Stream Gauge Frequencies - Assignment Example

TITLE By Name Course Professor University City/State Date Abstract This paper describes the estimation of design flood discharge for the Back Creek stream gauge site known as BEECHMONT (DNRM). The design flood discharge has been estimated for the Back stream gauge site using the hydro meteorological approach. The paper also analyses the stream gauge frequencies and applies the empirical formula in drawing conclusions. The flood frequency analysis has been based on consideration of outputs of four techniques namely: Annual series, Partial series, the palmen and the week’s method and the new regional flood frequency estimation (RFFE) developed as part of AR and R update (Palmen, 2011). The work involved obtaining and reviewing stream flow data from Queensland DNRM water Monitoring portal (http://water-monitoring.Information.qld.gov.au/), annual series FFA based on fitting a log person 3 (LP3) distribution to a yearly discharge maximum value, planning of the catchment area as well as the calculation of its area. Also, a compilation of estimates and selection of a set of design flood discharges and assessment of the rainfall frequency related to the January 2008 flood discharges event. Introduction A flood in the context of this paper is considered to be an unusually high stage of a river. Floods are categorized into three broad categories: Minor floods, moderate floods, and major floods. Minor floods because some slight levels of erosion but normally no major damages are experienced. Moderate floods cause damages to the nearby properties though they are not very massive. Major floods are naturally serious as they cause a threat to human life and destroy properties in huge figures (Riggs, 1973). Floods occur in flood plains along rivers when water in the rivers fills up the stream hence bursting and spilling over to the nearest flood plains. Hydraulic structures are usually raised to prevent damages caused by floods. When laying these structures, due considerations need to be taken into account because these structures are susceptible to collapsing due to pressure mounted by the floods. If the hydraulic structures are poorly designed and implemented, more damages are caused to the surrounding environment and properties. Certainly, when floods are harnessed at location, it accumulates, and potential energy builds up in the form of pressure which can cause massive damages in case of collapsing. Due to the probable damages that can be caused by these hydraulic structures, estimates of extreme flood flow are very critical. Estimates of extreme flood flow are also used by engineers when choosing the quality of hydraulic structure to be constructed. The selection of the hydraulic structure will also be determined by the availability of resources such as finances. Design flood is defined as the flood adopted for the design of hydraulic structures. Design flood takes into consideration two types of data: the entire flood hydrograph or the peak discharge of the flood hydrograph. This work takes into account the latter case for simplicity. This work considers a Weir as the key hydraulics structure Analyzing