River Flow Data Analysis

River Flow Data Analysis

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Summary:

The aim of this report is to analyse the river flow data from the River Severn in 2000 and 2001. The data readings will be taken from the Bewdley station 54001 over the 10 months of each year.

The data will be analysed in graphical and statistical format in order to view trends and relationships easier.

The results will be displayed as data i.e. either table format of raw data, from this graphs will be constructed to illustrate the various types of data and the way it will be displayed

Contents:

1. Introduction…………………………………………………………………Page 4

2. Background……………………………………………………………Page 5 & 6

3. Methodology………………………………………………………………. Page 7

4. Results………………………………………………………….Page 8,9,10 &11

5. Analysis……………………………………………………………………Page 12

6. Conclusion…………………………………..…………………………….Page 13

7. References………………………………………………………………..Page 14

8. Appendix 1……………………………………………………………….Page 15

1. Introduction:

The objectives of this report are to analyse the raw data readings of the station and process them into a statistical format in order to make comparisons and draw conclusions.

Initially the report will look at the river flow data of the River Severn in 2001, but will then compare the readings against those of year 2000 in order to view any relationships and changes.

2. Background:

Summary of flow measurement & flood monitoring

The UK gauging station network is one of the most densely populated when compared on compared on a global level. This is due to many factors such as climate change, geographical variances, usage of land and water utilisation. The data individual gauging stations collect vary widely depending on application and the area. Factors such as:

River flow measurement in essential in order to provide estimated projections in water supply, as well as reservoir levels in order meet the demand for water to domestic, agricultural and commercial sectors. This problem becomes more evident when most of the UK population and commercial sectors are concentrated in the driest parts of England, around the south east, where rainfall and river flow can vary significantly.

River flow has varied significantly over recent years due to droughts where there has not been enough rainfall, leading to reservoirs running extremely low. Then river flow has increased so quickly causing the concern to switch from droughts into risk of flooding. (2)

The majority of the river flow data readings taken by the 1300 monitoring stations are taken by a flow measurement weir as the one in the picture on the right. Readings are taken as the river flows past a notch in the weir. In order for the weir to give accurate results, the crest of the weir is to be kept sharp and sediment should not be allowed to build up behind the weir.

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(3)

There are many gauging station posted across the 209mile long stretch of the River Severn, which also inclines up to an altitude of 610 metres. Making it the largest river within the UK.

(1) http://www.ceh.ac.uk/data/nrfa/uk_gauging_station_network.html
(2) http://www.defra.gov.uk/environment/statistics/inlwater/iwlevels.htm
(3) http://www.british-hydro.org/mini-hydro/infopage.asp?infoid=368
Due to the UK being and island and surround by Atlantic Ocean, English Channel, Irish and North Sea on all four sides, the chances of flooding are considerably high. This makes the task of making flood risk assessments very difficult, due to the weather being able to change very quickly.

The environment agency uses technology to monitor the rainfall, river levels and sea condition 24 hours a day, in order to provide local forecasts for flooding. The system issues four levels of warnings, with the fourth level being an all clear.

The most common and well known flood defence are sandbags, which are regularly seen on the news along river banks when flood warnings are in effect. There are currently many products designed and available to consumer to protect against floods. These include flood guards and gates which are placed on the doors of a property as well as flood dams which can be used to protect a number of properties. The UK environment agency recommends using kite mark standard flood defences which have been tested and verified to work correctly. Below are some images of flood defence products available to consumers:


Flood Guard Flood Dam Flood Gate
(4)

(4) http://www.environment-agency.gov.uk/subjects/flood/826674/830330/877142/877272/?version=1&lang=_e
(5) http://www.environment-agency.gov.uk/subjects/flood/1217883/1218065/?version=1&lang=_e
(6) http://www.ukresilience.info/emergencies/weatherandflooding.shtm
(7) http://www.bbc.co.uk/weather/bbcweather/features/flood_warnings.shtml
(8) http://www.nwl.ac.uk/ih/feh/index.html
(9) http://www.defra.gov.uk/environ/fcd/wnew.htm
(10) http://www.waterpowermagazine.com/story.asp?sectioncode=130&storyCode=2039706

3. Methodology:

The data used to create the following tables was extracted from various web-sites, such as:
• www.nerc-wallingford.ac.uk/ih
• www.environment-agency.gov.uk

Which provided data from a large array of measuring stations across the UK over many years, which we shall be looking at 2000 and 2001.

