Total Maximum Daily Load (TMDL) for
Phosphorus in Findley Lake
Chautauqua County, New York
September 2008
Prepared for:
U.S. Environmental Protection Agency
Region 2
290 Broadway
New York, NY 10007
New York State Department of
Environmental Conservation
625 Broadway, 4th Floor
Albany, NY 12233
Environmental
Prepared by:
THE-
CADMUS
GROUP, INC.
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TABLE OF CONTENTS
1.0 INTRODUCTION 3
1.1. Background 3
1.2. Problem Statement 3
2.0 WATERSHED AND LAKE CHARACTERIZATION 4
2.1. History of the Lake and Watershed 4
2.2. Watershed Characterization 5
2.3. Lake Morphometry 9
2.4. Water Quality 10
3.0 NUMERIC WATER QUALITY TARGET 11
4.0 SO URGE ASSESSMENT 11
4.1. Analysis of Phosphorus Contributions 11
4.2. Sources of Phosphorus Loading 12
5.0 DETERMINATION OF LOAD CAPACITY 16
5.1. Lake Modeling Using the BATHTUB Model 16
5.2. Linking Total Phosphorus Loading to the Numeric Water Quality Target 16
6.0 POLLUTANT LOAD ALLOCATIONS 18
6.1. Wasteload Allocation (WLA) 18
6.2. Load Allocation (LA) 18
6.3. Margin of Safety (MOS) 20
6.4. Critical Conditions 19
6.5. Seasonal Variations 19
7.0 IMPLEMENTATION 21
7.1. Reasonable Assurance for Implementation 21
7.2. Follow-up Monitoring 24
8.0 PUBLIC PARTICIPATION 24
9.0 REFERENCES 25
APPENDIX A. AVGWLF MODELING ANALYSIS 27
APPENDIX B. BATHTUB MODELING ANALYSIS 41
APPENDIX C. TOTAL EQUIVALENT DAILY PHOSPHORUS LOAD ALLOCATIONS..46
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1.0 INTRODUCTION
1.1. Background
In April of 1991, the United States Environmental Protection Agency (EPA) Office of Water's
Assessment and Protection Division published "Guidance for Water Quality-based Decisions: The
Total Maximum Daily Load (TMDL) Process." In July 1992, EPA published the final "Water
Quality Planning and Management Regulation" (40 CFR Part 130). Together, these documents
describe the roles and responsibilities of EPA and the states in meeting the requirements of Section
303(d) of the Federal Clean Water Act (CWA) as amended by the Water Quality Act of 1987, Public
Law 100-4. Section 303(d) of the CWA requires each state to identify those waters within its
boundaries not meeting water quality standards for any given pollutant applicable to the water's
designated uses.
Further, Section 303(d) requires EPA and states to develop TMDLs for all pollutants violating or
causing violation of applicable water quality standards for each impaired waterbody. A TMDL
determines the maximum amount of pollutant that a waterbody is capable of assimilating while
continuing to meet the existing water quality standards. Such loads are established for all the point
and nonpoint sources of pollution that cause the impairment at levels necessary to meet the
applicable standards with consideration given to seasonal variations and margin of safety. TMDLs
provide the framework that allows states to establish and implement pollution control and
management plans with the ultimate goal indicated in Section 101 (a) (2) of the CWA: "water quality
which provides for the protection and propagation of fish, shellfish, and wildlife, and recreation in
and on the water, wherever attainable" (USEPA, 1991).
1.2. Problem Statement
Findley Lake (WI/PWL ID 0202-0004) is situated in the Town of Mina, within Chautauqua County,
New York. Over the past couple of decades, the lake has experienced degraded water quality that
has reduced the lake's recreational and aesthetic value. Recreational conditions in the lake have most
often been described as "slightly impaired" for most recreational uses, and the lake was also most
often described as having "definite algal greenness." Aquatic plants regularly grow to the lake
surface (though not in 2005), and continue to impact recreation. Recreational assessments generally
degrade during the summer, coincident with seasonal changes in water quality and weed densities-
both weeds and algae strongly influence recreation Lake productivity has been more variable in
recent years (lower in 2003 and 2004 and higher in 2005), although recreational assessments did not
substantially improve. This may be due to the continuing presence of nuisance weeds, although the
dominant plants may have changed from exotic plants (Eurasian water milfoil) to native plants (leafy
pondweed) (NYS DEC, 2006).
A variety of sources of phosphorus are contributing to the poor water quality in Findley Lake. The
water quality of the lake is influenced by runoff events from the drainage basin, as well as loading
from nearby residential septic tanks. In response to precipitation, nutrients, such as phosphorus
naturally found in New York soils drain into the lake from the surrounding drainage basin byway
of streams, overland flow, and subsurface flow. Nutrients are then deposited and stored in the lake
bottom sediments. Phosphorus is often the limiting nutrient in temperate lakes and ponds and can
be thought of as a fertilizer; a primary food for plants, including algae. When lakes receive excess
phosphorus, it "fertilizes" the lake by feeding the algae. Too much phosphorus can result in algae
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blooms, which can damage the ecology/aesthetics of a lake, as well as the economic well-being of
the surrounding drainage basin community.
Findley Lake is presently among the lakes listed on the 1999 Allegheny River drainage basin PWL,
with aesthetics, fishing and fish survival listed as impaired due to excessive nutrients, algae and weeds, and
reduced water clarity (NYS DEC, 2006). The results from state sampling efforts confirm eutrophic
conditions in Findley Lake, with the concentration of phosphorus in the lake exceeding the state
guidance value for phosphorus (20 ug/L or 0.020 mg/L, applied as the mean summer, epilimnetic
total phosphorus concentration), which increases the potential for nuisance summertime algae
blooms. In 2004, Findley Lake was added to the New York State Department of Environmental
Conservation (NYS DEC) CWA Section 303(d) list of impaired waterbodies that do not meet water
quality standards due to phosphorus impairments, but not designated as a "high priority for TMDL
development" (NYS DEC, 2004). Based on this listing, a TMDL for phosphorus is being developed
for the lake to address the impairment.
2.0 WATERSHED AND LAKE CHARACTERIZATION
2.1. History of the Lake and Watershed
Findley Lake was created by Alexander Findley in 1812 when damming the outlet of two ponds.
The two ponds originally formed from melting glacial ice about 16,000 or 17,000 years ago (Shermet
and Wilson, 2007). The dam is now controlled by the Findley Lake Property Owners, Inc. (Boria
and Wilson, 2002). Lake level is regulated using a mechanical gate in the spillway at the lake outlet.
Summer lake levels are maintained at about 1,420 feet above mean sea level (Boria and Wilson,
2002).
Findley Lake was sampled in 1937 as part of the Conservation Department (predecessor to NYS
DEC) Biological Survey of the Allegheny River basin. This survey showed oxygen deficits starting
at a depth between 15 and 20 feet from the lake surface. The field notes for the 1937 survey
included the following (NYS DEC, 2006):
"This, the westernmost lake in Neu> York State, is a very irregularly shaped body of water with numerous shallow bays and
several islands. The level is maintained by a dam at the north end. A large part of the south end is a shalkw area with flat
bottom covered with a thick growth of hornwort, watenveed, and bobbins pondweed. These plants cover almost the entire
bottom and apparently have been the most successful invaders of what was once a wooded area, as evidenced by the numerous
large submerged stumps. In this same weed bed are found many plants of the broad-leaved pondweed (P.amplifolius), of
najad and bladderwort, as well as the ubiquitous waterRRes and water shield. Akng the marshy shore, at the south end of
the lake, are extensive marshes of cattail and large floating masses of water smartweed. Other large weed beds were found at
the north end of the lake and along the east side.
Findley Lake has very poor bottom chemical conditions in the face of which it will be difficult if not impossible to improve
production by stocking akne. To form the present lake, an 8-foot dam was built across the outlet of two small ponds. The
total area of the two ponds was slightly more than half the area of the new lake. As a result about one-half of Findley Lake
is less than 10 feet deep. Within recent years this shallow area has become quite completely choked with vegetation. During
the summer this vegetation becomes so dense that only the tops are alive. In the lower levels where sufficient light fails to
penetrate, the vegetation is dead or dying. While green plants normally aerate the water, here so little of the plant actually is
green that stagnant conditions prevail on the bottom. It is not unusual for algal and rooted aquatic plant growths to become
sufficiently unpleasant although these growths seldom become sufficiently abundant to affect fish life adversely. The conditions
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in Findley Lake, however, leads one to conclude that vegetation may become so abundant as to be detrimental to fishing and
fish production.
bottom samples of water taken among the vegetation at a depth of 8 feet had only 0.4 parts per million of oxygen. In contrast to this in
deeper water where vegetation is lacking and when surface winds can mix the water more compktely, at a depth of 14 feet there wen
3.96 parts per million of oxygen at one station. At this same station below the plane of the 14-foot contour or in that areas not greatly
affected by surface winds, the oxygen dropped from 0.84 pans per million at 15 feet to 0.0 parts per million on the bottom at 31 feet.
from this it can be seen that among the vegetation the oxygen is less at 8feet than at almost twice the depth where the oxygen is lacking.
The bottom chemical conditions were inadequate for fish needs. A probably contributing factor is the natun of the bottom. Most of the
area flooded when the dam was built was low, muck land that in earlier times had probably been covered by natural ponds.
To remedy the condition here will not be easy. Weed elimination by chemical methods is out of the question for the present
since so far as is known, chemicals sufficiently strong to eliminate rooted vegetation on a large scale would kill all fish life.
Algal blooms in water supply reservoirs are controlled by chemical means but here it probably could not be done without some
harmful effect to fish life. Mechanical methods are the only safe means of removing rooted aquatic plants, laborious as the
task may be. Wood saws or rakes may be used for the purpose but it should be pointed out that the weeds should be
completely removed after they are cut for two reasons: (1) if left in the water to decompose and use up oxygen, the main
purpose of their destruction would be defeated and (2) since many aquatic plants reproduce asexually, more cutting is not
sufficient to stop their growth or to prevent them from spreading into other suitable areas. The process would have to be
repeated as often as necessary"
Findley Lake was sampled by NYS DEC as part of the state ambient lake monitoring program
(referred to as the Lake Classification and Inventory (LCI) Survey) in 1976 and 1985. Findley Lake
has been sampled as part of the NYS DEC Citizen Statewide Lake Assessment Program (CSLAP)
since 1986. Water quality monitoring in Findley Lake historically indicated that the lake possesses
relatively high nutrient (particularly phosphorus) readings, resulting in "slightly" to "substantially"
impaired recreational conditions (NYS DEC, 2006).
2.2. Watershed Characterization
Findley Lake has a direct drainage basin area of 3,000 acres excluding the surface area of the lake
(Figure 1). The topography of the basin exhibits irregularly-shaped rolling hills and valleys.
Elevations in the lake's basin range from approximately 1,742 feet above mean sea level (AMSL) to
as low as 1,420 feet AMSL at the surface of Findley Lake. The lake lies atop the Allegheny Plateau
located south of and above the Lake Erie Plain (Boria and Wilson, 2002). The basin's uplands are
composed of many small drumlins with intervening shallow swales or sometimes deep ravines.
These features were sculpted about 18,000 to 16,000 years ago by the great Laurentide glacier, along
with the central valley now occupied by Findley Lake. As the glacier melted away it left deposits of
mostly sand and gravel under and marginal to Findley Lake. Ravines were cut during the past 16,000
years and continue to be carved today. Streams in these ravines cut, rearrange, and deposit rock and
soil. For example, Buesink's Creek has a large gravel fan deposit that extends under the lake. The
land uses and septic systems are on and in the surface of this glacial and post-glacial terrain (Shermet
and Wilson, 2007).
