Physical Geography Filed Work The aim of this field work was to study the long profile and its characteristics of the River Lym. The velocity then drops at the mouth where it tends to deposit its load The velocity then drops at the mouth where it tends to deposit its load This is the general long profile of the river where the gradient decreases as you go down the river. The source is highest point of the river basin which usually is a mountain. This is the general long profile of the river where the gradient decreases as you go down the river.
The source is highest point of the river basin which usually is a mountain. The hypothesis of this field trip was to compare the long profile of a model river to the long profile of the River Lym and its characteristics. I came up with this hypothesis by researching into the geographical theory of the long profile of a model river and then wanted to compare it to a standard river in order to see which anomalies are present that is affected by nature or human activity.
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Velocity then increases as you go down stream because the cross sectional area increases relative to the wetted perimeter which reduces friction despite the changes in gradient. Velocity then increases as you go down stream because the cross sectional area increases relative to the wetted perimeter which reduces friction despite the changes in gradient. I carried out my field work along the River Lym in Lyme Regis which is on the South West Coast of England shown in the map.
Hazard| Danger | Likely hood | Risk| Precaution| Response| Vegetation HazardSpikes/stinging nettles| 1| 3| 3| Be vigilant| Wash yourself| Slipping on rocks| 3| 1| 3| Don’t climb on them| First aid| Tripping and slipping| 3| 2| 6| Enter at a safe point and don’t jump in| Get out and rest| Hypothermia | 3| 1| 3| Check weatherAppropriate clothing| Hospital | Drowning | 3| 1| 3| Sensible in the riverNo less than threeLook out for each other| Funeral| Ranging pole injuries| 2| 1| 2| Be careful| First aid| Traffic danger| 3| 1| 3| Single file facing oncoming trafficDesignating cross points| Ambulance|
English Channel English Channel I choose these study sites using stratified sampling. I had decided that I wanted to study five points of the River Lym. The main factors that determined these sites were accessibility and safety. I decided that all sites must have roughly an equal spread. The first site was chosen at the highest accessible point of the river. And our last point was chosen in the urban area, as close as you could get to the mouth considering safety issues. Then each point was chosen at roughly one kilometre apart to make it systematic but then repositioned due to accessibility factors.
For example our second study site was on privet land so we relayed on the permission of using this land to study the river. Our third site was on a public park which leads on from and to inaccessible areas so we carried out the field work in the park. The forth site was part of a forest which then passed by a horse field where we studied. Therefore I believe that all these sites are good representatives of the course of the River Lym. At each study site we collected a number of primary data across and along the channel.
Our aim was to work out the cross section, wetted perimeter, velocity, bed load size and roundness as well as the gradient in order to compare these results with our geographical knowledge that we had gained by studying a model river from the textbook. The cross sectional area was predicted to increase and was worked out by multiplying the depth and width together. We measured the width using a tape measure by taking measures from the bank at the current flow on both sides. We then divided the width by 10 and took 11 readings of the depth across the channel using a meter ruler.
I then calculated the mean or average depth which I multiplied by the width to get the cross section. By taking 11 measurements, our data was reliable because each measure represented the width well. We therefore used systematic sampling by measuring after exactly every tenth of the width to make our readings unbiased. Problems that occurred with this were that bed load may not have given an exact reading of the bed. Pools and riffles may make a difference in the average depth because there wasn’t an exact spot where we carried out our work, there was rather a range of 50 meters where we carried out our investigation and so ue to the nature of rivers there could have been great variations between a couple of meters and so there may be slight anomalies. It wasn’t very efficient to have 11 measurements on the first site because each measurement was very close to each other, likewise on third and fourth site the distance between each measurement was further apart and so we may miss something out. Furthermore, on the fourth site the depth was greater than a meter and so we had to make use of the meter ruler differently be measuring twice.
I represented my data on a graph which showed the cross section of the present flow and of the bank full. I plotted all eleven points of the depth that we collected to show the shape of the cross section of all sites onto the same scale. This made it easy to compare and interpret. It allows you to visualise how much and how efficient the water is flowing at and how much more the bank can hold before risk of flooding. However it does not show exactly how deep or wide it is or show any numbers, you can only tell how large it is relative to the other sights.
