Supplementary Information for
Nitrate Flux in the Mississippi River
GREGORY F. MCISAAC*, MARK B. DAVID*, GEORGE Z. GERTNER* AND DONALD A.
GOOLSBY †
*University of Illinois Department of Natural Resources and Environmental Sciences, W-503 Turner Hall,
1102 S. Goodwin Ave., Urbana, IL 61801, USA, †US Geological Survey Denver Federal Center, MS 406,
Denver, CO 80225, USA.
email: gmcisaac@uiuc.edu
Additional details of the model development, testing and utilization, not presented
in the brief communication titled “Mississippi River nitrate flux” are provided here.
Calculated values of annual NANI for the Mississippi River Basin (MRB) appear in
Figure 1s together with measured annual values of water yield and riverine nitrate flux
for the Lower Mississippi River (including the Old River outflow), USA.
METHODS
Observed Riverine Nitrate Concentrations and Flux
Riverine nitrate concentration was measured at St. Francisville, Louisiana, between
1955 and 1999 as described by Goolsby et al. (1999). Annual water yield was based on
discharge measured at Tarbert’s Landing, Mississippi, and the flow diverted to the
Atchafalaya River via the Old River outflow. Ninety eight percent of the entire MRB lies
upstream of these measurement locations. Between 1955 and 1967, water samples were
collected daily and combined for periods of 10 to 30 days to form a composite sample
that was analyzed for nitrate using phenoldisulfonic acid method. Nitrate flux during this
period was estimated by multiplying the composite concentration by the discharge
measured during the period that samples were collected. After 1967, water samples were
taken approximately once per month and were not composited. Nitrate flux for this
period was calculated using the rating curve approach as described by Cohn et al. (1992)
and Goolsby et al. (1999). In employing the rating curve method, it was observed that
the concentration at St. Francisville depended on discharge at Tarbert’s landing and the
proportion of discharge coming from the Mississippi River above the Missouri River,
which has considerably greater nitrate concentration than the Missouri or the Ohio
Rivers. The rating curve that accounted for the greatest portion of variation in daily
nitrate flux at St. Francisville was based on discharges from the Mississippi River at
Alton, Illinois and discharge at the mouths of the Missouri and Ohio Rivers.
In the early 1970s, the method of determining nitrate concentrations was changed to
automated cadmium reduction. There does not appear to be any change in concentrations
or flux estimates associated with the changes in methodologies employed during different
periods (Goolsby et al., 1999; Goolsby and Battaglin, 2001).
Estimating NANI
With minor exceptions, we used methods described by Goolsby et al. (1999) to
estimate the relevant annual N inputs and outputs for the 20 states that cover 89% of the
MRB. Inputs and outputs were expressed per unit area (3.39 million km2) of the 20
states, and this N use intensity was assumed to represent the 11% of the drainage basin
that lie outside of these states. Approximately 8% of the 20-state region lies outside of
the MRB.
2
The NANI approach consists of the sum of N inputs in fertilizer, biological fixation,
and atmospheric NOy deposition, minus the net N exported from the basin in food and
feed products (Howarth et al., 1996). The net N export from the basin in food and feed
products was calculated as the sum of N in harvested crops, hay and pasture, minus the N
retained in animal wastes and for human consumption. The N excreted in livestock waste
was estimated based on population sizes and estimates of excreted N per capita for
different species and age classes. Actual N consumption by domestic animals in the
basin will be greater than the excreted N by the amount that is assimilated into animal
tissues and animal products (e.g., milk and eggs). By using estimated manure production
to estimate N retention, the N assimilated into animal based food products was implicitly
estimated. The per capita N consumption by humans was estimated to be 4.53 kg N yr-1
based on analysis of protein consumption and age structure of the Illinois population
(David and Gentry, 2000). This N was also subtracted from the harvested N as the
NANI approach assumes that the quantity of N in food products that exceeds the
estimated demands of the human population would be exported from the basin and thus
not available for transport to surface waters.