There are 5 main tributaries that together drain 62% of Findley Lake's basin (Shermet and Wilson,
2007). The largest volume of water to the lake is from Buesink's Creek located on the northeast
shore. Water exits the lake at the north into the West Branch of the French Creek, which gradually
winds west then south, flowing into French Creek at Wattsburg, Pennsylvania (Boria and Wilson,
2002).
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Figure 1. Findley Lake Direct Drainage Basin
Legend
| [ Basin Boundar
Roacls
Streams
Existing land use and land cover in the Findley Lake drainage basin was determined from digital
aerial photography and geographic information system (GIS) datasets. Digital land use/land cover
data were obtained from the 2001 National Land Cover Dataset (Homer, 2004). The NLCD is a
consistent representation of land cover for the conterminous United States generated from classified
30-meter resolution Landsat thematic mapper satellite imagery data. High-resolution color
orthophotos were used to manually update and refine land use categories for portions of the
drainage basin to reflect current conditions in the drainage basin (Figure 2). Ground-truthed land
use data were provided by Chautauqua County to further improve on land use class adjustments
performed within the basin. These data were particularly useful when development was obscured by
forest canopy. Appendix A provides additional detail about the refinement of land use for the
drainage basin. Land use categories (including individual category acres and percent of total) in
Findley Lake's drainage basin are listed in Table 1 and presented in Figures 3 and 4.
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Figure 2. Aerial Image of Findley Lake
Table 1. Land Use Acres and Percent in Findley Lake Drainage Basin
Land Use Categoi
Open Water
Agriculture
Forest
Wetlands
Acres % of Drainage Basin
TOTAL
1,023
631
393
242
233
9
1,719
14
3,000
0.1%
34%
21%
13%
8%
8%
0.3%
57%
0.5%
100%
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Figure 3. Percent Land Use in Findley Lake Drainage Basin
Wetland Open Water
0.5% \r 0.1%
Developed Land
8%
Figure 4. Land Use in Findley Lake Drainage Basin
Legend
^J Water
| | Low Development
| High Development
U Hay/Pasture
] Row Crops
| Conifer Forest
] Mixed Forest
| Decidous Forest
| | Wooded Wetland
] Emergent Wetland
| Quarry
Transitional
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2.3. Lake Morphometry
Findley Lake is a 296 acre waterbody at an elevation of about 1,420 feet AMSL. Figure 5 shows a
bathymetric map for Findley Lake based on data provided by Chautauqua County (Boria and
Wilson, 2002). Table 2 summarizes key morphometric characteristics for Findley Lake.
Figure 5. Bathymetric Map of Findley Lake
Fiudlev Lake
Legend
0-5 feet
5-10 feet
| 10-15 feet
| 15-20 feet
| 20-25 feet
| 25-30 feet
| 30-35 feet
I 35--40 feet
0-4
0.8
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1.6
Table 2. Findley Lake Characteristics
Surface Area (acres)
Elevation (ft AMSL)
Maximum Depth (ft)
Mean Depth (ft)
Length (ft)
Width at widest point (ft)
Shoreline perimeter (ft)
Direct Drainage Area (acres)
Watershed: Lake Ratio
Mass Residence Time (years)
Hydraulic Residence Time (years)
296
1,420
40
11
6,573
2,670
30,177
3,000
10:1
0.3
0.7
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2.4. Water Quality
CSLAP is a cooperative volunteer monitoring effort between NYS DEC and the New York
Federation of Lake Associations (FOLA). The goal of the program is to establish a volunteer lake
monitoring program that provides data for a variety of purposes, including establishment of a long-
term database for lakes in New York State, identification of water quality problems on individual
lakes, geographic and ecological groupings of lakes, and education for data collectors and users. The
data collected in CSLAP are fully integrated into the state database for lakes, have been used to assist
in local lake management and evaluation of trophic status, spread of invasive species, and other
problems seen in the state's lakes.
Volunteers undergo on-site initial training and follow-up quality assurance and quality control
sessions are conducted by NYS DEC and trained NYS FOLA staff. After training, equipment,
supplies, and preserved bottles are provided to the volunteers by NYS DEC for bi-weekly sampling
for a 15 week period between May and October. Water samples are analyzed for standard lake water
quality indicators, with a focus on evaluating eutrophication status-total phosphorus, nitrogen
(nitrate, ammonia, and total), chlorophyll a, pH, conductivity, color, and calcium. Field
measurements include water depth, water temperature, and Secchi disk transparency. Volunteers
also evaluate use impairments through the use of field observation forms, utilizing a methodology
developed in Minnesota and Vermont. Aquatic vegetation samples, deepwater samples, and
occasional tributary samples are also collected by sampling volunteers at some lakes. Data are sent
from the laboratory to NYS DEC and annual interpretive summary reports are developed and
provided to the participating lake associations and other interested parties.
As part of CSLAP, a limited number of water quality samples were collected in Findley Lake during
the summers of 1986-2007. In addition to the CSLAP data, Findley Lake water quality data
collected by Chautauqua County during the summer of 1998 were also obtained (Boria and Wilson,
2002). The results from these sampling efforts show eutrophic conditions in Findley Lake, with the
concentration of phosphorus in the lake violating the state guidance value for phosphorus (20 ug/L
or 0.020 mg/L, applied as the mean summer, epilimnetic total phosphorus concentration), which
increases the potential for nuisance summertime algae blooms. Figure 6 shows the summer mean
epilimnetic phosphorus concentrations for phosphorus data collected during all sampling seasons
and years in which Findley Lake was sampled as part of CSLAP; the number annotations on the bars
indicate the number of data points included in each summer mean.
In 2003 and 2004, nutrient levels at the bottom of Findley Lake were comparable to those at the lake
surface, although historically highly elevated readings suggested the bottom waters are poorly
oxygenated and contribute to increases in surface water nutrient levels throughout the summer.
This deepwater oxygen deficit was recorded in the lake at least back to the 1930s (NYS DEC, 2007).
In 2007, however, phosphorus levels in the bottom waters averaged 0.20 mg/L, about four times
higher than the levels in the epilimnion. The bottom waters of Findley Lake are showing nutrient
levels substantially higher than those at the lake surface. When the water over the bottom sediments
is devoid of dissolved oxygen (a condition known as anoxic; without oxygen) phosphorus is no
longer chemically bound to the sediments and is free to rise out of the sediments into the over laying
water. This, in turn, can result in algae blooms.
10
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Figure 6. Summer Mean Epilimnetic Total Phosphorus Levels in Findley Lake
Total
Phosphorus 30
(ug/D
Phosphorus Water Quality Target (20 ug/L)
3.0 NUMERIC WATER QUALITY TARGET
The TMDL target is a numeric endpoint specified to represent the level of acceptable water quality
that is to be achieved by implementing the TMDL. The water quality classification for Findley Lake
is B, which means that the best usages of the lake are primary and secondary contact recreation and
fishing. The lake must also be suitable for fish propagation and survival. New York State has a
narrative standard for nutrients none in amounts that will result in growths of algae, weeds and
slimes that will impair the waters for their best usages (6 NYSCRR Part 703.2). As part of its
Technical and Operational Guidance Series (TOGS 1.1.1 and accompanying fact sheet, NYS, 1993),
NYS DEC has suggested that for waters classified as ponded (i.e., lakes, reservoirs and ponds,
excluding Lakes Erie, Ontario, and Champlain), the epilimnetic summer mean total phosphorus
level shall not exceed 20 ug/L (or 0.02 mg/L), based on biweekly sampling, conducted from June 1
to September 30. This guidance value of 20 ug/L is the TMDL target for Findley Lake.
4.0 SOURCE ASSESSMENT
4.1. Analysis of Phosphorus Contributions
The Arc View Generalized Watershed Loading Function (AVGWLF) watershed model was used in
combination with the BATHTUB lake response model to develop the Findley Lake TMDL. This
approach consists of using AVGWLF to determine mean annual phosphorus loading to the lake,
and BATHTUB to define the extent to which this load must be reduced to meet the water quality
target. This approach required no additional data collection thereby expediting the modeling efforts.
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The GWLF model was developed by Haith and Shoemaker (1987). GWLF simulates runoff and
stream flow by a water-balance method based on measurements of daily precipitation and average
temperature. The complexity of GWLF falls between that of a detailed, process-based simulation
model and a simple export coefficient model that does not represent temporal variability. The
GWLF model was determined to be appropriate for this TMDL analysis because it simulates the
important processes of concern, but does not have onerous data requirements for calibration.
AVGWLF was developed to facilitate the use of the GWLF model via an Arc View interface (Evans,
2002). Appendix A discusses the setup, calibration, and use of the AVGWLF model for lake TMDL
assessments in New York.
4.2. Sources of Phosphorus Loading
AVGWLF was used to estimate long-term mean annual phosphorus (external) loading to Findley
Lake for two different time periods 1990-2002 and 2003-2007. The estimated mean annual
external loads of 1,009 Ibs/yr (1990-2002) and 939 Ibs/yr (2003-2007) of total phosphorus to
Findley Lake were estimated to originate from the sources listed in Table 3 and shown in Figure 7.
The 1990-2002 run was performed using the National Land Cover Database (NLCD) 2001 as is.
For the 2003-2007 run, minor corrections were made to the NLCD 2001 based on analysis of
orthoimagery (in particular, the reduction of cropland in the basin); further, the 2003-2007 model
run also takes into account the adoption of nutrient management best management practices
(BMPs) on 80% of the cropland in the basin. The TMDL for Findley Lake was developed using the
2003-2007 model run; therefore, the detailed source loading results (discussed in the following
sections) are only provided for the 2003-2007 run. Appendix A provides the detailed simulation
results from AVGWLF.
Table 3. Estimated Sources of Phosphorus Loading to Findley Lake
Source
Hay/Pasture
Cropland
Forest
Developed Land
Stream Bank
Mean Annual Total Phosphorus Load (Ibs/yr)
1990 - 2002
60
400
11
3
0.4
2003 - 2007
62
330
11
3
0.4
Groundwater
TOTAL
111
1,009
108
12
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Figure 7. Estimated Sources of Annual Total Phosphorus Loading to Findley Lake
1990 - 2002
2003 - 2007
4.2.1. Residential On-Site Septic Systems
Residential on-site septic systems contribute an estimated 425 Ibs/yr of phosphorus to Findley Lake,
which is about 45% of the total loading to the lake. Residential septic systems contribute dissolved
phosphorus to nearby waterbodies due to system malfunctions. Septic systems treat human waste
using a collection system that discharges liquid waste into the soil through a series of distribution
lines that comprise the drain field. In properly functioning (normal) systems, phosphates are
adsorbed and retained by the soil as the effluent percolates through the soil to the shallow saturated
zone. Therefore, normal systems contribute very little phosphorus loads to nearby waterbodies. A
septic system (ponding) malfunction occurs when there is a discharge of waste to the soil surface
(where it is available for runoff); as a result, malfunctioning septic systems can contribute high
phosphorus loads to nearby waterbodies. Short-circuited systems (those systems in close proximity
to surface waters where there is limited opportunity for phosphorus adsorption to take place) also
contribute significant phosphorus loads; septic systems within 250 feet of the lake are subject to
potential short-circuiting, with those closer to the lake more likely to contribute greater loads.