flood recurrence Recurrence interval can be defined as the average time length that separates two flood events. The reciprocal of flood recurrence is the probability that the flood will occur during a one year period. Stream gauges provide continuous records of discharge at fixed stations along a river. The largest event in a given year is called the annual peak discharge (Palmen, 2011). The analysis of flood recurrence used in this exercise has utilized a data set comprising of annual peak discharges for N consecutive years. The recurrence interval for each event in the data set has been being computed using the Weibull equation, R = (N+1)/M Where R = recurrence interval (years), N = the number of annual peak discharge events in the data set, and M = the rank of the event (1 for the largest, 2 for the 2nd largest, and so on).The relationship between flood size and frequency is established by plotting R vs. discharge for each point in a data set, with R plotted on a log-scale x-axis and discharged plotted on a linear-scale y-axis (Rahman, 2015). A curve in shape to the data has been used to forecast the size and frequency of future discharge events. The curve was also extrapolated to determine the 100-year and 500-year floods. Basic gauge information Location Catchment area Start of stream gauging Start of rainfall measurements Maximum observed discharge When maxima reading was taken Lat: -281242 Long: 153.1888 7km 5/06/1971 23/1/1996 0.474 11/10/2010 Weir A weir is a water barrier that is constructed across a river with the purpose of regulating flow rates of water in streams during periods of high discharge. Water overflows past the weirs when the targeted level is reached. The main aim of the weir in our work is to reduce the volume of water flowing down stream during the rainy seasons. This weir will hence reduce the level of damages experienced when it floods. https://water-monitoring.information.qld.gov.au/ Hydraulic control https://water-monitoring.information.qld.gov.au/ Crossection https://water-monitoring.information.qld.gov.au/ Rating curve https://water-monitoring.information.qld.gov.au/ 143 gauges have been made since the inception of the site to the end of 2016 water year. The rating curve is comprehensive in the information it displays but falls short of details of individual stages. This is because Logarithmic curves are used in analyzing the huge amount of data hence barely detailed. Recorded years with missing data were listed below: HYMONTH V114 Output 20/04/2017 Site 146014A Back Creek at Beechmont Site 146014A VarFrom 140 Stream Discharge in Cumecs VarTo 151 Total Stream Discharge Volume in Megalitres Figures are for period ending 24:00:00 Mean Annual Missing Nov Dec Jan Feb Mar Apr May Jun Jul Aug Jul Aug Sep Monthly Total Missing Days 36.4 20.9 261.3 1551.4 844.2 1644 441 216.8 132.5 95.5 132.5 95.5 65.9 448.5 5382.1 0 617.7 240.9 193 563.1 365.1 239 155.5 102.3 1887.9 333.2 1887.9 333.2 199.2 557.2 6687 0 157.4 242.7 10284 1243.7 4276.6 1405.6 922.4 1049.3 341.1 252.8 341.1 252.8 129 1709.4 20513.3 0 175.7 113.3 157.3 305.6 647 256.9 149.6 101.2 91.3 72.2 91.3 72.2 91.7 192.8 2314.1 0 233.4 189.3 2019.8 4286.3 