Theoretically the width should increase because lateral erosion increases downstream. According to the graph the width does increase at each site apart from the fifth site. This is because the river has been channelized at the fjord in the urban area. This is artificial narrowing to speed up water into mill chase; it can also be seen as speeding up possible flood waters out of town. Geographically the depth should also increase because the volume of water increases which allows the shape of the channel shape to become more efficient.
The graph supports this theory in general; however they may be some anomalies within cause by pools and riffles. Therefore, the textbook stated that the cross section should increase downstream which according to the graph is proved apart from the anomaly at site five caused by channelization. Our methodology and skills were accurate but the data may have great variation because there had not been any precipitation for at least four days and so the present flow was very low relative to what it may have been if it had rained before.
The wetted perimeter was predicted to increase downstream as the discharge increases. When the discharge was low, we used a tape measure which we laid out onto the river bed and took readings from where the current flow met the bank. At higher discharge we used a chain which we laid out on the river of the bed to the present flow, then removed it from the water and measured it when it was laid out on the bank straight. The water was very cold and so we had to consider safety first and act sensible in order not to fall in. sing a chain is productive because it is dense and so goes to the bed of the river. However because we could not see the bed there may be some mistakes that are caused by pools that disallowed the chain to be on a straight line. The wetted perimeter can be seen on the graph showing the cross section on the previous page. The graph shows that the wetted perimeter increases downstream from site one to four where it is 8. 6 meters. However it then falls to 3. 5 meters on site five.
This is because the river has been channelized, as I mentioned earlier that it is narrowed to reduce the wetted perimeter in relation to cross sectional area and discharge and so water flows a lot faster, this is to reduce the chance of flooding although it increases the risk of flooding downstream. This is done by hard engineering. Again, because we had a certain area within a range of about 50 meters to carry out or field work the wetted perimeter could have varied if we moved our study station five meters either way.
The discharge on that day was also very low relative to the discharge after a rainfall and so the wetted perimeter which is the length of the bed that touches the water would have been much less because the present flow was low. You can see on the graph that the bank full perimeter was much greater. Therefore our results could have been more accurate if there had been precipitation two days before. The velocity is predicted to increase downstream because the channel becomes more efficient. We measured the velocity at each site over 10 meters.
First we pointed out ten meters using a tape measure and two ranging poles which indicated the boundaries. Then one of us dropped an orange peel whiles the other timed how long it would take for the orange peel to travel from one ranging pole to the other (over 10 meters) using a stop watch. We repeated this three times. I then worked out the mean time and from this I worked out the velocity which is also the speed at which the water travels at (meters per second). We used an orange peel because if we fail to catch it after ten meters then it could float along with the river without harming the environment because it is biodegradable.
Whereas although a table tennis ball would be perfect for the job, we would most likely lose it and so it may harm the environment or damage an ecosystem. Problems that occurred when we were calculating the velocity was that the orange peel got caught onto obstacles very frequently, particularly where the discharge was very low and on meanders. I used a scatter diagram to show the changes in velocity from one site to the other. According to my studies the velocity should increase downstream because the channel becomes more efficient due to friction, the graph proves this.
However, the velocity drops at site two, this is because this was on a meander, and it was on a riffle which had a change of gradient and so the peel got caught every time and took very slowly to travel across the pool. We found that the water travels very fast on riffles but may be caught onto a rock which affected our readings and travels very slow in pools. Therefore if our ten meters covered two very large pools with one very small riffle or none then it may not be reliable. Likewise if there are two large riffles with one small pool then the velocity could be misrepresentative.
To then improve our data we could have instead measured the velocity across a longer distance. However this would be difficult because you have to be able to see where the orange peel is. Therefore, we could have measured the velocity over three different lengths and so you measure the first ten meters, then from there you measure the next ten meters and then again. Repeat this once again and then work out the mean. This would give us a more representative reading. The discharge was predicted to increase because there would be more tributaries joining downstream and the drainage area would be greater.