Additional details about the process for estimating the population served by normal and
malfunctioning systems within the lake drainage basin is provided in Appendix A.
GIS analysis of septic parcel data from Chautauqua County for the basin shows approximately 61
houses within 50 feet of the shoreline and 213 houses between 50 and 250 feet of the shoreline; all
of the houses are assumed to have septic systems (Figure 8). Nearly all of the septic systems around
Findley Lake are within a sand and gravel aquifer, which permits the rapid movement of
groundwater (Boria and Wilson, 2002). Within 50 feet of the shorelines, 100% of septic systems
were categorized as short-circuiting. Between 50 and 250 feet of the shoreline, 50% of septic
systems were categorized as short-circuiting, 10% were categorized as ponding systems, and 40%
were categorized as normal systems.
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Figure 8. Findley Lake Septic Parcel Distribution
Data Source:
Ch:iut:uiqu;i Coimty, 200"
Legend
0 Septics Within 50 feet
© Septics 50 - 250 feet
Septics Outside of 250 feet
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To convert the estimated number of septic systems to population served, an average household size
of 2.61 people per dwelling was used based on the circa 2000 U.S. Census Bureau estimate for
number of persons per household in New York State. Further, in order to account for summer
populations at a campground and camp near the lake, an additional 225 individuals were added to
the septic loading numbers for the summer period. To account for seasonal variations in
population, data from the 2000 census were used to estimate the percentage of seasonal homes for
the town(s) surrounding the lake. Approximately 30% of the homes around the lake are assumed to
be year-round residences, while 70% are seasonally occupied (i.e., June through August only). The
estimated population in the Findley Lake drainage basin served by normal and malfunctioning
systems is summarized in Table 4.
Table 4. Population Served by Septic Systems in the Findley Lake Drainage Basin
Normally Functioning Ponding Short Circuiting Total
September May
Tune August (Summer)
17
78
131
550
215
940
14
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4.2.2. Agricultural Runoff
Agricultural land encompasses 1,023 acres (34%) of the lake drainage basin and includes hay and
pasture land (21%) and row crops (13%). Overland runoff from agricultural land is estimated to
contribute about 392 Ibs/yr of phosphorus loading to Findley Lake, which is 42% of the total
phosphorus loading to the lake. Phosphorus loading from agricultural land originates primarily from
soil erosion and the application of manure and fertilizers. Implementation plans for agricultural
sources will require voluntary controls applied on an incremental basis.
4.2.3. Urban and Residential Development Runoff
Developed land comprises 242 acres (8%) of the lake drainage basins. Stormwater runoff from
developed land contributes about 3 Ibs/yr of phosphorus to Findley Lake, which is less than 1% of
the total phosphorus loading to the lake. This load does not account for contributions from
malfunctioning septic systems.
In addition to the contribution of phosphorus to the lake from overland urban runoff, additional
phosphorus originating from developed lands is leached in dissolved form from the surface and
transported to the lake through subsurface movement via groundwater. The process for estimating
subsurface delivery of phosphorus originating from developed land is discussed in the Groundwater
Seepage section (below).
Phosphorus runoff from developed areas originates primarily from human activities, such as
fertilizer applications to lawns. Shoreline development, in particular, can have a large phosphorus
loading impact to nearby waterbodies in comparison to its relatively small percentage of the total
land area in the drainage basin.
4.2.4. Forest Land Runoff
Forested land comprises 1,719 acres (57%) of the lake drainage basin. Runoff from forested land is
estimated to contribute about 11 Ibs/yr of phosphorus loading to Findley Lake, which is about 1%
of the total phosphorus loading to the lake. Phosphorus contribution from forested land is
considered a component of background loading.
4.2.5. Groundwater Seepage
In addition to nonpoint sources of phosphorus delivered to the lake by surface runoff, a portion of
the phosphorus loading from nonpoint sources seeps into the ground and is transported to the lake
via groundwater. Groundwater is estimated to transport 108 Ibs/yr (11%) of the total phosphorus
load to Findley Lake. With respect to groundwater, there is typically a small "background"
concentration owing to various natural sources. In the Findley Lake drainage basin, the model-
estimated groundwater phosphorus concentration is 0.01 mg/L. The GWLF manual provides
estimated background groundwater phosphorus concentrations for >90% forested land in the
eastern United States, which is 0.006 mg/L. Consequently, about 60% of the groundwater load (65
Ibs/yr) can be attributed to natural sources, including forested land and soils.
15
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The remaining amount of the groundwater phosphorus load transported to the lake through
groundwater (about 43 Ibs/yr) likely originates from developed land sources (i.e., leached in
dissolved form from the surface). Table 5 summarizes this information.
Table 5. Sources of Phosphorus Transported in the Subsurface via Groundwater
65 60%
43 40%
108 100%
4.2.6. Other Sources
Atmospheric deposition, wildlife, waterfowl, and domestic pets are also potential sources of
phosphorus loading to the lake. All of these small sources of phosphorus are incorporated into the
land use loadings as identified in the TMDL analysis (and therefore accounted for). Further, the
deposition of phosphorus from the atmosphere over the surface of the lake is accounted for in the
lake model. Atmospheric phosphorus loads (75 mg/m2/yr) were specified using data collected by
USGS from a collection site at Mendon Ponds County Park, in New York (Sherwood, 2005).
Internal loading was not considered in the development of this TMDL due to lack of data to
confirm internal loading. However, NYS DEC acknowledges the need for additional monitoring to
determine if phosphorus migrates from the hypolimnion to the epiliminion, and if phosphorus
release from sediment plays a significant role in phosphorus loading in Findley Lake. It will be
important to make this determination in the near future as there is the possibility that, after external
sources are reduced, internal loading may become increasingly more important in the total
phosphorus loading in the lake, leading to a delay in water quality improvements.
5.0 DETERMINATION OF LOAD CAPACITY
5.1. Lake Modeling Using the BATHTUB Model
BATHTUB was used to define the relationship between phosphorus loading to the lake and the
resulting concentrations of total phosphorus in the lake. The U.S. Army Corps of Engineers'
BATHTUB model predicts eutrophication-related water quality conditions (e.g., phosphorus,
nitrogen, chlorophyll a, and transparency) using empirical relationships previously developed and
tested for reservoir applications (Walker, 1987). BATHTUB performs steady-state water and
nutrient balance calculations in a spatially segmented hydraulic network. Appendix B discusses the
setup, calibration, and use of the BATHTUB model.
5.2. Linking Total Phosphorus Loading to the Numeric Water Quality Target
In order to estimate the loading capacity of the lake, simulated phosphorus loads from AVGWLF
were used to drive the BATHTUB model to simulate water quality in Findley Lake. AVGWLF was
used to derive a mean annual phosphorus loading to the lake for the period 1990-2007; the
16
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individual year results were run through BATHTUB and compared against the observed summer
mean phosphorus concentration for years with observed in-lake data (Figure 9). The TMDL for
Findley Lake was developed using the long-term mean annual phosphorus loading to the lake for the
period 2003-2007. Using this load as input, BATHTUB was used to simulate water quality in the
lake. The combined use of AVGWLF and BATHTUB provides a good fit to the observed data for
Findley Lake (Figure 9).
Figure 9. Observed vs. Simulated Summer Mean Epilimnetic Total Phosphorus
Concentrations (ug/L) in Findley Lake
60
50
40
Total
Phosphorus 30
(ug/D
20
10
Illlllllllllllll
Illlllllllllllll
Observed Simulated V-
The BATHTUB model was used as a "diagnostic" tool to derive the total phosphorus load
reduction required to achieve the phosphorus target of 20 ug/L. The loading capacity of Findley
Lake was determined by running BATHTUB iteratively, reducing the concentration of the drainage
basin phosphorus load until model results demonstrated attainment of the water quality target. The
maximum concentration that results in compliance with the TMDL target for phosphorus is used as
the basis for determining the lake's loading capacity. This concentration is converted into a loading
rate using simulated flow from AVGWLF.
The maximum annual phosphorus load (i.e., the annual TMDL) that will maintain compliance with
the phosphorus water quality goal of 20 ug/L in Findley Lake is a mean annual load of 242 Ibs/yr.
The daily TMDL of 0.7 Ibs/day was calculated by dividing the annual load by the number of days in
a year. Lakes and reservoirs store phosphorus in the water column and sediment, therefore water
quality responses are generally related to the total nutrient loading occurring over a year or season.
For this reason, phosphorus TMDLs for lakes and reservoirs are generally calculated on an annual
or seasonal basis. The use of annual loads, versus daily loads, is an accepted method for expressing
nutrient loads in lakes and reservoirs. This is supported by EPA guidance such as The Lake
Restoration Guidance Manual (USEPA, 1990) and Technical Guidance Manual for Performing Waste Load
17
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Allocations, Book IV, lakes and Impoundments, Chapter 2 Eutrophication (USEPA, 1986). While a daily
load has been calculated, it is recommended that the annual loading target be used to guide
implementation efforts since the annual load of total phosphorus as a TMDL target is more easily
aligned with the design of BMPs used to implement nonpoint source and stormwater controls for
lakes than daily loads. Ultimate compliance with water quality standards for the TMDL will be
determined by measuring the lake's water quality to determine when the phosphorus guidance value
is attained.
6.0 POLLUTANT LOAD ALLOCATIONS
The objective of a TMDL is to provide a basis for allocating acceptable loads among all of the
known pollutant sources so that appropriate control measures can be implemented and water quality
standards achieved. Individual wasteload allocations (WLAs) are assigned to discharges regulated by
State Pollutant Discharge Elimination System (SPDES) permits (commonly called point sources)
and unregulated loads (commonly called nonpoint sources) are contained in load allocations (LAs).
A TMDL is expressed as the sum of all individual WLAs for point source loads, LAs for nonpoint
source loads, and an appropriate margin of safety (MOS), which takes into account uncertainty
(Equation 1).
Equation 1. Calculation of the TMDL
TMDL = YWLA + T.LA +MOS
6.1. Wasteload Allocation (WLA)
There are no wastewater treatment plant discharges of treated wastewater effluent in the Findley
Lake drainage basin. There are also no Municipal Separate Storm Sewer Systems (MS4s) in the
basin. Therefore, the WLA is set at 0 (zero), and all of the loading capacity is allocated as a gross
allotment to the load allocation.
6.2. Load Allocation (LA)
The LA is set at 218 Ibs/yr. Nonpoint sources that contribute total phosphorus to Findley Lake on
an annual basis include loads from developed land, agricultural land, and malfunctioning septic
systems. Table 6 lists the current loading for each source and the load allocation needed to meet the
TMDL; Figure 10 provides a graphical representation of this information. Phosphorus originating
from natural sources (including forested land, wetlands, and stream banks) is assumed to be a minor
source of loading that is unlikely to be reduced further and therefore the load allocation is set at
current loading.
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6.3. Margin of Safety (MOS)
The margin of safety (MOS) can be implicit (incorporated into the TMDL analysis through
conservative assumptions) or explicit (expressed in the TMDL as a portion of the loadings) or a
combination of both. For the Findley Lake TMDL, the MOS is explicitly accounted for during the
allocation of loadings. An implicit MOS could have been provided by making conservative
assumptions at various steps in the TMDL development process (e.g., by selecting conservative
model input parameters or a conservative TMDL target). However, making conservative
assumptions in the modeling analysis can lead to errors in projecting the benefits of BMPs and in
projecting lake responses. Therefore, the recommended method is to formulate the mass balance
using the best scientific estimates of the model input values and keep the margin of safety in the
"MOS" term.