The data is converted into a graphical and statistical format, in order to view trends and relationships better. Most of this will be done using the spreadsheet package Microsoft Excel 2003, which will plot the graphs, and also be able to calculate the averages. The cumulative frequency along with the lower and upper quartiles will also be calculated.

4. Results:



In order to calculate the monthly averages I used a function within Microsoft excel which automatically calculated the arithmetic mean of all of the days within the month, and then divided the total by the number of days to provide the mean. For example I used the formula: =AVERAGE(B6:B36) to calculate the average for January.

Month Monthly Average
January 88.88535
February 113.5661
March 73.69874
April 99.85163
May 50.28584
June 21.7859
July 28.71426
August 30.17755
September 16.90443
October 84.55765
November 47.206
December 66.69284





Lower quartile = 238

Upper Quartile = 358

95th Percentile = 360


In order to calculate the monthly averages I used a function within Microsoft excel which automatically calculated the arithmetic mean of all of the days within the month, and then divided the total by the number of days to provide the mean. For example I used the formula: =AVERAGE(B6:B36) to calculate the average for January.

Month Monthly Average
January 86.71387
February 95.32897
March 77.32065
April 91.34267
May 38.60355
June 42.00033
July 29.33581
August 18.82194
September 55.59667
October 113.3274
November 268.7433
December 207.0171





Lower quartile = 248

Upper Quartile = 353

95th Percentile = 364

5. Analysis:

After looking over the Daily readings for 2001 and the Monthly Average 2001 it is clear to see that the river flow was at its peak in both February and April where the flow is consistently high. February with a monthly average of around 115 cubic metres per second and a daily peak value of 248 cubic metres per second and April, with a month average of 95 cubic metres per second and a daily peak value of 239 cubic metres per second.

The river flow then drops from May to September with the lowest average monthly flow of 18 cubic metres per second. This river flow then rapidly rises again in October to a monthly average value of 82 cubic metres per second. This can be explained by season changes affecting the weather, with the autumn and winter month being colder, providing more rainfall and less evaporation, and spring and summer being warmer and causing water to evaporate. The Cumulative frequency curve show a positive coloration.

The Daily reading and Month Average for 2000 show that river flow being very low from January to August with the lowest monthly average being 20 cubic metres per second and the lowest daily flow rate of 10 cubic metres per second. This is explained by the fact that in the year 2000 there was very low rainfall, as well as the year being unusually warm, leading to a drought up until the beginning of September.

The river flow then rises rapidly reaching a peak of 500 cubic metres per a second in November, the highest value of the year, with a monthly average of 260 cubic metres per second. This was caused by a unusual heavy downpour of rain after an extremely dry year, which caused authority to be on flood alert in some areas.

By comparing the year 2000 to 2001 it can be seen that the average monthly river flow of 2000 is significantly lower than that of 2001. the only except to this is the month November and December of 2000 which saw river flow rates up to 3 times more than those on 2001.

Both cumulative frequency graph of both year are very similar, with the exception of the gradient of the graph of year 2000 be greater than that of 2001. This is due to the extremely high flow rate toward the end of 2000 in November and December

6. Conclusion:

The main finding of this report show the effect of seasonal and climate variation on river flow rates. This was shown by the differences between the year 2000 and 2001.

The report allowed good use of analytical skills and reading of graphical data which in turn made it easier to find the relationships between the months and years.

7. References:

(1) http://www.ceh.ac.uk/data/nrfa/uk_gauging_station_network.html

(2) http://www.defra.gov.uk/environment/statistics/inlwater/iwlevels.htm

(3) http://www.british-hydro.org/mini-hydro/infopage.asp?infoid=368

(4) http://www.environment-agency.gov.uk/subjects/flood/826674/830330/877142/877272/?version=1&lang=_e

(5) http://www.environment-agency.gov.uk/subjects/flood/1217883/1218065/?version=1&lang=_e

