USDA
Lake Waco-Bosque River Initiative
Strategies for Monitoring
Nonpoint Source Runoff
Anne McFarland and Larry Hauck
TR0115
December 2001
TIAER
Texas Institute for Applied Environmental Research
Tarleton State University • Box T0410 • Tarleton Station • Stephenville, Texas 76402
254.968.9567 • Fax 254.968.9568
[email protected]
Strategies for Monitoring Nonpoint Source Runoff
Acknowledgments
This study was supported financially by the United State Department of Agriculture Lake
Waco-Bosque River Initiative with additional funding provided by the State of Texas. The
authors wish to acknowledge the support of landowners who allowed access to their land for
stream monitoring sites. The authors also wish to acknowledge the dedicated work of the
many field personnel and laboratory chemists involved with TIAER’s water quality
monitoring program. Particularly with regards to storm water monitoring as rain often falls
on weekends requiring staff to be on call seven days a week.
Mention of trade names or commercial products does not constitute their endorsement.
For more information about this document or any other document TIAER produces, send email to [email protected].
Authors
Anne McFarland, Research Scientist, TIAER, [email protected]
Larry Hauck, Assistant Director of Environmental Sciences, TIAER, [email protected]
USDA02.09
2
Strategies for Monitoring Nonpoint Source Runoff
Abstract
Water quality monitoring in most cases is labor intensive and expensive. Careful planning is
needed to make sure monitoring efforts are designed to provide appropriate information with
the level of confidence needed to address local watershed-planning goals and objectives.
Monitoring nonpoint source pollution adds an extra level of complexity to most monitoring
programs with the need to measure storm water as well as base flow conditions. In
developing any type of monitoring plan, there are six primary points to consider: (1) what to
monitor, (2) where to monitor, (3) what type of monitoring to implement, (4) how often to
monitor, (5) how long to monitor, and (6) what resources are available for monitoring. The
strategies used for monitoring generally consider a balancing of cost versus necessary
confidence in the data collected. Unnecessary monitoring is wasteful; while insufficient
monitoring may lead to false conclusions or an inability to meet desired monitoring
objectives. The purpose of this report is to provide an overview of considerations for stream
monitoring with emphasis on monitoring in areas where agricultural nonpoint source
pollution is prominent. Examples are drawn from TIAER’s extensive monitoring program of
stream sites within the North Bosque River watershed.
3
Strategies for Monitoring Nonpoint Source Runoff
4
Contents
Chapter 1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Chapter 2
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Chapter 3
What to Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
General Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Defining Water Quality Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Monitoring and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Concentrations and Loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Chapter 4
Where to Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Spatial Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Determining Sources of Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Evaluating Effectiveness of Control Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Surrogate Sampling Locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Location within a Water Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Chapter 5
What Type of Monitoring to Implement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Chapter 6
How Often to Monitor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Average Annual Concentrations from Routine Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Annual Average Concentrations for Storm Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Annual Loadings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Integration Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Regression Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Results Comparing Loading Estimation Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Chapter 7
How Long to Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Chapter 8
What Resources are Available for Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Chapter 9
Summary & Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Appendix A
Sampling Sites, Weather, and Monitoring Protocols . . . . . . . . . . . . . . . . . . . 63
Sampling Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Precipitation and Hydrologic Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Data Collection Methods and Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Appendix B
Average Daily Flow at Sampling Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Appendix C
Impact of Sampling Frequency on Annual Average Concentration . . . . . . 73
Appendix D
Regression Estimation of Concentrations and Loadings . . . . . . . . . . . . . . . 77
Appendix E
Comparison of Annual Loading Estimation Methods . . . . . . . . . . . . . . . . . . 85
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Strategies for Monitoring Nonpoint Source Runoff
6
Tables
Table 1
Characteristics of seven selected monitoring sites. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Table 2
Comparison of water quality for impacted and least impacted watersheds . . . . . . . . . . . . 24
Table 3
Annual average percent error in PO4-P loading estimates compared to “true” loadings . 45
Table 4
Annual average percent error in TP loading estimates compared to “true” loadings . . . . 45
Table 5
Annual average percent error in TSS loading estimates compared to “true” loadings . . . . 46
Table 6
Minimal detectable change in storm water quality with varying years of monitoring . . . . 48
7
Strategies for Monitoring Nonpoint Source Runoff
8
Figures
Figure 1 North Bosque River watershed and selected sampling sites. . . . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 2 Annual average chlorophyll-a as a function of annual average time-weighted PO4-P . . . . 18
Figure 3 Phosphorus concentrations, stream volume, and loadings along the North Bosque River. 20
Figure 4 Cumulative daily differences in PO4-P and TP loadings and discharge volume for 1998. . 23
Figure 5 Variation in constituent concentrations with discharge at North Bosque River . . . . . . . . . . 30
Figure 6 Variation in NO2-N+NO3-N concentrations over time during six storm events . . . . . . . . . 31
Figure 7 NO2-N+NO3-N concentration during selected storm events in relation to discharge . . . . . 32
Figure 8 TSS concentration during selected storm events in relation to discharge at site BO070. . . . 32
Figure 9 Variation in TSS concentrations over time during six storm events . . . . . . . . . . . . . . . . . . . . 33
Figure 10 Comparison of flow-weighted and time-weighted averaging of storm samples.. . . . . . . . . 35
Figure 11 Impact of sampling interval on average annual concentration at site BO070.. . . . . . . . . . . . 38
Figure 12 Estimates of annual storm event mean concentrations based on subsampling. . . . . . . . . . . 40
Figure 13 Number of storm events needed to obtain certain allowable percent errors in EMCs. . . . . 41
9
Strategies for Monitoring Nonpoint Source Runoff
10
CHAPTER 1
Introduction
The purpose of this report is to provide insight and information for stream monitoring as part
of a watershed planning process that must consider agricultural nonpoint source pollution.
The information discussed herein does not directly address monitoring for broad state and
national programmatic needs that involve numerous river basins, such as, monitoring
required under sections 305(b) and 303(d) of the Clean Water Act to determine the water
quality conditions of all water bodies of a state and to list impaired water bodies. Rather, the
monitoring addressed herein focuses on individual watersheds where monitoring is needed
in response to existing water quality impairment or is being proactively pursued as part of a
watershed management plan to prevent a threatened water body from becoming impaired.
Successful watershed management approaches will require environmental monitoring.
Monitoring is a linchpin of the Planned Intervention Microwatershed Approach (PIMA), an
approach to watershed management being refined and demonstrated under the USDANatural Resources Conservation Service funded Agriculture and the Environment: Lake
Waco-Bosque River Watershed Project (USDA Initiative). PIMA is an assessment-based,
community-led, and performance driven approach to ameliorate agricultural runoff
problems. Other watershed management approaches will contain similar characteristics, but
the point is that there is a reliance on monitoring to support the assessment-based and
performance-driven aspects of this and similar approaches. Assessment monitoring is needed
to define the water quality problem, determine the sources of the problem and their
geographic extent, provide geographic focus on hot spots, and specify allowable pollutant
loadings by source and geographic area. Performance-driven implies that success is measured
not only in the implementation of agricultural best management practices, but ultimately in
improvement of water quality in receiving waters.
This report addresses several relevant issues concerning strategies to monitor agricultural
nonpoint source pollution. In some instances monitoring, by itself, may provide the
information needed to meet objectives, whereas in other instances monitoring may be in a
support role, for example, providing data for the application of computer models. Some
pertinent objectives that can be addressed and supported by monitoring include the
following:
•
Identifying water quality problems—Is there impairment and if so, what pollutants are of
concern? What are current loads or levels of pollutants?
•
Defining discrepancies in current conditions compared to desired conditions—What loads
or levels of pollutants are desired or allowable to overcome impairment, and how do
current conditions compare to desired conditions?
•
Determining sources of pollution—What sources are contributing pollutants and how
much? In other words, where is the impairment coming from?
11
Strategies for Monitoring Nonpoint Source Runoff
•
Making waste load allocations—How should pollutant loads be allocated between
sources to meet desired loads or concentrations? (Computer models are often used to
meet this objective in conjunction with watershed specific monitoring data.)
•
Recommending the implementation of pollution controls—What controls should be
implemented to reduce impairment from different sources? (Monitoring may also
supplement the application of computer models in meeting this objective.)
