EPA/600/R-01/075
September 2001
A Landscape Assessment of the
Catskill/Delaware Watersheds
1975-1998
New York City's Water Supply Watersheds
M.H. Mehaffey1, M.S. Nash1, T.G. Wade2, C.M. Edmonds1,
D.W. Ebert1, K.B. Jones1, and A. Rager3
1 U.S. Environmental Protection Agency, Office of Research and Development, National Exposure
Research Laboratory, Environmental Sciences Division, Las Vegas, Nevada
2 U.S. Environmental Protection Agency, Office of Research and Development, National Exposure
Research Laboratory, Environmental Sciences Division (Research Triangle Park)
3 Lockheed Martin Environmental Services, Las Vegas, Nevada
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EPA/600/R-01/075
September 2001
A Landscape Assessment of the
Catskill/Delaware Watersheds
1975-1998
New York City's Water Supply
Watersheds
M.H. Mehaffey1, M.S. Nashl, T.G. Wade2, C.M. Edmonds1,
D.W. Ebert1, K.B. Jones1, and A. Rager3
Found in Section 1 of this document:
Title Page
Abbreviations
Executive Summary
Found in Section 2 of this document:
Chapter 1
Chapter 2
Chapters
Chapter 4
Found in Section 3 of this document:
Chapters
Chapters
Chapter?
Chapters
Found in Section 4 of this document:
Appendices
Reference Materials
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Chapter 5. Landscape Change
Page - 34
The landscape is transformed from one cover type
to another by a number of different mechanisms.
Human-induced changes (suburbanization, farming,
and logging) and natural changes such as fires and
flooding are the most common drivers of land
cover change over time (Forman, 1995b). This
chapter provides an assessment of the land cover
and land use changes which have taken place in
the CD watersheds across a 25-year time span.
Change in fie Watershed
Between the mid-1970s and the late 1990s, a total
of 8% of the CD watersheds changed from one
cover type to another. The majority of the change is
from agriculture to forest (5% of the area) or forest
to agriculture (3% of the area). During the past two
decades many acres of pasture have been
released allowing forest regrowth to occur and
resulting in a 2% net increase in secondary forest
cover across the watersheds. The decrease in
percent agriculture within the CD watershed is
reflected in other related metrics, such as the
human use index, percent agriculture on erodible
soil, and agriculture on slopes greater than 5,10,
and 15% (Table 5.1). The next largest change,
following agriculture and forest, is an increase in
urban development of less than 1 % across the
watersheds (Figure 5.1 b). The majority of the
change in urban development occurred between the
mid-1970s and the mid-1980s which corresponds
to increases in population.
The rate of change was fairly consistent throughout
the two decades, with the exception of a slight
increase in change from agriculture to forest during
the mid-1980s to the early 1990s (Figures 5.1 a and
c). The CD watersheds which had the greatest
percentage of change from agriculture to forest
classification are the Cannonsville, Pepacton, and
Schoharie. Vegetation change between the mid-
1970s and the late 1990s in these three watersheds
resulted in a net increase of forest cover by 5, 3,
and 2%, respectively (Table 5.2).
Table 5.1. Change in Agriculture Metrics in the Catskill/Delaware Watersheds (mid-1970s to late 1990s)
Watershed
Agriculture
k>0.3
km2 %
Agriculture
Slope >5%
km2 %
Agriculture
SI ope > 10%
km2 %
Agriculture
Slope > 15%
km2 %
Cannonsville
Pepacton
Ashokan
Neversink
Schoharie
Rondout
0.24
0.58
0.40
0.02
0.00
0.00
0.02
0.06
0.06
0.01
0.00
0.00
46.00
12.00
20.00
0.05
1.00
0.59
3.92
1.28
3.03
0.02
0.13
0.24
21.00
4.00
9.00
0.05
0.25
0.42
1.74
0.37
1.34
0.02
0.03
0.17
6.00
0.48
2.00
0.02
0.16
0.01
0.49
0.05
0.25
0.01
0.02
0.00
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Page - 35
The only watershed showing a net loss in forest
cover is the Ashokan (Table 5.2). With the
exception of one subwatershed, which had no
change between the mid-1970s and the late
1990s, all of the Ashokan subwatersheds lost
forest cover during the past two decades (Figure
5.2). The loss of forest in the Ashokan and its
subwatersheds is likely related to increases in
urban development (Figure 5.1 b).
Outside of the Ashokan, there are only three other
subwatersheds which have a net loss in forest
cover across time, one each in the Cannonsville,
Schoharie, and Rondout (Figure 5.2). The
Cannonsville subwatershed forest loss is the result
of increases in urban and agriculture land use,
while the Schoharie subwatershed lost forest as
the result of urban growth and increases in bare
ground (ski resort development) (Figure 5.1 b, c,
and d). Loss of forest cover in the Rondout
reservoir subwatershed is also caused by urban
growth.
Barn, hay fie Id, and row crops in Cannonsville near
Hobart.
Strip cropping (corn, alfalfa, pasture) in Cannonsville,
North of New Delhi.
(a)
95
o> 90
_^_^^
' -fr- 4
x X x x
1975 1985 1991 199ฃ
Imagery Year
Cannonsville
Schoharie
O
Pepacton
Ashokan
A
Neversink
Rondout
O
Cannonsville
0
Pepacton
Ashokan
A
Neversink
Rondout
o
I
(b)
0.6
0.5
CD
f 0.4
CD
s ฐ-3
Q_
0.2
(d)
0.3
0.25
S 0.15
Q_
0.1
005
0
Urban
_
^ -f~
* * *
A ./
T^-^-"*
^ T T T
T T T T
1975 1985 1991 199ฃ
Imagery Year
Barren
^=3=^^x^*
^^^
A A
1975 1985 1991 199E
Imagery Year
Cannonsville
Schoharie
O
Pepacton
Ashokan
A
Neversink
Rondout
O
Cannonsville
0
Pepacton
Ashokan
A
Neversink
Rondout
O
Figure 5.1. Change in percent (a) forest, (b) urban, (c) agriculture, and (d) barren in the Catskill/Delaware
watersheds from mid-1970s to late 1990s.
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Page - 36
Table 5.2. Land Cover/Use Change (mid-1970s to Iate1990s) in the Catskill/Delaware Watersheds
Total Change
Watershed
Cannonsville
Schoharie
Pepacton
Ashokan
Neversink
Rondout
km2
162
53
80
5
4
8
%
14
7
9
1
2
3
Ag to Forest
km2
107
33
55
2
2
4
%
9
4
6
< 1
1
2
Forest to Ag
km2
55
19
24
3
2
4
%
5
2
3
< 1
1
2
Net Change
to Forest
km2
52
14
32
-1
0
0
%
5*
2
3
<1
0
0
* Seeming inaccuracies in net change results are the result of rounding.
