CALCULATING DAILY PARTICULATE PHOSPHORUS LOADS FROM DISCRETE SAMPLES AND DAILY FLOW DATA

1
CALCULATING DAILY PARTICULATE PHOSPHORUS LOADS
FROM DISCRETE SAMPLES AND DAILY FLOW DATA
Lorangelly Rivera-Torres1, Dr. Ghebremichael
Lula2 1Cell and Molecular Biology, Universidad
Metropolitana, San Juan, Puerto Rico,
2Rubenstein School of Enviromental Resources,
University of Vermont
ABSTRACT
Phosphorus is a vital mineral that is found in
different forms all through nature. Phosphorus in
water systems causes algae blooms which can cause
toxicity in the water and shift the nutrient
balance decreasing oxygen, fish populations and
habitats within the water. Its main sources are
manure, fertilizer and certain cleaning products
which get transported through the sewage system.
Particulate Phosphorus is the particle form of
phosphorus. Its main form of transportation is
through sediment runoff mostly caused by
storm-related events. Since its primary sources
are manure and fertilizer, agricultural areas are
primarily responsible for pollution in the water
system. Because of limited resources and
personnel, few particulate phosphorus
concentration samples are taken however, daily
flow is available because it can be measured
automatically. Yet daily phosphorus concentration
(Load) is needed to study and asses water quality
status. Therefore, the goal of this study was to
use discrete sample data and daily flow data and
estimate daily phosphorus concentration and Load.
Data was obtained from a monitoring station of an
Agricultural sub-watershed of the Lake Champlain
Basin (located between Vermont, New York State
and Quebec, Canada). Different types of rating
curves were used to estimate daily concentration
and Load, which gave the most accurate results.
Then their performance was tested using scatter
plot, Nash-Sutcliffe coefficient and basic
statistics. Cohns and the linear rating curves
were better predictions in the study. Using daily
particulate phosphorus concentration prediction,
water quality status could be determined.  
Figure 3. Results from chosen methods and
observed data
INTRODUCTION
Testing all rating curves were tested for
performance with the following Scatter Plot (for
visual observation Nash-Sutcliffe coefficient NS
(Martinez and Rango, 1989) Basic Statistics
(mean, standard deviation, minimum and maximum)

•  Particulate phosphorus concentrations are needed
  to asses the best management practices and revise
  water quality status.
• Throughout the environment we can find different
  types of phosphorus particulate phosphorus is
  the particle form of phosphorus.
• The amount of particulate phosphorus in a water
  system is important because it affects fish
  population, oxygen levels, aquatic wildlife
  therefore causing toxicity in the water system.
• It is primarily produced by sediment runoff,
  manure and fertilizer.

RESULTS
Table 1. presents the Linear Logarithm and the
Power Method that had very similar results. They
both had 0.11 NS values and they both
overestimate the minimum and underestimate the
maximum. Based on these results, these two
methods were disqualified in this study. The
Linear method had a good NS value at 0.6. It had
a very precise mean value of 0.063. Of all the
methods, it was the one that overestimated the
minimum the most, although the maximum value was
good at 0.45. It was the one that had the best
performance overall. Cohns Method had an average
NS value of 0.44. The mean value was better than
both the Linear Logarithm and Power Method.
Therefore, the two methods selected for
predicting daily particulate phosphorus were
Cohns and the linear methods. Predicted results
of estimated daily particulate phosphorus by
these two methods are presented in Figure 3. For
visual comparison purposes, Figure 3 has included
measured concentration data of particulate
phosphorus.
METHODS

• Data Used data was obtained from a monitoring
  station of an agricultural watershed, a
  sub-watershed of the Lake Champlain Basin
  (located between Vermont, New York State, and
  Quebec, Canada).
• 96 Samples of particulate phosphorus were used
  (20015, 200219, 200319, 200415, 200515,
  200613, 200711) Figure 1.
• Daily stream flow rate (10/09/01 04/03/08)
  Figure 2.

CONCLUSIONS
Figure 2.
Figure 1.
Table 1. Performance of Rating Curves
Cohns Method and the Linear Method were found to
be the best tools in estimating daily
particulate concentration. Daily concentration
values from discrete data, which will help
analyze and study water quality data, and asses
the effects of best management practices (BMPs)
for restoring or improving water quality.
Performance Methods Observed Data Linear Logarithm Method Power Method1 Linear Method2 Cohns Method3
NS ------------- 0.11 0.11 0.6 0.44
mean 0.036 0.046 0.046 0.063 0.52
Standard Deviation 0.07 0.01 0.01 0.06 0.04
Minimum 0.009 0.012 0.012 0.033 0.013
Maximum 0.56 0.089 0.089 0.446 0.253
REFERENCES

• Martinez, J., and A. Rango. 1989. Merits of
  Statistical criteria for the performance of
  hydrological models. Water Resources Bulletin 25
  (2)421-432.
• Cohn,T. A.,D.L. Caulder, E.J. Gilroy, L. D.
  Zynjuk, ad R.M. Summers. 1992. The validity of a
  simple statistical model for estimating fluvial
  constituent loadsan empirical study involving
  nutrient loads entering Chesapeake Bay. Water
  Resources Research, 28(9) 2353-2363

• Rating Curves Different types of rating curves
  were used to estimate daily concentration
• Linear Logarithm Method (Log (C) abLog(Q))
• Power Method (CaQ b)
• Linear Method (Ca bQ)
• Cohns Method (Cohn et al., 1992) (Ln(C) ß0 ß1
  lnQ/Qm ß2 (lnQ/Qm)2 ß3 lnT-Tm ß4
  (lnT-Tm)2 ß5 sin2p T ß6 cos2p T )

• VT EPSCoR Award NSF EPS 0701410, Complex Systems
  Modeling from Environmental Problem Solving For
  funding and resources
• NSF MIE For travel expenses
• Dr. Ghebremichael Lula, RSENR, UVM For
  providing the data and helping in analyzing the
  data and preparing the poster
• Dr. Negatu K. Adane, CALS,Plant and Soil Science
  Department, UVM For mentoring

ACKNOWLEDGMENTS
y 1.1947(flow) 2.247 1 Y
0.052(flow)0.1947 2 Y 0.0267x
0.0332 3Ln(PP) -2.20 0.424 Ln (Q/Qm) 0.0515
Ln(aQ/Qm)2 0.069 (T-Tm) 8.81 (T-m)2
0.397 sin(2p T) 0.523 cos(2p T)
Where C is particulate phosphorus concentration
and Q is flow.