•
Evaluating effectiveness of implemented control practices—Are load allocations being
met with implemented control practices? If not, how effective are implemented control
practices and what more may be needed?
•
Assessing improvement—Is water quality getting closer to the desired level over time?
Agricultural nonpoint source pollution to surface water bodies is stochastic in nature, because
its occurrence is determined by rainfall-runoff events. A successful nonpoint source
monitoring plan must meet the challenges presented by the stochastic nature of the source and
may need to address several different temporal (e.g., within storm variations of water quality
or annual averages) and spatial (e.g., major rivers, first- or second-order streams, or even
edge-of-field) scales. Based on the above objectives, there are six primary points to consider in
developing a monitoring plan.
•
What to monitor?
•
Where to monitor?
•
What type of monitoring to implement?
•
How often to monitor?
•
How long to monitor?
•
What resources are available for monitoring?
This report will be organized around these six points, which when considered in aggregate,
define how a monitoring plan will obtain observational data that meets desired objectives. In
many instances the general discussion will be embellished through specific examples taken
from monitoring efforts in the North Bosque River watershed under the USDA Initiative. The
streams and rivers in the North Bosque River watershed are characteristically intermittent in
flow with agricultural nonpoint source contributions dominating storm water quality
conditions. Despite the dominance of agricultural contributions during storm events, the
North Bosque River is strongly influenced by effluent from municipal wastewater treatment
plants at low flow conditions, which prevail during and following the typical hot, dry Texas
summer.
12
CHAPTER 2
Background
To provide the reader a proper setting for examples using data from the North Bosque River
watershed, a brief overview is provided of the watershed and selected monitoring sites. More
detail on monitoring within the North Bosque River watershed may be found in Appendix A
and other documents on the monitoring efforts of the USDA Initiative (e.g., Easterling, 2000;
Kiesling et al., 2001; McFarland and Hauck, 1999; McFarland et al., 2001).
The North Bosque River watershed of north central Texas is typical of many watersheds in the
region. The dominant land covers are rangeland and woods. Improved pasture and some row
crop farming are found to some degree throughout the watershed, though row crop is most
common in the southern portions of the watershed, particularly in the floodplain of the North
Bosque River close to the city of Clifton, Texas (Figure 1). The headwaters of the North Bosque
River originate in Erath County and contain a large number of dairy operations and the
watershed’s largest city, Stephenville. Within the headwaters, fields receiving solid and liquid
dairy manure are a significant land use, and henceforth these fields will be referred to as dairy
waste application fields.
The North Bosque River has been on every State of Texas section 303(d) list since 1992 for
water quality impairments, of which the most relevant impairments are excessive nutrient
and aquatic plant concentrations. Generally, the section 303(d) listing has attributed the source
of nutrients and the resulting over abundance of aquatic plants as originating from animal
feeding operations, and more specifically, dairy operations. Research for the USDA Initiative
has determined that phosphorus is the nutrient that controls (or limits) aquatic plant growth
in the North Bosque River (Kiesling et al., 2001). Dairy operations and municipal wastewater,
in that order, are the major controllable sources of phosphorus to the river (McFarland and
Hauck, 1999). Because the greatest density of dairy cows and the largest municipal
wastewater treatment plant discharge, which is from the city of Stephenville, are in the
headwaters of the North Bosque River, an anthropogenically driven nutrient gradient exists
along the North Bosque River with highest nutrient concentrations in the headwaters and
decreasing nutrient concentrations in the downstream direction.
Climatically, annual rainfall averages a little over 76 cm (30 in) per year across the watershed.
Rainfall typically follows a slightly bimodal pattern with peaks in the spring and fall. On
average the wettest month is May and the driest month is January. Most tributaries to the
North Bosque River are highly intermittent and frequently become dry soon after each rainfall
runoff event. Winter rains corresponding with the low evapotranspiration of that time of year
can, however, establish a base flow that persists well into the spring in some years. Ground
water does not contribute significantly to a base flow component in either the North Bosque
River or its tributaries, especially in the upper half of the watershed. Hence, the North Bosque
River typically becomes dominated by municipal wastewater treatment plant effluent in the
summer and early fall.
13
Strategies for Monitoring Nonpoint Source Runoff
Figure 1 North Bosque River watershed and selected sampling sites.
From the network of monitoring sites operated for the USDA Initiative, seven sites were
selected to represent a variety of drainage area sizes and differences in land covers (Figure 1
and Table 1). The monitoring at each site consisted of storm event sampling performed with
automatic sampling equipment and biweekly (once every two weeks) manual grab sampling
taken whenever flow was present at a site. Samples collected at each site were analyzed for a
number of water quality constituents; however, the constituents presented in this report focus
specifically on nitrite-nitrogen plus nitrate-nitrogen (NO2-N+NO3-N), orthophosphate
phosphorus (PO4-P; often referred to as soluble reactive phosphorus), total phosphorus (TP;
comprised of both soluble and particulate phosphorus forms), and total suspended solids
(TSS). These four water quality parameters were selected because they include two water
soluble (dissolved) constituents (NO2-N+NO3-N and PO4-P), a water suspended constituent
(TSS), and a constituent that contains suspended and soluble components (TP). The seven
sites used in this report not only have a wide variation in drainage area size and land cover,
but also water quality response. Some key characteristics of each site are provided in Table 1.
14
Chapter 2
Background
Table 1 Characteristics of seven selected monitoring sites.
(Source: Adams and McFarland, 2001)
Drainage Area
(ha)
Dominate Land Use/Land Cover
Water Quality and Hydrology
SF020, South Fork of N.
Bosque River
848
Wood/range (96%)
Least impacted small watershed site; highly
intermittent flow
NF020, North Fork of N.
Bosque River
791
Dairy waste application fields (45%),
wood/range (39%)
Water quality highly impacted by nonpoint
sources; highly intermittent flow
NC060, Neils Cr.
35,147
Wood/range (77%), pasture
and cropland (23%)
Ecoregion reference site (or least impacted
site); intermittent flow
GC100, Green Cr.
.
26,165
Wood/range (71%), dairy waste
application fields (7%)
Water quality moderately impacted by
nonpoint sources; intermittent flow
BO040, N. Bosque River
below Stephenville, TX
25,719
Wood/range (51%), dairy waste
application fields (12%)
Water quality impacted point and nonpoint
sources; perennial flow
BO070, N. Bosque River
at Hico, TX
93,248
Wood/range (68%), dairy waste
application fields (7%)
Water quality moderately impacted by point
and nonpoint sources; nearly perennial flow
BO090, N. Bosque River
near Clifton, TX
253,740
Wood/range (72%), pasture
and cropland (23%)
Low impacts from point and nonpoint
sources; perennial flow
Site/Location
The intermittent flow patterns observed in the North Bosque River are typical of patterns
observed throughout portions of the central and western United States, and therefore this
system provides a relevant example for many watersheds. As a caution, when strong surface
water and ground water interactions occur and the base flow component comprises a
significant portion of total flow, this difference in hydrology from the North Bosque River
watershed must be considered in light of comments and recommendations within this report,
which are largely determined from a storm water driven surface water system.
15
Strategies for Monitoring Nonpoint Source Runoff
16
CHAPTER 3
What to Monitor
General Considerations
The Clean Water Act refers to maintaining the physical, chemical, and biological integrity of
our Nation’s waters. For monitoring, this means there are a multitude of constituents that
could be monitored. The key is to determine which constituents are of primary importance in
a given watershed. Often there is a specific concern that drives water quality monitoring and
defines what constituents should be analyzed. For point source discharges from industries or
wastewater treatment plant facilities, specific chemicals must be monitored in the effluent
discharged. A National Pollutant Discharge Elimination System (NPDES) permit will specify
what must be monitored. If there is a question as to whether or not a point source is meeting
its permitted discharge limit, the answer to the question of what to monitor and where is quite
simple—at the end of pipe, for the permit specified constituents. Compliance monitoring is
appropriate to this situation.
With regard to nonpoint source pollution, what constituents to monitor may not be as clear.