Cannonsville
Schoharie
Land Cover Change
^H Agriculture to Forest
^B Forest to Agriculture or Urban
Net Change Forest Cover
^^ Net Loss
== No Change
Net Gain
Ashokan
Rondout
Figure 5.2. Vegetation change between forest
cover and agricultural or urban land use from mid-
1970s to late 1990s in the Catskill/Delaware
watersheds. The metrics were calculated as total
net change divided by subwatershed area.
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Page - 37
Change in the Riparian Buffer
A riparian buffer can carry out the functions of
filtering and sequestering nonpoint pollution.
However, when riparian vegetation is replaced by
agricultural or urban development, the natural
buffering capacity is lost and it becomes a potential
source of nutrient, bacterial, chemical, anderosional
pollution (Lowranceetal., 1984). Riparian buffer
make up a large proportion of the CD watersheds.
As a result of high stream density, an average of
44% of the land is located within 120 m of a stream.
Therefore, a large percentage (68%) of the total
vegetation change observed between the mid-
1970s and the late 1990s took place within riparian
buffers.
Riparian buffer changes are greatest in the
Cannonsville, Pepacton, and Schoharie watersheds,
resulting in net gains in the amount of forest cover in
the 60-m riparian from 2 to 4% (Table 5.3). The
largest increases in forest cover occurred between
the mid-1980s and the early 1990s (Figure 5.3a). In
the Cannonsville watershed the amount of forest
gain in the riparian buffer was slightly lower than the
watershed as a whole, suggesting that more
conversion from agriculture to forest occurred farther
than 60 m from streams.
A decreasing trend in riparian agriculture occurred
during the same 10 years (mid-1980s to early
1990s) as forest increases, followed by a leveling
off (Figure 5.3c). The percentage change in bare
ground fluctuated between each of the four time
periods with no obvious trend across time (Figure
5.3d). Urban development increases in the riparian
buffer of the CD watersheds were greatest between
the mid-1970s and the mid-1980s paralleling
watershed results (Figure 5.3b).
When assessing riparian buffer changes at the
subwatershed scale, the range of gains and losses
is considerably larger than suggested by the change
in the watershed. Changes in the subwatershed
riparian buffer range from forest cover losses of 3%
to gains of 14% (Figure 5.4). In five of the CD
subwatersheds forest percentages remained the
same or decreased in the 120-m buffer over time
(Figure 5.4); however, these same subwatersheds
were shown to have an increase in percent forest
cover across the whole area (Figure 5.2). Four
Cannonsville subwatersheds had the highest net
gains in riparian forest cover. All of the
subwatersheds in the Ashokan had net decreases
in riparian forest cover with time, which is most likely
related to urbanization along major roads paralleling
nearby streams.
Table 5.3. Total Land Cover, Agriculture (Ag), and Forest Change in the Catskill/Delaware Watersheds Riparian
Buffer (60-m) from mid-1970s to late 1990s
Total Change
Watersheds
Cannonsville
Schoharie
Pepacton
Ashokan
Neversink
Rondout
km2
95
37
50
4
2
5
%
20
10
13
2
2
4
Ag to Forest
km2
59
22
34
2
1
2
%
11
5
8
1
1
2
Forest
km2
36
15
16
2
1
2
toAg
%
8
4
4
1
1
2
Net Change
to Forest
km2
22
7
18
0
0
0
%
4*
2
4
0
0
0
* seeming inaccuracies in net change results are the result of rounding
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Page - 38
(a)
100
90
0>
2 80
Q.
70
60
Forest (60-m)
_ _ _
o ~5
. *
A
^m
m r^"
1975 1985 1991 1998
Imagery Year
(c) Agriculture (60-m)
30
25
1 20
I 15
10
5
^
O O O ป
Cannonsville
Schoharie
O
Pepacton
Ashokan
A
Neversink
Rondout
0
Cannonsville
Schoharie
Pepacton
Ashokan
A
Neversink
Rondout
0
1975 1985 1991 1998
Imagery Year
(b)
1.2
1
0.8
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Page - 39
Landscape Change Summary
Across all six watersheds there was a total of 2%
gain in forest cover. The majority of the change was
between agriculture land use and forest land cover,
with only a small portion of forest loss as a result of
urbanization. The increases in urban percentages
occurred during the period of greatest population
increase from the m id-1970s to the m id-1980s. A
majority of the forest increases were between the
mid-1980s and the early 1990s, with a further small
increase between the early 1990s and the late
1990s. The Cannonsville watershed had the
greatest number of subwatersheds showing a net
gain in forest cover percentages, while the Ashokan
was the only watershed to have an overall loss in
forest cover with time. All but one of the
subwatersheds in the Ashokan lost forest cover
during the past two decades. Most of the losses in
the Ashokan were the result of increased urban
development between the mid-1970s and the mid-
1980s and increased agriculture land use between
the mid-1980s and the early 1990s. In general,
changes occurring in the riparian buffer parallel
watershed and subwatershed results. Forest
cover gains in the subwatershed riparian buffers
ranged between 1 and 14% and are mostly the
result of shifts from agriculture to forest. Riparian
forest losses ranged between 0 and 3%, with the
highest losses occurring in the Ashokan
subwatershed buffer.
Tributary of the East Branch Delaware River in the Pepacton.
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Page - 40
Chapter 6. Surface Water Quality
A large portion of the water collected in the
reservoirs of the CD watersheds is supplied by
surface water runoff. The biophysical setting within
the watershed influences the quantity and quality of
surface water entering the streams and reservoirs
(Herlihyetal., 1998). The rate of water runoff
depends on properties such as forest, slope, and
water-holding capacity (Nash etal., 1992 and 1999).
Therefore, amounts of surface water total nitrogen,
phosphorus, and fecal coliform bacteria are
expected to be strongly affected by topography, soil,
and vegetative cover (Slaymaker, 2000). In this
chapter, spatial and temporal variation of the three
measurements of water quality are examined. An
average across the most recent five years of water
data (1994 -1998) at all water sample site is used
for spatial estimates. Temporal patterns of fecal
coliform bacteria, total nitrogen and total
phosphorous, discharge, and precipitation are
determined using 8 to 10 years of data.
Spatial Variation
Like many of the landscape metrics, water quality
measurement averages (1994-1998) of fecal
coliform bacteria, total nitrogen, and total
phosphorus are highest in the northwest and lowest
in the southeast in the CD watersheds (Figures
6.1 a, b, and c). The lowest average concentrations
of total nitrogen, phosphorus, and fecal coliform
bacteria counts are found within the Catskill Park
boundary and other areas of low human use (Figure
2.3b). Median fecal coliform bacteria counts ranged
from 0 to 200 CFU/100 ml. Maximum fecal coliform
bacteria counts are sometimes greater than 10,000
CFU/100 ml at sites in the Ashokan, Cannonsville,
Pepacton, and Schoharie watersheds (Table D-3).
