Master of
Engineering
Project:
Building a Model to
Assess the Effects
of Road Salt on
Water Quality
By Breann Liebermann
Cornell Biological and Environmental Engineering
December 2014
1
Contents
Introduction ................................................................................................................................................... 3
Objectives ..................................................................................................................................................... 4
Methods ........................................................................................................................................................ 5
Site Description......................................................................................................................................... 5
Data Collection ......................................................................................................................................... 6
Model Description and Assumptions ........................................................................................................ 7
TWMC and FWMC Calculations ........................................................................................................... 10
Results ......................................................................................................................................................... 12
Model Performance ................................................................................................................................. 12
Current Trends: Have Chloride Concentrations Been Too High? .......................................................... 19
Future Scenario: Will Chloride Concentrations Be Too High? .............................................................. 21
TWMC and FWMC Estimates................................................................................................................ 24
Discussion ................................................................................................................................................... 26
Model Limitations and Improvements .................................................................................................... 26
Takeaways............................................................................................................................................... 28
Sources ........................................................................................................................................................ 28
Appendix A: Road Salt Application ........................................................................................................... 29
Appendix B: Streamflow Samples .............................................................................................................. 30
Appendix C: Runoff Samples ..................................................................................................................... 31
Appendix D: Soil Samples .......................................................................................................................... 34
Acknowledgements ..................................................................................................................................... 35
2
INTRODUCTION
Application of road salt began in the United States in the 1940’s as a response to the
“bare pavement” policy that emerged in highway departments so people could travel safely on
roads in all weather conditions (Bradof, 1994). Though there have been many changes between
the 1940’s and the present day, one thing has remained constant: road salt is the predominant
deicer. There are no laws limiting the amount of road salt applied; as much as needed for road
safety is applied. Annually, approximately 20 million tons of road salt is applied in the United
States (Novotny & Stefan, 2012). It is an effective and affordable deicer, but it comes with its
risks.
Because road salt is highly soluble, it is easily transported when snow and ice on the road
melts (Novotny & Stefan, 2012). The problem is, as Novotny and Stefan (2012) report, “chloride
can be toxic to freshwater organisms”. Numerous studies have been conducted on the effects of
chloride on aquatic life and plant communities. Findings have included negative impacts on
amphibian embryos (Karraker & Ruthig, 2009) and amphibian tadpoles (Denoël et al., 2010).
Because of the clear link between chloride and effects on amphibian health, the US
Environmental Protection Agency (EPA) has developed guidelines on recommended maximum
chloride short-term and long-term exposure levels (Mullaney, Lorenz, & Arntson, 2009).
However, no such guidelines exist for local flora. Effects on flora have been found to be death
(Parida & Das, 2005), favorable conditions for non-native invasive species (Richburg, Patterson,
& Lowenstein, 2001) and even ecosystem function changes and decreases in biodiversity
(Daaehler, 2003). Though chloride is not harmful to humans, the frequency and high levels at
which it is applied in many areas have led to significant changes in local and seasonal water
3
quality (Gardner & Royer, 2010, Cassanelli & Robbins, 2013). Effects on aquatic organisms,
local flora, and water quality may be exacerbated in the near future due to anticipated climate
change, including changes in temperature and the frequency and duration of storm events (US
EPA, 2014).
This study will focus on the Fall Creek watershed in Ithaca, NY. Climatic, hydrologic,
and field data will be gathered to build a model that predicts chloride concentrations in the
watershed given a known amount of road salt applied. Chloride is used here as a proxy for road
salt. The implications of the predicted chloride concentrations will be explored, and an analysis
of future conditions will be conducted to address the potential effects of climate change.
OBJECTIVES
It is clear that high concentrations of road salt can have an impact on plants and aquatic
life in the watershed. Effects have already been discussed but include death of local flora and
fauna, favorable conditions for invasive plant species, and mortality in amphibian eggs. Thus,
data on road salt concentrations in the watershed could be incredibly useful in determining
whether the amount of road salt applied has potential to cause harmful effects. My first objective
is to determine whether the amount of road salt applied in Ithaca in the past, present, and future
is at levels harmful to aquatic life. To answer this question, I will gather historical and current
road salt application data. The main deliverable will be a model that estimates chloride
concentrations (used as a proxy for road salt) given a certain input of road salt and known
environmental conditions. I will sample stream water and runoff in the Fall Creek watershed to
evaluate the performance of the model. Model predictions of chloride concentrations will be
compared to toxicity standards for aquatic life to determine whether their health is at risk. Next, I
will estimate future environmental conditions and road salt inputs based on climate change
4
research to determine if there is a greater risk ahead for toxic chloride levels. This model could
be useful for many other areas of study as well. Most watersheds or municipalities do not
routinely sample water for chloride concentrations, but many areas do apply road salt. Thus, a
model that estimates chloride concentrations in a watershed could be very useful in estimating
risk associated with road salt application without the need for laborious and expensive field
sampling.