2156.8 592.3 415.7 228.7 159.9 122.1 159.9 122.1 89.1 887.6 10651.4 0 228.2 222.1 113 210.4 555.7 383.4 291.9 182.7 175 90.2 175 90.2 91.3 221.7 2660.3 0 56.7 25.4 36.3 53.5 1175.3 825.4 311.7 149.5 120.7 98.9 120.7 98.9 124.5 252.8 3034.2 0 99.8 161.8 548.7 516.3 376.2 118.1 90.1 374.8 232.8 139.2 232.8 139.2 74.1 233.8 2806.1 0 101.4 66.9 39.4 75.6 31.6 22.2 1702.5 255.9 168 98.3 168 98.3 60.9 224.5 2694.4 0 36.1 58.4 62.3 873.7 351.5 337.6 283.5 154.7 140 97.6 140 97.6 87.1 210.3 2524.1 0 63.3 61.8 240.3 61.7 152.1 111 895.8 1848.2 842.6 467.1 104.1 82.9 88.4 [255] [255] 85 752.8 1194.4 803.7 290.8 173.7 1358.1 446.3 354.5 441.6 297.7 842.6 467.1 279.1 430.9 5171.2 0 132.2 112.4 62.6 50.7 32.1 20.4 27 20.9 31.7 29.5 441.6 297.7 199.2 549 6587.7 0 26.6 25.2 25.8 17.6 252.4 43.2 873.7 381.6 242.9 307.8 373.2 191.4 164.9 [255] [255] 19 138 194.9 142.2 74.5 72.4 3837.2 508.2 1164.3 1201.4 247.4 31.7 29.5 22 60.6 727.6 0 121.1 871.4 530.3 240.9 300.9 7719.2 1832.1 823 383.7 271.5 242.9 307.8 247.8 209 2508.4 0 93.6 257.4 218.2 1992.8 948.7 3864.1 1127.9 972.8 344.7 132.1 1201.4 247.4 373.7 684.7 8216.6* 0 41.7 46.9 94.4 615.5 182.7 99.2 87.1 73.8 64.9 29 383.7 271.5 140.9 1115.5 13386.3* 0 8.1 1423.3 493.3 528.9 737.4 741.2 396.3 231.6 168.3* 79.4 344.7 132.1 80 842.8 10113.8 0 100 57.9 34.9 33.7 42.1 13.6 19.3 8.4 53.6 17.7 64.9 29 18.2 116.7 1400.1 0 23.9 50.6 30.1 63.6 304.9 169.5 91.3 50.9 32.7 17.4 168.3* 79.4 138.4 412.9 4955.4 0 6.2 11.4 16.9 378.3 122.5 52.7 59.9 45.6 22.8 32.8 53.6 17.7 61.7* 44.4 533 0 76.2 158.3 997.4 318 184.2 87.7 5587.9 488.1 208.6 101.6 32.7 17.4 18.3 74.1 889.3 0 60.8 185.8 529.1 397.6 192.5 108.3 319.1 191.1 137.6 80.5 22.8 32.8 14.2 64.6 774.7 0 190.6 140.3 212.5 226.3 123.7 83.6 86.6 46.7 60.3 143.5 208.6 101.6 59.8 690.3 8283.6 0 158.5 253.5 312.3 1015.8 1295.6 472.9 285.7 321 635.3 322.7 137.6 80.5 76.2 194.3 2332 0 593.8 370.8 300.8 180.2 144.5 91.9 247.2 127.6 90.2 60.1 60.3 143.5 370.5 153.6 1842.8 0 185.6 114.7 73.4 3563.8 374.1 170.4 124.9 84.1 70.4 47.1 635.3 322.7 204.8 459.9 5519.3 0 115.8 97.3 35.6 23.7 33.8 21.8 20 30.4 7.2 22.5 90.2 60.1 35.9 233.4 2800.3 0 3.4 19.1 0.4 136.8 209.7 102.5 226.1 193.9 137.4 79.4 70.4 47.1 33.3 407.8 4893.6 0 46.7 197.7 444.3 631.7 1603.2E 247.4 110 74.1 50.8 40.1 7.2 22.5 11.6 38 456.4 0 213.8 780.2E 247.3E 125.2E 73.3 48.5 40.2 511.8E 229.1E 103.5 137.4 79.4 50.9 96.9 1162.7 0 66.2 169.3E 294.6E 278.1E 507.8E 359.3E 155.5 107.1 90.5E 66.8E 50.8 40.1 38.2 298.2E 3578.9E 0 46.7 27.6 36.1 15.8 16.5 8.1 14.1 28.3 13.4 55.8E 229.1E 103.5 70.3 209.0E 2508.1E 0 107.6F 138.4E 3652.8E 1316.7E 325.5E 153 102.4E 283.8E 158.6 113.9 90.5E 66.8E 86.1E 186.7E 2240.2E 0 342.4E 403.5E 227.2P 225.0E 196.6 653.8E 1069.1E 1002.3E 446.1E 170.6 13.4 55.8E 39.9 28.7E 345.0E 0 79.3E 134.4E 116.9E 1943.4E 544.6E 236.3E 265.2E 136.9 105.6 101.4E 158.6 113.9 112.5 544.9E 6538.6E 0 297.1E 2105.6E 2505.3E 390.0E 223.1E 139.2E 142.6E 112.6 84.9F 162.2E 446.1E 170.6 89.1 408.0E 4895.6E 0 132.9E 184.0E 3714.1E 1331.4E 740.6 346.3 208.7E 224.1E 256.1E 181.5E 105.6 101.4E 101.5 318.7E 3824.7E 0 73 59.6 6759.7E 1177.2E 2232.8E 432.1 201.6 208.4 443.4 222.7 84.9F 162.2E 133.2 582.4E 6988.5E 0 