The TMDL contains an explicit margin of safety corresponding to 10% of the loading capacity, or
24 Ibs/yr. The MOS can be reviewed in the future as new data become available.
6.4. Critical Conditions
TMDLs must take into account critical environmental conditions to ensure that the water quality is
protected during times when it is most vulnerable. Critical conditions were taken into account in the
development of this TMDL. In terms of loading, spring runoff periods are considered critical
because wet weather events transport significant quantities of nonpoint source loads to lakes.
However, the water quality ramifications of these nutrient loads are most severe during middle or
late summer. Therefore, BATHTUB model simulations were compared against observed data for
the summer period only. Furthermore, AVGWLF takes into account loadings from all periods
throughout the year, including spring loads.
6.5. Seasonal Variations
Seasonal variation in nutrient load and response is captured within the models used for this TMDL.
In BATHTUB, seasonality is incorporated in terms of seasonal averages for summer. Seasonal
variation is also represented in the TMDL by taking 17 years of daily precipitation data when
calculating runoff through AVGWLF, as well as by estimating septic system loading inputs based on
residency (i.e., seasonal or year-round). This takes into account the seasonal effects the lake will
undergo during a given year.
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Table 6. Total Annual Phosphorus Load Allocations for Findley Lake1
Source
Agriculture
Developed Land*
Septic Systems
Forest, Wetland, Stream Bank, and
Natural Background
LOAD ALLOCATION
Point Sources
WASTELOAD ALLOCATION
LA +WLA
Margin of Safety
TOTAL
Total Phosphorus Load (Ibs/yr)
Current Allocated Reduction
392
46
425
76
114
28
0
278
18
425
/o Reduction
71%
39%
100%
76
939
0
0
939
___
939
218
0
0
218
24
242
721
0
0
721
77%
0%
0%
77%
1 - Note: The values reported in Table 6 are the annually integrated values. The daily equivalent values are provided in
Appendix C of the TMDL support document.
* Includes phosphorus transported through surface runoff and subsurface (groundwater)
Figure 10. Total Phosphorus Load Allocations for Findley Lake (Ibs/yr)
Septic Systems
0 Ibs/yr
Developed Land
28 Ibs/yr
Forest, Wetland,
Stream Bank, and
Natural
Background
76 Ibs/yr
Point Sources
0 Ibs/yr
Margin of Safety
24 Ibs/yr
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7.0 IMPLEMENTATION
One of the critical factors in the successful development and implementation of TMDLs is the
identification of potential management alternatives, such as BMPs and screening and selection of
final alternatives in collaboration with the involved stakeholders. The ongoing watershed protection
efforts (e.g., watershed characterization, restoration, and volunteer monitoring) have already been
outlined in the 2002, The State of Findley Lake report and the 2002, The Management of Findley
Lake and its Watershed report. Coordination with state agencies, federal agencies, local
governments, and stakeholders such as Findley Lake Property Owners Inc. (FLPO), Findley Lake
Watershed Management Team, the general public, environmental interest groups, and
representatives from the nonpoint pollution sources will ensure that the proposed management
alternatives are technically and financially feasible. NYS DEC, in coordination with these local
interests, will address the sources of impairment, using primarily non-regulatory tools in this
watershed, matching management strategies with sources, and aligning available resources to effect
implementation.
NYS DEC recognizes that TMDL designated load reductions alone may not be sufficient to restore
eutrophic lakes. The TMDL establishes the required nutrient reduction targets and provides some
regulatory framework to effect those reductions. However, the nutrient load only affects the
eutrophication potential of a lake. The implementation plan therefore calls for the collection of
additional monitoring data, as discussed in Section 7.2, and consideration of the recommendations
summarized in The State of Findley Lake report and the Management of Findley Lake and its
Watershed report, which discusses in-lake measures, such as water level control, that may need to be
taken to supplement the nutrient reduction measures required by the TMDL.
7.1. Reasonable Assurance for Implementation
This TMDL was written based upon the elimination of phosphorus loading from septic systems in
conjunction with significant reductions from agricultural and developed lands. Meeting the
necessary load reductions using this approach is the most technically achievable alternative.
However, all of the reductions would be voluntary, so funding and public acceptance of this
approach is essential to meeting the goals of the TMDL.
7.1.1. Recommended Phosphorus Management Strategies for Septic Systems
Due to the fact that septic systems are a major source of loading in the Findley Lake Watershed,
restoration depends on elimination of that source. A systematic approach, such as the formation of
a sanitary sewer district and discharge of treated wastewater outside of the watershed, is essential to
achieving the load reductions specified above.
In the interim, a surveying and testing program should be implemented to document the location of
septic systems and verify failing systems requiring replacement in accordance with the State Sanitary
Code. State funding is also available for a voluntary septic system inspection and maintenance
program or a septic system local law requiring inspection and repair. Property owners should be
educated on proper maintenance of their septic systems and encouraged to make preventative
repairs.
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To further assist municipalities, NYS DEC is involved in the development of a statewide training
program for onsite wastewater treatment system professionals. A largely volunteer industry group
called the Onsite Wastewater Treatment Training Network (OTN) has been formed. NYS DEC has
provided financial and staff support to the OTN during the last five years.
7.1.2. Recommended Phosphorus Management Strategies for Agricultural Runoff
The New York State Agricultural Environmental Management (AEM) Program was codified into
law in 2000. Its goal is to support farmers in their efforts to protect water quality and conserve
natural resources, while enhancing farm viability. AEM provides a forum to showcase the soil and
water conservation stewardship farmers provide. It also provides information to farmers about
Concentrated Animal Feeding Operation (CAFO) regulatory requirements, which helps to assure
compliance. Details of the AEM program can be found at the New York State Soil and Water
Conservation Committee (SWCC) website, http://www.nys-soilandwater.org/aem/index.html.
Using a voluntary approach to meet local, state, and national water quality objectives, AEM has
become the primary program for agricultural conservation in New York. It also has become the
umbrella program for integrating/coordinating all local, state, and federal agricultural programs. For
instance, farm eligibility for cost sharing under the SWCC Agricultural Non-point Source Abatement
and Control Grants Program is contingent upon AEM participation.
AEM core concepts include a voluntary and incentive-based approach, attending to specific farm
needs and reducing farmer liability by providing approved protocols to follow. AEM provides a
locally led, coordinated and confidential planning and assessment method that addresses watershed
needs. The assessment process increases farmer awareness of the impact farm activities have on the
environment and by design, it encourages farmer participation, which is an important overall goal of
this implementation plan.
The AEM Program relies on a five-tiered process:
Tier 1 Survey current activities, future plans and potential environmental concerns.
Tier 2 Document current land stewardship; identify and prioritize areas of concern.
Tier 3 Develop a conservation plan, by certified planners, addressing areas of concern tailored to
farm economic and environmental goals.
Tier 4 Implement the plan using available financial, educational and technical assistance.
Tier 5 Conduct evaluations to ensure the protection of the environment and farm viability.
Chautauqua County Soil and Water Conservation District should continue to implement the AEM
program on all farms in the watershed, focusing on identification of management practices that
reduce phosphorus loads. These practices would be eligible for state or federal funding and because
they address a water quality impairment associated with this TMDL, should score well.
Tier 1 could be used to identify farmers that for economic or personal reasons may be changing or
scaling back operations, or contemplating selling land. These farms would be candidates for
conservation easements, or conversion of cropland to hay, as would farms identified in Tier 2 with
highly-erodible soils and/or needing stream management. Tier 3 should include a Comprehensive
22
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Nutrient Management Plan with phosphorus indexing on any cropland where this is not already
being done. This action was also recommended in The Management of Findley Lake and its
Watershed report. Additional practices could be fully implemented in Tier 4 to reduce phosphorus
loads, such as conservation tillage, stream fencing, rotational grazing and cover crops.
Although additional management practices could further reduce phosphorus loads from cropland,
management practices alone could not achieve the load allocation for agriculture. Conversion of a
significant portion of the cropland to hayland or conservation easements would be needed.
Targeting these conversions to highly erodible land and riparian buffers would yield the best
reductions. Riparian buffers reduce losses from upland fields and stabilize stream banks in addition
to the reductions from taking the land in buffers out of production.
7.1.3. Recommended Phosphorus Management Strategies for Urban Storm water Runoff
In March 2002, NYS DEC issued SPDES general permits GP-02-01 for construction activities, and
GP-02-02 for stormwater discharges from MS4s in response to the Federal Phase II Stormwater
rules. These permits were re-issued, effective May 1, 2008 as GP-0-08-001 and GP-0-08-002,
respectively. GP-0-08-002 applies to urbanized areas of New York State, so it does not cover the
Findley Lake Watershed.
Stormwater management in rural areas can be addressed through the Nonpoint Source Management
Program. There are several measures, which, if implemented in the watershed, could directly or
indirectly reduce phosphorus loads in stormwater discharges to the lake or watershed. Many of the
following measures are also recommended in the Management of Findley Lake and its watershed
plan.
Public education regarding:
Lawn care, specifically reducing fertilizer use or using phosphorus-free products, now
commercially available;
Cleaning up pet waste; and
Discouraging waterfowl congregation by restoring natural shoreline vegetation.
Management practices to address any significant existing erosion sites.
Construction site and post construction stormwater runoff control ordinance and inspection and
enforcement programs.
Pollution prevention practices for road and ditch maintenance.
Management practices for the handling, storage and use of roadway deicing products
7.1.4. Additional Protection Measures
Measures to further protect water quality and limit the growth of phosphorus load that would
otherwise offset load reduction efforts, as outlined in The Management of Findley Lake and its
Watershed reports should be considered. The basic protections afforded by local zoning ordinances
could be enhanced to limit non-compatible development, preserve natural vegetation along
shorelines and tributaries and promote smart growth. Identification of wildlife habitats, sensitive
23
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environmental areas, and key open spaces within the watershed could lead to their preservation or
protection byway of conservation easements or other voluntary controls.
7.2. Follow-up Monitoring
A targeted post-assessment monitoring effort will determine the effectiveness of the
implementation plan associated with the TMDL. Findley Lake will be sampled at its deepest
location (approximately 12 meters), during the warmer part of the year (May through September) on
8 sampling dates. Grab samples will be collected at 1.5 meter and in the hypolimnion. The samples
will be analyzed for the phosphorus series (total phosphorus, total soluble phosphorus, and soluble
reactive phosphorus), the nitrogen series (nitrate, ammonia and total nitrogen), and chloride. The
epilimnetic samples will be analyzed for chlorophyll a and the Secchi disk depth will be measured.
A simple macrophyte survey will also be conducted one time during mid-summer.
In recent years, this monitoring has been done through CSLAP. If CSLAP is discontinued at this
lake, the sampling will be repeated at a regular interval. The initial plan will be to set the interval at
5 years, but could vary based on the speed and extent of implementation In addition, as the
information on the DEC GIS system is updated (land use, BMPs, etc.), these updates will be
applied to the input data for the BATHTUB and AVGWLF models. The information will be
incorporated into the New York State 30 5 (b) report as needed.
8.0 PUBLIC PARTICIPATION
NYS DEC met with local representatives from the Chautauqua County Water Quality Task Force
on October 24, 2007 to discuss TMDL development, refine data and to receive local input. A
second meeting was held during the public comment period on August 19, 2008 to review the draft
TMDL. Notice of availability of the draft TMDL was made to local government representatives and
interested parties. This draft TMDL was public noticed in the Environmental Notice Bulletin on
July 23, 2008. A 30-day public review period was established for soliciting written comments from
stakeholders prior to the finalization and submission of the TMDL for EPA approval. NYS DEC
did not receive written comments during the public review period. Written comments submitted
prior to the public review period and verbal comments received during the second meeting have
been addressed.
