Download thermal and nutrient dynamics in a chronically

THERMAL AND NUTRIENT DYNAMICS IN A CHRONICALLY
EUTROPHIED DRINKING WATER RESERVOIR
_______________
A Thesis Proposal (in progress)
Presented to the
Faculty of
San Diego State University
_______________
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
in
Geography
_______________
by
Raymond Lee
Fall 2012
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Introduction
Water quality in drinking water reservoirs, including in San Diego, is adversely affected by
vernal and summertime algal blooms. The blooms can cause hypoxia and health, odor and taste
problems, in spite of water treatment (Falconer 1999; Izaguirre et al., 1999; Paerl et al., 2001).
Copious algal production leads to high levels of total organic carbon (TOC) in the influent at
water treatment facilities. High TOC results in an upper limit to the amount of chemical
treatment that can be applied because disinfection is both expensive and introduces harmful
disinfection byproduct (DBP) contaminants directly proportional to the presence of precursor
organic matter (Camel and Bermond 1998; Richardson 2003; Singer 1994; Symons et al., 1975).
The levels of DBPs must not surpass federal standards that were set to protect human health
(U.S. EPA 2006).
High inputs of nitrogen (N) and phosphorus (P) are the primary catalysts for algal growth and
TOC production. Nutrients are natural or anthropogenic in origin and are delivered to a reservoir
either through atmospheric deposition (Luo et al., 2011; Michalski 2004) or, more prominently,
through inflow and internal cycling. Inflow includes transfers of water from other reservoirs
(Fornarelli and Antenucci, 2011); water imported from outside of the watershed that drains to the
reservoir; and surface runoff and groundwater inflow from the watershed. San Diego purchases a
blend of imported water, which, historically, accounts on average for ~80% of San Diego’s
annual water supply (CSD 2011). A majority of the imported water is sourced from the Colorado
River (high in N and Total Dissolved Solids (TDS)) and the balance comes from the Sacramento
River (high in P, TOC, and bromide). The relative proportions of water in the blend fluctuate.
The quality of imported raw water is generally better than local inflow from surface water, but
the supply cost is greater by more than an order of magnitude. In 2012, the baseline supply cost
of water, which includes transportation and storage of water, is $52 acre foot
for local raw
water and $699 acre foot for imported raw water (price does not include a fixed cost of $187
acre foot ; SFID 2012). It is in the interest of municipal water districts to improve the water
quality of local runoff because the supply cost is much lower, and because high nutrient loading
can result in algal growth even if imported water is of high quality.
For reservoirs which receive a high proportion of local runoff in their water balance, land use in
the watershed is a significant determinant of external nutrient loading. Agricultural areas leach
excess nutrients from fertilizer application (Li et al., 2011; Sheeder and Evans, 2004), which
leads to increased algal production in water bodies downstream (Savage et al., 2010). Dairy
farms, which produce cattle manure, have been shown to increase P concentrations in stormwater
runoff (McFarland and Hauck, 1999). Urbanization introduces fertilizer from residential lawns
(Kelling and Peterson, 1975), which, in some American cities, are growing bigger in proportion
to the lot size of new homes (Robbins and Birkenholtz, 2003), and golf courses (King et al.,
2007). Treated wastewater effluents enter streams (Bowen and Valiela, 2001; Kalscheur et al.,
2012; McLaughlin et al., 2006). Generally, higher quantities of nutrients are exported from urban
areas than from natural areas due to the prevalence of impervious surface, which increases runoff
during precipitation events and removes sinks capable of biological uptake (Lewis and Grimm,
2007; Tang et al., 2011). However, forest fires can contribute P by atmospheric deposition from
smoke and ash during the fire (Spencer et al., 2003) or ash can be transported through surface
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flow, depending on the severity of the burn, speed of vegetation recovery and volume and
intensity of rainfall following the fire (Feikema et al., 2011).