The second objective of my study is to determine the current time-weighted and flowweighted mean chloride concentrations. These parameters each provide different depictions of
the average chloride concentration. The time-weighted mean concentration (TWMC) is the
average concentration seen by aquatic life in streams, while the flow-weighted mean
concentration (FWMC) is the average concentration seen by the outlet of the watershed
(Heidelberg College, 2005). The TWMC is useful for assessing whether aquatic life is at risk of
chronic toxicity levels. The FWMC will supplement data from a previous 2012 study of the Fall
Creek Watershed, in which FWMCs for chloride were modeled for data collected from 1972
through 2003 and were predicted for the future (Shaw et al. 2012). The FWMC I determine for
2014 will be compared to the paper’s modeled value to assess the performance of their model.
METHODS
SITE DESCRIPTION
The study site is Fall Creek watershed which includes the towns of Ithaca, Freeville, and
Dryden, NY. This site was chosen for three main reasons: 1) because precipitation, snowmelt,
and temperature data was readily available, 2) because field data could be easily collected, and 3)
because I hoped to gain a better understanding of water quality in the region I live in.
5
DATA COLLECTION
Road salt data in tons applied per winter was obtained from the New York State Office of
General Services (NY State Government, 2014). This data was available for Tompkins County
from the winter of 2002-2003 to 2013-2014. Since the model is applied to Fall Creek, not
Tompkins County, the amount of road salt applied was scaled down based on the areas of
Tompkins County and Fall Creek watershed. This calculation and the specific areas were based
on that done in a paper published by Shaw, Marjerison, Bouldin, Parlange, and Walter (2012).
Temperature, precipitation, and snow depth data was obtained from the National Climatic
Data Center (http://www.ncdc.noaa.gov/) for the Cornell University station in Ithaca, NY.
Streamflow data was obtained from the USGS website for Fall Creek
(http://water.usgs.gov/data/).
Two types of field samples were taken: stream samples and runoff samples. Stream
samples were taken from Fall Creek at the USGS gauging station upstream from Beebe Lake.
These samples were taken initially twice a week and eventually less frequently, approximately
once a week from March 26th to August 11th for a total of 22 sampling dates. Samples were taken
by the author, Breann Liebermann, until May 5th and by an undergraduate researcher, Sarah
Nadeau, until the end of the sampling period. Water samples were also taken in four detention
basins on campus and in nearby areas: the Library Annex basin, LARTU (Large Animal
Research and Teaching Unit) basin, Oxley basin, and EHOB (East Hill Office Buildings) basin.
Technically, EHOB is not located in Fall Creek watershed, but because of its close proximity and
ease of collection it was included in this study. Collection bottles were left in basins and during
rain events, runoff water collected in the bottles. These samples were obtained after most, but not
all storm events. Table 1 shows the number of sampling days for each basin. Breann
6
Liebermann, Sarah Nadeau, Felice Chan, and Sharon Zhang collected runoff samples throughout
the study period. Both stream and runoff samples were analyzed using an Ion Chromatography
(IC) system in the Cornell Soil and Water Lab for chloride concentrations. Breann Liebermann,
Lauren McPhillips, and Sarah Nadeau analyzed samples on the IC.
Basin
# of
days
sampled
Annex
13
LARTU
13
Oxley
8
EHOB
10
Table 1: Number of sampling days in each basin
MODEL DESCRIPTION AND ASSUMPTIONS
The purpose of the model is to approximate chloride concentrations in the watershed
given a certain amount of road salt applied. In many areas, water quality data is not regularly
collected, and this model could approximate chloride as a water quality parameter to inform
scientists and policy-makers. The model involves two functions: one manipulating road salt
inputs and one that is based on hydrologic processes. The model assumes a well-mixed
watershed, meaning spatial variation is assumed to be negligible. This would mean that chloride
concentration in soil water is all the same in the entire watershed, and similarly for chloride in
runoff and streamflow.
The first function written converts from road salt applied in tons per year to chloride
applied in milligrams per day. The only input required is a vector of tons of road salt applied
each winter. I have assumed that the amount of road salt reported in the delivery schedule is
7
equal to the amount applied in a given year, the application rate in Tompkins County is
equivalent to the application rate in Fall Creek, the composition of road salt is primarily sodiumchloride (NaCl), and no sand is mixed in. I have also assumed that in a given year road salt is
applied equally each day from Nov 1- March 31. This assumption was made for model
simplicity. This function also assumes that no road salt is applied the rest of the year and that
road salt is the only factor contributing to chloride inputs to the watershed.