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9.0 REFERENCES
ASCE Task Committee on Definition of Criteria for Evaluation of Watershed Models of the
Watershed Management Committee, Irrigation and Drainage Division, 1993. Criteria for evaluation
of watershed models. Journal of Irrigation and Drainage Engineering, Vol. 199, No. 3.
Boria, W. T. and M. P. Wilson (editors). January 2002. The State of Findley Lake. Chautauqua
County Department of Health: Mayville, New York.
Day, L.D., 2001. Phosphorus Impacts from Onsite Septic Systems to Surface Waters in the
Cannonsville Reservoir Basin, NY. Delaware County Soil and Water Conservation District, Walton,
NY, June, 2001.
Evans, B.M., D.W. Lehning, K.J. Corradini, Summary of Work Undertaken delated to Adaptation of
AVGWEFfor Use in New England andNew York. 2007.
Evans, B.M., D.W. Lehning, K.J. Corradini, G.W. Petersen, E. Nizeyimana, J.M. Hamlett, P.D.
Robillard, and R.L. Day, 2002. A Comprehensive GIS-Based Modeling Approach for Predicting
Nutrient Loads in Watersheds. Journal of Spatial Hydrology, Vol. 2, No. 2.
Haith, D.A. and L.L. Shoemaker, 1987. Generalized Watershed Loading Functions for Stream Flow
Nutrients. Water Resources Bulletin, 23(3), pp. 471-478.
Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan. 2004. Development of a 2001 National Eandcover
Database for the United States. Photogrammetric Engineering and demote Sensing, Vol. 70, No. 7, July 2004,
pp. 829.
National Atmospheric Deposition Program (NRSP-3). 2007. NADP Program Office, Illinois State
Water Survey, 2204 Griffith Dr., Champaign, IL 61820.
New York State Department of Environmental Conservation and New York Federation of Lake
Associations, 2006. 2005 Interpretive Summary, New York Citizens Statewide Lake Assessment
Program, Findley Lake.
New York State, 2004. New York State Water Quality Report 2004. NYS Department of
Environmental Conservation, Division of Water, Bureau of Watershed Assessment and
Management.
New York State, 1998. 6 NYS Codes Rules and Regulations, Part 703.2, Narrative Water Quality
Standards.
New York State, 1993. New York State Fact Sheet, Ambient Water Quality Value for Protection of
Recreational Uses, Substance: Phosphorus, Bureau of Technical Services and Research. NYS
Department of Environmental Conservation.
Shermet, D. M. and M. P. Wilson (editors). March 2007. Sources of Unwanted Nutrients at Findley
Lake, NY. State University of New York, Fredonia, New York.
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Sherwood, D.A., 2005, Water resources of Monroe County, New York, water years 2000-02
atmospheric deposition, ground water, streamflow, trends in water quality, and chemical loads in
streams: U.S. Geological Scientific Investigations Report 2005-5107, 55 p.
United States Army Corps of Engineers, Engineer Research and Development Center, 2004. Flux,
Profile, and BATHTUB: Simplified Procedures for Eutrophication Assessment and Prediction.
.
USEPA. 2002. Onsite Wastewater Treatment Systems Manual. EPA/625/R-00/008. February 2002.
USEPA, 1999. Protocol for Developing Sediment TMDLs (First Edition). EPA 841-B-99-004.
Office of Water (4503F), United States Environmental Protection Agency, Washington, DC.
USEPA. 1990. The Lake and Reservoir Restoration Guidance Manual. 2nd Ed. and Monitoring Lake and
Reservoir Restoration (Technical Supplement). Prepared by North American Lake Management Society.
EPA 440/4-90-006 and EPA 440/4-90-007.
USEPA. 1986. Technical Guidance Manual for Performing Wasteload Allocations, Book IV:
Lakes, Reservoirs and Impoundments, Chapter 2: Eutrophication. EPA 440/4-84-019, p. 3-8.
United States Census Bureau, Census 2000 Summary File 3 (SF 3) - Sample Data. H18.
AVERAGE HOUSEHOLD SIZE OF OCCUPIED HOUSING UNITS BY TENURE Average
Household Si^e of Occupied housing Units by Tenure. 2007, http://factfinder.census.gov/
Watts, S., B. Gharabaghi, R.P. Rudra, M. Palmer, T. Boston, B. Evans, and M. Walters, 2005.
Evaluation of the CIS-Based Nutrient Management Model CANWET in Ontario. In: Proc. 58th
Natl. Conf. Canadian Water Resources Assoc., June 2005, Banff, Canada.
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APPENDIX A. AVGWLF MODELING ANALYSIS
Northeast A VGWLF Model
The AVGWLF model was calibrated and validated for the northeast (Evans et al, 2007). AVGWLF
requires that calibration watersheds have long-term flow and water quality data. For the northeast
model, watershed simulations were performed for twenty-two (22) watersheds throughout New York
and New England for the period 1997-2004 (Figure 11). Flow data were obtained directly from the
water resource database maintained by the U.S. Geological Survey (USGS). Water quality data were
obtained from the New York and New England State agencies. These data sets included in-stream
concentrations of nitrogen, phosphorus, and sediment based on periodic sampling.
Figure 11. Location of Calibration and Verification Watersheds for the Northeast AVGWLF
Model
I | Counties
| | Calibration
~~| Verification
Initial model calibration was performed on half of the 22 watersheds for the period 1997-2004. During
this step, adjustments were iteratively made in various model parameters until a "best fit" was achieved
between simulated and observed stream flow, and sediment and nutrient loads. Based on the
calibration results, revisions were made in various AVGWLF routines to alter the manner in which
model input parameters were estimated. To check the reliability of these revised routines, follow-up
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verification runs were made on the remaining eleven watersheds for the same time period. Finally,
statistical evaluations of the accuracy of flow and load predictions were made.
To derive historical nutrient loads, standard mass balance techniques were used. First, the in-stream
nutrient concentration data and corresponding flow rate data were used to develop load (mass) versus
flow relationships for each watershed for the period in which historical water quality data were
obtained. Using the daily stream flow data obtained from USGS, daily nutrient loads for the 1997-2004
time period were subsequently computed for each watershed using the appropriate load versus flow
relationship (i.e., "rating curves"). Loads computed in this fashion were used as the "observed" loads
against which model-simulated loads were compared.
During this process, adjustments were made to various model input parameters for the purpose of
obtaining a "best fit" between the observed and simulated data. With respect to stream flow,
adjustments were made that increased or decreased the amount of the calculated evapotranspiration
and/or "lag time" (i.e., groundwater recession rate) for sub-surface flow. With respect to nutrient loads,
changes were made to the estimates for sub-surface nitrogen and phosphorus concentrations. In regard
to both sediment and nutrients, adjustments were made to the estimate for the "C" factor for cropland
in the USLE equation, as well as to the sediment "a" factor used to calculate sediment loss due to
stream bank erosion. Finally, revisions were also made to the default retention coefficients used by
AVGWLF for estimating sediment and nutrient retention in lakes and wetlands.
Based upon an evaluation of the changes made to the input files for each of the calibration watersheds,
revisions were made to routines within AVGWLF to modify the way in which selected model
parameters were automatically estimated. The AVGWLF software application was originally developed
for use in Pennsylvania, and based on the calibration results, it appeared that certain routines were
calculating values for some model parameters that were either too high or too low. Consequently, it
was necessary to make modifications to various algorithms in AVGWLF to better reflect conditions in
the Northeast. A summary of the algorithm changes made to AVGWLF is provided below.
ET: A revision was made to increase the amount of evapotranspiration calculated automatically by
AVGWLF by a factor of 1.54 (in the "Pennsylvania" version of AVGWLF, the adjustment factor
used is 1.16). This has the effect of decreasing simulated stream flow.
GWR: The default value for the groundwater recession rate was changed from 0.1 (as used in
Pennsylvania) to 0.03. This has the effect of "flattening" the hydrograph within a given area.
GWN: The algorithm used to estimate "groundwater" (sub-surface) nitrogen concentration was
changed to calculate a lower value than provided by the "Pennsylvania" version.
Sediment "a" Factor: The current algorithm was changed to reduce estimated stream bank-
derived sediment by a factor of 90%. The streambank routine in AVGWLF was originally
developed using Pennsylvania data and was consistently producing sediment estimates that were
too high based on the in-stream sample data for the calibration sites in the Northeast. While the
exact reason for this is not known, it's likely that the glaciated terrain in the Northeast is less
credible than the highly credible soils in Pennsylvania. Also, it is likely that the relative
abundance of lakes, ponds and wetlands in the Northeast have an effect on flow velocities and
sediment transport.
Lake/Wetland Retention Coefficients: The default retention coefficients for sediment, nitrogen
and phosphorus are set to 0.90, 0.12 and 0.25, respectively, and changed at the user's discretion.
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To assess the correlation between observed and predicted values, two different statistical measures
were utilized: 1) the Pearson product-moment correlation (R2) coefficient and 2) the Nash-Sutcliffe
coefficient. The R2 value is a measure of the degree of linear association between two variables, and
represents the amount of variability that is explained by another variable (in this case, the model-
simulated values). Depending on the strength of the linear relationship, the R2 can vary from 0 to 1,
with 1 indicating a perfect fit between observed and predicted values. Like the R2 measure, the Nash-
Sutcliffe coefficient is an indicator of "goodness of fit," and has been recommended by the American
Society of Civil Engineers for use in hydrological studies (ASCE, 1993). With this coefficient, values
equal to 1 indicate a perfect fit between observed and predicted data, and values equal to 0 indicate that
the model is predicting no better than using the average of the observed data. Therefore, any positive
value above 0 suggests that the model has some utility, with higher values indicating better model
performance. In practice, this coefficient tends to be lower than R2 for the same data being evaluated.
Adjustments were made to the various input parameters for the purpose of obtaining a "best fit"
between the observed and simulated data. One of the challenges in calibrating a model is to optimize
the results across all model outputs (in the case of AVGWLF, stream flows, as well as sediment,
nitrogen, and phosphorus loads). As with any watershed model like GWLF, it is possible to focus on a
single output measure (e.g., sediment or nitrogen) in order to improve the fit between observed and
simulated loads. Isolating on one model output, however, can sometimes lead to less acceptable results
for other measures. Consequently, it is sometimes difficult to achieve very high correlations (e.g., R
above 0.90) across all model outputs. Given this limitation, it was felt that very good results were
obtained for the calibration sites. In model calibration, initial emphasis is usually placed on getting the
hydrology correct. Therefore, adjustments to flow-related model parameters are usually finalized prior
to making adjustments to parameters specific to sediment and nutrient production. This typically
results in better statistical fits between stream flows than the other model outputs.
For the monthly comparisons, mean R2 values of 0.80, 0.48, 0.74, and 0.60 were obtained for the
calibration watersheds for flow, sediment, nitrogen and phosphorus, respectively. When considering
the inherent difficulty in achieving optimal results across all measures as discussed above (along with the
potential sources of error), these results are quite good. The sediment load predictions were less
satisfactory than those for the other outputs, and this is not entirely unexpected given that this
constituent is usually more difficult to simulate than nitrogen or phosphorus. An improvement in
sediment prediction could have been achieved by isolating on this particular output during the
calibration process; but this would have resulted in poorer performance in estimating the nutrient loads
for some of the watersheds. Phosphorus predictions were less accurate than those for nitrogen. This is
not unusual given that a significant portion of the phosphorus load for a watershed is highly related to
sediment transport processes. Nitrogen, on the other hand, is often linearly correlated to flow, which
typically results in accurate predictions of nitrogen loads if stream flows are being accurately simulated.