Baseline monitoring of a board spectrum of constituents may be needed to establish the
general health or conditions of a stream. Baseline monitoring should consider not only
chemical and physical constituents, but a general evaluation of the biological health of a
stream including habitat assessment and monitoring of benthic invertebrates, fish, and algae
populations (e.g., USEPA, 1990; Davis and Simon, 1994; Barbour et al., 1999). An assessment or
reconnaissance of land uses in the watershed may also be key to defining potential pollutants
for monitoring. For example, certain pesticides or herbicides are often associated with specific
crops, such as atrazine with corn.
Defining Water Quality Problems
What to monitor is a very important consideration regardless of the monitoring objective.
However, this consideration is paramount when the objective is to identify the pollutant(s)
causing water quality impairment. For example, the water quality impairment or effect may
be the death of fish, but the cause may be toxins, low dissolved oxygen, or other factors that
need to be determined.
Within the North Bosque River nutrients (the causative factor) are being intensively
monitored due to their impact on algal growth (the response factor). Nutrients, generally
either nitrogen or phosphorus, are often limiting factors to algal growth and generally much
easier to monitor than algal abundance or growth rates. The nutrients themselves are not the
problem per se, but the excessive growth of algae caused by nutrients may be. In the North
Bosque River, phosphorus has been identified as the primary nutrient limiting algal growth
along much of the River (Matlock and Rodriguez, 1999; Dávalos-Lind and Lind, 1998). By
evaluating relationships between in-stream phosphorus concentrations and algal abundance
17
Strategies for Monitoring Nonpoint Source Runoff
as represented by chlorophyll-a concentrations (Figure 2), a potential stream target based on
PO4-P was developed for the North Bosque River (Kiesling et al., 2001). Development of
nutrient targets for the North Bosque River are presented in detail in Kiesling et al. (2001). EPA
has also recently published reports specifically outlining the development of nutrient criteria
for rivers and streams (USEPA, 2000a) and lakes and reservoirs (USEPA, 2000b). Intensive well
designed studies are needed to develop these relationships, but once developed, these
relationships can aid greatly in directing the constituents to be evaluated in a monitoring
program.
Annual Average CHLA (µg/L)
Figure 2 Annual average chlorophyll-a as a function of annual average time-weighted PO4-P
concentrations for North Bosque River and tributary sites. Figure excludes PO4-P saturated sites.
Adapted from Kiesling et al. (2001).
60
y = 13.14Ln(x) - 35.94
2
R = 0.71
50
40
30
20
10
0
0
50
100
150
200
250
300
350
Annual Average Time-Weighted PO4-P (µg/L)
Monitoring and Modeling
Baseline monitoring may also be used to obtain data for the calibration and validation of instream water quality or lake models. Water quality models are often used to provide
information for watershed planning efforts that may not be easily determined through direct
monitoring activities. Watershed loading models are tools for simulating the movement of
precipitation and pollutants from the landscape to surface and/or ground waters. Hydrologic
water quality models are tools for simulating fate and transport of constituents in receiving
waters, i.e., streams, lakes, and estuaries. Some models contain both watershed loading and
water quality components. Models are particularly useful in projecting the impact of different
implementation practices and in determining needed load allocations to obtain water quality
targets. Models may also be used to take the place of intensive monitoring efforts, but when
used without monitoring data for calibration, they should be considered only a guide to what
water quality might be like rather than an indicator of actual water quality.
Many sophisticated water quality models exist, such as SWAT (Soil Water Assessment Tool;
Arnold et al., 1998) and CE-QUAL-W2 (Cole and Buchak, 1995), which were applied for the
USDA Initiative. These sophisticated models can be very helpful in providing information in
the watershed planning process, but require extensive watershed specific information to
operate. Monitoring data may be needed to calibrate specific model coefficients and to
18
Chapter 3
What to Monitor
provide data for overall model calibration and verification. Each water quality model is
unique in its purpose, characteristics, and data needs, thus, before using a given model, the
model should be thoroughly reviewed to make sure it meets the objectives of the planning
process and that the data needed to operate the model are available or can be collected. A
review of the general principles behind most surface water quality models can be found in
Thomann and Mueller (1987) and Chapra (1997), and a basic description of many models is
contained in Shoemaker et al. (1997).
Concentrations and Loading
Water quality data are generally measured as instantaneous concentrations, but the loading or
mass of a constituent added to a water body or transported by a river over time may be just as
important. To obtain loading, pollutant concentration and water flow must be measured,
because loading by definition is determined as water volume times pollutant concentration.
The difference between concentrations and loadings can sometimes be confusing. For
example, along the North Bosque River, a clear decrease in phosphorus concentrations occurs
from sites near the headwaters to sites near the mouth of the river (Figure 3). In contrast, the
loading of phosphorus increases from upstream to downstream, because the volume of
discharge increases dramatically between the three sites in this example.
Constituent concentrations provide information on the exposure level to aquatic plants and
animals at the time of the sample. For toxicants, the exposure concentration is often expressed
as an LD50 or the concentration at which 50 percent of test organisms are killed in a given time
period, such as 24 hours (see Rand and Pertocelli, 1985). For constituents, such as nutrients,
concentrations give us an idea of their availability for aquatic plant growth, although even
nutrients in some forms, for example elevated NH3-N concentrations, can be toxic to fish and
other aquatic life (see Boyd, 1990).
Multiplying concentrations by flow lets us know how much of a given constituent or the load
of that constituent that has passed a given point in a stream or been added to a lake or
reservoir. In streams and rivers, where flow continually moves waterborne constituents
downstream, concentration information may be more important to watershed planning
processes than load information with regard to exposure levels. For lakes and reservoirs,
loadings from tributary streams and rivers may be as important information as the actual
concentration of these loadings. This is because lakes and reservoirs have longer retention
times than streams and rivers. A longer retention time allows for inflows to become mixed or
integrated with resident waters. It is, thus, important to know both the concentration and
loading of inflows into a lake or reservoir to assess their potential impact on in-lake water
quality. For example, a high constituent concentration inflow at low flow will often have less
of an impact on a lake than the same or even lesser concentration at a high flow. Due to the
longer residence time, lakes and reservoirs also generally have a greater natural capacity to
assimilate constituent loadings than streams or rivers. Assimilation, in this context, refers to
the retention of a constituent in a form that is not easily released to the water column or
readily available for biotic uptake (Reddy et al., 1996).
To calculate the loading of a constituent, concentration data must be combined with stream
flow data. There are a number of different methods to calculate loadings. An integration
approach is generally the most accurate, but also one of the most data intensive methods for
calculating loadings (Roberston and Roerish, 1999). Various loading calculation methods and
the associated sampling effort needed are discussed in more detail in Chapter 6, “How Often
to Monitor. “
19
Strategies for Monitoring Nonpoint Source Runoff
Figure 3 Phosphorus concentrations, stream volume, and loadings along the North Bosque River
for 1997. Concentrations represent annual flow-weighted averages.
20
CHAPTER 4
Where to Monitor
Location of sampling sites is another consideration not only regarding spatial aspects within a
watershed, but also in regard to spatial aspects within a water body at a given location. Within
a watershed, the size of the drainage area monitored, how many tributaries are located in the
watershed area, and critical areas or sources, may influence the location of sampling sites
within a watershed. For a given sampling location spatial aspects include depth of sampling
and, for streams, lateral position along the cross section.
Spatial Considerations
As pointed out in the NRCS National Handbook of Water Quality Monitoring (Clausen, 1996),
the scale or size of the watershed to monitor is probably one of the most difficult decisions in a
water quality monitoring project. Some factors that should be considered are:
•
Stream order—Should headwater streams be monitored or is the primary interest only
along the main stem of a river? Headwater streams are associated with much of the
loading of nutrients to downstream water bodies (Peterson et al., 2001), and main stem
river sites provide an integration of the various tributaries flowing into a river. The
purpose of monitoring, for example defining sources of a pollutant to a river versus
monitoring loadings from a river into a lake, will help define the stream order to monitor.
•
Stream permanence—Is the stream intermittent or perennial? In the North Bosque River
watershed most of the tributaries are intermittent with little or no flow between storm
events. Even reaches of the North Bosque River can go dry during periods of low rainfall
and high evaporation that often occur in the summer. Very little water appears to be
contributed to the North Bosque River from groundwater seepage, while in other areas
groundwater contributions may be a primary source of base flow contributions. Stream
permanence is very important for biological monitoring as the biotic community may be
controlled by periodic lack of flow. Hendon et al. (1998) found that macroinvertebrate
communities in the North Bosque River watershed were more influenced by persistence
in flow (i.e., intermittent versus perennial conditions) than variation in water quality
between sampling sites with similar flow regimes.