Autochthonous nutrients, particularly P, which are generated internally, can be a more serious
threat to water quality than allochthonous nutrients, as excess nutrient inputs accumulate in a
water body over time. Internal cycling occurs when a legacy of nutrient loading is coupled with
hypoxic conditions in the hypolimnion and sediment. Under anoxic conditions, ferric ions of iron
reduce to the ferrous form, which leads to the dissolution of the P and iron (Fe) bond and causes
sediments to release soluble reactive phosphorus (SRP) and Fe, as well as ammonia (NH ) and
manganese (Mn), to the hypolimnion (Beutel, et al., 2007; Correll, 1998). Warmer water
temperatures have also been shown to increase release of P from sediment (Duan et al., 2012).
The liberated P further enhances algal growth and decay in the euphotic zone upon entrainment
(Effler et al., 1986), vertical eddy diffusion (Stauffer 1987) or mixis (Cooke et al., 1993). Algae
eventually sink into the hypolimnion and sediment, feeding microbial bacteria that exert high
oxygen demand. This positive feedback further degrades water quality and reinforces a cycle of
nutrient supply, production of labile organic matter and DO depletion.
In addition to the importance of nutrient loading, the development of hypoxia in a water body
also depends on hydrodynamic circulation. Hypoxic conditions commonly occur following the
development of thermal stratification. Warm summer temperatures cause the formation of a
density gradient in the vertical water column and the separation of warm surface waters from
cold bottom waters. When a lake is stratified, aeration from the atmosphere or plant respiration
cannot replenish the hypolimnion because oxygen cannot penetrate the dense upper boundary of
the hypolimnion. Usually, this stratified state persists until autumn, when the temperature in the
water column homogenizes and the water can recirculate. The risk of anoxia depends on the
concentration of dissolved oxygen (DO) in the hypolimnion at the onset of stratification and the
duration of the stratified state.
The length and strength of summer thermal stratification are determined primarily by weather –
related factors (Boehrer and Schultze, 2008), such as the penetration of solar radiation, sensible
heat exchange with the atmosphere and wind mixing. A warming climate increases the potential
for anoxia because it can move the onset of stratification earlier in the year, prolonging its
duration (Foley et al., 2012; Minns et al., 2011; Robertson and Ragotzkie, 1990; Wang et al.,
2012; Winder and Schindler, 2004); intensify vertical temperature gradients (Jankowski et al.,
2006); and raise overall lake temperature (Arhonditsis et al., 2004), which is correlated with
increasing microbial metabolism (Pettersson 1998). Wind can decrease anoxia by mechanically
turning over the water to allow circulation or push oxygen across the thermocline and reduce
Volumetric Hypolimnetic Oxygen Demand (VHOD; Foley et al., 2012), the volume of oxygen
consumed by microbial bacteria in the hypolimnion and sediment.
Additional factors include lake morphometry, expressed as the Osgood Index (Osgood 1988), the
ratio of mean depth ( ̅) to the square root of lake surface area (A0.5). Values of ̅/ . > 8
signify deeper lake depths compared to surface area and longer stratification, whereas ̅/ . < 6
or 7 represent shorter stratification, a larger interface between the hypolimnion and sediment,
greater mixing in the water column, enhanced vertical migration of P and higher DO depletion
rates (Nurnberg 1995; Osgood 1988).
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Management of a reservoir can also affect anoxia, as a reservoir functions differently from a
natural lake. Hydraulic conditions can increase eutrophication irrespective of external nutrient
addition (Zohary and Ostrovsky, 2011). Shallow reservoirs generally have shorter water
retention times and also experience lower P retention due to more intense mixing (Straskraba et
al., 1995). Additionally, when the water supply in a reservoir becomes extremely low, riparian
vegetation may spread, causing biomass to accumulate (Coops et al., 2003). Subsequent die-off
of the vegetation upon refilling the reservoir can release a shock of nutrient and carbon input to
the lake and enhance anoxia (Geraldes and Boavida, 2003). High amplitude of seasonal and
multi-annual water level fluctuation from drinking water drawdown during summer drought in a
Mediterranean climate can prematurely destroy hydrodynamic stability and send nutrient-rich
water from the hypolimnion to the epilimnion during the algal growing season, rather than later
in the autumn (Naselli-Flores, 2003). The choice of the location of the dam outlet for withdrawal
of water can also impact stability. Drawing cold hypolimnetic water leaves behind a greater
proportion of warm epilimnetic water, which disturbs the stability of the thermocline and
transports bottom nutrients to be entrained in the epilimnion (Nowlin et al., 2004). Drawing
water from the epilimnion can counteract the effects of climate warming, while drawing from the
hypolimnion can exacerbate them (Moreno-Ostos et al., 2008).
