exercise 1 - solution
1. Make plot between flow and phosphorus concentration!
2. Analyze the tendency of variety of phosphorus concentration in dependence
of the flow!
3. Point the questionable measures, which are declined from the common tendency!
1.
2. The phosphorus concentration increases when the flow decreases. The phosphorus concentration decreases when the flow increases. The observed monitoring data are measured in the cases of low water in the period 30.07.1993 to 15.03.1994. The measured phosphorus concentrations vary in range from 0.084 mg/L (24.08.93) to 0.96 mg/L (14.09.93). The differences are more than 10 times during this period while the measured discharges are practically constant - 0.15-0.2 m3/s. This shows that the variations of the phosphate concentrations are mainly product of point sources of pollution with changeable monthly working regime.
3. Some of the measured monitoring data in the period 30.07.93 to 15.03.94 are not in accordance with the basic tendency of variation between flow and phosphate concentration.
exercise 2 - solution
1. The first step is the correlation between the calculated daily phosphorus loads (P) in kg/day and measured daily discharges in m3/day to be established. This means the flow to be calculated in m3/day and in l/day and the phosphorus concentration to be diverted in phosphorus load in kg/day as follows:
Flow [m3/day] = Flow [m3/s]·86400 [s/day];
Flow [l/day] = Flow [m3/day]·1000;
Phosphate load [kg/day] = Phosphate concentration [mg/L] · Flow [l/day]
/ 1000000.
2. The correlation (linear, logarithmic, polynomial, power and exponential) between flow in m3/day and phosphorus load in kg/day is made (Figure 1 to 5).
Figure 1 Linear Correlation
Figure 2 Logarithmic Correlation
Figure 3 Polynomial Correlation
Figure 4 Power Correlation
Figure 5 Exponential Correlation
The best regression is turned out linear with correlation coefficient 0.6342 and polynomial - with correlation coefficient 0.6386. The observed mean monthly phosphorus loads of the river for Rossitza subbasin for the investigated period 1990-1995 is prepared using linear and polynomial correlation, presented in Figure 6 and Table 2.
Attention: In common case the best correlation's dependencies between flow and nutrient load are linear and power.
Month |
Observed |
Observed |
Days per month |
ObsP,Polyn. |
ObsP,Polyn. |
ObsP,Polyn. |
ObsP,Polyn. |
HYDROLOGICAL YEAR | |||||||
90,APR | 1.580 | 136512.000 | 30 | 23.173 | 695.177 | 25.488 | 764.637 |
MAY | 5.890 | 508896.000 | 31 | 62.814 | 1947.245 | 99.965 | 3098.906 |
JUN | 3.220 | 278208.000 | 30 | 37.930 | 1137.894 | 53.827 | 1614.813 |
JUL | 0.670 | 57888.000 | 31 | 15.157 | 469.877 | 9.763 | 302.656 |
AUG | 0.560 | 48384.000 | 31 | 14.197 | 440.101 | 7.862 | 243.731 |
SEP | 0.710 | 61344.000 | 30 | 15.507 | 465.211 | 10.454 | 313.629 |
OCT | 0.530 | 45792.000 | 31 | 13.935 | 431.990 | 7.344 | 227.661 |
NOV | 0.610 | 52704.000 | 30 | 14.633 | 438.995 | 8.726 | 261.789 |
DEC | 5.580 | 482112.000 | 31 | 59.871 | 1855.986 | 94.608 | 2932.845 |
JAN | 0.990 | 85536.000 | 31 | 17.962 | 556.815 | 15.293 | 474.074 |
FEB | 1.350 | 116640.000 | 28 | 21.135 | 591.781 | 21.514 | 602.378 |
MAR | 4.400 | 380160.000 | 31 | 48.796 | 1512.683 | 74.218 | 2300.743 |
91,APR | 8.140 | 703296.000 | 30 | 84.611 | 2538.326 | 138.845 | 4165.341 |
MAY | 26.400 | 2280960.000 | 31 | 289.459 | 8973.222 | 454.378 | 14085.703 |
JUN | 10.300 | 889920.000 | 30 | 106.247 | 3187.397 | 176.170 | 5285.085 |