•
Weather variability—For paired watershed studies, it is important that similar weather
conditions occur within the two watersheds of interest for comparison purposes.
Variability in weather conditions, particularly with regard to rainfall, also plays an
important role in determining the likelihood of rainfall-runoff events which help
determine the frequency of sampling that can be expected as well as the length of time
that might be needed to collect the data to meet monitoring objectives.
•
Heterogeneity of land uses—The heterogeneity of land uses generally increases with
watershed size. For determining source contributions of a constituent by land use, a fairly
small watershed may need to be monitored to isolate the water quality impact associated
with a specific land use.
21
Strategies for Monitoring Nonpoint Source Solution
•
Number of land owners—If the purpose of monitoring is to influence the management
behavior of landowners within a given watershed, the area monitored may need to be
small enough that landowners can directly associate what they do on the land with water
quality measurements. If the area is too large in comparison to the number of landowners
or management units in a watershed, individuals may more easily dismiss their
contribution as being less significant compared to others.
•
Watershed geology—Background water quality can vary greatly with geology. For
example, karst geology is associated with cave formations, which allow preferential flow
of pollutants into groundwater. Areas with karst geology may need special considerations
for groundwater monitoring. The geology of an area also defines the mineral make up of
the soil, which in turn defines the soil’s ability to bind up potential pollutants such as
phosphorus. For example, Harris et al. (1994) found that sandy soils in Florida had a low
phosphorus retention capacity even in the clay-fraction which had a high cation exchange
capacity (CEC). Although a high CEC is often associated with a higher ability for a soil to
bind anions, such as phosphorus, the dominance of noncrystalline-silica clays in the clay
fraction of these soils had a very low affinity for phosphorus.
•
Watershed topography—Steeper slopes will lead to more runoff and greater potential
movement of pollutants than shallower slopes. Topography also determines the dendritic
pattern of a the stream system.
Determining Sources of Pollution
When a monitoring objective is to determine sources of pollutants, the location of a pollutant
source is important in considering the location of sampling sites. As a stream sampling site
gets further downstream from the pollutant source, there is a greater chance for dilution from
other inflows and for physical and biochemical processes to act upon waterborne constituents.
During periods of low flow, there may even be incomplete transport of pollutants from one
point to another along a stream. These stored pollutants will likely be transported with the
next significant rainfall-runoff event. The phenomenon of pollutant storage is aptly shown in
Figure 4 through the differences in cumulative loadings of PO4-P and TP and differences in
cumulative flow volume between three North Bosque sites. The three sites, in an upstream to
downstream direction, are BO040 (North Bosque River below Stephenville), BO070 (North
Bosque River at Hico), and BO090 (North Bosque River near Clifton). Site BO040 is
immediately below (400 m) the city of Stephenville Wastewater Treatment Plant discharge and
consequently loadings at this site reflect this substantial source. For this example differences in
loadings and flow volume were calculated on a daily basis by subtracting values at BO040
from BO070 and by subtracting values at BO070 from BO090. These differences were then
accumulated daily. Each cumulated difference was divided by the drainage area between the
two sites to normalize the data on a per hectare basis, so the two sets of differences (BO070 BO040 and BO090 - BO070) would be visually discernible on the same plot.
For the reaches between BO040 and BO070 and between BO70 and BO090, a clear decrease in
the cumulative mass of PO4-P occurred from June through December 1998. A somewhat
similar decrease in cumulative discharge is noted between BO090 and BO070, but not between
BO070 and BO040. These decreases in cumulative loadings of PO4-P indicate that soluble
phosphorus, as represented by PO4-P, contributed at upstream points in the North Bosque
River watershed are not being transported during this period to BO090 as PO4-P. Pooling of
water along the stream or slower flows allow more time for the conversion of PO4-P to less
soluble forms via binding with sediment particles and biological uptake by plants and algae
during the extended period of low flow (see Froelich, 1988). For the North Bosque River,
transformations of soluble phosphorus to less soluble forms appear to occur primarily during
22
Chapter 4
Where to Monitor
Figure 4 Cumulative daily differences in PO4-P and TP loadings and discharge volume for 1998
at selected sites along the North Bosque River. From upstream to downstream, site BO040 is located
about 34 river kilometers above site BO070, and site BO070 is located about 73 river kilometers above site
BO090.
23
Strategies for Monitoring Nonpoint Source Solution
extended low flow periods and not during storm events due to rapid transport associated
with elevated flows (see McFarland et al., 2001). A similar decrease in TP was indicated for the
reach between BO070 and BO090. The decrease in cumulative TP loadings shown between
BO070 and BO090 indicates a settling or binding of TP loadings along the North Bosque River
prior to reaching sampling site BO090. These settled loadings are generally resuspended and
moved further downstream with subsequent elevated flows associated with storm events.
In a heterogeneous land-use watershed such as the North Bosque River, isolating
contributions of pollutants between nonpoint sources will require some forethought and
planning. In some instances it may prove practical and expeditious to establish monitoring
sites to isolate individual sources, e.g., placing a site on a first-order stream that originates
within the boundaries of a specific agricultural enterprise of interest. Such an approach was
not taken in the North Bosque River watershed for the following reasons. First, the water
quality problems in the watershed were contentious and volatile, and water quality
information collected from a specific operation would be subject to third party open records
requests and that information could potentially be used by that third party in legal action
against the cooperating operation—at best an undesirable outcome and possibly an outcome
that undermines agricultural support for the entire watershed project. Second, monitoring
that isolates a specific enterprise, thus, reflects the soils, slopes, facilities, management, and
other factors unique to that operation, and therefore monitoring results may not be
representative of typical water quality conditions from that land use. However such focused
monitoring may be very appropriate for specific circumstances, and monitoring data from
such sites may be crucial in calibrating field and landscape loading models. If land-use
specific monitoring occurs, multiple sites representing each land-use should be monitored to
obtain statistically, representative results.
The approach in the North Bosque River watershed was to locate sites on streams with
drainage areas including at least two and frequently even more dairy operations. Such site
placement made the data much less usable in court against an individual operation and
resulted in integrated water quality measurements across the variety of conditions and
practices on all operations above the site. A strong negative to this approach in sample site
selection, particularly in a heterogeneous watershed, is that other land uses and land covers
will undoubtedly be a significant part of the site's drainage area.
An example of some of the complicating aspects of drainage area size, site location, and
heterogeneity of land uses is depicted by four sampling sites within the North Bosque River
watershed (Table 2). The four sites presented in Table 2 may be roughly paired based on
drainage area size. Within each pair one site represents a least impacted watershed and the
other a watershed impacted by dairy operations.
Table 2 Comparison of water quality for impacted and least impacted watersheds
of different sizes (Source: Adams and McFarland, 2001).
Site
Drainage Area
(ha)
General Water Quality Description
Median Storm Event PO4-P
(mg/L)a
SF020
NF020
848
791
Control, least impacted
Impacted by dairy operations
0.030
2.020
NC060
GC100
35,147
26,165
Control, least impacted
Impacted by dairy operations
0.010
0.090
a. Represents the median of storm samples collected from January 1, 1996 through December 31, 2000.
For data collected between January 1996 and December 2000, the median storm event PO4-P
concentration of the two least impacted sites was approximately the same, whereas the
median storm event PO4-P concentration varied over one order-of-magnitude between the
24
Chapter 4
Where to Monitor
two watersheds impacted by dairy operations. Several factors can help explain this large
difference in median PO4-P concentrations between impacted stream sites: 1) the larger
watershed will exhibit a greater assimilative capacity than the smaller watershed, 2)
environmental conditions and individual dairy operation practices will vary between
watersheds, and 3) most importantly, the land use within smaller watershed (NF020)
comprises 45 percent dairy waste application fields, while the land use within larger
watershed (GC100) comprises only 7 percent dairy waste application fields (Table 1).