Sites having the highest average, median, and
maximum total nitrogen content are located on the
West Branch Delaware river in the Cannonsville
watershed. Three sites in the Cannonsville
watershed have greaterthan 1.5 mg/L median total
nitrogen concentrations and are located on the
upper portion of the West Branch Delaware river
(Figure 6.1 b). Total phosphorus median
concentration values ranged from 3 to 111 i/g/L
across the watersheds. Similar to total nitrogen, the
highest phosphorus average, median, and maximum
values are found in the Cannonsville and Schoharie
watersheds (Table D-1).
In general the average and median total nitrogen,
phosphorus, and fecal coliform bacteria did not
exceed state and federal surface water standards.
However, in watersheds having the most human use
(i.e., Cannonsville, Schoharie, and Pepacton), afew
water sampling sites have maximum values that
approach or slightly exceed established standards.
Often these sites are located downstream of point
sources, such as sewage treatment facilities, dairy
farms, and landfills. The NYCDEP monitors both
upstream and downstream of treatment plants to
determ ine general effectiveness of each treatment
plant (Figure 6.2). Furthermore under the MOA the
NYCDEP is committed to upgrading all wastewater
treatment plants in order to meet phosphorus effluent
discharge limits and remove the presence of
protozoan pathogens.
Over 70% of the monitored point source sites have
greater median nutrient concentrations and fecal
coliform bacteria counts downstream. Upstream
and downstream differences are greatest in the
Cannonsville and Schoharie watershed sites for all
three water parameters (Table 6.1). Differences in
median values for the selected treatment plant sites
in each watershed ranged from 0.08 to 0.38 mg/L
nitrogen, 6 to 82 i/g/L phosphorus, and -8 to 20
CFU/100 ml fecal coliform bacteria. These results
suggest that until treatment plant upgrades are
implemented by the NYCDEP, nutrients and fecal
coliform bacteria contributions from effluent will
continue to be a problem for a number of streams in
the CD watersheds.
Temporal Variation
Climate in the CD watersheds includes mild
summers and cold winters. Yearly precipitation
(rainfall and snowfall) can average as much as 1650
mm (65 in.), with snowfall accounting for up to 18%
of the yearly total (Murdoch and Barnes, 1996).
Overtime, precipitation rates vary, and in turn
influence discharge, surface water runoff, nutrients,
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Page - 41
Median
Fecal Coliforms CFU/100 ml)
0- 42
42-81
81 -121
121-160
160-200
A Point Source
Cannonsville
(a)
(b)
Median
Total Nitrogen (mg/L)
0-0.5
0.5-0.8
0.8-1.1
1.1 -1.5
1.5-1.8
A Point Source
Ashokan
Rondout
Median
Total Phosphorus (ug/L)
3-24
24-46
46- 67
67- 89
89-111
A Point Source
Figure 6.1. Median (1994-1998) (a) fecal
coliform bacteria, (b) total nitrogen, and (c)
total phosphorus within the Catskill/Delaware
subwatersheds.
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Page - 42
(a)
Median
Total Nitrogen (mg/L)
0-0.5
0.5-0.8
0.8-1.1
1.1 -1.5
1.5-1.8
A Point Source
Schoharie
Ashokan
Rondout
(b)
D)
E 3
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Page - 43
Table 6.1. Mean and Median Total Nitrogen, Total Phosphorous, and Fecal Coliform Bacteria (1994-1998) in
Surface Water Sample Sites Upstream and Downstream of Sewage Treatment Plants in the Catskill/Delaware
Watersheds
Total Nitrogen
(mg/L)
Watersheds*
Ashokan
Cannonsville
Pepacton
Rondout
Schoharie
Site
E3
E15
WSPA
WSPB
PMSA
PMSB
RGA
RGB
S1
S2
Stream Location
Upstream
Downstream
Upstream
Downstream
Upstream
Downstream
Upstream
Downstream
Upstream
Downstream
Mean
0.27
0.36
0.94**
1.39
0.37
0.50
0.35
0.44
0.35
0.73
Median
0.29
0.37
0.92
1.30
0.38
0.49
0.36
0.44
0.36
0.60
Total Phosphorus
(ug/L)
Mean
15.95
28.57
31.17
102.76
16.85
27.87
12.37
17.96
14.71
48.20
Median
14.00
23.00
28.00
110.00
15.00
25.00
11.00
17.00
11.00
37.00
Fecal Coliform B
(CFU/100ml)
Mean
14.38
19.42
94.76
197.44
93.30
75.80
82.00
97.76
30.19
47.23
Median
6.00
10.00
20.00
40.00
28.00
20.00
24.50
22.00
4.00
12.00
there are no sewage treatment plants with up
' red color = close to or exceeding federal and
and downstream monitoring in the Neversink watershed
state surface water standards
and fecal coliform bacteria input to streams.
Examining long-term precipitation and surface water
measurements provides a picture of trends and
changes over time.
Rainfall and Discharge
The average monthly rainfall in the CD watersheds
from 1987 through 1998 ranges between 79 and 112
mm (3.1 and 4.4 in.) with the highest monthly rainfall
average occurring in the Neversink watershed (Table
6.2). Variation in the amount of rainfall is random
and does not change with time at any of the six rain
gauge sites selected for temporal analysis.
The highest average monthly discharge occurs at the
stream gauge in the Ashokan watershed. However,
the widest range of discharge occurs at the stream
gauge in the Cannonsville watershed. In contrast to
rainfall, a significant 12-month cyclical pattern occurs
in discharge with time at all six stream gauges
selected for temporal analysis (Figures D-1, 3, 5, 7,
9, and 11). The maximum discharge measurements
are generally seen during the months of April and
May. Since discharge tends to be skewed by large
storm events the median values are lower than the
mean.
Peaks and depressions in monthly discharge are
synchronized with rainfall (Figures D-1, 3,5,7, and
11). Cross correlation between discharge and
rainfall indicates that discharge has an immediate
response to rainfall. The instantaneous affect of
rainfall on discharge suggests that flow and
precipitation sample sites are sufficiently close
together to insure that distance between sites is not
impacting the relationship between rainfall and
discharge.