Nitrogen fertilizer input was taken from state-level fertilizer sales statistics compiled
by Alexander and Smith (1990), Battaglin and Goolsby (1995) and USDA (1998). NOy
deposition from 1984-96 was estimated based on the National Atmospheric Deposition
Program measurement of wet NO3- deposition and assuming dry NO3- deposition to be
0.7 times the wet (Goolsby et al., 1999). In contrast to the CENR analysis, we assumed
NOy deposition from 1955-1984 was proportional to national estimated NOx emissions
(USEPA, 2000), following the approach used by David and Gentry (2000). For each year
3
prior to 1984, the 1994-1996 average NOy deposition was multiplied by the annual
estimated NOx emissions divided by the estimated 1984-1996 NOx emissions. For
instance, estimated national NOx emissions in 1955 were 48% of 1984-96 average
emissions; therefore, the estimated NOy deposition for 1955 was 48% of the 1984-96
estimated deposition.
Biological N2 fixation in association with legume crops, hay and pastures was
estimated based on values reported in the literature for soybeans (0.91 kg N bu-1), alfalfa
(218 kg N ha-1 yr-1), non-alfalfa hay (116 kg N ha-1 yr-1) and pasture (15 kg N ha-1 yr-1)
(David and Gentry 2000; Goolsby et al. 1999; Jordan and Weller, 1996). Soybean
production and areas devoted to hay and pasture were taken from state level historical
statistics compiled by USDA (1998). Nitrogen harvested in corn, soybeans, wheat,
sorghum and hay was estimated from state level crop production statistics multiplied by
the average N content of each product (Table 1). Except for soybeans, these values were
identical to those used by Goolsby et al. (1999). For soybeans Goolsby et al. (1999) used
a value of 1.78 kg N bu-1, which is considerably greater than 1.5 kg N bu-1 used by
Burkhart and James (1999) or 1.61 kg N bu-1 used by Kellog et al. (2000).
Human population was estimated from state level US Census data and interpolation
between census years. Livestock population sizes were taken from year-end inventories
in state level production statistics compiled by USDA (1998).
The year-end inventories
were assumed to be representative of average populations during the entire year for all
species except for steers, turkeys and two thirds of heifers which were assumed to
produce manure N for an average of 170, 122 and 170 days, respectively (Goolsby et al.
1999).
4
NANI does not address exchanges with soil organic N and implicitly assumes that
organic N mineralization and immobilization are approximately equal. This latter
assumption is reasonable if soil organic N content is relatively steady over time. The
limited number of measurements and simulation studies that are available to assess
changes in soil organic matter in the MRB suggest that soil organic N levels in cultivated
soils have been, on average, approximately constant from 1950 to 1999 (Paul et al., 1997;
Aref and Wander, 1998; Buyanovsky et al., 1997; Donigian et al., 1997; Patwardhan et
al., 1997). Some studies have simulated or measured increases in organic carbon in the
top 20 cm of the soil profile after 1970, which appears to be due to increases in yield and
changes in tillage practices (Donigian et al., 1997; Patwardhan et al., 1997). However, in
some locations, it has been demonstrated that the increase in the top 20 cm from reduced
tillage is roughly equal to a decrease lower in the soil profile so that no net change has
occurred (Wander et al., 1998; Yang and Wander, 1999). Additionally, Buyanovsky et
al. (1997) reported that there was no change in soil N in long-term research plots in
Missouri where an increase in soil organic carbon was measured.
Model Development and Diagnostic Testing
Calculated values of NANI provide an estimate of N that is available for
denitrification, immobilization, leaching to ground water, and transport to surface waters.