While dairy waste application fields occur on only seven percent of the land area of GC100,
they contribute a disproportionate amount of the nutrients expected in stream flow. Above
GC100, over 70 percent of the land area is comprised of wood-range, which is considered the
background condition for the watershed, and about 22 percent is associated with pasture and
cropland not used for dairy waste application. Estimated export coefficients by land use
within the Bosque River watershed for the period November 1, 1995 through March 30, 1998
indicate that dairy waste application fields export 22 times more PO4-P than pasture-cropland
and 44 times more PO4-P than wood-rangeland resulting in a relatively small areal extent
associated with dairy waste application fields being the major controllable nonpoint source of
nutrients above GC100 (see McFarland and Hauck, 1999). This finding indicates that in
determining sources a small portion of a watershed may be an important contributing source
that needs to be monitored.
Evaluating Effectiveness of Control Practices
Sampling site location is very important in evaluating the effectiveness of control practices.
The upstream-downstream approach is one common statistical design recommended for
evaluating nonpoint source management practices (Spooner et al., 1985). While the impact of
upstream water quality on downstream water quality can be a complicating factor in
evaluating water quality data, nesting, in this case, can greatly aid evaluating the effectiveness
of control practices by isolating drainage area contributions along a stream. Differences
between paired observations at the upstream and downstream sites are used to evaluate
treatment impacts. Ideally, pre- and posttreatment observations are collected, so the treatment
impact can be isolated from potential variations in rainfall and flow between the two
monitoring periods. Even within a relatively small area, inherent differences in such factors as
geology, soils, and slope can cause differences in water quality separate from the treatment
effect, so if only post-treatment samples are collected, it is difficult to determine if observed
differences are due to the treatment or some other inherent difference. Care must be taken in
site selection for evaluating the effectiveness of control practices, because sites that are too far
apart may have no relationship between them, while sites that are too close together may not
show a treatment difference if the treatment contribution area is relatively small.
A paired watershed approach is another recommended method that allows researchers to
measure the responsiveness of water quality to land use changes (Spooner and Line, 1993). A
paired watershed approach is similar to the upstream-downstream approach but involves
monitoring two similar watersheds for a control period and then applying a treatment to one
of the watersheds while continuing the same practices as during the “control” period on the
second watershed. A comparison of changes in the relationship between the two watersheds
for the control and treatment periods is used to determine changes in water quality due to the
treatment (e.g., Clausen and Spooner, 1993). The paired watershed approach works best when
watersheds are in close or immediate proximity to one another to minimize spatial variability
in weather conditions, especially rainfall, between the two watersheds.
25
Strategies for Monitoring Nonpoint Source Solution
Surrogate Sampling Locations
Sometimes it is not practical to directly monitor the location of interest due to difficulty in
accessing the site or expense of monitoring. In such cases, a surrogate for the location may be
appropriate depending on the objectives of monitoring. For example, in the upper portion of
the North Bosque River watershed, 40 Public-Law 566 (PL-566) reservoirs exist. These PL-566
reservoirs were built in the 1950s and 1960s to reduce and retard high flows related to
flooding. The streams flowing into most of these reservoirs are intermittent and are
characterized by episodic runoff events1. Although not their intended purpose, these small
reservoirs act as integrators of water quality for the drainage above them. During small storm
events, these reservoirs may catch all the water that comes into them (McFarland and Hauck,
1995a). Water quality within these small reservoirs is directly related to the quality of the
water flowing into them (McFarland and Hauck, 1995b). Therefore, under certain monitoring
situations it may be appropriate to use these reservoirs as surrogate sampling sites of the
water quality flowing into them, thus, avoiding the expense of storm water monitoring that
would be necessary to characterize water quality associated with intermittent stream flow.
Location within a Water Body
Determining where to sample within a water body may be just as important as determining
which water bodies to sample within a watershed. Klemm et al., (1990) outlined a number of
considerations for biological sampling, such as making sure stream depth, substrates, and
velocity are similar between sampling locations. Several references go into detail on selecting
appropriate stream and lake or reservoir sampling sites (e.g., Sanders et al., 1990; Clausen,
1996; TNRCC, 1999). General site selection criteria includes such factors as making sure
appropriate habitat and land uses are represented, that the stream section monitored has a
straight uniform cross section, and that a stream site is not located at obstructions. The vertical
and horizontal velocity profiles of a stream should also be considered in placement of stream
sampling sites. In general, the larger the stream system, the more important variations in the
vertical and horizontal profile become. Velocities will be highest at the top and in the middle
of a stream and slowest toward the bottom and outside edges of a stream. These variations in
velocity can influence the transport of constituents and determine the type of sampling
needed for a given site.
Velocity is also an important consideration in defining mixing zones associated with point
source contributions. Mixing zones represent limited and defined areas where initial dilution
of permitted or authorized discharges may occur. Water quality within mixing zones will be
changing and may not be representative throughout the entire stream profile as the discharge
volume for the point source is diluted by stream water. Mixing zones will vary depending on
the size and rate of the discharged contribution compared to stream flow and volume.
Suggested procedures on selecting site locations with regard to mixing zones for stream water
quality sampling are provided by Montgomery and Hart (1972) and Sanders et al. (1977). For
macroinvertebrate monitoring, Klemm et al. (1990) suggest including samples along both
stream banks as well as midchannel, if a site is directly below a discharge that is not well
mixed.
1
26
Sites SF020 and NF020 represent two such intermittent stream sites. The charts of average daily
flow for sites NF020 and SF020 show extended periods of low or no flow punctuated by dramatic
rises and falls in flow in response to rainfall (see Appendix B for details).
Chapter 4
Where to Monitor
Where two streams debouch or merge, the complete mixing of their waters does not occur
instantaneously. A downstream reach is required for waters of the streams to completely mix.
The length of this reach will be a function of many variables including geomorphology, stream
flow, and sinuosity. Sampling within this mixing zone or reach can result in erroneous
characterization of the combined water quality of the two streams and should be avoided for
the majority of monitoring purposes. When locating monitoring sites below two debouching
streams, keep in mind that under some circumstances the distance required for complete
mixing may be surprisingly long.
The depth at which a sample is taken is another consideration in determining sampling site
location. Although differences with depth are more often associated with lake and reservoir
systems, depth can also be a factor is stream systems, particularly in larger streams. Sediment
and sediment bound constituents can be expected to show a distinct gradient with depth,
because lighter particles, such as clay and silt, can stay suspended in the current, while larger
particles, such as sand and rocks, will move along the stream bottom. A couple of other
important constituents that may show distinct differences with depth include dissolved
oxygen and water temperature. Water temperatures and dissolved oxygen concentrations
generally decrease with increasing depth in lakes and reservoirs, particularly during summer
months. Stratification of dissolved oxygen and temperature in streams is less common, but
can occur and is most likely associated with base flow conditions in deeper pools or within
slow moving, deep streams.
27
Strategies for Monitoring Nonpoint Source Solution
28
CHAPTER 5
What Type of Monitoring to
Implement
The type of sampling to implement depends on the purpose of monitoring as well as the
variability in constituent concentrations with flow and depth at a given site. In dealing with
vertical and horizontal variability in water quality at a site, samples may need to be collected
at varying depths and widths. Sampling at increments by depth is often used for constituents
that are known or expected to have potential changes with depth, such as temperature and
dissolved oxygen in lakes or reservoirs, which exhibit a high potential for seasonal
stratification. Within streams, monitoring vertical and horizontal differences in the
concentration of different sediment fractions may be important. The USGS water sampling
handbook (USGS, 1999) covers in detail methods for collecting depth and width integrated
samples from streams to obtain a composite, discharge-weighted sample that is proportional
to total stream flow. For stream sites, integrated sampling can be very intensive and
impractical at high flows when a sampler cannot be lowered properly through the vertical or
horizontal profile. For most nonpoint source sampling of stream sites, discrete samples are
collected at one point within the stream profile using grab or automated samplers. However,
modifying the intake of an automatic sample to include a float to raise a perforated intake line
can allow depth-integrated sampling during elevated flows (e.g. Clausen, 1996).
While depth and width integrated sampling is sometimes necessary, most stream sampling
with regard to nonpoint source contributions occurs as discrete samples collected at one point
within the stream profile using manual grab sampling techniques or automated samplers.