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Page - 44
Table 6.2. Descriptive Statistics for Monthly Precipitation(1987-1998), Discharge (1987-1998), Total Nitrogen
(1990-1998), Total Phosphorus (1990-1998), and Fecal Coliform Bacteria (1987-1998) at Select Surface Water
Sample Sites in the Catskill/Delaware Watersheds
Watershed
Ashokan
Variable
Precipitation (mm)
Discharge (ff/sec)
Total Nitrogen (mg/L)
Total Phosphorus (ug/L)
Fecal Coliform (CFU/100ml)
Mean
101.09
735.22
0.17
11.44
28.22
Median
101.60
517.00
0.17
10.00
4.00
Minimum
10.41
149.47
0.02
6.00
2.00
Max
262.38
2,927.60
0.60
30.00
210.00
Cannonsville
Neversink
Pepacton
Rondout
Schoharie
Precipitation (mm)
Discharge (ft3/sec)
Total Nitrogen (mg/L)
Total Phosphorus (ug/L)
Fecal Coliform (CFU/100ml)
Precipitation (mm)
Discharge (ft3/sec)
Total Nitrogen (mg/L)
Total Phosphorus (ug/L)
Fecal Coliform (CFU/100ml)
Precipitation (mm)
Discharge (ft3/sec)
Total Nitrogen (mg/L)
Total Phosphorus (ug/L)
Fecal Coliform (CFU/100ml)
Precipitation (mm)
Discharge (ff/sec)
Total Nitrogen (mg/L)
Total Phosphorus (ug/L)
Fecal Coliform (CFU/100ml)
Precipitation (mm)
Discharge (ft3/sec)
Total Nitrogen (mg/L)
Total Phosphorus (ug/L)
Fecal Coliform (CFU/100ml)
93.22
583.06
0.99
31.49
86.21
112.78
195.08
0.31
5.61
8.52
85.60
54.67
0.42
10.34
27.86
97.79
103.80
0.32
7.06
23.13
79.25
49.02
0.25
13.21
80.46
81.79
326.00
0.92
27.00
20.00
100.58
117.00
0.29
4.00
3.00
84.07
34.00
0.36
8.00
7.00
91.69
64.00
0.30
5.00
8.00
76.20
23.00
0.24
11.00
16.00
9.40
27.58
0.43
11.50
1.50
3.05
2.47
0.13
2.00
1.00
5.59
8.86
0.07
2.00
1.00
2.03
1.60
0.03
5.00
1.00
224.03
2,756.60
1.82
86.50
853.33
12.70
19.26
0.12
2.00
1.00
259.33
898.77
0.86
107.00
78.33
213.36
257.73
0.91
127.67
302.00
271.53
442.77
0.88
98.00
404.00
261.87
296.57
0.51
36.00
2,816.25
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Page - 45
Total Nitrogen
The Cannonsville sample site has the highest
monthly mean, median, minimum, and maximum
nitrogen valueforthe sampling period (Table 6.2).
The Pepacton site had the second highest recorded
mean, median, and maximum monthly nitrogen value.
The average monthly nitrogen values at the other
water chemistry sample sites range between 0.17
and 0.32 mg/L. The median values are similar to the
means suggesting a fairly evenly distributed set of
data.
Trends analyses for four of the six water chem istry
sites indicate an overall decrease in monthly nitrogen
values since 1990 (Figures D-2, 8, 10, and 12).
However, no significant change in time took place at
the Ashokan and Neversink sites. The rate of
change in nitrogen at these sample sites is slight and
remains near the average throughout time.
A12-month cyclic pattern in monthly total nitrogen is
present at all six water chem istry sample sites, with
maximum values generally occurring during the
winter and spring months (Appendix D-1, 3, 5, 7, 9,
and 11). The Ashokan water chemistry sample site
was the only site that didn't show an immediate
nitrogen concentration response to greater
discharge. Nitrogen concentrations at the other five
sites respond quickly to changes in discharge,
suggesting that nitrogen contributions from the
surrounding landscape are expected to increase
during high rainfall and snowmelt events.
Total Phosphorus
The Cannonsville water chem istry sample site has
the highest mean and median monthly total
phosphorus (31.49 i/g/L) concentrations, which are
more than twice those of the other five sites (Table
6.2). The Ashokan and Schoharie site had the
second highest average total phosphorus
concentration (11.44 and 13.21 i/g/L). The lowest
average monthly phosphorus concentration values
are at the Neversink and Rondout sites. Median total
phosphorus values are only slightly lower than mean
values and the relative ranking of the water chem istry
sites is the same as for the means.
Total phosphorus concentration significantly
increases over time at the Ashokan and Schoharie
sample sites (Figures D-2 and 12). However, the
monthly total phosphorus concentrations at the
Cannonsville and Neversink watershed sites
decrease (Figures D-2 and 6). The remaining water
chemistry sample sites did not show any significant
trends in time.
Time series analyses indicated no significant cyclic
pattern in monthly total phosphorus at any of the six
water chemistry sample sites. There is a slight delay
in response (1 to 2 months) of phosphorus
concentrations to discharge at the Schoharie and
Ashokan sites. At the site in the Cannonsville
watershed there is an immediate total phosphorus to
discharge response (Figure D-1). The other three
sites did not show any significant response to
discharge. The lack of a consistent response to
discharge suggests that monthly total phosphorus
concentrations were less tightly coupled to surface
water runoff than total nitrogen.
Fecal Coliform Bacteria
Monthly fecal coliform bacteria counts over the
sampling period are highest at the Cannonsville site,
with the widest range of values at the Schoharie
sample site. The average and maximum monthly
counts at the Neversink site are more than two times
lower than the other five sites. Like discharge data,
the fecal coliform bacteria counts peak a few times a
yearwith the majority of the counts being lower. This
type of skewed data results in the lower median
values seen in Table 6.2.
The only site to show any significant decreasing trend
in monthly fecal coliform bacteria counts is the one
located in the Schoharie watershed (Figure D-12). A
slight negative slope can be seen at the otherfive
sites, however the trend is not significant.
Only the Ashokan and Neversink sample sites have a
significant 12-month cyclic pattern (Figures D-3 and
5). However, all the watershed sample sites have
highervalues of surface waterfecal coliform bacteria
-------
Page - 46
during the summer months (e.g., July and August)
and lower values in winter (November and
December). Fecal coliform bacteria have a delayed
response (1 to 5 months) to discharge in all but the
Schoharie sample site, which did not respond to
changes in discharge. These results suggest a
potential dilution effect in spring followed by higher
reproduction rates in the warm summer months when
discharge is low.
Water Quality Summary
Average monthly measurements of fecal coliform
bacteria, total nitrogen, and total phosphorus appear
to be greatest in the northwest watersheds where
human use is higher and least in the southeast
watersheds where human use is lower. Point source
contributions are influencing downstream sample
sites by increasing nutrient concentrations and, to a
lesser degree, fecal coliform bacteria counts.
There is an overall decreasing trend in monthly total
nitrogen concentrations with time at four of the six
water chemistry sample sites selected for temporal
analysis. There doesn't appear to be any consistent
trend in monthly total phosphorus concentrations. The
Cannonsville sample site, which has the highest
average nutrient concentrations, is the only site
where a decreasing trend over time is observed for
both total nitrogen and phosphorus. Fecal coliform
bacteria counts are highest in the warm summer
months for all sample sites and did not change over
time at five of the six sample sites. Only the
Schoharie watershed sample site has a significant
decreasing trend with time in fecal coliform bacteria.