Some portion of the NANI may be transported to surface water quickly whereas other
fractions may be transported slowly after cycling through crop residues and soil organic
matter. We tested linear and non-linear relationships between NANI and flow weighted
nitrate concentration and riverine nitrate flux, in which NANI was averaged over 1 to 12
5
years and lagged over 0 to 8 years to account for transport time and temporary storage of
NANI in the basin. Models that combined NANI and water yield, similar to the Caraco
and Cole (1999) model, were also tested. Functional forms and time periods over which
NANI was averaged and lagged were evaluated on the basis of model root mean square
error, coefficient of determination, and autocorrelation of residuals. The significance of
the residual autocorrelation function was tested using the Ljung-Box Q statistic.
We evaluated the effect of co-linearity of water yield and NANI during 1960-1998 by
conducting the regression analysis with the 1973-1998 data, since the correlation between
water yield and NANI terms was not significant during this period. The resulting
coefficients were not statistically different than those appearing in Equation [1]. For this
period, the following equation accounted for 92% of the variation in annual nitrate flux:
NLM = 0.26*W0.94*e(0.17*NANI2-5+0.08*NANI6-9))
[1s]
Symbols used in Eq. [1s] retain the same meaning as defined for Eq. [1]. The
residual autocorrelation function was not statistically significant (p<0.05). As with Eq.
[1], the coefficient of NANI2-5 is statistically greater than the coefficient of NANI6-9
(p<0.05). None of the coefficients in Eq. [1s] are statistically different from the
corresponding coefficient in Eq. [1], suggesting that the effects of colinearity of
independent variables in Eq. [1] are not great. Although the coefficients in the two
equations are not statistically different, the larger coefficients of NANI2-5 and NANI6-9
in Eq. [1s] will result in estimated mean nitrate flux values that are more sensitive to
changes in NANI than predicted by the coefficients in Eq. [1].
6
We also conducted an uncertainty analysis of our annual nitrate-N flux predictions
1960-98 with and without reduced fertilizer application rates. Depending on the N input
term considered, we assumed that our estimates of N inputs were within 5 and 30% of the
true value as indicated in Table 2. Monte Carlo simulations were conducted assuming a
uniform distribution of values for input variables within the range of uncertainty, and
preserving the correlation between these input terms. We also accounted for uncertainty
in regression parameter estimation. We then calculated 95% confidence intervals for the
mean annual predicted nitrate flux, and partitioned the variance in the nitrate flux
predications according to uncertainty in estimated N input terms and regression
parameters. This analysis indicated that 95% of the variance in estimated nitrate flux
was due to statistical estimation of the regression parameters, and 5% of the variance was
due to uncertainty in estimating the components of NANI (Table 3). This result was due,
in part, to the effect of averaging NANI over 4-year intervals, which reduces random
estimation errors, and due to the correlation among N input terms.
NANI includes N contributed from point source input of municipal sewage by the
inclusion of N retained in the drainage basin for human consumption. However, our
analysis does not include estimates of industrial point sources of N input. According to
US EPA data on permitted N discharges to the Mississippi River system, this represented
a relatively small input in 1996 when it was 6% of riverine nitrate flux(Goolsby et al.,
1999). Estimates of industrial point source inputs in the 1970s suggest that these inputs
may have declined over time due to regulations imposed by the Clean Water Act, but
consistently collected data are not available. Goolsby et al. (1999) assumed that
industrial point source inputs were constant between 1951 and 1996.
7
The regression analysis conducted in this study can compensate for constant errors in
estimating NANI by adjusting the coefficients to optimize the fit between the estimated
NANI and observed nitrate flux. If, for instance, the true value of NANI was consistently
10% greater than our estimated value, the resulting regression coefficients would be 10%
lower than if we had used the true value of NANI, and the estimated nitrate flux would
not be influenced by such an error. On the other hand, regression analysis can not
compensate for trends in errors in estimating NANI. If the true value of NANI has
different temporal trend than our estimate, then the true relationship between NANI and
riverine nitrate flux will be different than the relationship we identified using our estimate
of NANI. Further research is needed to more precisely quantify changes in N inputs and
outputs. Estimates of biological N fixation appear to be more uncertain than other
estimates of N sources and sinks considered in this study.