As expected, the monthly Nash-Sutcliffe coefficients were somewhat lower due to the nature of this
particular statistic. As described earlier, this statistic is used to iteratively compare simulated values
against the mean of the observed values, and values above zero indicate that the model predictions are
better than just using the mean of the observed data. In other words, any value above zero would
indicate that the model has some utility beyond using the mean of historical data in estimating the flows
or loads for any particular time period. As with R2 values, higher Nash-Sutcliffe values reflect higher
degrees of correlation than lower ones.
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Improvements in model accuracy for the calibration sites were typically obtained when comparisons
were made on a seasonal basis. This was expected since short-term variations in model output can
oftentimes be reduced by accumulating the results over longer time periods. In particular, month-to-
month discrepancies due to precipitation events that occur at the end of a month are often resolved by
aggregating output in this manner (the same is usually true when going from daily output to weekly or
monthly output). Similarly, further improvements were noted when comparisons were made on a
mean annual basis. What these particular results imply is that AVGWLF, when calibrated, can provide
very good estimates of mean annual sediment and nutrient loads.
Following the completion of the northeast AVGWLF model, there were a number of ideas on ways
to improve model accuracy. One of the ideas relates to the basic assumption upon which the work
undertaken in that project was based. This assumption is that a "regionalized" model can be
developed that works equally well (without the need for resource-intensive calibration) across all
watersheds within a large region in terms of producing reasonable estimates of sediment and
nutrient loads for different time periods. Similar regional model calibrations were previously
accomplished in earlier efforts undertaken in Pennsylvania (Evans et al., 2002) and later in southern
Ontario (Watts et al., 2005). In both cases this task was fairly daunting given the size of the areas
involved. In the northeast effort, this task was even more challenging given the fact that the
geographic area covered by the northeast is about three times the size of Pennsylvania, and arguably
is more diverse in terms of its physiographic and ecological composition.
As discussed, AVGWLF performed very well when calibrated for numerous watersheds throughout
the region. The regionalized version of AVGWLF, however, performed less well for the verification
watersheds for which additional adjustments were not made subsequent to the initial model runs.
This decline in model performance may be a result of the regionally-adapted model algorithms not
being rigorous enough to simulate spatially-varying landscape processes across such a vast
geographic region at a consistently high degree of accuracy. It is likely that un-calibrated model
performance can be enhanced by adapting the algorithms to reflect processes in smaller geographic
regions such as those depicted in the physiographic province map in Figure 12.
Fine-tuning & Re-Calibrating the Northeast A VGWLF for New York State
For the TMDL development work undertaken in New York, the original northeast AVGWLF
model was further refined by The Cadmus Group, Inc. and Dr. Barry Evans to reflect the
physiographic regions that exist in New York. Using data from some of the original northeast model
calibration and verification sites, as well as data for additional calibration sites in New York, three new
versions of AVGWLF were created for use in developing TMDLs in New York State. Information on
the fourteen (14) sites is summarized in Table 7. Two models were developed based on the following
two physiographic regions: Eastern Great Lakes/Hudson Lowlands area and the Northeastern
Highlands area. The model was calibrated for each of these regions to better reflect local conditions, as
well as ecological and hydrologic processes. In addition to developing the above mentioned
physiographic-based model calibrations, a third model calibration was also developed. This model
calibration represents a composite of the two physiographic regions and is suitable for use in other areas
of upstate New York.
30
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Figure 12. Location of Physiographic Provinces in New York and New England
| Atlantic Coastal Pine Barrens
j Eastern Great Lakes and Hudson Lowlands
j Erie Drift Plain
| Laurentian Plains and Hills
j North Central Appalachians
j Northeastern Coastal Zone
j Northeastern Highlands
j Northern Appalachian Plateau and Uplands
| Northern Piedmont
j Ridge and Valley
Table 7. AVGWLF Calibration Sites for use in the New York TMDL Assessments
Site
Owasco Lake
West Branch
Little Chazy River
Little Otter Creek
Poultney River
Farmington River
Saco River
Squannacook River
Ashuelot River
Laplatte River
Wild River
Salmon River
Norwalk River
Lewis Creek
Location
NY
NY
NY
VT
VT/NY
CT
ME/NH
MA
NH
VT
ME
CT
CT
VT
Physiographic Region
Eastern Great Lakes/Hudson Lowlands
Northeastern Highlands
Eastern Great Lakes /Hudson Lowlands
Eastern Great Lakes/Hudson Lowlands
Eastern Great Lakes/Hudson Lowlands & Northeastern
Highlands
Northeastern Highlands
Northeastern Highlands
Northeastern Highlands
Northeastern Highlands
Eastern Great Lakes /Hudson Lowlands
Northeastern Highlands
Northeastern Coastal Zone
Northeastern Coastal Zone
Eastern Great Lakes /Hudson Lowlands
31
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Set-up of the "New York State" AVGWLF Model
Using data for the time period 1990-2007, the calibrated AVGWLF model was used to estimate
mean annual phosphorus loading to the lake. Table 8 provides the sources of data used for the
AVGWLF modeling analysis. The various data preparation steps taken prior to running the final
calibrated AVGWLF Model for New York are discussed below the table.
Table 8. Information Sources for AVGWLF Model Parameterization
WEATHER.DAT file
Data
Source or Value
Historical weather data from Cobleskill, NY and
Maryland, NY National Weather Services Stations
TRANSPORT.DAT file
Data
Basin size
Land use/cover distribution
Curve numbers by source area
USLE (KLSCP) factors by source area
ET cover coefficients
Erosivity coefficients
Daylight hrs. by month
Growing season months
Initial saturated storage
Initial unsaturated storage
Recession coefficient
Seepage coefficient
Initial snow amount (cm water)
Sediment delivery ratio
Soil water (available water capacity)
Source or Value
GIS/derived from basin boundaries
GIS/derived from land use/cover map
GIS/derived from land cover and soil maps
GIS/derived from soil, DEM, & land cover
GIS/derived from land cover
GIS/ derived from physiographic map
Computed automatically for state
Input by user
Default value of 10 cm
Default value of 0 cm
Default value of 0.1
Default value of 0
Default value of 0
GIS/based on basin size
GIS/derived from soil map
NUTRIENT.DAT file
Data
Dissolved N in runoff by land cover type
Dissolved P in runoff by land cover type
N/P concentrations in manure runoff
N/P buildup in urban areas
N and P point source loads
Background N/P concentrations in GW
Background P concentrations in soil
Background N concentrations in soil
Months of manure spreading
Population on septic systems
Per capita septic system loads (N/P)
Source or Value
Default values /adjusted using GWLF Manual
Default values/adjusted using GWLF Manual
Default values/adjusted using AEU density
Default values (from GWLF Manual)
Derived from SPDES point coverage
Derived from new background N map
Derived from soil P loading map/adjusted using
GWLF Manual
Based on map in GWLF Manual
Input by user
Derived from census tract maps for 2000 and house
counts
Default values/adjusted using AEU density
32
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Land Use
The 2001 NLCD land use coverage was obtained, receded, and formatted specifically for use in
AVGWLF. The New York State High Resolution Digital Orthoimagery (for the time period 2000
2004) was used to perform updates and corrections to the 2001 NLCD land use coverage to more
accurately reflect current conditions. Each basin was reviewed independently for the potential need
for land use corrections; however individual raster errors associated with inherent imperfections in
the satellite imagery have a far greater impact on overall basin land use percentages when evaluating
smaller scale basins. As a result, for large basins, NLCD 2001 is generally considered adequate,
while in smaller basins, errors were more closely assessed and corrected. The following were the
most common types of corrections applied generally to smaller basins:
1) Areas of low intensity development that were coded in the 2001 NLCD as other land use types
were the most commonly corrected land use data in this analysis. Discretion was used when
applying corrections, as some overlap of land use pixels on the lake boundary are inevitable due
to the inherent variability in the aerial position of the sensor creating the image. If significant
new development was apparent (i.e., on the orthoimagery), but was not coded as such in the
2001 NLCD, than these areas were re-coded to low intensity development.
2) Areas of water that were coded as land (and vise-versa) were also corrected. Discretion was used
for reservoirs where water level fluctuation could account for errors between orthoimagery and
land use.
3) Forested areas that were coded as row crops/pasture areas (and vise-versa) were also corrected.
For this correction, 100% error in the pixel must exist (e.g., the supposed forest must be
completely pastured to make a change); otherwise, making changes would be too subjective.
Conversions between forest types (e.g., conifer to deciduous) are too subjective and therefore
not attempted; conversions between row crops and pasture are also too subjective due to the
practice of crop rotation. Correction of row crops to hay and pasture based on orthoimagery
were therefore not undertaken in this analysis.
Phosphorus retention in wetlands and open waters in the basin can be accounted for in AVGWLF.
AVGWLF recommends the following coefficients for wetlands and pond retention in the northeast:
nitrogen (0.12), phosphorus (0.25), and sediment (0.90). Wetland retention coefficients for large,
naturally occurring wetlands vary greatly in the available literature. Depending on the type, size and
quantity of wetland observed, the overall impact of the wetland retention routine on the original
watershed loading estimates, and local information regarding the impact of wetlands on watershed
loads, wetland retention coefficients defaults were adjusted accordingly. The percentage of the
drainage basin area that drains through a wetland area was calculated and used in conjunction with
nutrient retention coefficients in AVGWLF. To determine the percent wetland area, the total basin
land use area was derived using Arc View. Of this total basin area, the area that drains through
emergent and woody wetlands were delineated to yield an estimate of total watershed area draining
through wetland areas. If a basin displays large areas of surface water (ponds) aside from the water
body being modeled, then this open water area is calculated by subtracting the water body area from
the total surface water area.
33
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On-site Wastewater Treatment Systems ("septic tanks")
GWLF simulates nutrient loads from septic systems as a function of the percentage of the
unsewered population served by normally functioning vs. three types of malfunctioning systems:
ponded, short-circuited, and direct discharge (Haith et al., 1992).
Normal Systems are septic systems whose construction and operation conforms to
recommended procedures, such as those suggested by the EPA design manual for on-site
wastewater disposal systems. Effluent from normal systems infiltrates into the soil and enters
the shallow saturated zone. Phosphates in the effluent are adsorbed and retained by the soil and
hence normal systems provide no phosphorus loads to nearby waters.
Short-Circuited Systems are located close enough to surface water (~15 meters) so that
negligible adsorption of phosphorus takes place. The only nutrient removal mechanism is plant
uptake. Therefore, these systems are always contributing to nearby waters.
Ponded Systems exhibit hydraulic malfunctioning of the tank's absorption field and resulting
surfacing of the effluent. Unless the surfaced effluent freezes, ponding systems deliver their
nutrient loads to surface waters in the same month that they are generated through overland
flow. If the temperature is below freezing, the surfacing is assumed to freeze in a thin layer at
the ground surface. The accumulated frozen effluent melts when the snowpack disappears and
the temperature is above freezing.
Direct Discharge Systems illegally discharge septic tank effluent directly into surface waters.
