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

METHODS

RESULTS

* 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(2π T) + 0.523 cos(2π T)

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. Cohn’s 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 Cohn’s 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.

•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

•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 loads:an empirical study involving nutrient loads entering Chesapeake Bay. Water Resources Research, 28(9): 2353-2363

Lorangelly Rivera-Torres1, Dr. Ghebremichael Lula2

1Cell and Molecular Biology, Universidad Metropolitana, San Juan, Puerto Rico, 2Rubenstein School of Enviromental Resources, University of Vermont

CONCLUSIONS

Cohn’s 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.

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. Cohn’s and the linear rating curves were better predictions in the study. Using daily particulate phosphorus concentration prediction, water quality status could be determined.  

ABSTRACT

INTRODUCTION

• 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.

Where C is particulate phosphorus concentration and Q is flow.

Performance Methods

Observed Data

Linear Logarithm Method*

Power Method*₁

Linear Method*₂

Cohn’s Method*₃

NS ------------- 0.11 0.11 0.6 0.44

mean 0.036 0.046 0.046 0.063 0.52Standard Deviation

0.07 0.01 0.01 0.06 0.04

Minimum 0.009 0.012 0.012 0.033 0.013Maximum 0.56 0.089 0.089 0.446 0.253

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 (2001=5, 2002=19, 2003=19, 2004=15, 2005=15, 2006=13, 2007=11) Figure 1.

• Daily stream flow rate (10/09/01 – 04/03/08) Figure 2.

Figure 3. Results from chosen methods and observed data

Table 1. Performance of Rating Curves

REFERENCES

ACKNOWLEDGMENTS

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)

Rating Curves: Different types of rating curves were used to estimate daily concentration

• Linear Logarithm Method (Log (C)= a+bLog(Q))• Power Method (C=a*Q b)• Linear Method (C=a + b*Q)

• Cohn’s Method (Cohn et al., 1992) (Ln(C)= β0 + β1 ln[Q/Qm] + β₂ (ln[Q/Qm])^2 + β₃ ln[T-Tm] + β₄ (ln[T-Tm])^2 + β₅ sin[2π T] + β₆ cos[2π T] )

Figure 1. Figure 2.