(6) http://www.ukresilience.info/emergencies/weatherandflooding.shtm

(7) http://www.bbc.co.uk/weather/bbcweather/features/flood_warnings.shtml

(8) http://www.nwl.ac.uk/ih/feh/index.html

(9) http://www.defra.gov.uk/environ/fcd/wnew.htm

(10) http://www.waterpowermagazine.com/story.asp?sectioncode=130&storyCode=2039706


8. Appendix 1: Bewdley River Flow Data (2001).

Daily mean gauged discharges (cubic metres per second)


DAY JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
1 100.367 73.213 46.467 64.137 55.249 30.008 14.507 19.228 22.901 20.283 67.298 135.935
2 146.508 70.439 44.103 65.188 47.24 25.992 14.11 18.502 21.123 65.567 57.352 138.563
3 179.458 77.377 39.065 68.573 42.769 25.32 14.081 16.559 15.518 58.896 48.406 104.15
4 152.259 137.483 37.524 98.261 41.916 25.137 14.204 15.903 14.774 40.923 43.901 94.488
5 123.278 193.402 36.943 162.881 37.24 24.512 30.495 16.461 14.231 42.696 41.164 134.928
6 102.057 203.017 36.405 203.264 34.506 23.403 29.797 19.177 14.968 48.632 38.871 145.224
7 88.679 173.593 40.693 229.335 33.306 22.814 19.232 22.394 14.867 65.243 40.187 117.834
8 80.927 162.519 62.162 237.54 32.114 22.585 18.209 31.029 14.391 90.075 49.756 94.883
9 78.962 134.835 55.357 218.045 32.029 19.916 19.671 32.296 13.094 156.793 94.514 78.866
10 72.103 101.48 47.08 195.728 39.171 17.768 17.402 23.328 15.447 137.528 63.848 68.819
11 65.269 97.826 56 156.393 49.35 18.535 17.477 18.78 14.132 86.733 48.631 60.471
12 60.678 203.228 60.139 111.081 39.7 18.202 24.832 19.462 14.042 63.66 44.73 56.377
13 55.183 234.767 55.911 73.087 36.455 18.93 31.673 62.489 14.893 51.276 47.67 52.104
14 53.171 246.612 52.938 60.816 50.741 18.174 25.948 55.405 25.749 43.253 49.641 45.667
15 51.725 184.142 48.359 48.886 85.735 20.476 29.134 39.638 31.751 40.359 39.743 39.566
16 49.022 133.12 48.052 47.654 91.678 25.106 25.029 44.655 20.628 38.19 38.16 36.505
17 42.889 103.201 68.423 47.491 100.233 41.212 24.943 58.961 18.01 36.598 34.233 36.112
18 40.275 83.946 80.587 48.229 136.472 32.519 36.428 48.386 17.978 39.286 32.674 35.012
19 37.882 72.287 83.143 52.023 93.17 25.815 94.928 39.157 15.735 69.924 31.889 33.867
20 35.629 66.169 73.172 47.913 64.183 23.68 70.99 43.678 15.425 95.49 32.527 34.095
21 36.432 61.193 72.832 44.627 53.191 23.325 44.91 43.796 15.794 174.039 30.1 36.52
22 56.812 58.624 103.998 46.625 46.865 22.952 39.592 33.597 13.106 136.956 31.524 34.09
23 120.267 57.08 159.891 93.118 42.086 18.799 37.866 29.303 13.715 99.422 39.65 34.586
24 171.688 54.652 172.948 107.414 39.817 16.156 31.548 27.438 14.359 87.565 32.972 33.311
25 166.703 48.278 140.161 93.444 38.851 16.298 28.591 25.033 14.493 87.09 30.12 33.396
26 127.273 48.572 109.928 94.687 35.718 16.053 25.383 24.184 14.5 132.446 41.954 43.321
27 124.958 49.609 91.45 70.803 33.065 15.825 23.299 22.531 15.174 186.728 46.401 43.019
28 101.715 49.187 102.563 66.775 31.751 15.127 22.876 23.486 15.953 163.954 50.838 79.621
29 81.861 103.446 76.995 32.099 15.144 21.66 19.531 21.25 110.792 58.286 80.493
30 75.096 84.033 67.536 32.225 13.794 21.804 18.696 19.132 83.196 109.14 56.863
31 76.32 70.888 29.936 19.523 22.421 67.694 48.888
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