Research Problems and Study Design
Figure 1. Land use map of Lake Hodges watershed showing land uses; USGS stream gages: (SY) Santa
Ysabel Station, (G) Guejito Station, (SM) Santa Maria Station; and nutrient sampling sites: (1) Temescal
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Creek, (2) Santa Ysabel Creek, (3) Guejito Creek, (4) Santa Maria Creek, (5) Cloverdale Creek, (6) San
Dieguito River, (7) Sycamore Creek, (8) Green Valley Creek, (9) Kit Carson Creek, (10) Felicita Creek, (11)
Del Dios Creek.
Water quality is severely degraded Lake Hodges in San Diego County. This is exhibited by
seasonal algal blooms and low dissolved oxygen concentrations, which persist in the
hypolimnion for most of the year.
The overarching research question is: W
hy is DO chronically low or absent in the hypolimnion of Lake Hodges (?)?
This study will address the question in two parts, the first of which uses a survey of thermal
behavior and oxic conditions in four drinking water reservoirs—Barrett, Hodges, Sutherland and
Morena (Figure 1)—in San Diego County. The four reservoirs are in a semi-arid region and in
relatively close proximity, but have different amounts of land use in their contributing
watersheds. The second part of the study will be a more focused investigation of the nutrient
dynamics in Lake Hodges, including lake modeling.
To answer the principal research question, this study addresses several other research questions,
including:
(1) What is the duration and length of thermal stability in the four study reservoirs? Are
there any differences in stability among the four reservoirs, controlling for interannual
variability and other factors that influence stability, such as lake volume? How does
climate variability influence lake temperatures and thermal stratification?
Lake temperature profile data at all four study reservoirs from 19xx to 20xx will be analyzed to
determine the Schmidt Stability index (Hutchinson 1957; Idso 1973), which reflects the intensity
of stability and the duration of lake stratification. These are important determinants of the risk of
anoxia in the hypolimnion. The analysis will look for temporal trends in lake stratification, with
particular attention to the date of the onset of stratification, duration of the stratified season and
the mean and maximum annual stability of the water column. A preliminary analysis suggests
that mean annual lake volume is significantly correlated (p-value < 0.001) with mean annual
stability at all of the study reservoirs. Other variables which may contribute to the stability of a
lake, such as air temperature and windspeed, will be regressed against stability. Additionally,
thermal profile data will be reviewed for temporal trends in volume-weighted temperatures of the
whole lake, epilimnion and hypolimnion. Warming lake temperatures may be caused by climate
change and may impact anoxia.
(2) What is the spatial extent and duration of anoxia, expressed in terms of the Anoxic
Factor, in the study reservoirs? Are there any differences among the reservoirs,
controlling for interannual variability and trends in thermal stability or land use? Which
factors contribute to the exceptional anoxia at Lake Hodges?
Lake oxygen profile data at all four study reservoirs from 19xx to 20xx will be analyzed to
determine their Anoxic Factors (AF), a metric which gives the theoretical number of days in a
year for which an area equal to the surface area of a lake is anoxic (Nurnberg 1995). Explanatory
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variables, such as mean and maximum annual stability, mean annual lake volume, lake
temperature, air temperature and percentages of urban and agricultural land use in the watershed,
will be regressed against AF. Lake morphometry and the ratio of watershed area to lake volume
will also be considered.
(3) How does internal loading of P compare with external loading?
The imported loads of P will be compared to the volume of P cycling in the lake and leaving
through the effluent. Annual external loading of P to Lake Hodges will be back-calculated using
the daily loads extrapolated from rating curves of nutrient loading in the four primary inflows,
including San Dieguito River, Kit Carson Creek, Felicita Creek and Green Valley Creek. San
Dieguito River flows through a large, agricultural area, and the other streams flow through urban
neighborhoods.