90.7 55.9 47.7 29.7 634.6E 94.8 58.4E 50.6 35.5 48 256.1E 181.5E 107.9 628.9E 7547.0E 0 14.5 71.8 484.4E 572.5E 364.8 703.1 858.5E 196 114.1 74.8 443.4 222.7 141.8 1002.5E 12029.7E 0 181.3 109.9 177 100.3F 90.2F 71.4F 68.3 1077.7E 250.2 188.8 35.5 48 32.7 105.2E 1262.5E 0 Nov Dec Jan Feb Mar Apr May Jun Jul Aug 114.1 74.8 57.4 294.1E 3528.6E 0 36.4 20.9 261.3 1551.4 844.2 1644 441 216.8 132.5 95.5 250.2 188.8 170.2E 213.0E 2556.3E 0 1981 1984 The following table shows a table with truncated years on the bases of years with more than four missing data. Sorted in descending order Year Peak discharge 1973/74 20513.3 1988/89 13386.3 1975/76 10651.4 1989/90 10113.8 1995/96 8283.6 1987/88 8216.6 1972/73 6687 1983/84 6587.7 1998/99 5519.3 1971/72 5382.1 1982/83 5171.2 1991/92 4955.4 2000/1 4893.6 1977/78 3034.2 1978/79 2806.1 1999/0 2800.3 1979/80 2694.4 1976/77 2660.3 1980/81 2524.1 1986/87 2508.4 1996/97 2332 1974/75 2314.1 1997/98 1842.8 1990/91 1400.1 2002/3 1162.7 1993/94 889.3 1994/95 774.7 1985/86 727.6 1992/93 533 2001/2 456.4 Probability of occurrence The likelihood of occurrence is calculated by the formula; 100(2n-1)/2y where: y is the number of data collection years, and n is the rank of each event. Year peak monthly discharge Ranking Exceedance 1973/74 10284 1 1.2 1988/89 7719.2 2 3.5 1995/96 5587.9 3 5.8 1975/76 4286.3 4 8.1 1989/90 3864.1 5 10.5 1987/88 3837.2 6 12.8 2000/1 3563.8 7 15.1 1972/73 1887.9 8 17.4 1982/83 1848.2 9 19.8 1979/80 1702.5 10 22.1 1971/72 1644 11 24.4 1991/92 1423.3 12 26.7 1983/84 1358.1 13 29.1 1998/99 1295.6 14 31.4 1977/78 1175.3 15 33.7 1980/81 873.7 16 36.0 1986/87 873.7 17 38.4 2011/12 740.6 18 40.7 2014/15 703.1 19 43.0 1974/75 647 20 45.3 2003/4 631.7 21 47.7 1990/91 615.5 22 50.0 1999/0 593.8 23 52.3 1976/77 555.7 24 54.7 1978/79 548.7 25 57.0 1996/97 529.1 26 59.3 2012/13 443.4 27 61.6 1994/95 378.3 28 64.0 1997/98 370.5 29 66.3 1993/94 304.9 30 68.6 2015/16 250.2 31 70.9 2002/3 226.1 32 73.3 2004/5 213.8 33 75.6 2008/9 196.6 34 77.9 1985/86 186.1 35 80.2 2007/8 158.6 36 82.6 2005/6 155.5 37 84.9 2009/10 136.9 38 87.2 2010/11 133.2 39 89.5 2001/2 115.8 40 91.9 1992/93 100 41 94.2 2013/14 94.8 42 96.5 2006/7 46.7 43 98.8 Return period for the flood discharge of 1500 cumecs. 1500 cumecs of the flood are the value by which if exceeded, the flood becomes problematic. The return period is computed from the formula: 100/fe where fe is the probability of occurrence by the flood discharges. The magnitude of the floods is also significant because it helps in determining the levels of destruction that are caused by flooding (Rahman, 2015). Magnitude at each point in time can be computed by finding the values of y in the equation of curve representing return periods versus peak monthly values. LP3 table peak monthly discharge Log(Q)=x (x-M)^2 (x-M)^3 M 10284 4.012162 9.1 27.3 1 7719.2 3.887572 3.6 6.7 2 5587.9 3.747249 0.6 0.4 3 4286.3 3.632083 0.1 0.0 4 3864.1 3.587048 2.0 -2.8 5 3837.2 3.584014 5.8 -14.1 6 3563.8 3.551913 11.9 -41.0 7 1887.9 3.275979 22.3 -105.4 8 1848.2 3.266749 32.9 -188.5 9 1702.5 3.231087 45.8 -310.1 10 1644 3.215902 60.6 -471.7 11 1423.3 3.153296 78.3 -692.4 12 1358.1 3.132932 97.4 -960.6 13 1295.6 3.112471 118.5 -1290.6 14 1175.3 3.070149 142.3 -1697.9 15 873.7 2.941362 170.5 -2226.9 16 873.7 2.941362 197.6 -2778.6 17 740.6 2.869584 228.9 -3463.8 18 703.1 2.847017 260.9 -4214.6 19 647 2.810904 295.5 -5078.8 20 631.7 2.800511 331.2 -6028.1 21 615.5 2.789228 369.1 -7089.8 22 593.8 2.77364 409.1 -8274.7 23 555.7 2.74484 451.8 -9602.7 24 548.7 2.739335 495.5 -11031.0 25 529.1 2.723538 541.8 -12611.0 26 443.4 2.646796 593.1 -14443.4 27 378.3 2.577836 646.3 -16430.0 28 370.5 2.568788 698.6 -18465.1 29 304.9 2.484157 757.1 -20832.8 30 250.2 2.398287 818.1 -23397.9 31 226.1 2.354301 878.9 -26054.6 32 213.8 2.330008 940.6 -28849.7 33 196.6 2.293584 1005.3 -31874.4 34 186.1 2.269746 1071.3 -35062.9 35 158.6 2.200303 1142.4 -38613.4 36 155.5 2.19173 1211.6 -42174.2 37 136.9 2.136403 1286.2 -46127.7 38 133.2 2.124504 1359.8 -50143.4 39 115.8 2.063709 1439.2 -54596.5 40 100 2 1521.0 -59319.0 41 94.8 1.976808 1601.9 -64111.4 42 46.7 1.669317 1708.2 -70602.1 43 Mean 2.8 std dev 0.565219826 Skew 0.211385068 ARI (yrs) 2 5 10 20 50 100 Annual FFA 813 2772 4237 5712 7661 9137 Partial Series FFA peak monthly discharge Ranking order 10284 1 7719.2 2 5587.9 3 4286.3 4 3864.1 5 3837.2 6 3563.8 7 1887.9 8 1848.2 9 1702.5 10 1644 11 1423.3 12 1358.1 13 1295.6 14 1175.3 15 873.7 16 873.7 17 740.6 18 703.1 19 647 20 631.7 21 615.5 22 593.8 23 555.7 24 548.7 25 529.1 26 443.4 27 378.3 28 370.5 29 304.9 30 250.2 31 226.1 32 213.8 33 196.6 34 186.1 35 158.6 36 155.5 37 136.9 38 133.2 39 115.8 40 100 41 94.8 42 46.7 43 Return period for the flood discharge of 1500 cumecs. 1500 cumecs of flood is the value by which if exceeded, the flood becomes problematic. The return period is computed from the formula: 100/fe where fe is the probability of occurrence by the flood discharges. The magnitude of the floods is also very important because it helps in determining the levels of destruction that are caused by flooding (Riggs, 1973). Magnitude at each point in time can be computed by finding the values of y in the equation of curve representing return periods versus peak monthly values. No of water years = 43. It’s usual to assume K = N Hence N = 43 Threshold discharge, Q = 1500 m3/s ARI (yrs) 2 5 10 20 50 100 Annual FFA 933 1349 2040 3424 7574 14492 Palmen and Weeks design discharges estimates: ARI (yrs) 2 5 10 20 50 100 P & W Discharge 25 52.49 80.5 118 189 200 IFD chart of design rainfalls for the Back Creek catchment: Evaluation of design discharge estimates: Discharge (m3/s) ARI (Years) 2 5 10 20 50 100 Annual 813 2772 4237 5712 7661 9137 Partial 933 1349 2040 3424 7574 14492 Palmen and Weeks 25 52.49 80.5 118 189 200 Selected Discharge 933 2772 4237 5712 7661 14492 References Palmen, L.B., and Weeks, W.D. (2011). Regional Flood Frequency for QueenslandUsing the Quantile Regression Technique. Australian Journal of Water Resources,15 (1), 47-57. Queensland Government: Water Monitoring Information Portal https://watermonitoring.information.qld.gov.au/ Rahman, A. K. Haddad, G. Kuczera., and Weinmann E. (2015) ARR - A Guide to floodestimation. Riggs, H. C. (1973). Regional Analyses of Stream Flow Characteristics. US Government Printing Office. Read More