The Thornthwaite-Mather model for watershed yield is a well-known hydrologic model
(Thornthwaite & Mather 1955). It calculates soil water, watershed storage, and river discharge
based on precipitation, snowmelt, evapotranspiration, available water capacity in the soils, and a
reservoir coefficient. In BEE 6740 Ecohydrology , as a class a Thornthwaite-Mather model was
written in R, the statistical computing software. I have made several edits to that function so that
it acts as a simple “salt budget” function. In addition to calculating water quantity (soil water,
runoff, and streamflow), the salt budget function also calculates water quality in terms of the
concentration of chloride in soil water, runoff, and streamflow. Chloride is used as a proxy for
road salt because it is largely stable.
The second function written is the salt budget function using the Thornthwaite-Mather
model . Inputs include mass of chloride applied, precipitation, snowmelt, evapotranspiration (all
on a daily time step) available water capacity in the soils, a runoff coefficient, and a reservoir
coefficient. Available water capacity in the soils was assumed to be 150 and the reservoir
coefficient was assumed to be 0.25, based on class discussions in BEE 6740 Ecohydrology.
These values are specific for the Fall Creek watershed. The runoff coefficient was assumed to be
0.10, based on an assumption made in the paper published by Shaw et al. (2012). This means that
10% of road salt is lost to direct runoff.
8
It is assumed that there is no chloride in the watershed on day 1. Soil water is initialized
to be equal to the available water content. Streamflow is initialized as the streamflow recorded
by the USGS gauging station on the day that the model begins. Storage is initialized to be initial
streamflow divided by the reservoir coefficient. The initial concentration of chloride in soil
water, streamflow, and runoff are all set as 0. A running total of the mass of chloride on the road,
in soil water, in runoff, in storage, and in streamflow is all calculated on a daily time step. The
concentration of chloride in streamflow and runoff is then calculated by dividing each day’s
mass by the volume of water in streamflow and runoff respectively. Figure 1 shows the general
schematic. There are three different hydrologic scenarios possible for each day, and calculations
are slightly different for each scenario:
1) Daily precipitation (rain plus snowmelt) is less than potential evapotranspiration which
causes the soil to dry. If it is winter, road salt accumulates on the road but does not enter
the soil water because there is not enough water to transport it off the roads. No runoff or
excess water is generated. Streamflow is directly dependent on storage.
2) Daily precipitation is greater than potential evapotranspiration, but soil is not filled to
field capacity. There is no runoff or excess water generated. All road salt that has
accumulated on the road is washed into the soil, so the mass of chloride in soil water
includes the mass that was already there plus the additional mass from the road.
Streamflow is directly dependent on storage.
3) Daily precipitation is greater than potential evapotranspiration and soil has filled above
field capacity. All road salt that has accumulated on the road is washed out into runoff
and soil water. Overland runoff is generated and is directly dependent on excess water
(amount of water above field capacity). Runoff is transported directly to streamflow.
9
Excess water enters storage. A proportion of storage is carried to streamflow. Streamflow
is thus dependent on storage and runoff.
Figure 1: Model schematic. Note abbreviations:
P: precipitation
ET: potential evapotranspiration
Cl: chloride
SW: soil water
R: runoff
X: excess
f: unitless coefficient of runoff
S: storage
Res_coef: reservoir coefficient
Q: streamflow
Model outputs include streamflow in mm/day, and the concentration of chloride in soil
water, runoff, and streamflow in mg/L.
TWMC AND FWMC CALCULATIONS
TWMC and FWMC were determined for Fall Creek for November 1, 2013 to October
31, 2014. Since stream samples were only collected from March 2014 to August 2014, an
equation for chloride concentration given a specific stream flow was determined for two
different time periods. Figure 2 shows the two different curves.
10
50.00
45.00
Chloride (mg/L)
40.00
Winter/Spring
35.00
30.00
25.00
Summer/Fall
20.00
15.00
Poly. (Winter/Spring)
10.00
y = 0.0419x2 - 1.6522x + 45.343
R² = 0.6675
Log. (Summer/Fall)
5.00
0.00
1
10
Streamflow (m^3/s)
100
y = -5.405ln(x) + 34.939
R² = 0.7418
Figure 2: Chloride-streamflow curves
The winter/spring time period is clearly distinct from the summer/fall time period.
Winter/spring here is defined as November 1 to June 21, signifying the time period of active road
salt application and the flushing out of chloride from the watershed. Summer/fall is defined as
June 22 to October 31, representing the growing season in the northeast. As shown in Figure 2,
the winter/spring curve is best defined by a second order polynomial curve with an R-squared
value of 0.67. The summer/fall curve is best defined by a logarithmic curve with an R-squared
value of 0.74. These curves were then used to estimate chloride levels in Fall Creek given known
stream flow values.