The rating curves will model nutrient loading as a function of stream discharge. They will be
made with flow data, provided by the City of San Diego and Weston Solutions, and nutrient data,
provided by the City of San Diego and the author. Stormwater grab samples will be collected in
the field throughout the wet season (December 2012—March 2013) and analyzed for their
chemistry. Lake P released from the sediment will be derived from nutrient concentration data,
provided by the City of San Diego, taken at the surface, middle of the water column and
sediment. Effluent P data will be provided by the Santa Fe Irrigation District.
Additionally, Lake Hodges will be modeled with a lake thermal and nutrient model, CE-QUALW2, using bathymetric, meteorological, thermal, nutrient and DO input data. The model will
elucidate processes in the development of thermal stability and sediment release of P. The model
will be run for different future scenarios, including increasing air temperatures, due to climate
warming, and various TP loading scenarios, as a result of anticipated increases in urbanization
and impervious surface cover in the watershed or possible increased nutrient uptake in
constructed wetland projects. The model results will provide additional insight on whether the
significant problem at Hodges is external or internal nutrient loading.
Methods
Study Site
Lake Hodges is located approximately 50 km north of the city of San Diego and 20 km inland
from the coast (33.045, -117.128611) at an altitude of 67 m a.s.l. It is a drinking water reservoir,
which also serves as a recreational area for fishing and boating. When the lake is at full capacity,
surface area is 4.99 km and maximum water depth is 35 m. The Osgood hypsometric ratio is
6.2, which suggests that the lake is shallow and there is a large interface between the
hypolimnion and sediment, making it vulnerable to hypoxia (Nurnberg 1995). Typically, it is a
warm monomictic lake (Hutchinson 1957), but can be considered continuous warm polymictic
(Lewis 1983) when decreased inflow and drawdown result in low water volume. From 2002 to
2004, the lake did not stratify because lake volume was low enough to allow deeper solar
penetration and thermal homogenization. Oxygen in the hypolimnion is chronically low and
commonly completely depleted before fall turnover, leading to summer anoxic conditions and
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nuisance algal blooms. Water quality in Lake Hodges is poor—Lake Hodges is 303(d) listed for
color, mercury, Mn, TN, TP, turbidity and pH (SWRCB, 2010).
Hodges water has three major problems. First, it contains elevated levels of TOC, which causes
chlorine in the water treatment to react and form elevated levels of DBPs, primarily,
trihalomethanes (THMs) and haloacetic acids (HAAs). Second, there is an elevated level of Mn,
which could cause discoloration of the water. Third, the prevalence of algae-produced chemicals,
primarily Geosmin and 2-methylisoborneol (MIB), impart an objectionable taste and odor (SFID
2012). There is currently no treatment scheme to destroy or remove these chemicals, due to
prohibitive costs.
Between 1989 and 2010, local runoff accounted for 96% of inflow to Lake Hodges and direct
precipitation accounted for 4%, which suggests that land use is potentially a dominant control on
water quality (City of San Diego, unpublished data). The largest watershed that drains to Lake
Hodges is the San Dieguito River, which drains urban, agricultural and natural land use. Flow is
intermittent, but can be substantial during the wet season. Minor tributaries include Del Dios
Creek, Felicita Creek, Green Valley Creek and Kit Carson Creek, all of which drain
predominantly urban land use (Figure 1). The five inflows together drain a catchment of
approximately 641 km , the largest in the study, hereafter called the “Hodges watershed”.
Land use in Hodges watershed is quite different from the other study watersheds (Table xx).
Hodges watershed has considerable urban and agricultural areas (22.4% and 14.3% of the
watershed area, respectively; Figure 1). Agricultural land use is separated into two categories:
golf courses, orchards and vineyards (lower impact), and poultry ranches and dairy farms (higher
impact). The area under agricultural land use is more stable over time than the area under urban
land use, which in 1986 was 11.6% and is projected to be 57.8% in 2050, higher than in the other
three study watersheds. Currently, Barrett, Morena and Sutherland reservoirs are in sparsely
urbanized watersheds (3.7%, 6.6% and 2.9%, respectively). There is less agriculture in Barrett
and Morena watersheds (1.1% and 2.2%, respectively), but more in Sutherland (19.2%).