Total nitrogen concentrations have a strong 12-
month cyclical pattern and an instant response to the
rate of discharge. Maximum values are often seen
during the spring and winter months. The relationship
between peak total nitrogen levels and discharge
suggests that a greater contribution from surrounding
landscape occurs as a result of increases in surface
runoff during high rainfall and snowmelt. Total
phosphorus and fecal coliform bacteria are less
influenced by discharge and surface water runoff than
total nitrogen. There is, however, a slight seasonal
effect on fecal coliform bacteria with higher values
occurring during the summer months (July and
August).
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Page - 47
Chapter 7. Landscape and Water Relationships
An imprint of landscape condition is collected and
transported to the streams via surface runoff. The
impact of land cover and use can be seen in the
measurements of nutrient concentrations and fecal
coliform bacteria counts. The previous chapters
present an overview of spatial and temporal aspects
of landscape and water parameters. This chapter
focuses on relationships between landscape and
water quality data within the 32 EPA delineated
subwatersheds within the CD watersheds (Figure
2.8). The following subsections discuss regression
analyses on the mid-1980s, early 1990s, and late
1990s data, as well as trends across the three time
periods.
Regression Models
The riparian metrics are highly correlated with whole
watershed metrics and were therefore eliminated
from the regression. The forest cover metric was
also eliminated, since in the CD watersheds the
percent of forest is simply the inverse of the
percentage of agriculture and other land uses make
up only a small percentage of the area. Of the
remaining landscape metrics calculated, multiple
regression analyses for total nitrogen, total
phosphorus, and fecal coliform bacteria indicated
seven that are significant to the final models. In
general, metrics in the final model which are an
estimate of land use are positively related to water
quality measurements (Table 7.1). Therefore, the
greater the percentage of land use in the watershed,
the more total nitrogen, total phosphorus, and fecal
coliform bacteria present in the surface water. Two
measurements of land use that are positively related
to water quality, and consistently present in all three
models, are percent agriculture and percent urban
development. The combined effect of these two land
uses strongly influences water quality measurements,
explaining between 25 and 75% of the model
variation (Partial R2).
By examining the magnitude of the coefficients (B),
an indication of how contributions of a particular land
use change between time periods can be
determined. For example, the contribution of
percent agricultural land use to each of the surface
water quality measurements decreases with time
from the m id-1980s to the late 1990s. Three land
use measurements having for the most part a
weaker positive relationship to water quality and
explaining only 3 to 46% of the variability are percent
barren, percent agriculture on steep slopes, and
percent agriculture on erodible soils in the
subwatersheds. The inclusion of these metrics in the
regression models indicates that land uses which
affect the rate of erosion, also affect concentrations
of total nitrogen and total phosphorus and counts of
fecal coliform bacteria in surface water. The only
metric consistently having a negative relationship to
measurements of surface watertotal nitrogen, total
phosphorus, and fecal coliform bacteria was stream
density. The negative value of the stream density
metric most likely reflects the affect of water volume
flowing through the streams. As stream density
increases, the quantity of water reaching a site
increases, diluting nutrient concentrations.
Total Nitrogen
Since total nitrogen measurements did not begin
until 1990, the regressions were run for only the early
1990s and late 1990s time periods. The landscape
measurements in the nitrogen regression model are
strongly related (79%) to surface watertotal nitrogen
concentrations (Table 7.1). Stream density, percent
agriculture, and percent urban land use are the
dominant landscape metrics in the subwatersheds
for both time periods. More than half of the nitrogen
variability is explained by the percentage of
agriculture land use in the subwatersheds. However,
the contribution of agriculture and urban land use, as
indicated by the magnitude of their coefficients (I3>),
decreases with time. The relationship between
stream density and total nitrogen concentration
indicates that subwatersheds having greater stream
mileage per hectare would be expected to have a
lower average total nitrogen. The other two land
uses which are significant, but explain only small
amounts of the variability in the average total
nitrogen concentration data, are percent agriculture
on erodible soils and percent barren within the
subwatersheds. In the early 1990s the percentage of
-------
Page - 48
Table 7.1. Regression Model Estimates (B), Partial R2 and Model R2 for Landscape Metrics and Surface
Water Total Nitrogen, Total Phosphorus, and Fecal Coliform Bacteria for mid-1980s, early 1990s, and late
1990s
Mid-1980s Early 1990s Late 1990s
B Partial R2 B Partial R2 B Partial R2
Regression Models
Log Total Nitrogen
Stream Density - - 0.921 9.6 0.840 7.2
Agriculture - - 0.046 59.3 0.039 64.9
Urban - - 0.312 6.2 0.256 4.0
Ag. on Erodible Soil - - 0.182 4.3
Barren - - - - 1.018 3.0
Model R2 79.4 79.1
Log Total Phosphorous
Stream Density - - 0.574 3.0 0.928 7.0
Agriculture 0.052 50.5 0.047 69.5 0.032 43.1
Urban - - 0.233 4.3 0.362 5.4
Ag. on Erodible Soil - - - - 0.426 7.6
Model R2 50.5 76.8 63.1
Log Fecal Coliform Bacteria
Erodible Soil 0.271 16.6 0.206 8.5 0.132 3.3
Urban 0.409 15.9 0.428 10.5 0.389 12.2
Agriculture 0.043 31.0 0.048 48.4 0.046 12.7
Ag. onSlopes >15% - - 1.099 5.1 1.494 46.1
Model R2 63.5 72.5 74.3
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Page - 49
agriculture on erodible soil has a weak relationship
to total nitrogen and by the late 1990s it is not
included as part of the model. The percent of barren
in the subwatersheds was only important in the late
1990s nitrogen model. Those subwatersheds
having the highest amount of barren land cover have
a greater amount of total nitrogen in the stream.
Total Phosphorous
As in the case of average total nitrogen, the
percentage of agriculture in the subwatersheds has
the strongest relationship to total phosphorus
concentrations in all three time periods, explaining
43 to 70% of the variability. In the mid-1980s the
percentage of agriculture in the subwatersheds is
the only variable with a strong relationship to total
phosphorous (51 %). However, from mid-1980s to
late 1990s the influence of percent total agriculture in
the model (B) decreases and other metrics, such as
percent agriculture on erodible soils, stream density,
and urban development make a more significant
contribution. In the early and late 1990s, stream
density, percent agriculture, and urban development
in the subwatersheds explain more than a half of the
variability in total phosphorus concentration. Afourth
metric, percentage of the subwatersheds having
agriculture on erodible soil, explains an additional
8% of the variability in the late 1990s model.
Fecal Coliform Bacteria
There are four significant measures of land cover
and land use included in the fecal coliform bacteria
model. These landscape measurements have a
strong relationship to fecal coliform bacteria counts
explaining 64 and 74% of the variation in the data.