I worked out the discharge by multiplying the cross sectional area with the velocity for each site, I then also worked out the discharge for bank full, this allowed me to see how much water the river is carrying and how much water it is capable of carrying. I also used a scatter graph to represent this data because it allows you to plot more than one type of data onto the same graph and scale in this case being the discharge of the present flow and the bank full flow. Theoretically the discharge should increase downstream because more water is added as you go down the river by tributaries and surface runoff.
Therefore the bank full discharge should also increase. The graph shows exactly that. There is a huge jump between the third and fourth site, this is because there are two tributaries joining the River Lym. This is shown on the field sketch on page two. Although the results follow the theory, there is a huge difference between bank full and present flow discharge. This is to do with the amount of water there is in the river currently. On the third of April when we carried out our field work, the discharge was very low after an unusual dry march.
There had not been any rainfall the previous days and the land was dry, therefore the level of water in the river was generally very low. Our results could have been improved if it therefore had rained which would cause a great change into our data. The channel should become more efficient downstream because the volume of water and erosion increases, therefore friction is minimised and so the hydraulic radius increases. Geographers therefore calculate the hydraulic radius to measure efficiency by dividing cross sectional area by the wetted perimeter. also used a scatter graph for this data so that the trend can be shown clearly. However, because it isn’t a line graph or a line of best fit, you cannot predict what the hydraulic radius could or should be for any points in between or to predict the hydraulic radius for the confluence point at the mouth of the river. The textbook mentioned that the hydraulic radius should increase downstream because although the wetted perimeter increases, the volume of water relative to it increases by far more and so the cross sectional area increases at a greater rate relative to the wetted perimeter.
This reduces the amount of water that touches the bed relative to the amount of water that flows which is another way of saying there is less friction and so the channel is more efficient. The graph shows that the hydraulic radius increases downstream. At site one the wetted perimeter was great in relation to the cross section as shown in the first diagram. Whereas at site two the hydraulic radius is greater than that of site three and four, this is because the wetted perimeter is low relative to the cross section.
Relating back to the graph that shows the cross section, the roundness of the cross section increases efficiency, in other words, as the bed’s shape is closer to the shape of a cylinder, it is more efficient. Therefore pipes are round because they have minimum friction and so the content flows faster. And so because the bed is roundish at site two the hydraulic radius is greater. The channel is more efficient than expected at site five; this is because it has been channelized.
The aim of channelizing is to speed up the water, this is done by increasing the efficiency be increasing the hydraulic radius by narrowing the channel so that the cross section is much greater relative to the wetted perimeter. If the volume of water increased then the channel would have been a lot more efficient and so if it had rained earlier then the wetted perimeter would increase a bit because the current flow would rise, but the cross sectional area would increase greatly in relation to the wetted perimeter and so hydraulic radius would increase.
The bed load size should decrease downstream due to erosion. We divided the width by ten, then for every tenth of the width we picked up a bed load and measured the longest side of the load using a 30 cm ruler. This meant we had to pick it up from very cold water and so the methodology was unpleasant, however, as there were 11 samples per site it made our data reliable. We used systematic random sampling to collect our data because we randomly picked the first load that came into grip at first every tenth of the width.
This made our data very representative. The graph shows that the sediment size increased downstream whereas the theory states that it should decrease because the bed load erodes as it is transported downstream. At site one; the bed load was 0. 01 cm, this was because it was at the source where the water didn’t have enough erosive power to break rocks and so the bed load consisted of silt trough out. The bed load then increased after site two and onwards which contrasts with the theory. This is however caused by a change in rock type.
The River Lym starts on lime stone and clay which is easily eroded and then goes through chert rock which is a lot harder and so requires a lot more erosive power to erode. Therefore chert cobbles replace it and so the size increases downstream. The bed load size at site five increases more than expected, this is because the river is channelized at this point and so the rocks were man made. You also have to consider that as tributaries join, the load that is transported will also add to the load of the River Lym.