The watershed area to lake volume ratio is high in Lake Hodges, compared to the other study
reservoirs, which further indicates that activities in the watershed may have a significant
influence on the quality of surface runoff and, by extension, the receiving lake.
Sutherland Reservoir is the smallest of the study reservoirs, located about 72 km northeast of San
Diego, upstream of Lake Hodges. Though Hodges actually drains the Sutherland watershed,
water that is collected in Sutherland Reservoir is exchanged between it and San Vicente
Reservoir, keeping Sutherland watershed hydrologically isolated from Hodges watershed. Barrett
Reservoir has the largest volume of the study reservoirs, located at the confluence of
Cottonwood and Pine Valley Creeks, about 56 km east of San Diego. It is connected to Morena
Reservoir, located 24 km to the east, the highest and most remote of all the reservoirs in the San
Diego reservoir system.
Table 1. Characteristics of study reservoirs: Watershed Area; V—lake volume; —lake surface area;
depth; —mean depth;
—mean maximum annual Stability; —mean annual Stability;
length of stratified season;
—mean annual Anoxic Factor.
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—maximum
—mean
Reservoir
Barrett
Hodges
Morena
Sutherland
Watershed
Area
(km2)
V
(km )
A
(km )
z
(m)
z
(m)
z/A .
(m km )
S
(J m )
S
(J m )
Stratıfıed Season
(days)
AF
(days)
641
26.21
24.16
2.19
3.26
41.5
25.05
11.22
7.57
855.40
375.38
253.67
174.02
140
14.99
1.28
31.4
38.5
6.66
4.22
231.51
79.31
146.64
63.74
339
294
19.43
2.49
7.01
10.4
6.2
9.2
264.97
728.03
121.7
312.17
200
221.14
161.64
187.43
Data
At Lake Hodges and Sutherland, water quality parameters, including temperature, DO, pH,
specific conductivity and TDS, were sampled throughout the water column at approximately 1 m
depth intervals in the lacustrine zone, approximately monthly from 1992 to 2011. At Lake
Barrett and Morena, sampling occurred biweekly, and weekly in some cases, from 1989 to 1992,
then monthly until 2012. Where more than one sample was collected per month, data from the
first sample event of each month was kept for the analysis in order to be consistent among
reservoirs.
Meteorological data were obtained from the National Climatic Data Center (NCDC) for the
National Oceanic and Atmospheric Administration (NOAA) weather station closest to each
reservoir. The parameters used in the study include daily maximum air temperature, minimum
air temperature, total precipitation and mean windspeed, if available. Additional meteorological
data, including solar radiation, was obtained for Lake Hodges from the University of California
Pest Management Program (UC IPM). Meteorological data covered the same time span as the
water quality data.
At Lake Hodges, nutrients, including NH , nitrate, nitrite, nitrate plus nitrite, TKN?, TN, and TP,
were sampled by the City of San Diego at the surface, a fixed point in the water column
(reservoir gauge 75), and 1 m off the bottom, weekly from 2008 to 2012. Reservoir gauge 75
refers to … Nutrients were also sampled at ten tributaries in the Hodges watershed (Figure 1)
monthly from 2003 to 2012. Nutrient concentration in the effluent was sampled weekly from
2008 to 2011. At Lake Barrett and Lake Morena, the same analytes were sampled at the surface
only at the surface (?) twice yearly from 1992 to 1995, then quarterly from 1996 to 2012. No
samples were collected a Sutherland (?).
Land use data were obtained from the San Diego Association of Governments Regional GIS
Data Warehouse database (SANDAG). The GIS layers are a composite of aerial photography
and data from the County Assessor Master Property Records file. Land use information has been
verified as accurate by each of the local jurisdictions and the County of San Diego.