References
Alexander, R.B.and R.A. Smith. 1990. County level estimates of nitrogen and
phosphorus fertilizer use in the United States 1945 to 1985. US Geological Survey
Open-File Report 90-130. Reston, VA.
Aref, S., and M.M. Wander. 1998. Long-term trends of corn yield and soil organic matter
in different crop sequences and soil fertility treatments in the Morrow Plots. Adv.
Agron. 62:153-197.
Battaglin, W.A. and D.A. Goolsby. 1995. Spatial data in geographic information system
format on agricultural chemical use, land use, and cropping practices in the United
8
States. US Geological Survey Water Resources Investigations Report 94-4176.
Denver, CO.
Buyanovsky, G. A., J. R. Brown and G. H. Wagner. 1997. Sanborn Field: Effect of 100
years of cropping on soil parameters influencing productivity. In: E. A. Paul K.
Paustian, E.T. Elliott, and C.V. Cole (eds.). Soil Organic Matter in Temperate
Agroecosystems: Long-Term Experiments in North America. CRC Press Inc. P.205225
Caraco, N. F., and J.J. Cole. 1999. Human impact on nitrate export: an analysis using
major world rivers. Ambio 28:167-170.
Cohn, T.A., D.L. Caulder, E.J. Gilroy, L.D. Zynjuk, and 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 28:2352-2363.
David, M.B,. and L.E. Gentry. 2000. Anthropogenic inputs of nitrogen and phosphorus
and riverine export for Illinois, USA. J. Environ. Qual. 29:494-508.
Donigian, A.S., A.S. Patwardhan, Jr., R.V. Chinnaswamy, and T.O. Barnwell. 1997.
Modeling soil carbon and agricultural practices in the central US: an update of
preliminary study results. p. 499-518. In R. Lal, J.M. Kimble, R.F. Follett, and B.A.
Stewart. (ed.) Soil processes and the carbon cycle. CRC Press, Boca Raton, FL.
Goolsby, D.A., and W.A. Battaglin. 2001. Long-term changes in concentrations and flux
of nitrogen in the Mississippi River Basin, USA. Hydrol. Processes 15:1209-1226.
Goolsby, D.A., W.A. Battaglin, B.T. Aulenbach, and R.P. Hooper. 2001. Nitrogen input
to the Gulf of Mexico. J. Environ. Qual. 30:329-336.
9
Goolsby, D.A., W.A. Battaglin, G.B. Lawrence, R.S. Artz, B.T. Aulenbach, R.P. Hooper,
D.R. Keeney, and G.J. Stensland. 1999. Flux and Sources of Nutrients in the
Mississippi-Atchafalaya River Basin. Topic 3. Report of the Integrated Assessment
on Hypoxia in the Gulf of Mexico. NOAA Coastal Ocean Program Decision Analysis
Series No. 17. NOAA Coastal Ocean Program, Silver Spring, MD.
Howarth, R.W., G. Billen, D. Swaney, A. Townsend, N. Jaworski, K. Lajtha, J.A.
Downing, R. Elmgren, N. Caraco, T. Jordan, F. Berendse, J. Freney, V. Kudeyarov,
P. Murdoch, and Z. Zhao-Liang. 1996. Regional nitrogen budgets and riverine N & P
fluxes for the drainages to the North Atlantic Ocean: natural and human influences.
Biogeochemistry 35:75-139.
Jordan, T.E., and D.W. Weller. 1996. Human contributions to terrestrial nitrogen flux.
BioScience 46:655-664.
Kellogg, R.L., C.L. Lander, D.C. Mioffitt, and N. Gollehon. 2000. Manure Nutrients
Relative to the Capacity of Cropland and Pasture Land to Assimilate Nutrients:
Spatial and Temporal Trends for the United States. USDA-NRCS-ERS. Publication
nps00-0579. Washington, DC. Available on the internet at
http://www.nhq.nrcs.usda.gov/land/pubs/manntr.html (last referenced 8/01).