Fecal coliform bacteria is positively related to
percent erodible soil, urban development and
agriculture within the subwatersheds. Unlike total
nitrogen and phosphorous, the influence (B) of
percent urban and percent agricultural in the fecal
coliform bacteria model remains the same across
time periods. However, the total model variability
explained by percent agriculture within the
subwatersheds ranges from 13 to 48%. In the early
1990s fecal coliform bacteria responded positively
to the percentage of agriculture on slopes greater
than 15% within the subwatersheds. The amount of
variability percent agriculture on very steep slopes
explains increases from 5 to 46% between the early
and late 1990s. The overall contribution of this
metric, as indicated by the larger coefficient, also
increases with time.
Model Application
Using the late 1990s regression models, an
estimate was made of potential total nitrogen,
phosphorus, and fecal coliform bacteria
contributions for all 79 subwatersheds based on the
late 1990s land cover (Figure 7.1). The spatial
distribution of human use is the most important
factor affecting the maps of watershed pollution
potential. The highest level of estimated nutrients
and fecal coliform bacteria are located within
Cannonsville subwatersheds. The West Branch
Delaware River subwatershed has the greatest
fecal coliform bacteria and total phosphorus
measures due to the influence of the percentage of
urban and agriculture on slopes >15% within the
subwatershed. A similar effect of urban land use on
fecal coliform bacteria and phosphorus can be seen
in the lower ranking of the Ashokan subwatersheds.
The subwatersheds around the Cannonsville
Reservoir have the highest nitrogen content as a
result of the high percentage of transitional land
upstream of the lake.
The accuracy of applying stepwise regression
models to other subwatersheds was tested by
exam ining water sample data from four sites not
used to develop the models. The observed
nitrogen, phosphorus, and fecal coliform bacteria
means from the new sites are all within the 95%
confidence intervals of predicted values from
subwatersheds having comparable landscape
metrics (Table 7.2; Figure 7.2).
-------
Page - 50
West Branch
Delaware River
(a) Fecal Conforms (CFU/100 ml)
13.7
29.6
49.7
75.9
-29.6
-49.7
-75.9
-175.4
175.4-296.4
(b) Total Nitrogen (mg/L)
0.16-0.36
0.36-0.58
0.58-0.96
0.96-1.70
1.70-4.90
Ashokan
Rondout
(c) Total Phosphorus (tvg/L)
I 15.3-23.6
ZD 23.6-34.6
^l 34.6-64.1
^H 64.1 -107.3
Figure 7.1. Predicted average (late 1990s) (a) fecal
coliform bacteria, (b) total nitrogen and (c) total phosphorus
within the Catskill/Delaware subwatersheds based on the
regression models.
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Page - 51
Table 7.2. Average Observed Total Nitrogen (TN), Total Phosphorus (TP), and Fecal Coliform Bacteria (FC) from
Four Surface Water Sample Sites not used in the Landscape Models Compared with Model Predicted Upper and
Lower 95% Confidence Interval (Cl) Values from Subwatersheds having Similar Land Cover Percentages
Model Site
Lower 95% Cl
Upper 95% Cl
New Site
Observed
-mg/L-
TN log(TN) TN log(TN)
mg/L
TN log(TN)
BRD*
C-38
E12I
NK7A
BRD
C-38
E12I
NK7A
0.13
0.63
0.16
0.13
TP
3.82
1.27
6.49
1.13
2.03
0.47
1.82
2.01
log(TP)
1.34
0.24
1.87
0.12
0.42
2.23
0.69
0.43
-ug/L--
TP
22.87
104.58
59.74
21.54
0.87
0.80
0.37
0.85
log(TP)
3.13
4.65
4.09
3.07
NWBR**
CWB
SCL
NEBR
NWBR
CWB
SCL
NEBR
0.29
0.92
0.30
0.25
TP
3.91
34.85
20.49
3.50
1.24
0.08
1.20
1.39
-ug/L-
log(TP)
1.36
3.55
3.02
1.25
CFU/100ml
FC log(FC) FC log(FC)
CFU/100ml
FC log(FC)
BRD
C-38
E12I
NK7A
4.10
55.70
4.57
4.53
1.41
4.02
1.52
1.51
47.94
796.32
52.98
52.46
3.87
6.68
3.97
3.96
NWBR
CWB
SCL
NEBR
9.48
347.76
21.09
8.64
2.25
5.85
3.05
2.16
The four model sites and their corresponding subwatershed locations can be seen in Figure 3.2.
' The four new sites and their corresponding subwatersheds are NWBR (West Branch Neversink River), CWB(Wright Brook),
SCL (Stony Clove Creek), NEBR (East Branch Neversink River); their location within the Catskill/Delaware watersheds can
be seen in Figure 2.8.
-------
Page - 52
1
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"* '
-2
2 5
(a) A
O
T *
A A
o ฐ o
T
T T
T Lower Cl
A Upper Cl
O New Site
NWBR CWB SCL NEBR
5
>
o 4
.c
Q.
ฐ 3
CL
"ro
o 2
\
Q
(b) *
A
0
A o A
T
ฎ o
T T
T Lower Cl
A Upper Cl
O New Site
NWBR CWB SCL NEBR
ซ 7
tj 6
CO
E 5
ฃ
0 4
"ro
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Page - 53
Trends in Water and Landscape
The general direction of change in surface water
nitrogen, phosphorus, fecal coliform bacteria, and
landscape metric percentages with time indicates
that those land uses shown to be significant for
single-point-in-time comparisons (i.e., the late
1990s image data compared with 1994 -1998
water data) are also important to change through
time (mid-1980s to late 1990s) comparisons of
water and landscape data.
Decreasing trends through time of percent
agriculture land use and increasing trends through
time of percent forest cover within the
subwatersheds tend to coincide with decreasing
total nitrogen concentrations (Table 7.3). In five
subwatersheds total nitrogen decreases, while
percent agriculture increases and percent forest
decreases. However, in three of these
subwatersheds, percent agriculture on erodible or
sloped soils has decreased, suggesting the
possibility of decreased nutrient runoff to streams
from these types of farm fields (Table 7.4).
From 1990 to 1998 only four water chem istry
sample sites showed a decreasing trend in total
phosphorus concentration with time (Table 7.3). In
these four subwatersheds there is a decrease in the
percentage of total agriculture and an increase in
percent forest cover. In all but one of these
subwatersheds there was also an increase in
riparian forest cover and a decrease in the amount
of agriculture on sloped soils. Nine sites had slight
increasing trends in total phosphorus which appear
to be related to greater percentages of human use,
particularly in the riparian buffer.