GWLF requires an estimation of population served by septic systems to generate septic system
phosphorus loadings. In reviewing the orthoimagery for the lake, it became apparent that septic
system estimates from the 1990 census were not reflective of actual population in close proximity to
the shore. Shoreline dwellings immediately surrounding the lake account for a substantial portion of
the nutrient loading to the lake. Therefore, the estimated number of septic systems in the drainage
basin was refined using a combination of 1990 and 2000 census data and GIS analysis of
orthoimagery to account for the proximity of septic systems immediately surrounding the lake. If
available, local information about the number of houses within 250 feet of the lakes was obtained
and applied. Great attention was given to estimating septic systems within 250 feet of the lake (those
most likely to have an impact on the lake). To convert the estimated number of septic systems to
population served, an average household size of 2.61 people per dwelling was used based on the
circa 2000 USCB census estimate for number of persons per household in New York State.
GWLF also requires an estimate of the number of normal and malfunctioning septic systems. This
information was not readily available for the lake. Therefore, several assumptions were made to
categorize the systems according to their performance. These assumptions are based on data from
local and national studies (Day, 2001; USEPA, 2002) in combination with best professional
judgment. To account for seasonal variations in population, data from the 2000 census were used to
estimate the percentage of seasonal homes for the town(s) surrounding the lake. The failure rate for
septic systems closer to the lake (i.e., within 250 feet) were adjusted to account for increased loads
due to greater occupancy during the summer months. If available, local information about seasonal
occupancy was obtained and applied. For the purposes of this analysis, seasonal homes are
considered those occupied only during the month of June, July, and August.
34
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Groundwater Phosphorus
Phosphorus concentrations in groundwater discharge are derived by AVGWLF. Watersheds with a
high percentage of forested land will have low groundwater phosphorus concentrations while
watersheds with a high percentage of agricultural land will have high concentrations. The GWLF
manual provides estimated groundwater phosphorus concentrations according to land use for the
eastern United States. Completely forested watersheds have values of 0.006 mg/L. Primarily
agricultural watersheds have values of 0.104 mg/L. Intermediate values are also reported. The
AVGWLF-generated groundwater phosphorus concentration was evaluated to ensure groundwater
phosphorus values reasonably reflect the actual land use composition of the drainage basin and
modifications were made if deemed unnecessary.
Point Sources
If permitted point sources exist in the drainage basin, their location was identified and verified by
NYS DEC and an estimated monthly total phosphorus load and flow was determined using either
actual reported data (e.g., from discharge monitoring reports) or estimated based on expected
discharge/flow for the facility type.
Concentrated Animal Feeding Operations (CAFOs)
A state-wide Concentrated Animal Feeding Operation (CAFO) shapefile was provided by NYS
DEC. CAFOs are categorized as either large or medium. The CAFO point can represent either the
centroid of the farm or the entrance of the farm, therefore the CAFO point is more of a general
gauge as to where further information should be obtained regarding permitted information for the
CAFO. If a CAFO point is located in or around a basin, orthos and permit data were evaluated to
determine the part of the farm with the highest potential contribution of nutrient load. In Arc View,
the CAFO shapefile was positioned over the basin and clipped with a 2.5 mile buffer to preserve
those CAFOS that may have associated cropland in the basin. If a CAFO point is found to be
located within the boundaries of the drainage basin, every effort was made to obtain permit
information regarding nutrient management or other best management practices (BMPs) that may
be in place within the property boundary of a given CAFO. These data can be used to update the
nutrient file in AVGWLF and ultimately account for agricultural BMPs that may currently be in
place in the drainage basin.
Municipal Separate Storm Sewer Systems (MS4s)
Stormwater runoff within Phase II permitted Municipal Separate Storm Sewer Systems (MS4s) is
considered a point source of pollutants. Stormwater runoff outside of the MS4 is non-permitted
stormwater runoff and, therefore, considered nonpoint sources of pollutants. Permitted Stormwater
runoff is accounted for in the wasteload allocation of a TMDL, while non-permitted runoff is
accounted for in the load allocation of a TMDL. NYS DEC determined there are no MS4s in this
basin.
35
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AVGWLF Model Simulation Results (2003-2007^
Input Transport File
Edit Transport File
-I lxi
Rural LU
HAWPAST
CROPLAND
FOREST
WETLAND
Aiea (ha)
Month Ket
Season Eros Stream Ground
Coef Extract Extract
Bare Land Area (ha) CN K
Urban LU Area (ha) CN K
175 10.192
LO_INT_DEV [73
HI INT DEV
Antecedent Moisture Condition
Day 1 Day 2 Day 3 Day 4 Day 5
[ci " Ei " [01 ~ W~ ~ W~
[
transedil:! .dat
t3 avgwlf
Init Unsat Stor (cm) \f\
Init Sat Stor (cm) |Q
Recess Coef (1/dia) n ri5
Seepage Coef (1/dia:) n
Tile Drain Density IQ
Initial InitSnow (cm) g
Sed Delivery Ratio |g -|g
Sediment A Factor |7.2660E-05
Unsat Avail Wat (cm) |-| .86226
Tile Drain Ratio [rfi
Load Transport File M
Save File
Close
36
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Input Nutrient File
3 Edit Nutrient File
Runoff Loads by Source
Rural Runoff Dis N mg/L Dis P mg/L
HAY/PAST 12.9 :0.15
CROPLAND |2.S ]0.2
FOREST J0.19 10.006
WETLAND 10.19 |0.006
Manure
Urban Build-Up N kg/ha/d P kgrtia/d
LO_INT_DEV 10.055 10.008
HI INT DEV 10.101 10.011
t3 avgwlf
tjjRunfiles
tjj Output
<~^ Revisions
Nitrogen and Phosphorus Loads from Point Sources and Septic Systems
Point Source Loads/Discharge
Month
KgN
Discharge
MGD
Septic System Loads
Normal Ponding Short Cine Direct
Systems Systems Systems Discharge
[67 IT? |131
[312
[312
[312
j67
i67
[67
[67
167
[67~
TS"
[TB~
l7~
[17
J55T
[550~
[550~
(s
fis
[Ts
s
Per capita tank effluent
N (g/d) P(g/d)
\\2~~ |2.5
Growing season N/P Uptake Sediment
N (g/d) P (g/d) N (mg/Kg) P (mg/Kg)
[1.6 I0.4 13000.0 1392.1
Groundwater
N(rng/L)
10.934
P (mg/L)
10.01
Tile Drainage (mg/L)
N P Sed
bT" [50~
Load Nutrient File
Save File
Close
37
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Simulated Hydrology Transport Summary
GWLF Transport Summary for Findley_Revision_2c
Period of analysis 6 years, from Apr 2002 to Mar 2008
Units in Centimeters
Month
APR
MAY
JUN
JIJL
AUG
SEP
OCT
NOV
DEC
JAN
FEE
MAR
Total
Prec ET
|8.72 |5.40
|9.68 |8.54
|6.73 |6.65
J12.38 |11.11
|9.08 |7.94
|12.60 |7.92
|9.83 |6.06
|9.47 |3.53
|8.73 (1.26
(8.08 J0.49
|5.85 |0.32
(6.63 |2.12
|107.8 |61.34
Go Back
Extraction
1 0.00
1 0.00
1 0.00
1 0.00
1 0.00
1 0.00
|0.00
1 0.00
IQ.OQ
1 0.00
1 0.00
1 0.00
1 0.00
Runoff
0.06
|0.19
1 0.00
1 0.04
0.05
1 0.09
|0.13
1 0.23
1 0.38
|0.13
|0.32
|0.57
|2.19
Loads by Month I
Subsurface Point Src
Flow Flow
1 5. 68
1 2. 70
|1.14
1 0.45
1 0.66
1 2. 77
1 3. 33
1 3. 71
|6.16
1 7. 00
1 4. 71
1 6. 24
1 44. 56
Print |
1 0.00
1 0.00
1 0.00
1 0.00
1 0.00
]o.oo
1 0.00
1 0.00
1 0.00
1 0.00
1 0.00
1 0.00
1 0.00
CExptMOo 31
Tile Drain
IO.QQ
IQ.QQ
|o.oo
IQ.QQ
|o.oo
|o.oo
|o.oo
|o.oo
|o.oo
|o.oo
|o.oo
|o.oo
|o.oo
5E&::)|
Stream
Flow
|5.74
|2.90
1.14
1 0.49
1 0.71
1 2. 86
1 3. 47
1 3. 94
1 6. 54
J7.13
1 5. 03
6.81
|46.75
1
Close |
38
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Simulated Nutrient Transport Summary
GWLF Transport Summary for Findley_Revision_2c
Period of analysis 6 years, from Apr 2002 to Mar 2008
I^^H Kg XI 000
Month
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
JAN
FEE
MAR
Total
Erosion
|308.2
1435.4
l>
[262.2
|617.3
1 537. 6
1 267.1
|136.0
(91.7
1 55. 4
|54.4
]21.4
1 44. 5
1 2831. 3
Sediment Dis N
H.6
|21.5
J0.3
|5.3
|5.2
|13.8
|18.1
] 40. 3
1 90.1
|21.3
1 76. 2
|187.5
|481.2
1 668. 3
1 352. 5
|194.1
|126.3
|152.5
|341.0
|415.4
|471.4
1 769. 9
1 832.1
1 604. 8
1 822. 5
1 5750. 7
Go Back Loads
' 1~ if riniT Tri"" tP F f
Nutrient Loads [Kg]
Total N
|671.2
|421.0
(194.1
|143.8
|l72.Q
|384.8
|471.8
1 598. 5
1 1055. 2
|897.2
|846.1
1 1420.1
1 7275. 8
by Source
3| Print
DisP
|17.2
(13.6
(35.5
1 36. 3
(36.7
|13.6
|17.3
|19.5
1 26.0
(20.1
|18.4
23.8
(277.8
Close
J
Total P
|17.6
1 22.5
1 35. 5
38.5
39.3
|19.3
24.6
1 36.0
1 63.1
28.5
1 49. 7
|101.4
1 476.1
39
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Simulated Total Loads by Source
GWLF Total Loads for Findley_Revision_2c
Period of analysis: 6 years, from Apr 2002 to Mar 2008
Source 'Hal lcml
HAY/PAST [254 [il~~
CROPLAND pfTEJ [gT~
FOREST |698 ~ [lT~
WETLAND [6~~ JF~
LO_INT_DEV |73 ~ [si"
HI_INT_DEV (5 ~ [32~4
Tile Drainage
Stream Bank
Groundwater
Point Sources
Septic Systems
Totals [1214 2.2
Kg X 1000 |
Erosion
|276.3
|2407.5
|6S.4
|o.o
|78.6
|2.4
1
1
1
1
1
1
: 2831 3
Sediment
|46.2
|402.2
|11.1
;0.0
|10.7
[0.2
I
I
I
!