The calculation for TWMC weighs the concentration of each sample by the time period it
represents, and is given by the following formula:
∑𝑛1(𝑐𝑖 ∗ 𝑡𝑖 )
𝑇𝑊𝑀𝐶 =
∑𝑛1(𝑡𝑖 )
11
The calculation for FWMC weighs the concentration of each sample by the time and flow
it represents, and is given by the following formula:
𝐹𝑊𝑀𝐶 =
∑𝑛1(𝑐𝑖 ∗ 𝑡𝑖 ∗ 𝑞𝑖 )
∑𝑛1(𝑡𝑖 ∗ 𝑞𝑖 )
RESULTS
MODEL PERFORMANCE
Predicting streamflow
Figure 3 shows a visual comparison of modeled streamflow to actual streamflow across
the time period of interest (November 1, 2002 to September 15, 2014). Modeled base streamflow
is an under-prediction of actual streamflow. The model both over and under-predicts peak flows.
The root mean square error (RMSE) between modeled and actual streamflow was calculated to
evaluate model performance. The RMSE gives the standard deviation of the model prediction
error, with a smaller value indicating better model performance (Bigiarini). The formula for
RMSE is √∑[𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑𝑖 − 𝑚𝑜𝑑𝑒𝑙𝑒𝑑𝑖 ]2 . The RMSE across all years of interest is 0.37 mm/day.
12
Figure 3: Modeled streamflow (black) and actual streamflow (red)
Predicting chloride concentration in streamflow
Figures 4, 5, and 6 show the model predictions of chloride concentrations in soil water,
runoff and streamflow for the entire time period of interest. Figures 7, 8, and 9 show the
predictions of chloride in soil water, streamflow and runoff for just one year (2013).
13
Figure 4: Model predictions of chloride in soil water across all years
Figure 5: Model predictions of chloride in runoff across all years
14
Figure 6: Model predictions of chloride in streamflow across all years
Figure 7: Model predictions of chloride in soil water in 2013
15
Figure 8: Model predictions of chloride in runoff in 2013
Figure 9: Model predictions of chloride in streamflow in 2013
Modeled chloride concentrations were compared to trends reported by Bouldin (2005),
who studied samples taken from Fall Creek between 1972 and 2003. During that time period, he
16
found that chloride concentrations in streamflow decreased from between 20 to 25 mg/L (winter)
to 8 to 10 mg/L (spring, summer, early fall). He also found that concentrations taken during
snow melt or winter rain after salt had been applied reached levels as high as 70 mg/L. He also
found that concentrations increased over the years from 11 mg/L in 1972 to 19 mg/L by 2003.
My model predicts that after road salt is not applied (April through October) concentrations are
around 50 mg/L. Winter spikes in concentration are oftentimes above 100 mg/L and are as high
as 200 mg/L. Overall, my model’s yearly trends generally match what Bouldin found (higher
concentrations in the winter, lower concentrations the rest of the year), but my predictions are
higher than what he had found. It is important to keep in mind that my model predictions are for
a different time period than his samples, and that overall chloride concentrations may have in
fact risen.
Modeled chloride concentrations from this spring and summer were compared to
streamflow and runoff samples collected that were analyzed for chloride. Figure 10 shows the
comparison. The model over-predicts chloride in streamflow for the spring and summer of 2014
by on average 22 mg/L, which indicates weak performance of the model. However, one
promising aspect of the model is it did seem to capture the decline in chloride levels seen around
the end of March, and the rise back up to average levels throughout April. Unfortunately, no
samples were taken in the middle of May to compare to the model prediction of a sharp decline
in chloride levels. The model did not capture the variation seen in chloride levels through June,
July, and early August.
17
Chloride in Streamflow
67
Model
estimate
62
Actual
Chloride (mg/L)
57
52
47
42
37
32
27
22
17
3/2/2014
4/1/2014
5/1/2014
5/31/2014
6/30/2014
7/30/2014
8/29/2014
Figure 10: Model performance of predicting chloride in streamflow
Predicting chloride concentrations in runoff
Unfortunately, the model predicted no chloride in runoff during the sampling time period.
Runoff samples from the detention basins did in fact contain chloride, sometimes concentrations
of up to 4000 mg/L. Figure 11 shows the variation of chloride concentrations in runoff samples
across and within basins. Variation within basins is sometimes seen because multiple bottles
were left in each detention basin and after some storm events more than one sample was
obtained. Variation between basins is also evident- EHOB and LARTU typically had higher
chloride than Annex and Oxley. Although it was predicted that a well-mixed assumption would
not hold true, the variation across and within basins is evidence that the watershed is certainly
not well-mixed. This would mean that chloride concentration is dependent on spatial location.
This will be elaborated further in the Discussion section.
18
Chloride in Runoff
4500.00
4000.00
Chloride (mg/L)
3500.00
3000.00
Annex
2500.00
EHOB
2000.00
LARTU
1500.00
Oxley
1000.00
500.00
0.00
4/1/2014
5/1/2014
5/31/2014
6/30/2014
7/30/2014
Figure 11: Great variation seen in chloride in runoff
CURRENT TRENDS: HAVE CHLORIDE CONCENTRATIONS BEEN TOO HIGH?