Manual sampling is often used in determining baseline conditions within a water body and is
generally used for compliance and assessment monitoring (sometimes called ambient
monitoring) by regulatory agencies. Manual sampling generally consists of dip or point
sample. A dip sample commonly involves wadding into a stream and dipping a narrowmouthed bottle into the water or lowering a bottle with a weight into the water from a bridge
or other structure. Generally, dip samples are collected below the water surface at a depth of
about 0.3 meters (1 ft). A point sample involves specialized equipment and involves lowering
the sampler to a specified depth for sample collection or using a single-stage sampler that
collects a sample when the water level rises to a specified height.
Automated or pump samplers are most often used for monitoring storm runoff when changes
in discharge occur rapidly making it difficult and potentially dangerous for individuals to
manually collect samples. Flow varies greatly over a storm event as well as the concentration
of different constituents. For example at North Bosque River site BO070 near Hico, Texas, a
typical storm event may show a rapid rise in the concentration of TSS and TP peaking near the
time of maximum discharge. Whereas PO4-P and NO2-N+NO3-N concentrations actually
decrease initially and then increase on the falling portion of the hydrograph (Figure 5).
29
Strategies for Monitoring Nonpoint Runoff
Figure 5 Variation in constituent concentrations with discharge at North Bosque River
site BO070 from a March 1998 storm.
30
Chapter 5
What Type of Monitoring to Implement
The pattern of concentrations for a given constituent can also vary greatly between events. In
the storm event presented in Figure 5, NO2-N+NO3-N indicates a fairly constant increase in
concentration with increased elapsed time during the event. In Figure 6, NO2-N+NO3-N
concentrations are compared for six different storm events with storm 1 representing the same
storm event as shown in Figure 5. The other five events presented show varying, often
complex patterns in NO2-N+NO3-N concentrations (Figure 6a). When NO2-N+NO3-N
concentrations are normalized for the maximum concentration in each event and elapsed time
is also normalized (Figure 6b), there is a clear indication that the maximum NO2-N+NO3-N
concentration for a storm can occur at any time during an event. There does appear to be a
weak pattern of decreasing NO2-N+NO3-N concentrations at the beginning of several of the
storm events, which appears to be related to instantaneous discharge (Figure 7). For dissolved
constituents, such as NO2-N+NO3-N, a decrease in concentration during storm events with
increased flow is not uncommon and is attributed to a dilution effect associated with initial
runoff volumes generally containing lower concentrations of soluble constituents than later in
the event (see Reckhow et al., 1980).
Figure 6 Variation in NO2-N+NO3-N concentrations over time during six storm events
for a) actual concentrations and elapsed time and b) normalized concentrations and elapsed time. Data
for North Bosque River site BO070.
31
Strategies for Monitoring Nonpoint Runoff
NO2-N+NO3-N (mg/L)
Figure 7 NO2-N+NO3-N concentration during selected storm events in relation to discharge
at site BO070.
10.00
1.00
0.10
0.01
1
10
100
1,000
Instantaneous Discharge (m3/s)
Storm 1
Storm 2
Storm 3
Storm 4
Storm 5
Storm 6
For particulate or water suspended constituents such as TSS, a very different response pattern
for storm event concentrations is observed. With TSS, the maximum TSS concentration is
generally a function of flow energy with peak TSS concentrations occurring near peak flow in
the hydrograph (Figure 8). Peak TSS concentrations generally occur early in a storm event
(Figure 9a & 9b). These phenomena are most likely the result of dislodgement and transport of
sediment particles from the land surface during the initial stages of runoff and the
resuspension of sediment within the stream bed with increasing stream flow energy. For water
suspended constituents, such as TSS, concentrations generally decrease as flow energy
decreases during the falling portion of the hydrograph.
Figure 8 TSS concentration during selected storm events in relation to discharge at site BO070.
TSS (mg/L)
10,000
1,000
100
10
1
1
10
100
1,000
Instantaneous Discharge (m3/s)
Storm 1
32
Storm 2
Storm 3
Storm 4
Storm 5
Storm 6
Chapter 5
What Type of Monitoring to Implement
Figure 9 Variation in TSS concentrations over time during six storm events
for a) actual concentrations and elapsed time and b) normalized concentrations and elapsed time. Data
from North Bosque River site BO070.
Because concentration patterns can vary greatly between constituents within storm events
and for the same constituent between events, storm sampling must be performed to allow for
this variability. It may be necessary to sample several events and represent several points in
time during each event to properly characterize storm water contributions of a given
constituent or set of constituents. While manual grab sampling can be used to sample storm
events, if intensive storm sampling is necessary to represent several points during an event,
the use of automated samplers is recommended. Automated samplers allow samples to be
collected at prescribed time or flow intervals and are generally more reliable than manual
sample collection when specific sample intervals are required. Automated samplers also help
minimize potential safety risks to individuals conducting wet-weather sampling. In some
cases, probes for continuous, real-time sampling may be used.
Although real-time, continuous monitoring is most often conducted for research purposes,
general applications are becoming more frequent. Stable and chemically sensitive probes for
33
Strategies for Monitoring Nonpoint Runoff
continuous, real-time sampling are commonly available for constituents, such as water
temperature, specific conductance, dissolved oxygen, chlorophyll-a, turbidity, and pH. Probes
are also available for constituents, such as ammonium, nitrate, and soluble phosphorus, but
the sensitivity of these probes should be considered before using them for monitoring. The
chemical sensitivity currently associated with probes for constituents such as ammonium may
allow monitoring in enriched systems such as wastewater or industrial discharges, but in
many natural waters, these probes may not be sensitive enough to provide useful data. The
reliability and sensitivity of equipment for continuous real-time monitoring is improving
rapidly, as well as the type of constituents that can be monitored. Efforts are also being made
by several researchers to use constituents that can be reliably monitored using continuous,
monitoring probes as surrogates to predict concentrations of other constituents, such as
chloride and sulfate from measurements of specific conductance and fecal coliform and total
phosphorus from measurements of turbidity (e.g., Christensen et al., 2000).
While automated samplers allow the collection of multiple samples over a storm event, it may
be desirable to composite these samples. A composite sample is basically a collection of single
samples combined into one sample by some factor, generally time or flow in the case of storm
events. Depth composited samples are also common when evaluating the vertical profile of a
water body. Compositing is often desirable to decrease the number of samples needed for
laboratory analyses while attempting to maximize the amount of information obtained from
the sample. While time-compositing of storm samples is sometimes used because of the
simplicity with which it can be implemented, time-composited samples will typically provide
a skewed characterization of water quality during a storm event. Further, a time-weighted
concentration has no intrinsic relationship to the stream hydrograph precluding the use of
such concentrations to calculate loadings, unless flow or concentration is constant over the
period of sampling. However, time-weighted sampling may be totally appropriate for
instances when monitoring is addressing time-related issues, such as exposure time of
organisms to acute or chronic concentrations of toxins or average nutrient concentrations
available to periphyton.
Flow weighting of storm samples is generally preferred if storm samples need to be
composited. Flow compositing of storm sample is more complicated than time compositing,
because flow compositing requires measurement of stream stage or level throughout the event
and knowledge of the stage-discharge relationship at the sampling site. The following
example shows how time compositing and flow compositing of samples can lead to different
interpretations of average storm event concentrations for constituents.
In evaluating sequentially collected data from six storm events at BO070, fairly large
differences in event mean concentrations (EMCs) occurred, particularly for TP and TSS,
depending on how data were averaged (Figure 10; storms 1 through 6 match the storms
presented in Figures 5-9). For most storms, flow weighting substantially increased the EMC
for TP and TSS compared to time weighting, because the largest TP and TSS concentrations
typically occurred with the highest discharges. In contrast, for NO2-N+NO3-N and PO4-P, the
greatest EMC for most storms occurred when data were time weighted rather than flow
weighted, however, trends in EMCs across storms were not as consistent for these constituents
as for TP and TSS. Generally, when the flow-weighted EMC is greater than the time-weighted
EMC, the higher constituent concentrations are occurring with higher flows, and the converse
is true when the flow-weighted EMC is less than the time-weighted EMC. As indicated in
Figures 6 and 9, different storm events can lead to different patterns of concentration response
for the same constituent, and, thus, differences when averaged if either flow weighting or time
weighting is used.