Field methods
Grab sampling will be done during storm events at three inflow sites to Lake Hodges, including
San Dieguito River, Kit Carson Creek and Green Valley Creek. Multiple (~3-5) samples (1L)
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will be collected in duplicate to capture the rising limb, peak and falling limb of the event. All
unfiltered samples will be stored in bottles at 4°C and transported to the Ecology Analytical Lab
at San Diego State University (SDSU), where they will be filtered (0.45 um) and analyzed
according to the Kjeldahl method for concentrations of NH , NO , total Kjeldahl Nitrogen
(TKN) and TP. Discharge is measured at each location by Weston Solutions, who installed
bubbler-type depth meters (check my terminology) at a xx-minute sampling interval.
Analysis
Temperature and stability
Volume-weighted water temperature will be calculated for the epilimnion, hypolimnion and the
whole lake for each observation of water temperature using:
T or T or T =
1
V
t V
(1)
where is the temperature of the epilimnion,
is the temperature of the hypolimnion,
is the
temperature of the whole lake,
is the volume of the layer of interest (epilimnion, hypolimnion
or whole lake), is the temperature of a depth interval in that layer,
is the volume of the
depth interval and n is the number of depth intervals. The summation will be taken over all
depths at 1 m intervals from the initial depth of the layer of interest to the bottom of that layer.
The strength of thermal stratification in the reservoir will be expressed in terms of Schmidt
Stability Index, S, J m (Hutchinson 1957), which indicates the minimum amount of work that
must be done by wind to return a lake to an isothermal state. S will be calculated from
differences in water density in the water column using equation (8) in Idso (1973):
S=
( −
∗
)(
−
∗
)
(2)
where g is acceleration due to gravity, A is surface area of the lake, A is lake area at depth z, p
is water density as calculated from the temperature at depth z, p* is the lake’s mean water
density, and z* is the depth where the mean water density occurs. The summation will be done
for all depths (z) at an interval (d ) of approximately 1 m from the surface (z ) to the maximum
depth (z ).
Water density will be calculated as a function of temperature using the Thiessen-ScheelDiesselhorst Equation (Maidment 1993):
p = 1000 1 −
t + 288.94
(t − 3.9863)
508929.2(t + 68.12963)
where p is water density (kg m ) and t is temperature (°C).
Page | 9
(3)
The date of stratification onset will be considered as the day of the year when Schmidt Stability
in the water column reaches a minimum threshold of 100 J m and the date of fall turnover will
be the day of the year when Schmidt Stability falls below 100 J m . This threshold was selected
because it generally correlates with the development of a thermocline, defined as a minimum
temperature gradient of 1° C in the water column (Wetzel 2001). An initial review of the data
showed that the estimated days of the start and end of stratification did not usually coincide with
an observation of lake water temperature, so piecewise linear interpolation between observation
dates and general rounding rules were used to determine them.
Anoxia
Hypolimnetic oxygen will be expressed as the Anoxic Factor, the number of days in a year for
which an area equivalent to the surface area of a lake is anoxic. Anoxic water is defined as
having an oxygen concentration of 1.5 mg L or less (Nurnberg 1995). AF will be calculated for
each year with observations n as:
AF = ∑
(4)
where the duration of anoxia (t in days) is multiplied by the shallowest anoxic plane (a in m )
and divided by the lake surface area (A , m ) for period i.
AF was chosen as a measure of anoxia because it accounts for the high fluctuations in water
volume that occurs in many reservoirs. Comparison of AF is a more suitable approach than
VHOD to evaluate anoxia in the hypolimnion because VHOD determines only the speed of
developing anoxia but provides no further information about the anoxia after that (Nurnberg
2004). The hypolimnion in Lake Hodges was anoxic for most of the study period, which makes
determination of the VHOD and interannual comparisons difficult.
Nutrients
Annual TP concentration rating curves will be developed using flow measurements and observed
TP concentrations when samples were taken. Nutrient concentrations can then be back-calculated
for the periods for which there are flow data, but not nutrient data. The relationship between flow
rates and nutrient concentration, a power function, will be fitted on a log-log plot:
Ci = aQib
(5)
where Ci is predicted TP concentration (mg L-1) at time i, Qi is streamflow (cfs) at time i and a
and b are regression coefficients.