Mullaney, Lorenz, and Arntson (2009) reported that the EPA recommendation of
chloride in aquatic life for chronic exposure is a four day average of 230 mg/L with an
occurrence interval of once every three years. The acute recommendation is 860 mg/L (a one
hour average) and the recurrence interval is less than once every three years.
Figure 12 shows predicted chloride in streamflow plotted against the chronic toxicity
standard of 230 mg/L. My model predicts that during the entire period in which data was
available (November 1st, 2002 to September 15th, 2014), chloride levels in streamflow did not
exceed the chronic toxicity recommendation. On January 6th, 2014, the predicted chloride level
was 230 mg/L, however that is the one day average, and the four day average preceding,
following, or surrounding that day is less than 230 mg/L. Furthermore, data from spring and
summer of 2014 suggest that the model tends to over-predict chloride concentrations in
streamflow, so actual concentrations may have been even lower.
19
The story with runoff is slightly different. Figure 13 shows predicted chloride in runoff
plotted against the acute toxicity standard of 860 mg/L. At least once every year except 2013 the
model predicts that the standard was exceeded which is much more frequent than the
recommended recurrence interval of once every three years. In November and December of
2011, the model predicts that the standard was exceeded over 5 times. It is difficult to evaluate
how well the model predicts chloride in runoff, because on the days that runoff samples were
collected, the model predicted no chloride in runoff. However, there is reason to believe stream
concentrations near runoff entry points may be elevated to toxic levels because of concentrated
runoff. With the model improvements, statements can be made with more certainty as to whether
aquatic life is at risk from chloride levels.
Figure 12: Comparing modeled chloride in streamflow to chronic toxicity of 230 mg/L (red line)
20
Figure 13: Comparing modeled chloride in runoff to acute toxicity of 860 mg/L (red line)
FUTURE SCENARIO: WILL CHLORIDE CONCENTRATIONS BE TOO HIGH?
Given the anticipated future changes in temperature, precipitation, and other climatic
variables, I used the model to estimate future chloride concentrations and evaluate whether
aquatic life would be at risk from chloride toxicity. To model these projected changes, I modified
the 12 years of precipitation and temperature data (2002 to 2014) and assigned these altered
values to the next 12 years (2014 to 2026). There are many estimates of exactly how temperature
and precipitation may change as a result of climate change. I used estimates published by the
EPA. According to the EPA, average U.S. temperatures are expected to increase by 2.2 to 6.1
degrees Celsius by 2100 (EPA 2014). For simplicity, I increased minimum and maximum
temperatures by 5 degrees Celsius. Projected precipitation changes are less explicitly reported.
21
The EPA reports that northern areas of the US will be wetter and will experience a higher
proportion of precipitation falling as rain instead of snow (EPA 2014). Thus, it is unclear exactly
how much more precipitation is expected. In my future scenario, I assumed a 20% increase from
the past 12 years of precipitation data. Because precipitation and temperature change in the
future scenario, it is likely that road salt application would also change as a result. It is unclear
exactly what a 5 degree temperature increase combined with a 20% precipitation increase would
mean for total yearly road salt applications. An increase in precipitation would lead to more
snow, but this is balanced by an increase in temperature, which may mean more rain events
instead of snow events. Thus, to account for both changes I have assumed a 10% increase in total
yearly road salt applied. Potential evapotranspiration and snowmelt are recalculated based on
new temperature and precipitation values. I have assumed that all other conditions stay the same
(available water capacity, reservoir coefficient, and runoff coefficient).
Figures 14 and 15 show the results of chloride concentrations in streamflow and runoff
respectively, in the future climate change scenario as compared to if historical trends were to
continue (i.e. the past 12 years of temperature and precipitation data were to be replicated in the
next 12 years). In examining chloride in streamflow (figure 14), it appears that in the climate
change scenario, chloride levels actually decrease, despite the increase in road salt applied. This
may be because with greater precipitation, there is a larger volume of stream water to dilute
chloride. Over all years, chloride concentrations do not reach the recommended chronic toxicity
limit. In examining chloride in runoff (figure 15), changes are less clear. Chloride levels in the
climate change scenario are sometimes lower and sometimes higher than historical levels. The
recommended acute toxicity limit is exceeded approximately 10 times in 12 years in the climate
change scenario, suggesting that high levels of chloride in runoff may deliver super-concentrated
22
chloride water to streams which could harm aquatic life. This was a concern in the prior analysis
of the past 12 years of data as well.