As seen in the regression analyses, fecal coliform
bacteria trends across time appear to be related to
changes in human use practices and their location
within the subwatersheds. In subwatersheds having
significant increases in fecal coliform bacteria levels
with time, there are also increasing trends in the
percentage of agriculture on erodible soils, slopes
>15%, and in the riparian zone within the
subwatersheds.
Relationship Summary
Landscape metrics that have a strong positive
relationship with concentrations of total nitrogen,
total phosphorus, or fecal coliform bacteria are
percent urban and total agriculture within the
subwatersheds. These two land use measurements
also show up as being important in an assessment
of trends with time. The smaller contribution of
percent agriculture to surface water nutrient
concentrations in the late 1990s regression is
reflected in the percent forest cover gains and
agriculture losses through time. However, in a few
subwatersheds changes in land use within the 60-
and 120-m riparian buffer zones appear to be more
related to trends in water quality.
Stream density was the only landscape
measurement included in the regression models
with an inverse relationship to all three water quality
measurements. As the number of streams per area
increases, the amount of waterflowing past the
sampling point increases resulting in a dilution of
surface water nutrients and fecal coliform bacteria.
Three other metrics having a slight positive
relationship with water quality measurements in the
regression models are percent bare ground,
percent agriculture on slopes >15%, and percent
erodible soils within the subwatersheds. The
association between trends in time of landscape
percentages and total nutrients concentration data
was less obvious then in the regression. However,
trends in fecal coliform bacteria and percentage of
human use within the watershed show a similar
pattern to that seen in the regression models.
Despite decreasing trends at a majority of the water
chemistry sample sites in the northwest CD
watersheds (Cannonsville, Pepacton, and
Schoharie), predicted levels of total nitrogen, total
phosphorus, and fecal coliform bacteria within these
subwatersheds are higher than those in the
southeast as a result of the greater percentage of
human use.
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Page - 54
Table 7.3. Trends in Total Nitrogen (1990-1998), Total Phosphorus (1990-1998), Fecal Coliform Bacteria
(1987-1998), and Landscape Metrics (1987-1998) in 32 Catskill/Delaware Subwatersheds
Watershed
Ashokan
Ashokan
Ashokan
Ashokan
Ashokan
Ashokan
Ashokan
Ashokan
Cannonsville
Cannonsville
Cannonsville
Cannonsville
Cannonsville
Neversink
Neversink
Pepacton
Pepacton
Pepacton
Pepacton
Pepacton
Pepacton
Pepacton
Rondout
Rondout
Rondout
Rondout
Rondout
Schoharie
Schoharie
Schoharie
Schoharie
Schoharie
Site
bk
bnv
brd
e1
e10i
e12i
Ibk*
wdl
c-38
c-7*
c-79
c-8
wdhoa
nk6
nk7a*
p13
P21
p50
p52
p60*
P7
P8
rd1
rd4
rdoa*
rga
rk
fb4
s1
s10
s6i
s7i*
TN
TP
FC
FOR
AGT
ERD
SL5
SL10
SL15
URB
BAR
U_IN
* = sites also used in time series cross-correlation analysis with discharge and precipitiation; green = positive change (i.e.,
increasing forest cover, decreasing land use, decreasing nutrient concentrations, and decreasing fecal coliform bacteria counts),
gold = negative (i.e., decreasing forest cover, increasing land use, increasing nutrient concentrations, and increasing fecal
coliform bacteria counts), grey = no change; TN =Total Nitrogen; TP=Total Phosphorus; FC= Fecal Coliform Bacteria;
FOR=Forest; AGT=Agriculture; ERD=Agriculture on Erodible Soils; SL5, SL10, and SL15=Agriculture on 5%, 10%, and 15%
slope; URB=Urban; BAR=Barren; U_IN= U-lndex.
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Page - 55
Table 7.4. Trends in Total Nitrogen (1990-1998), Total Phosphorus (1990-1998), Fecal Coliform Bacteria
(1987-1998), and Riparian Landscape Metrics (1987-1998) in 32 Catskill/Delaware Subwatersheds
Watershed
Ashokan
Ashokan
Site
FOR
60m
bk
bnv
ACT
60m
URB
60m
BAR
60m
U_IN
60m
FOR
120m
AGT
120m
URB
120m
BAR
120m
U_IN
120m
Ashokan
brd
Ashokan
e1
:
Ashokan
e10i
Ashokan
e12i
Ashokan
Ibk
Ashokan
wdl
Cannonsville
c-38
Cannonsville
c-7
Cannonsville
c-79
Cannonsville
c-8
Cannonsville
wdhoa
Neversink
nk6
Neversink
nk7a'
Pepactpn
p13
Pepacton
p21
Pepacton
p50
Pepacton
p52
Pepacton
p60
Pepacton
Pepacton
p8_
Rondout
rd1
Rondout
rd4
Rondout
rdoa'
Rondout
rga
Rondout
rk
Schoharie
fb4
Schoharie
s1
Schoharie
s10
Schoharie
s6i
Schoharie
s7i
* = sites also used in time series cross-correlation analysis with discharge and precipitiation; green = positive change (i.e.,
increasing forest cover, decreasing land use, decreasing nutrient concentrations, and decreasing fecal coliform bacteria counts),
gold = negative (i.e., decreasing forest cover, increasing land use, increasing nutrient concentrations, and increasing fecal
coliform bacteria counts), grey = no change; TN =Total Nitrogen; TP=Total Phosphorus; FC= Fecal Coliform Bacteria;
FOR=Forest; AGT=Agriculture; URB=Urban; BAR=Barren; U_IN= U-lndex.
-------
Chapter 8. Conclusion
Page - 56
This final chapter provides a synopsis of the
landscape and water quality results. A summary of
land use metric percentages and trends and how
they are related to water quality is presented. The
summary section is followed by a set of
recommendations that have been developed based
on the results from this assessment and with regard
to current and proposed future management
practices.
Summary
Region 2 hydrologic units surrounding the CD
watersheds are in excellent environmental condition.
The forest cover in these HUCs is high and land use
is minimal (30% total agriculture, 15% urban;
Figures 4.2 and 4.8). In the smaller CD
subwatersheds agriculture land use percentages
range from 0 to 35% (Figure 4.3). However, due to
low population growth rates, percentages of urban
development in the CD subwatersheds only reach
3.7%. Percentages of riparian land use at the
regional scale are slightly lower than in the CD
watersheds and have a smaller range than the
subwatersheds. Agriculture and urban land use
make up from about 0 to 47% of the 60-m riparian
buffer in the CD subwatersheds (Table 4.2).
Water quality in the streams of the CD watersheds
remains high with only a few cases of exceedance of
federal surface water requirements. However,
despite the continued high quality of water in
the streams of CD watersheds, point source
(i.e., treatment plants) and nonpoint source
(near-stream land use) impacts to stream
condition remain a concern for New York City.