I
I
I
|o.o
8.2
1 478. 5
DisN
|105.4
1 259. 7
|13.9
[03
|0.0
[55
r
i
i
i
i
i
r
1 3402. 5
ID
1 367. 9
1 41 49. 6
Total Loads (Kg]
Total N
1 253. 2
1 680. 8
[49.4
0.4
|10.3
0.1
1
1
1
1
1
1
1
|o.o
,0.4
[3402.5
ID
|367.9
4764.8
DisP
Total P
;8.0 [28.2
J25.6 |149.6
,0.4
0.0
|o.o
[Ho
r
i
i
i
i
i
r
5.1
0.0
1.5
0.0
r
IP
|0.2
|48.9 |48.9
|o
|189.5
o
189.5
|273.4 |422.9
Go Back
! Export to JPEG M
Print
Close
to
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APPENDIX B. BATHTUB MODELING ANALYSIS
Model Overview
BATHTUB is a steady-state (Windows-based) water quality model developed by the U. S. Army
Corps of Engineers (USACOE) Waterways Experimental Station. BATHTUB performs steady-
state water and nutrient balance calculations for spatially segmented hydraulic networks in order to
simulate eutrophication-related water quality conditions in lakes and reservoirs. BATHTUB's
nutrient balance procedure assumes that the net accumulation of nutrients in a lake is the difference
between nutrient loadings into the lake (from various sources) and the nutrients carried out through
outflow and the losses of nutrients through whatever decay process occurs inside the lake. The net
accumulation (of phosphorus) in the lake is calculated using the following equation:
Net accumulation = Inflow Outflow Decay
The pollutant dynamics in the lake are assumed to be at a steady state, therefore, the net
accumulation of phosphorus in the lake equals zero. BATHTUB accounts for advective and
diffusive transport, as well as nutrient sedimentation. BATHTUB predicts eutrophication-related
water quality conditions (total phosphorus, total nitrogen, chlorophyll-a, transparency, and
hypolimnetic oxygen depletion) using empirical relationships derived from assessments of reservoir
data. Applications of BATHTUB are limited to steady-state evaluations of relations between
nutrient loading, transparency and hydrology, and eutrophication responses. Short-term responses
and effects related to structural modifications or responses to variables other than nutrients cannot
be explicitly evaluated.
Input data requirements for BATHTUB include: physical characteristics of the watershed lake
morphology (e.g., surface area, mean depth, length, mixed layer depth), flow and nutrient loading
from various pollutant sources, precipitation (from nearby weather station) and phosphorus
concentrations in precipitation (measured or estimated), and measured lake water quality data (e.g.,
total phosphorus concentrations).
The empirical models implemented in BATHTUB are mathematical generalizations about lake
behavior. When applied to data from a particular lake, actual observed lake water quality data may
differ from BATHTUB predictions by a factor of two or more. Such differences reflect data
limitations (measurement or estimation errors in the average inflow and outflow concentrations) or
the unique features of a particular lake (no two lakes are the same). BATHTUB's "calibration
factor" provides model users with a method to calibrate the magnitude of predicted lake response.
The model calibrated to current conditions (against measured data from the lakes) can be applied to
predict changes in lake conditions likely to result from specific management scenarios, under the
condition that the calibration factor remains constant for all prediction scenarios.
Model Set-up
Using descriptive information about Findley Lake and its surrounding drainage area, as well as
output from AVGWLF, a BATHTUB model was set up for Findley Lake. Mean annual
phosphorus loading to the lake was simulated using AVGWLF for the period 1990-2007. The
TMDL for Findley Lake was developed using the long-term mean annual phosphorus loading to the
lake for the period 2003-2007. Using this load as input, BATHTUB was used to simulate water
41
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quality in the lake. After initial model development, NYS DEC sampling data were used to assess
the model's predictive capabilities and, if necessary, "fine tune" various input parameters and sub-
model selections within BATHTUB during a calibration process. Once calibrated, BATHTUB was
used to derive the total phosphorus load reduction needed in order to achieve the TMDL target.
Sources of input data for BATHTUB include:
Physical characteristics of the watershed and lake morphology (e.g., surface area, mean depth,
length, mixed layer depth) - Obtained from CSLAP and bathymetric maps provided by NYS
DEC or created by the Cadmus Group, Inc.
Flow and nutrient loading from various pollutant sources - Obtained from AVGWLF output.
Precipitation Obtained from nearby National Weather Services Stations.
Phosphorus concentrations in precipitation (measured or estimated), and measured lake water
quality data (e.g., total phosphorus concentrations) Obtained from NYS DEC or USGS.
Tables 9 12 summarize the primary model inputs for Findley Lake, including the coefficient of
variation (CV), which reflects uncertainly in the input value. Default model choices are utilized
unless otherwise noted. Spatial variations (i.e., longitudinal dispersion) in phosphorus
concentrations are not a factor in the development of the TMDL for Findley Lake. Therefore,
division of the lake into multiple segments was not necessary for this modeling effort. Modeling the
entire lake with one segment provides predictions of area-weighted mean concentrations, which are
adequate to support management decisions. Water inflow and nutrient loads from the lake's
drainage basin were treated as though they originated from one "tributary" (i.e., source) in
BATHTUB and derived from AVGWLF.
BATHTUB is a steady state model, whose predictions represent concentrations averaged over a
period of time. A key decision in the application of BATHTUB is the selection of the length of
time over which water and mass balance calculations are modeled (the "averaging period"). The
length of the appropriate averaging period for BATHTUB application depends upon what is called
the nutrient residence time, which is the average length of time that phosphorus spends in the water
column before settling or flushing out of the lake. Guidance for BATHTUB recommends that the
averaging period used for the analysis be at least twice as large as nutrient residence time for the lake.
The appropriate averaging period for water and mass balance calculations would be 1 year for lakes
with relatively long nutrient residence times or seasonal (6 months) for lakes with relatively short
nutrient residence times (e.g., on the order of 1 to 3 months). The turnover ratio can be used as a
guide for selecting the appropriate averaging period. A seasonal averaging period (April/May
through September) is usually appropriate if it results in a turnover ratio exceeding 2.0. An annual
averaging period may be used otherwise. Other considerations (such as comparisons of observed
and predicted nutrient levels) can also be used as a basis for selecting an appropriate averaging
period, particularly if the turnover ratio is near 2.0.
Precipitation inputs were taken from the observed long term mean daily total precipitation values
from the Cobleskill, NY and Maryland, NY National Weather Services Stations for the 2003-2007
period. Evapotranspiration was derived from AVGWLF using daily weather data (2003-2007) and a
cover factor dependent upon land use/cover type. The values selected for precipitation and change
in lake storage have very little influence on model predictions. Atmospheric phosphorus loads were
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specified using data collected by USGS from a collection site at Mendon Ponds County Park, in
New York (Sherwood, 2005). Atmospheric deposition is not a major source of phosphorus loading
to Findley Lake and has little impact on simulations.
Lake surface area, mean depth, and length were derived using GIS analysis of bathymetric data.
Depth of the mixed layer was estimated using a multivariate regression equation developed by
Walker (1996). Existing water quality conditions in Findley Lake were represented using an average
of the observed summer mean phosphorus concentrations for years 2003-2007. These data were
collected through NYS DEC's CSLAP. The concentration of phosphorus loading to the lake was
calculated using the average annual flow and phosphorus loads simulated by AVGWLF. To obtain
flow in units of volume per time, the depth of flow was multiplied by the drainage area and divided
by one year. To obtain phosphorus concentrations, the nutrient mass was divided by the volume of
flow.
Internal loading rates reflect nutrient recycling from bottom sediments. Internal loading rates are
normally set to zero in BATHTUB since the pre-calibrated nutrient retention models already
account for nutrient recycling that would normally occur (Walker, 1999). Walker warns that
nonzero values should be specified with caution and only if independent estimates or
measurements are available. In some studies, internal loading rates have been estimated from
measured phosphorus accumulation in the hypolimnion during the stratified period. Results from
this procedure should not be used for estimation of internal loading in BATHTUB unless there is
evidence the accumulated phosphorus is transported to the mixed layer during the growing season.
Specification of a fixed internal loading rate may be unrealistic for evaluating response to changes in
external load. Because they reflect recycling of phosphorus that originally entered the reservoir from
the watershed, internal loading rates would be expected to vary with external load. In situations
where monitoring data indicate relatively high internal recycling rates to the mixed layer during the
growing season, a preferred approach would generally be to calibrate the phosphorus sedimentation
rate (i.e., specify calibration factors < 1). However, there still remains some risk that apparent
internal loads actually reflect under-estimation of external loads.
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Table 9. BATHTUB Model Input Variables: Model Selections
Water Quality Indicator
Phosphorus Calibration
Error Analysis
Availability Factors
Mass Balance Tables
Default model choice
Description
2nd Order Available Phosphorus
01 Decay Rate*
01 Model and Data*
00 Ignore*
Use Estimated Concentrations
Table 10. BATHTUB Model Input: Global Variables
Model Input
Averaging Period (years)
Precipitation (meters)
Evaporation (meters)
Atmospheric Load (mg/m2-yr)- Total P
Atmospheric Load (mg/m2-yr)- Ortho P
Default model choice
Table 11. BATHTUB Model Input: Lake Variables
Morphometry
Surface Area (km2)
Mean Depth (m)
Length (km)
Estimated Mixed Depth (m)
Observed Water Quality
Mean
1.078
0.613
75.48
43.52
Mean
35.76
0.2*
0.3*
0.5*
0.5*
1.2
3.3
1.998
3.3
Mean
NA
NA
NA
0.12
CV
0.5
Default model choice
Table 12. BATHTUB Model Input: Watershed "Tributary" Loading
Monitored Inputs
Total Watershed Area (km2)
Flow Rate (hm3/yr)
Total P (ppb)
Organic P (ppb)
Mean
12.14
5.68
75.05
48.71
0.1
0.2
0.2
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Model Calibration
BATHTUB model calibration consists of:
1. Applying the model with all inputs specified as above
2. Comparing model results to observed phosphorus data
3. Adjusting model coefficients to provide the best comparison between model predictions and
observed phosphorus data (only if absolutely required and with extreme caution.
Several t-statistics calculated by BATHTUB provide statistical comparison of observed and
predicted concentrations and can be used to guide calibration of BATHTUB. Two statistics
supplied by the model, T2 and T3, aid in testing model applicability. T2 is based on error typical of
model development data set. T3 is based on observed and predicted error, taking into consideration
model inputs and inherent model error. These statistics indicate whether the means differ
significantly at the 95% confidence level. If their absolute values exceed 2, the model may not be
appropriately calibrated. The Tl statistic can be used to determine whether additional calibration is
desirable. The t-statistics for the BATHUB simulations for Findley Lake are as follows:
Observed
Simulated
Average (03-0
In cases where predicted and observed values differ significantly, calibration coefficients can be
adjusted to account for the site-specific application of the model. Calibration to account for model
error is often appropriate. However, Walker (1996) recommends a conservative approach to
calibration since differences can result from factors such as measurement error and random data
input errors. Error statistics calculated by BATHTUB indicate that the match between simulated
and observed mean annual water quality conditions in Findley Lake is quite good. Therefore,
BATHTUB is sufficiently calibrated for use in estimating load reductions required to achieve the
phosphorus TMDL target in the lake.
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APPENDIX C. TOTAL EQUIVALENT DAILY PHOSPHORUS LOAD ALLOCATIONS
Total Phosphorus Load (Ibs/day)
Current Allocated Reduction
1.073
0.127
1.162
0.209
0.312
0.077
0.0
0.209
Source
Agriculture*
Developed Land*
Septic Systems
Forest, Wetland, Stream Bank, and
Natural Background
LOAD ALLOCATION
Point Sources
WASTELOAD ALLOCATION
LA +WLA
Margin of Safety
TOTAL
Includes phosphorus transported through surface runoff and subsurface (groundwater)
0.761
0.05
1.162
0.0
/o Reduction
2.571
0.0
0.0
2.571
___
2.571
0.598
0.0
0.0
0.598
0.1
0.698
1.973
0.0
0.0
1.973
___
71%
39%
100%
II I
77%
0%
0%
77%
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