Figure 14: Comparing historical trends to a climate change scenario for streamflow
concentrations
23
Figure 15: Comparing historical trends to a climate change scenario for runoff concentrations
TWMC AND FWMC ESTIMATES
TWMC
TWMC for chloride was determined to be 35.2 mg/L for Fall Creek for November 1,
2013 to October 31, 2014. This is the average chloride concentration that aquatic life is subject to
24
for a year-long period. This can be compared to the EPA’s chronic toxicity limit of 230 mg/L.
These results suggest that long-term chloride exposure levels are significantly below what is
considered harmful to them. In other words, these findings suggest that the level of road salt
applied in the winter of 2013/2014 were not at levels that would harm aquatic life that were
exposed to the long term average. However, the TWMC says nothing about the superconcentrated chloride hotspots seen in runoff as discussed in the previous two sections.
FWMC
FWMC for chloride was determined to be 35.9 mg/L for Fall Creek for November 1,
2013 to October 31, 2014. In Figure 16, predictions from Shaw et al. (2012) for FWMC for Fall
Creek are shown. My calculation for 2013/2014 is approximately 13 mg/L greater than what
their model estimates for FWMC for 2013/2014. This could be due to an underestimation in their
model, an overestimation of my model, or a combination of the two. This is consistent with the
overestimations of my model discussed in the previous sections.
25
Figure 16: from Shaw et al. (2012), “Comparison of the mixing model [heavy solid line] to the
observed annual flow-weighted mean Cl−; to fit the observations (symbols), the model assumes a
residence time of 50 years; the thin line indicates annual changes in incoming Cl− concentration
attributed to increasing road salt application
DISCUSSION
MODEL LIMITATIONS AND IMPROVEMENTS
When building models, there is always a tradeoff between accuracy and feasibility. Time,
resources, data availability, and background knowledge can limit the accuracy of a model.
Assumptions made for feasibility also can compromise accuracy. In this model, one of the main
constraints was time. Much of the initial research process was dedicated to learning to use R
which limited the amount of time spent with the more technical aspects of the model. A great
deal of time was also spent familiarizing myself with designing a research study which led to a
later field sampling start than anticipated. Another time constraint was how often runoff and
streamflow could be sampled. More samples leads to more comparisons to model predictions
which could lead to an improved model. However, more samples also means more time and
resources spent gathering and analyzing them. Thus, a tradeoff was made.
Availability of data was also dealt with. Data did not exist on the yearly amount of road
salt actually applied so the yearly scheduled amount to be applied was used. Depending on the
difference between scheduled and actual amounts, this could lead to inaccuracies in the model.
Furthermore, data did not exist on daily road salt applications. Since the model calculated
concentrations on a daily time step, an assumption had to me made about how yearly road salt
was distributed daily. My model assumed an equal distribution of road salt from November 1st
26
through March 31st. However, this is an oversimplification made that could affect model
accuracy.
Assumptions made for simplicity also affect the model accuracy. For example, my model
assumes that the only source of chloride in the watershed is from road salt, and it ignores sources
such as sewage. Also, the runoff coefficient, reservoir coefficient, and available water capacity
parameters calculated as a class were assumed to be the optimum values, which may not be the
case.
One of the main assumptions made was that the watershed was well-mixed, and that there
was no spatial variation in chloride concentrations. However, through spatial differences in
chloride in runoff it is clear that there is spatial variation. Furthermore, I suspect that distance
from roadways and sidewalks has great influence on chloride concentrations. The biggest
accuracy improvement to this model may be adding spatial variation. Another significant
improvement could be increasing the frequency and duration of the sampling period. In this
study, samples were only taken from March to August, so the accuracy of the model in
predicting chloride from September to February could not be determined. Sampling many times
a day could also be beneficial to determine whether acute toxicity levels have been met (which
are based on a one hour average). Adding a spatial component to the model and increasing
sampling would require significantly more time, resources, and background knowledge.
Simplifications were also made in examining a future scenario given climate change. It is
unclear exactly how precipitation and minimum and maximum temperatures will change in the
next 12 years, and the daily changes will not be constant. Projecting the past 12 years of data into
the future for baseline conditions is also not accurate, as weather patterns are extremely variable.
27
Furthermore, an assumption was made as to how road salt application rates would change based
on climatic changes, which introduces another uncertainty.
TAKEAWAYS
The goal of this project was to collect data in the field, use that data to build a model to
estimate the effects on water quality of road salt, and evaluate whether aquatic life was at risk
given current and future conditions. The main takeaway is that based on model estimates, overall
chloride concentrations in streamflow remain under recommended limits. However, model
estimates of current and future conditions suggest that surface runoff carries super-concentrated
doses of chloride that are occasionally above recommended acute levels. When this runoff enters
streams, aquatic life that is present may be harmfully impacted. Future investigations should look
into studying spatial variation of chloride in the watershed, focusing particularly on runoff from
sidewalks and roads that have the biggest potential to carry high concentrations of chloride.