If compositing must be done to allow estimation of constituent loadings, flow weighting is
preferred, because by definition loading is the integration of waterborne constituent
34
Chapter 5
What Type of Monitoring to Implement
Figure 10 Comparison of flow-weighted and time-weighted averaging of storm samples.
35
Strategies for Monitoring Nonpoint Runoff
concentration with flow over time. Using storm 1 as an example, the total storm volume
discharge was about 35,970,000 m3 (1,271,000,000 ft3). This translates into a mass loading for
TSS of 37,200 metric-tons (41,000 tons) using the flow-weighted EMC of 1,033 mg/L TSS and
12,200 metric-tons (13,400 tons) using the time-weighted EMC of 339 mg/L TSS. Using an
integration approach of discharge with water quality for the individual sequential samples, a
TSS loading of 37,200 metric-tons (41,000 tons) was calculated. The integrated loading equals
the loading estimated using the flow-weighted EMC and provides the best estimate of actual
storm loading. The time-weighted average EMC greatly underestimated TSS loadings in
comparison. While the differences were most dramatic for TSS, similar differences in loading
estimates could be expected for the other constituents depending on the weighting of
composited samples.
36
CHAPTER 6
How Often to Monitor
Data collection efforts generally represent a compromise between confidence in the data and
available resources. Defining an appropriate frequency for sampling, particularly when
monitoring nonpoint source contributions is a formidable task. To illustrate this problem, we
present a comparison of several common sampling strategies that employ different sampling
frequencies to estimate annual concentrations and loadings of PO4-P, TP, and TSS by
subsampling data from seven sampling sites within the North Bosque River watershed. As
presented previously (Table 1), the seven sites selected for these comparisons provide a
variety of water quality and hydrologic conditions, which may be summarized as follows:
•
BO040 - water quality highly impacted by point and nonpoint sources; perennial flow
•
BO070 - water quality moderately impacted by point and nonpoint sources; nearly
perennial flow
•
BO090 - water quality slightly impacted by point and nonpoint sources; perennial flow
•
NC060 - good water quality; intermittent flow
•
GC100 - water quality moderately impacted by nonpoint sources; intermittent flow
•
NF020 - water quality highly impacted by nonpoint sources; highly intermittent flow
•
SF020 - good water quality; highly intermittent flow.
Note that the above water quality characterizations are relative and not an indication of water
quality condition relative to state and federal surface water standards.
Average Annual Concentrations from Routine Sampling
Estimation of annual average concentrations are often an objective of routine monitoring
when assessing ambient water quality conditions. Routine monitoring generally involves
collecting near surface grab samples at set time intervals throughout the year. Common time
intervals used are biweekly, monthly, or quarterly. To evaluate how changing the time interval
might influence annual average concentrations, a data set containing routine biweekly grab
sampling data was subsampled on a monthly and quarterly basis for each of seven sites. Two
monthly subsamplings were evaluated. One using the first biweekly grab sample collected in
a month, and the second using the last biweekly grab sample collected in a month. For those
months in which three biweekly grab samples were collected, the middle sample collected in a
month was excluded. To represent the potential variability in quarterly sampling, six
subsampling options were evaluated assuming at least six biweekly samples collected in a
quarter. Q1 represented the first biweekly sample in a quarter, while Q6 represented the last
biweekly sample in a quarter. Subsampling options Q2 through Q5 represented the biweekly
sample closest to increments of 32, 48, 64, and 80 percent elapsed time in a given quarter.
Results comparing different grab sampling intervals are presented for North Bosque River site
BO070 in Figure 11.
37
Stategies for Monitoring Nonpoint Source Runoff
Figure 11 Impact of sampling interval on average annual concentration at site BO070.
B represents biweekly sampling, M1 and M2 represent monthly sampling schemes, and Q1 through Q6
represent quarterly sampling schemes.
38
Chapter 6
How Often to Monitor
Annual average stream concentrations of PO4-P, TP, and TSS at site BO070 clearly show an
increase in statistical confidence with increasing sampling frequency when using the annual
average of biweekly sampling as the “true” concentration. To summarize the results in Figure
11, a percent error from the annual average concentration based on biweekly sampling was
calculated for the monthly and quarterly sampling schemes. For PO4-P, percent error for
monthly sampling ranged from plus 18 percent to minus 17 percent, and percent error for
quarterly data was greater than plus or minus 50 percent. The percent error for TP was fairly
similar to that of PO4-P, but for TSS, the difference between sampling schemes increased
dramatically. For TSS, percent error for monthly sampling ranged from plus 81 percent to
minus 83 percent. For quarterly TSS samples, the disparity increased to plus 90 percent to
minus 419 percent that of average annual values based on biweekly sampling.
For site BO070, monthly sampling might be appropriate for measuring PO4-P or TP
concentrations if a 20 percent error compared to biweekly sampling is acceptable. For TSS,
monthly sampling was insufficient to maintain this same 20 percent margin of error compared
to biweekly sampling. Similar results were found for the other six sites evaluated, and results
for all seven sites are presented in Appendix C.
Annual Average Concentrations for Storm Events
Storm events will generally have concentrations for constituents that differ from base flow.
Because many routine sampling programs focus on base flow conditions, special monitoring
will be necessary to evaluate constituent concentrations associated with storm events. It is
generally infeasible, due to limitations in money and personnel, to monitor every storm event.
Therefore, it is important to know how many events need to be monitored to obtain a
reasonable estimate of annual average concentration of constituents associated with storm
events. This question was evaluated for seven selected stream sites within the North Bosque
River watershed by using successive storm events as subsamples to represent estimates of the
annual average storm event concentration. Flow-weighted mean concentrations of successive
storm events monitored during the year were compared with the annual average flowweighted concentration of all storm events in a given year. The subsampled mean
concentration was calculated for PO4-P, TP, and TSS by adding the mass associated with each
additional storm event and dividing by the cumulative storm volume to obtain a flowweighted mean value. Sampling procedures used for storm events are outlined in Appendix A
under the section, Data Collection Methods and Procedures. Masses for each constituent were
calculated by combining flow and water quality data using a midpoint, rectangular
integration method between water quality samples (Stein, 1977). Figure 12 shows storm event
mean concentrations for PO4-P, TP, and TSS at North Bosque River site BO070 for each
successive subsampling of events. In general, there is a stabilization in the mean concentration
for storm events as the number of storm events monitored increases indicating that not all
storms in a given year need to be monitored to obtain an estimate of the annual average storm
concentration for a constituent.
To estimate the number of storm events needed to obtain a reasonable estimate of annual
average storm concentrations for PO4-P, TP, and TSS, a percent error was calculated for each
subsample estimate in comparison to the annual average flow-weighted concentration using
all events. The number of storm events needed to obtain accuracy within a prescribed percent
error in a given year was evaluated using allowable percent errors of 10, 20, 30, 40, and 50
percent. Relatively little difference was noted in the number of storm events needed to meet
these allowable percent errors between sites within a given year, so annual data for all seven
sites were combined and averaged (Figure 13). As expected, the number of storm events that
should be monitored increases as the allowable percent error decreases. For PO4-P, at least
39
Stategies for Monitoring Nonpoint Source Runoff
Figure 12 Estimates of annual storm event mean concentrations based on subsampling
successive storm events at North Bosque River site BO070.
40
Chapter 6
How Often to Monitor
Figure 13 Number of storm events needed to obtain certain allowable percent errors in EMCs
for seven sampling sites in the North Bosque River watershed.
Number of Storms Monitored
10
9
8
7
6
5
4
3
2
1
0
10
20
30
40
50
Allowable Percent Error
PO4-P
TP
TSS
eight storms should be monitored or about 50 percent of all events, based on an average of 16
storms per year, to stay within an allowable percent error of 10 percent. Only three storms
need to be monitored if the allowable percent error is 40 percent. Storm event TP
concentrations appeared to be more stable than PO4-P or TSS concentrations in the North
Bosque River watershed as indicated by the need to monitor fewer storm events to obtain the
same percent error.
