature upstream. At the source of the river, the sediments aren’t exposed to as much erosion, and they have not been transported yet. As the river takes its course downstream, sediments collide with one another diminishing their size and breaking them apart.

The graph above displays the average length of the sediments from the source of the river to the mouth. The linear trendline has a slightly negative gradient which weakly corresponds to the Bradshaw’s model statement that load particle size decreases downstream. As our sample of rocks was random, this weakly negative trend line proves that most of the sediments picked out did decrease in size downstream.

Furthermore, the average sphericity of sediments in the “Baye De Clarens” river increases as the river gets closer to the mouth. The graph above demonstrates a linear trendline with a strong positive gradient. This indicates that angular rocks at the source of the river consequently become rounder therefore spherical when they get closer to the mouth.

Hypothesis 3 - Average velocity and river discharge will increase downstream. According to the Bradshaw model, average velocity increases downstream, and our data collected has corresponded to this statement.

The linear trendline has a strong positive gradient as average velocity increases when the river flows downstream. As more water is added to the river via tributaries, less of the water is in contact with the bed of the river and the mouth so there is less energy used to overcome friction. This leads the river to flow progressively faster downstream (“How Rivers Change from Source To mouth”, Cool geography). Although there are some anomalies that stand out such as areas 6.483 Km (site 11), 6.955 Km (site 13), and 7.57 Km (site 16) away from the source, the overall trend is positive. These anomalies may have been caused by the decrease in river slope downstream. If the river’s slope is decreased, water will flow at a

slower rate decreasing average velocity. There is a significant decrease in average velocity at 4.125 Km (site 8) from the stream. This may have been due to the narrow nature of this site with plunging pools slowing down the river’s flow.

The linear trendline has a strong positive gradient as average velocity increases when the river flows downstream. As more water is added to the river via tributaries, less of the water is in contact with the bed of the river and the mouth so there is less energy used to overcome friction. This leads the river to flow progressively faster downstream (“How Rivers Change from Source To mouth”, Cool geography). Although there are some anomalies that stand out such as areas 6.483 Km (site 11), 6.955 Km (site 13), and 7.57 Km (site 16) away from the source, the overall trend is positive. These anomalies may have been caused by the decrease in river slope downstream. If the river’s slope is decreased, water will flow at a

Finally, according to Bradshaw’s model of downstream change, the river’s discharge

increases downstream.

As seen from the graph above, the trendline has a strongly positive gradient suggesting that discharge increases overall, however it is not constant. At site 16, 7.757 Km (site 8) away from the source the river’s discharge suddenly peaks downwards as it did for average velocity. This reveals that average velocity and the river’s discharge are strongly correlated.

I will be using Spearman’s rank to determine the relationship between river velocity and river discharge as they are continuous variables. According to the Bradshaw model, velocity and discharge have a direct relationship. With the 16 pairs of data collected, a small sample, I will analyse the correlation between the quantitative variables statistically.

**Null hypothesis** - there is no statistically significant correlation between river velocity and river discharge.

**Alternative hypothesis** - there is a statistically significant correlation between river velocity river discharge.

Further calculations using values from the table -

\(1-\frac{(6\Sigma D^2)}{n^3-n}\)

\(1- \frac{(6×70.5)}{16-15}\)

\(1- \frac{4284}{3390}=0.896 \text{ is the Spearman’s rank coefficient}\)

According to the table of critical values of Rs at various significance levels, calculated Rs (0.896) is greater than the critical value (0.655) for 16 pairs of data. Therefore, reject null hypothesis at 1% significance level. There is a significant correlation at the 1% significance level.

As river velocity and discharge contain similar elements in the equations used to calculate their result, they are strongly correlated. Thus, they’re strong correlation supports hypothesis 3 since they both have significantly increased downstream corresponding to Bradshaw’s model.

In conclusion, the “Baye de Clarens” river corresponds to the Bradshaw model in terms of downstream change responding to the following research question: To what extent does the Baye de Clarens river correspond to the Bradshaw model in terms of downstream change? The data collected allowed me to investigate the river’s cross-sectional area, load particle size and the river’s average velocity and discharge. Overall, these factors corresponded to the model taking into consideration anomalies that were present. These anomalies may have been caused by human error or simply by the natural factors affecting the river’s behaviour. The factors investigated did not increase or decrease in a perfectly linear fashion however, the Bradshaw model does not state that these factors are linear.

*Hypothesis 1 was accepted as the cross-sectional area of the river did increase downstream.*

*Hypothesis 2 was accepted as load particle size decreased and average sphericity increased downstream.*

*Hypothesis 3 was accepted as average velocity and river discharge did increase downstream following a trendline with a strongly positive gradient.*

Therefore, the approval of all three of my hypotheses demonstrates that the Bradshaw model is a practical guidance to lead an investigation. However, each river is far more complex than a simple model due to the many factors that may be affecting it which may have caused the fluctuations and anomalies across the factors investigated.

"Baye de Clarens."* Wiki Wand*, www.wikiwand.com/de/Baye_de_Clarens. Accessed 29 Sept. 2022.

"Changing channel characteristics."* Coolgeography*, www.coolgeography.co.uk/A- level/AQA/Year%2012/Rivers_Floods/Channel%20characteristics/Channel%20Charac teristics.htm. Accessed 29 Sept. 2022.

"Google My Maps." Google, www.google.com/maps/about/mymaps/. Accessed 29 Sept. 2022.

"How rivers change from source to mouth." *Coolgeography,* www.coolgeography.co.uk/GCSE/Year%2010/PhysicalWorld/Drainage%20basin/how _rivers_change.htm. Accessed 29 Sept. 2022.

"How rivers change from source to mouth."* Jkgeography*, www.jkgeography.com/how-

rivers-change-from-source-to-mouth1.html. Accessed 9 Sept. 2022.

“River Processes”* BBC Bitesize*, 2019, www.bbc.co.uk/bitesize/guides/zcjprdm/revision/5. Accessed 29 Sept. 2022.

Franzi, David A. *Particle Shape Analysis*. 2003, serc.carleton.edu/files/NAGTWorkshops/sedimentary/activities/particle_shape.pdf. Accessed 8 Sept. 2022.