SOURCES
Bigiarini, M. Z. Root mean square error. R Documentation. Retrieved from
http://www.rforge.net/doc/packages/hydroGOF/rmse.html.
Bouldin, D. (2005). Manuscripts and water quality data for watersheds and lakes in central ny,
1972-2003: chloride in fall creek as influenced by road salt. Retrieved from
http://hdl.handle.net/1813/2547.
Bradof, K. (1994). The deicing debate: will it ever be put on ice? The Center for Science &
Environmental Outreach. Retrieved from
http://cseo.mtu.edu/community/publications/wellspring/deicingdebate.html.
Cassanelli, J. P., & Robbins, G. A. (2013). Effects of road salt on Connecticut’s groundwater: a
statewide centennial perspective. Journal of Environmental Quality, 42.
Daehler, C. C. (2003). Performance comparisons of co-occurring native and alien invasive
plants: implications for conservation and restoration. Annual Review of Ecology, Evolution, and
Systematics .
28
Denoël, M., Bichot, M., Ficetola, G. F., Delcourt, J., Ylieff, M., Kestemont, P., & Poncin, P.
(2010). Cumulative effects of road de-icing salt on amphibian behavior. Aquatic Toxicology,
99(2).
Gardner, K. M., & Royer, T. V. (2010). Effect of road salt applications on seasonal chloride
concentrations and toxicity in south-central Indiana streams. Journal of Environmental Quality,
39.
Heidelberg College. (2005). Time-weighted and flow-weighted mean concentrations. Water
Quality Laboratory. Retrieved from http://wql-data.heidelberg.edu/index2.html.
Karraker, Nancy E., & Gregory R. Ruthig. (2009). Effect of road deicing salt on the
susceptibility of amphibian embryos to infection by water molds. Environmental Research,
109(1).
Mullaney, J. R., Lorenz, D. L., & Arntson, A. D. (2009). Chloride in groundwater and surface
water in areas underlain by the glacial aquifer system, northern United States. United States
Geological Survey.
New York State Government. Office of General Services (2014). Road salt delivery schedules.
Retrieved from http://www.ogs.state.ny.us/purchase/spg/awards/01800DS00.HTM.
Novotny, E. V. & Stefan, H. G. (2012). Road salt impact on lake stratification and water quality.
Journal of Hydraulic Engineering, 138(12).
Parida, A. K., & Das, A. B. (2005). Salt tolerance and salinity effects on plants: a review.
Ecotoxicology and environmental safety, 60(3).
Richburg, J. A., Patterson, W. A., & Lowenstein, F. (2001). Effects of road salt and phragmites
australis invasion on the vegetation of a western Massachusetts calcareous lake-basin fen.
Wetlands, 21(2).
Shaw, S. B., Marjerison, D., Bouldin, D. R., Parlange, J., & Walter, M. T. (2012). Simple model
of changes in stream chloride levels attributable to road salt applications. Journal of
Environmental Engineering, 138(1).
Thornthwaite, C.W. & J.R. Mather. (1955). The water balance. Laboratory of Climatology, No.
8, Centerton NJ.
United States Environmental Protection Agency. (2014). Future climate change. Retrieved from
http://www.epa.gov/climatechange/science/future.html.
APPENDIX A: ROAD SALT APPLICATION
29
Tompkins County road salt application data obtained from
http://www.ogs.state.ny.us/purchase/spg/awards/01800DS00.HTM. Assumed 1750 km of roads
in Tompkins County and 588 km of roads in Fall Creek Watershed (Shaw et al. 2012).
Year
Tompkins County
(tons/year)
Fall Creek
(tons/year)
2002
28014
9413
2003
29710
9983
2004
32433
10897
2005
34313
11529
2006
33478
11249
2007
34276
11517
2008
36463
12252
2009
37485
12595
2010
37318
12539
2011
36012
12100
2012
31772
10675
2013
31475
10576
Table 2: Road salt applied in Tompkins County. 2002 corresponds to the winter of 2002-2003.
APPENDIX B: STREAMFLOW SAMPLES
Samples were taken from Fall Creek at the USGS gauging station upstream from Beebe Lake.
Samples were analyzed on the IC at the Cornell Soil and Water Lab.
Sample Date
Chloride (mg/L)
3/26/2014
40.39
3/28/2014
42.76
4/6/2014
24.87
4/8/2014
29.49
30
4/11/2014
32.95
4/16/2014
30.00
4/19/2014
34.19
4/23/2014
37.78
4/26/2014
38.49
4/30/2014
38.30
5/5/2014
37.28
5/28/2014
43.30
5/30/2014
40.20
6/6/2014
40.08
6/12/2014
38.72
6/17/2014
36.38
6/26/2014
20.73
7/7/2014
31.85
7/9/2014
26.82
7/21/2014
35.59
8/6/2014
22.71
8/11/2014
32.18
Table 3: Chloride concentration in Fall Creek
APPENDIX C: RUNOFF SAMPLES
Runoff samples were taken in four detention basins around Cornell University. Collection bottles
were left near the inlets of the basins and collected after storm events. Samples were analyzed on
the IC at the Cornell Soil and Water Lab.