Patwardhan, A.S., A.S. Donigian, Jr., R.V. Chinnaswamy, and T.O. Barnwell. 1997. A
retrospective modeling assessment of historic changes in soil carbon and impacts of
agricultural development in central USA, 1900 to 1990. p. 485-498. In R. Lal, J.M.
Kimble, R.F. Follett, and B.A. Stewart. (ed.) Soil processes and the carbon cycle.
CRC Press, Boca Raton, FL.
10
Paul, E.A., K. Paustian, E.T. Elliott, and C.V. Cole (eds.) 1997. Soil organic matter in
temperate agroecosystems: long term experiments in North America. CRC Press,
Boca Raton, FL.
US Department of Agriculture. 1998. National Agricultural Statistics Service historical
data. Accessed 7/98 at URL http://www.usda.gov/nass/pubs/histdata.htm.
USEPA. 2000. National Air Pollutant Emission Trends, 1900-1998. Office of Air Quality
Planning and Standards, Research Triangle Park, NC, USA. EPA-454-R/00-002.
Available on-line http://www.epa.gov/ttn/chief/trends/trends98/browse.html
Wander, M.M., M.G. Bidart, and S. Aref. 1998. Tillage impacts on depth distribution of
total and particulate organic matter in three Illinois soils. Soil Sci. Soc. Am. J.
62:1704-1711.
Yang, X., and M.M Wander. 1999. Tillage effects on soil organic carbon distribution and
storage in a silt loam soil in Illinois. Soil Tillage Res. 52:1-9.
11
Table 1s. Nitrogen content in crop and pasture products used to estimate annual N
harvested in MRB.
Crop
Harvest Unit
Alfalfa
Corn for Grain
Corn for Silage
Sorghum for grain
Sorghum for silage
Soybeans
Wheat
Other Hay
Pasture
ton
bu
ton
bu
ton
bu
bu
ton
ton
N
(kg)
23.6
0.331
3.27
0.363
6.70
1.61
0.499
20.0
20.0
12
Table 2s. Assumed uncertainty in the annual N input and output terms used in the
uncertainty analysis of riverine nitrate flux estimates produced by the regression equation
developed in this study. The uncertainty percentage indicated the range of probable
deviation between the annual estimated value and the true value of each variable.
Variable
Uncertainty
(%)
N fixation
30
N fertilizer use
5
N harvested in crops and pasture
10
N consumed by humans and excreted by livestock 10
Atmospheric NOy Deposition
10
Water Yield
5
13
Table 3s. Relative contributions to the variance in the estimated values of riverine nitrate
flux using Eq. [1] and assumed input uncertainties appearing in Table 2s.
Associated % of variance in
estimated riverine nitrate flux
Source of uncertainty
Regression parameter a†
Regression parameter b
Regression parameter c
Regression parameter d
N Fixation
N Fertilizer
N Harvest
N in animal manure and
human consumption
NOy Deposition
Water Yield
52.6
7.9
32.1
2.0
2.4
1.9
0.8
0.2
0.1
0.0
TOTAL
†
100.0
b
(c*NANI2-5 + d*NANI6-9)
Form of Equation: NLM = a*(W ) e
14
Figure Caption
Figure 1s. Time series of net anthropogenic N input (NANI) calculated for the
Mississippi River Basin, annual water yield measured at Tarbert’s Landing,
and the Old River outflow, and annual nitrate N flux estimated based on nitrate
concentrations measured at St. Francisville, Louisiana.
0.4
NANI
20
-1
0.3
Water Yield (m yr )
NANI and Riverine Nitrate
-1
-1
Flux (kg N ha yr )
25
15
0.2
10
Water
0.1
5
Nitrate
0
1950
1960
1970
1980
1990
0.0
2000
Year
15