In order to then improve our results we would have to study a river that only flows over the same rock type, where the source is on a mountain full of rocks, and is not channelized. It should also not have any tributaries. However that would be unrealistic because the aim of studying the River Lym is to identify and compare the differences that a model river may have in relation to a real one. I used a dispersion graph to represent the data of the bed load shape and size. The advantages for this were that it showed variation in size and shape.
Multiple data can show variation in the measurement being recorded. I could have used a bar chart to represent it but this would require discrete categories. Or I could have used a line or scatter graph of the mean size but this would not show the variation. I could also have showed it in a tally but this is visual so it is easy to read. It is also easy to draw because there are no calculations involved. However it doesn’t show where on the transect map each measurement was recorded so you cannot tell if the sediment changed with increased distance from bank.
The dispersion graph also doesn’t show the mean and so there is no clear correlation demonstrated from the data. The bed load shape should become rounder downstream due to erosion. We divided the width by ten, then for every tenth of the width we picked up a bed load and rated the roundness from one to six, six being round and one being angular. This meant we had to pick it up from very cold water and so the methodology was unpleasant, however, as there were 11 samples per site it made our data reliable.
We did this as well as calculating the bed load size as we were measuring the depth because it was at the same point of the river, and so multitasking saved time and the chance of making mistakes was less. We used systematic random sampling to collect our data because we randomly picked the first load that came into grip at first every tenth of the width. This made our data very representative. The graph shows that the sediment roundness on a scale of one to six at each site. The graph does not show a strong correlation but it shows that the bed load becomes rounder downstream in general although it does not change a lot.
You would also have to consider that the bed load that is transported from other tributaries may make a difference to our data. As well as the changes in geology, this may make it a lot harder to erode the load in order to make it rounder and so more erosive power is required. The data at site one was anomalies, this was because the entire bed load at the source was silt and so it did not fit the trend. The textbook states that the bed load shape should become rounder downstream because it is eroded by a process called attrition.
Therefore to improve our data the load at site one would have to be freshly eroded angular rock, then if no rocks were added and the geology remained the same and the erosive power was great then it would follow onto the predictions from my studies. The gradient should decrease away from the source towards the mouth. To measure the gradient we placed two ranging poles into the river, exactly 10 meters apart, preferably from a riffle to riffle if the bed was uneven. Then using a clinometer gun we worked out the angle at which the gradient was.
This may not have been very accurate because the bed may be uneven. The graph shows the gradient over ten meters at each study site. The data is shown on a line graph. It allows you to build a brief picture of the long profile of the river. Geographically, the gradient should be negative all the time; it should be very high at the source and then very gentle after the middle course, then almost nothing in the old course of the long profile of a model river. The graph shows that the gradient is very high at the young stage, and then decreases towards the middle course.
According to the data the gradient becomes positive then goes back to negative three which is saying that the potential energy is just as high as it was on the second study site which should not occur. Neither should the gradient become positive which it does again at site five. These anomalies could have been caused by an uneven bed; this is most likely to occur if you are measuring from pools and riffles because this would shift the level of the bed. Although we did measure from riffle to riffle, the amount of bed load and the size of the riffle may make a difference to the results.
Our data could have been improved if we took various readings in order to ignore the anomalies by working out which one had an even bed. To make our data more accurate we should have measured the gradient over a longer distance. However we only worked with 10 meters because it is practical. Or we could even work out the gradient using a map by looking at the contour lines, this would also allow us to draw out the whole long profile of the River Lym rather than five lengths, but then it would have been secondary data.
The use of ICT supported my filed work investigation because it helps me to sample my study sites and familiarize myself with the area. I used Google earth to investigate the drainage area, and then used street maps to stratify the study sites. The fieldwork investigation has improved my understanding of the original geographical theory that underpinned my fieldwork. I know understand that there will always be anomalies caused by human activities. For example, the hard engineering known as channelization to protect the urban area from flooding affected my results.
The nature left to its own wishes may also make a difference such as the change in geology that was experience. Physical geography is also known as earth science but unlike science where you can carry out investigations under certain conditions to make your results as fair as possible, you cannot control the conditions in geography and so you are trying to get as close to the real answer as possible by considering how much your data represents the real thing.