Annual load will be calculated as:
Annual TP Load = ∑
Q ∗ C ∗ 2.4466
where Qi is streamflow (cfs) at time i, Ci is predicted TP concentration (mg L-1) at time i and
2.4466 is the conversion factor from cfs and mg L -1 to kg day-1. Instantaneous load is derived by
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(6)
multiplying Qi, Ci and 2.4466; daily load is the summation of instantaneous loads over the day;
annual load is the summation of daily loads over 365 days.
Statistical Analysis
Trends in the Schmidt stability index and AF will be tested using…
Modeling
CE-QUAL-W2 was selected because it can simulate vertical mixing, thermocline development
and nutrient release from sediments (Cole and Wells, 2011). It is a dynamic 2-D model of lake
hydraulics and water quality, which was originally developed by the U.S. Army Corp of
Engineers and Portland State University. It has been used to successfully model thermal
stratification (Gelda et al., 1998; Ma et al., 2008) and water quality (Kuo et al., 2003; Wu et al.,
2006) in reservoirs.
A bathymetric contour map of Lake Hodges was hand digitized in ArcGIS and converted to a
Triangulated Irregular Network (TIN) file (Figure 2). The reservoir will be divided into 18
longitudinal segments, with lengths varying between 181 m to 1805 m, widths between 77 m and
1551 m and depth layers of either 5 ft or 10 ft (Figure 2). The exchanges of energy, water and
nutrients will be modeled using input variables, including climate, inflow, lake temperature, DO
and nutrient levels in the water column (See Table 2). The model will provide insight into
thermal and nutrient cycling processes in the lake and simulate scenarios in which TP loading
changes according to land use change.
Table 2. Data parameters which will be used to calibrate the CE-QUAL-W2 model.
Data (Input)
Bathymetry
Air temperature
Dewpoint
temperature
Wind speed
Wind direction
Cloud cover (MODIS
cloud mask product)
Short wave solar
radiation
Inflow
Nutrients
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Source
County of San Diego
NCDC
NCDC
Available Date Range
NA
1990-2012
1990-2012
Use in Model
Model input
Model input
Model input
NCDC
NCDC
NASA
1990-2012
1990-2012
2000-2012
Model input
Model input
Model input
UC IPM
1990-2012
Model input
Weston Solutions,
Author
County of San Diego,
Author
2008-2012?
Model input
2008-2012
Model input
Figure 2. A bathymetric representation of Lake Hodges as a TIN and longitudinal and vertical profiles in CEQUAL-W2.
Page | 12
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variability on the thermal properties of Lake Washington. Limnology and Oceanography,
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American Public Health Association (APHA). (1998) Standard methods for the examination of
water and wastewater, 20th Ed., Washington, D.C.
Beutel, M. W. (2003) Hypolimnetic anoxia and sediment oxygen demand in California drinking
water reservoirs. Lake and Reservoir Management, 19, 208-221.
Beutel, M. Hannoun, J. Pasek & K. Bowman Kavanagh (2007) Evaluation of hypolimnetic
oxygen demand in a large eutrophic raw water reservoir, San Vicente Reservoir, Calif.
Journal of Environmental Engineering-Asce, 133, 130-138.
Boehrer, B. & M. Schultze (2008) Stratification of lakes. Reviews of Geophysics, 46.
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Camel, V. & A. Bermond (1998) The use of ozone and associated oxidation processes in
drinking water treatment. Water Research, 32, 3208-3222.
Cole TM and Wells SA. (2011) CE-QUAL-W2: A Two-Dimensional, Laterally Averaged,
Hydrodynamic and Water Quality Model, Version 3.7, Department of Civil and
Environmental Engineering, Portland State University, Portland, Oregon.
Cooke, G. D., M.R. McComas, D.W. Waller, R.H. Kennedy (1977) The occurrence of internal
phosphorus loading in two small, eutrophic, glacial lakes in northeastern Ohio.
Hydrobiologia, 129.
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