Date
Site Name
Chloride (mg/L)
4/8/2014
Annex
11.27
4/18/2014
Annex
2.90
31
4/23/2014
Annex
5.18
4/30/2014
Annex
39.84
5/5/2014
Annex
22.92
5/16/2014
Annex
181.12
5/28/2014
Annex
628.00
5/30/2014
Annex
628.05
6/13/2014
Annex
1.67
6/18/2014
Annex
0.18
6/26/2014
Annex
4.66
6/26/2014
Annex
0.40
7/7/2014
Annex
0.47
4/8/2014
EHOB
410.88
4/18/2014
EHOB
508.47
4/18/2014
EHOB
296.46
4/30/2014
EHOB
1657.24
5/5/2014
EHOB
1839.93
5/16/2014
EHOB
248.28
5/28/2014
EHOB
1480.60
5/30/2014
EHOB
1480.62
6/13/2014
EHOB
630.79
6/18/2014
EHOB
653.62
6/26/2014
EHOB
571.93
7/9/2014
EHOB
426.25
4/8/2014
LARTU
3212.44
4/8/2014
LARTU
1251.05
4/16/2014
LARTU
994.04
32
4/18/2014
LARTU
3197.87
4/18/2014
LARTU
302.27
4/18/2014
LARTU
321.07
4/30/2014
LARTU
3836.95
4/30/2014
LARTU
1004.77
5/2/2014
LARTU
4070.12
5/16/2014
LARTU
94.00
5/28/2014
LARTU
135.40
5/28/2014
LARTU
340.00
5/30/2014
LARTU
135.44
5/30/2014
LARTU
339.98
6/3/2014
LARTU
422.00
6/3/2014
LARTU
525.28
6/13/2014
LARTU
20.94
6/13/2014
LARTU
88.80
6/18/2014
LARTU
1811.36
6/18/2014
LARTU
121.12
6/26/2014
LARTU
1076.78
6/26/2014
LARTU
144.08
7/9/2014
LARTU
131.65
7/9/2014
LARTU
1151.20
4/9/2014
Oxley
2.63
4/9/2014
Oxley
1.49
4/18/2014
Oxley
3.12
4/19/2014
Oxley
32.55
5/16/2014
Oxley
3.10
33
5/16/2014
Oxley
1.46
5/28/2014
Oxley
9.40
5/30/2014
Oxley
9.35
6/18/2014
Oxley
2.75
6/26/2014
Oxley
3.87
7/9/2014
Oxley
0.67
7/9/2014
Oxley
0.58
Table 4: Chloride concentration in detention basins
APPENDIX D: SOIL SAMPLES
Soil conductivity was collected in the field using a soil probe at four different sites. Breann
Liebermann, Bryan Finneran, Felice Chan, and Sharon Zhang collected soil conductivity data.
Site one is in front of the side Riley Robb parking lot in the grass next to the sidewalk. Site two
is in front of the wrestling building parking lot in the grass next to the sidewalk. Site three is near
the LARTU detention basin in the grass. Site four is in the grass alongside Tower Road near the
intersection with Judd Falls. Soil conductivity data was not ultimately used in this study.
Date
Site
Conductivity (mS)
4/9/2014
1
0.26
4/18/2014
1
0.38
4/22/2014
1
0.27
4/25/2014
1
0.39
4/29/2014
1
0.36
4/8/2014
2
2.69
4/11/2014
2
2.62
4/16/2014
2
2.38
4/19/2014
2
2.99
4/23/2014
2
1.03
4/28/2014
2
1.2
34
4/30/2014
2
1.38
4/10/2014
3
1.32
4/16/2014
3
1
4/21/2014
3
1.16
4/25/2014
3
0.9
4/30/2014
3
0.82
4/14/2014
4
1.48
4/17/2014
4
0.85
4/18/2014
4
1.01
4/21/2014
4
0.71
4/24/2014
4
0.71
4/28/2014
4
0.84
Table 5: Soil conductivity data
ACKNOWLEDGEMENTS
I would like to thank Bryan Finneran, Felice Chan, Sharon Zhang, and Sarah Nadeau for
their help with field work; Sarah Nadeau and Lauren McPhillips for their help with lab work; and
Josephine Archibald and Dan Fuka for their help with R coding. I would also like to extend a
huge thank you to Dr. Todd Walter for his help throughout the process in choosing a topic,
devising a sampling protocol, interpreting results, and being as excited about road salt as I am.
35