A recent mid-course report by the EPA
recommended that the city upgrade 34
sewage treatment plants and acquire more
"crucial" land during the years remaining
under the FAD (EPA, 2000).
In addition to inputs from waste treatment
plant facilities and land use, impacts to the
CD water supply watershed streams are also
related to terrain influences on runoff. The
steep slopes result in very rapid water flow
across the landscape and into the streams.
Therefore, nitrogen in the surrounding landscape will
be carried quickly in runoff to streams either in
solution or transported in the sediment. Stream total
phosphorus concentrations do not appear to
respond to rainfall-induced increases in discharge
as rapidly as nitrogen. This delay in response to
rainfall events suggests that base flow and ground
water play an important role in total phosphorus
contributions to the streams. Fecal coliform bacteria
levels in the streams do not respond to increases in
discharge from rainfall, but instead peak during the
warm summer months when watertemperature is
high, flow is low, and recreational and animal usage
is the greatest.
Much of the past research has investigated the
relationships between landscape and water quality
by examining water quality response to a
degradation in ecological condition. In this study we
have demonstrated that the same linkage between
landscape and water quality holds true under
improving ecological conditions. In the CD
watersheds, releasing agricultural fields from
farming has returned a small percentage (2%) of
land to secondary growth forest. With the exception
of a few subwatersheds the increase in forest cover
took place in the northwest. Since the majority of the
agriculture in the study area is located within 240 m
of a stream, much of the 2% change is located within
the riparian buffer.
Pepacton Reservoir, Pepacton watershed.
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Page - 57
Agriculture land use is the major contributor to
concentrations of total nitrogen and total phosphorus
in the streams, but its influence is reduced as
percentages within the watershed decrease. The
effect of decreasing agriculture and increasing forest
cover percentages is evident in the lower agriculture
contribution to surface water nutrient concentration
seen in the late 1990s regression analyses and in
the decreasing trends in total nitrogen across time at
many of the water chemistry sample sites. In
subwatersheds where there are no trends in
agricultural land use or forest cover, surface water
nutrient concentrations remain unchanged across
time.
Changes in total agriculture land use and forest
cover appear to have less influence on fecal coliform
bacteria trends. Fecal coliform bacteria are more
affected by percentage of agriculture land use on
slopes greater than 15% in the watershed. The
Cannonsville
influence of this type of land use on fecal coliform
bacteria increases as total agriculture percentages
decrease in the watershed. These results suggest
that nitrogen and phosphorus concentrations are
strongly related to land use proportions, while fecal
coliform bacteria counts are related more to land
use location within the watershed.
In general, application of the late 1990s regression
models demonstrated that the western watersheds,
which have the greatest percentage of human use,
would be expected to have higher stream total
nitrogen, total phosphorus, and fecal coliform
bacteria counts. A number of subwatersheds stand
out as being at risk from single or multiple land uses
(Figure 8.1). The landscape conditions in these
subwatersheds have a high potential for impacts to
waterquality.
Schoharie
Landscape Metrics Associated
with Surface Water Quality
n~rj] Lowest Percent Forest
[^7] Highest Percent Urban
E3 Highest Percent Agriculture on Slopes >15%
I I Highest Percent Agriculture
I I Highest Percent Agriculture on Erodible Soil
| | Highest Percent Bare Ground
^| Lowest Stream Density
Net Loss of Forest
Ashokan
Rondout
Figure 8.1. Catskill/Delaware subwatersheds having
landscape metrics associated with waterquality
degredation.
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Page - 58
Recommendations
Agriculture is the greatest human use of the land
occurring in the CD watersheds and one of the most
likely factors affecting water quality. Agricultural land
use can result in nonpoint pollution via runoff from
barnyards, pastures, and crop fields. Agricultural
practices can also lead to stream sedimentation by
increasing erosion rates. In response to potential
risks to the water supply, the Watershed Agriculture
Council began promoting whole farm planning. The
planning process is voluntary and implements farm-
specific best management practices (BMPs). Since
farming is important to the economic viability of the
area, continued education and enrollment of the land
owners in these types of programs offers an
attractive way of reducing nonpoint source pollution
to surface waters (Addiscott, 1997). However,
results from this study suggest that in addition to
farm-specific criteria, the Watershed Agriculture
Council may also want to consider gearing its
programs toward subwatershed specific needs.
Targeting the farms in an at risk subwatershed, may
achieve greater overall pollution reduction to the
water supply than random areawide enrollment. For
example, the subwatersheds of Third Brook and Elk
Creek have a high potential for pollution by nitrogen,
phosphorus, and fecal coliform bacteria (Figure 7.1).
Outreach in these subwatersheds m ight want to
focus on farms with cropping or pasture taking place
on steep slopes or erodible soils. Subwatersheds
having a low stream density and in close proximity to
a reservoir are more likely to contribute nutrients to
the reservoirs. Encouraging farmers within this type
of subwatershed to preserve wetland and riparian
areas through enrollment in wetland reserve and
forest easement programs would help buffer streams
and reservoirs from nutrient runoff impacts.
While comprising a much smaller percentage of the
CD watersheds than agriculture, urban land use
remains one of the key components in determining
water quality. The current regulations proposed in
the MOAfor improving exiting treatment plant
performance and restricting new waste treatment
plants should help reduce point source inputs in the
CD watersheds. However, in addition to waste
treatment plant inputs, high percentages of
impervious surfaces and agriculture have
increased discharge rates, sedimentation, and
pollutant runoff in a number of the subwatersheds.
Only after the current impacts are alleviated in the
at-risk subwatersheds can planning for future offset
needs be implemented.
An urban planning program that helps landowners
develop BMPs for golf courses, parks, backyard
gardens, and lawns could help address some of
the current impacts. Offsetting future land uses will
most likely require increasing the percentage of
forest cover, particularly in the riparian buffer. One
way to help promote more riparian forest is by
increasing the setback requirements for human
use from 30 to 60 or 120 m. Another
recommendation would be for the Watershed
Agriculture Council's Forestry Program to set up a
model forest in the riparian buffer of one of the
more urbanized areas. The study area would
provide an excellent opportunity for education
outreach and green space for the nearby
community.
Balancing water quality protection and economic
growth requires a great deal of thought,
coordination, and cooperation. Targeting
watersheds and farms for possible BMP
implementation depends on which pollutant is of
highest priority to the community. Numerous
groups depend on the water from the CD
watersheds for drinking, irrigation, recreational
use, and livestock production. As demonstrated by
the results of this study, human use of the
landscape has direct consequences on water
quality resources. Even changes as small as 2%
can have an effect. Whether or not the change is
beneficial to the quality of water in the CD water
supply rests on the choices made by those living in
the area. Economic and social incentives which
encourage forestry management, and agriculture
and urban planning for specific subwatershed
needs within the CD watersheds can help facilitate
the continued success of long-term watershed
management plans set forth in the MOA.
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