JUL | 22.200 | 1918080.000 | 31 | 237.933 | 7375.933 | 381.802 | 11835.847 |
AUG | 5.200 | 449280.000 | 31 | 56.282 | 1744.727 | 88.042 | 2729.287 |
SEP | 0.360 | 31104.000 | 30 | 12.455 | 373.652 | 4.406 | 132.189 |
OCT | 1.530 | 132192.000 | 31 | 22.729 | 704.597 | 24.624 | 763.341 |
NOV | 2.300 | 198720.000 | 30 | 29.602 | 888.057 | 37.930 | 1137.885 |
DEC | 1.330 | 114912.000 | 31 | 20.958 | 649.706 | 21.168 | 656.205 |
JAN | 1.840 | 158976.000 | 31 | 25.485 | 790.045 | 29.981 | 929.402 |
FEB | 2.510 | 216864.000 | 29 | 31.492 | 913.259 | 41.558 | 1205.191 |
MAR | 7.010 | 605664.000 | 31 | 73.570 | 2280.660 | 119.318 | 3698.867 |
92,APR | 16.500 | 1425600.000 | 30 | 172.218 | 5166.551 | 283.306 | 8499.165 |
MAY | 4.390 | 379296.000 | 31 | 48.703 | 1509.801 | 74.045 | 2295.386 |
JUN | 26.200 | 2263680.000 | 30 | 286.945 | 8608.364 | 450.922 | 13527.645 |
JUL | 3.920 | 338688.000 | 31 | 44.351 | 1374.878 | 65.923 | 2043.616 |
AUG | 0.900 | 77760.000 | 31 | 17.171 | 532.315 | 13.738 | 425.863 |
SEP | 0.440 | 38016.000 | 30 | 13.151 | 394.532 | 5.789 | 173.661 |
OCT | 0.430 | 37152.000 | 31 | 13.064 | 404.984 | 5.616 | 174.093 |
NOV | 0.760 | 65664.000 | 30 | 15.945 | 478.336 | 11.318 | 339.549 |
DEC | 0.820 | 70848.000 | 31 | 16.470 | 510.570 | 12.355 | 383.008 |
JAN | 0.950 | 82080.000 | 31 | 17.610 | 545.922 | 14.602 | 452.647 |
FEB | 0.980 | 84672.000 | 28 | 17.874 | 500.469 | 15.120 | 423.357 |
MAR | 3.680 | 317952.000 | 31 | 42.141 | 1306.375 | 61.776 | 1915.053 |
93,APR | 7.280 | 628992.000 | 30 | 76.191 | 2285.715 | 123.984 | 3719.517 |
MAY | 29.400 | 2540160.000 | 31 | 327.875 | 10164.129 | 506.218 | 15692.743 |
JUN | 2.860 | 247104.000 | 30 | 34.656 | 1039.680 | 47.606 | 1428.189 |
JUL | 1.100 | 95040.000 | 31 | 18.929 | 586.809 | 17.194 | 532.999 |
AUG | 0.290 | 25056.000 | 31 | 11.847 | 367.253 | 3.197 | 99.098 |
SEP | 0.230 | 19872.000 | 30 | 11.326 | 339.784 | 2.160 | 64.797 |
OCT | 0.250 | 21600.000 | 31 | 11.500 | 356.490 | 2.506 | 77.671 |
NOV | 0.480 | 41472.000 | 30 | 13.499 | 404.982 | 6.480 | 194.397 |
DEC | 0.990 | 85536.000 | 31 | 17.962 | 556.815 | 15.293 | 474.074 |
JAN | 0.550 | 47520.000 | 31 | 14.110 | 437.397 | 7.690 | 238.375 |
FEB | 0.820 | 70848.000 | 28 | 16.470 | 461.160 | 12.355 | 345.943 |
MAR | 2.500 | 216000.000 | 31 | 31.402 | 973.448 | 41.386 | 1282.951 |
94,APR | 8.470 | 731808.000 | 30 | 87.871 | 2636.137 | 144.547 | 4336.413 |
MAY | 4.860 | 419904.000 | 31 | 53.089 | 1645.746 | 82.166 | 2547.155 |
JUN | 4.000 | 345600.000 | 30 | 45.089 | 1352.682 | 67.306 | 2019.165 |
JUL | 10.700 | 924480.000 | 31 | 110.330 | 3420.219 | 183.082 | 5675.527 |
AUG | 1.270 | 109728.000 | 31 | 20.428 | 633.274 | 20.131 | 624.064 |
SEP | 0.230 | 19872.000 | 30 | 11.326 | 339.784 | 2.160 | 64.797 |
OCT | 1.290 | 111456.000 | 31 | 20.605 | 638.750 | 20.477 | 634.778 |
NOV | 1.250 | 108000.000 | 30 | 20.252 | 607.549 | 19.786 | 593.565 |
DEC | 3.360 | 290304.000 | 31 | 39.208 | 1215.453 | 56.246 | 1743.635 |
JAN | 7.878 | 680659.200 | 31 | 82.034 | 2543.051 | 134.317 | 4163.838 |
FEB | 7.968 | 688435.200 | 28 | 82.918 | 2321.703 | 135.873 | 3804.431 |
MAR | 9.073 | 783907.200 | 31 | 93.871 | 2909.996 | 154.967 | 4803.975 |
Where:
Obs P [kg/day, Linear correlation] = 0.0002·Flow [m3/day]
- 1.8145;
Obs P [kg/day, Polynomial correlation] = 1E-11·Flow [m3/day]
+ 0.0001·Flow [m3/day] + 9.335;
Obs P [kg/month] = Obs P [kg/day] · (Days/month).
Figure 6 Observed Monthly Phosphorus Load
for the Period 1990-1995