Annual Loadings
A further step in determining how often to monitor depends on whether or not estimates of
annual loadings are necessary to meet monitoring objectives. Estimating annual loadings
generally requires a combination of routine and storm event monitoring. As discussed in the
section above, few monitoring programs can afford to sample every storm event, because of
constraints in the availability of labor, equipment, and money. It may not be feasible to invest
in expensive automated sampling equipment for intensive storm sampling, so alternative
sampling strategies may need to be considered to obtain estimates of storm loadings. There
are also a number of different approaches available for estimating annual loadings (for
examples see Cohn, et al., 1989; Preston et al. 1989; and Robertson and Roerish, 1999).
To provide insight into the estimation of loadings and the impact of different sampling
strategies, two common loading calculation approaches, an integration and a regression
approach, were compared with different sampling strategies representing different levels of
monitoring intensity by subsampling from data available for seven sites within the North
Bosque River Watershed. An integration approach using all available monitoring data was
assumed to be the “true” estimate of loading for comparison purposes, because the
monitoring program within the North Bosque River watershed is highly intensive and tries to
capture every storm event (see McFarland and Hauck, 1999 for details).
41
Stategies for Monitoring Nonpoint Source Runoff
Integration Approach
In estimating loads, an integration approach of combining storm and grab sampling is
generally recommended (Robertson and Roerish, 1999). The integration approach assumes a
constant water quality between samples, although differences are expected between base flow
and storm flow in an area with large nonpoint or point source contributions. While it will
typically be infeasible to sample all storm events, it may be possible to sample a select number
of storm events and associate these data with elevated flows in the calculation of loadings.
Three subsets of data were used with the integration approach to estimate loadings. The first
data subset used only biweekly grab sampling data. The second and third data subsets
included biweekly grab sampling data but associated a volume-weighted event mean
concentration (EMC) with elevated flows. In the second subset, EMCs for constituents were
set annually based on an overall EMC of the first four storms in a given year. The first four
storm events were selected based on previous analyses indicating a plus or minus 20 to 30
percent error using four storm events compared to the annual average storm event
concentration for PO4-P, TP, and TSS (see Figure 13). In the third data set, EMCs for
constituents were set quarterly based on EMCs associated with the first storm in each quarter.
Data used in these evaluations were collected from January 1, 1995 to December 31, 1999.
Regression Approach
The second loading estimation method used a regression approach as presented by Cohn et al.
(1992) using a minimum variance unbiased estimator (Bradu and Mundlak, 1970). The
regression approach was based on the following seven parameter model:
1)
ln[C] = bo + b1ln[Qa] + b2{ln[Qa]}2 + b3[Ta] + b4[Ta]2 + b5sin[2pT] + b6cos[2pT] +e
where ln [ ] denotes the natural logarithm function, C is the instantaneous constituent
concentration on a given day, Qa is the average daily discharge adjusted with a centering
variable, T is time over the monitoring period measured in fractions of a year, and Ta is time
adjusted with a centering variable. The sine and cosine functions are designed to account for
periodic or cyclic variations in water quality, such as annual variations with season. The
values b0 through b6 represent coefficients estimated through the regression process. The error
or residual of the model is represented by e. The centering variables used for adjusting
discharge and time were calculated following procedures outlined in Cohn et al. (1992).
Centering adjustments of Q and T were made to avoid multicollinearity among the regression
equation coefficients. One of the potential advantages of the regression approach over the
integration approach to estimating loadings is that the regression approach provides
continuous estimates of daily concentrations with discharge and time rather than assuming
set values for concentrations.
Daily concentration estimates from the regression model were adjusted using a minimum
variance unbiased estimator (MVUE) and multiplied by daily discharge and summed by year
to estimate annual loadings. The estimator modifies the bias introduced in the
retransformation from “log space” to “real space” of the regression model estimates of
concentration (Cohn et al., 1989). The MVUE is generally considered applicable only if the
residuals of the equation are normally distributed. A Smearing bias estimator (Duan, 1983)
was also used and compared to results using the MVUE. The Smearing estimator is
recommended as a retransformation bias corrector when residuals are not normally
distributed (Hirshch et al., 1993). Consistently, the MVUE performed equal to or better than
the Smearing estimator even when residuals from the regression model were not normally
42
Chapter 6
How Often to Monitor
distributed based on a Shapiro-Wilks test of normality (a = 0.05). The results using the MVUE
are presented in the body of this report. The results using the Smearing estimator may be
referenced in Appendix D.
Four subsets of data were evaluated using the regression approach. The first included only
biweekly grab data. Biweekly grab data were then supplemented to include storm data. It was
assumed that intensive monitoring of individual storms was not an option and only one
sample was collected per storm event. Although TIAER’s storm sampling program is quite
intensive, these samples still represent only set time periods within an event (see Appendix A
for details on TIAER’s storm sampling protocols). Within a selected storm event, the sample
closest in time to the criteria outlined below was used as the representative sample. Storms
included in the different scenarios were selected to allow monitoring during normal working
hours (8 am to 5 pm), if manual sampling was involved. On average between 1995 and 1999
about 17 storms per year occurred at each of the 7 sampling sites evaluated. Assuming only a
limited number of storms could be monitored per year, sampling schemes were further
limited to sample only about three to eight storms per year focusing on larger events. This was
done by allowing sampling to occur only when instantaneous discharge levels exceeded a
specific limit in relation to “historical” average daily volume following procedures similar to
those outlined by Cohn et al. (1992).
Storm sampling included the following sampling scenarios:
•
Peak flow—Storm samples used in this scenario represented concentrations associated
with the peak discharge of storm events. Storm events were limited to those in which peak
flow was greater than the 50th percentile of average daily flow at a site as calculated from
available discharge data from January 1, 1995 to December 31, 1999. Storm events were
also constrained to have peak flow occurring between 9 am and 4 pm or normal working
hours allowing one hour of travel time at the beginning and end of the day. This scenario,
while fairly unrealistic in reality, was evaluated to determine how important it was in the
regression approach to include samples representative of the peak discharge.
•
Storm chasing—The events used for the storm chasing scenario were chosen based on the
constraints described above for the peak flow scenario, except a two-hour delay was
assumed in obtaining samples after peak discharge occurred. Peak discharge was, thus,
allowed to occur between 7 am and 2 pm for storm events to keep sampling within
normal working hours. Samples selected were, thus, representative of the hydrograph
two-hours after peak discharge rather than at peak discharge.
•
Set stage—The set stage scenario assumes a sampling device is available that will collect a
sample when stream flow rises above a certain level. Samples used in this scenario
represented the rising limb of storm events. To limit the number of storm events sampled,
only storms with instantaneous discharges exceeding the 95 percentile of average daily
flow for a given site were included in this scenario. No time of day restriction was
imposed, because it was assumed that samples could be retrieved during normal working
hours and still meet holding time restrictions for laboratory analysis.
These three scenarios used with the regression loading approach assume a knowledge of
historical and current flow conditions at each site. Historical data were used to set flow
thresholds, so monitoring efforts were concentrated on larger storm events. The underlying
assumption being that the purpose of storm monitoring was to best represent concentrations
associated with higher flows for input into the regression model for loading estimates as a
supplement to routine grab sampling data. It was assumed that most monitoring sites will
have some history of at least average daily flows which can be used as an indicator of larger
storm events based on instantaneous flows. For the data sets evaluated, the distribution of
average daily flows for each site was based on discharge data for the timeframe from which
data subsets were selected (January 1, 1995 through December 31, 1999). While not presented
43
Stategies for Monitoring Nonpoint Source Runoff
in the body of this report, the regression approach was also evaluated using the full data set of
storm and grab samples available for each site to see if increasing the number of samples
would improve the results of this approach. The results using the full data set are presented in
Appendix D.
These storm sampling scenarios used with the regression approach assumed that a single grab
sample was collected to represent each sampled storm event. While actually collecting a
sample during peak discharge of a storm event is somewhat unrealistic, storm chasing and set
stage sampling are reasonable approaches given adequate measurements of stream stage. All
loading estimates assumed the availability of an adequate and fairly accurate estimate of
discharge on at least a daily basis with real-time availability of stream level data.
Results Comparing Loading Estimation Approaches
In this section, the biweekly, storm chasing, and set stage scenarios using the regression
method for estimating loadings are presented in comparison to the integration method
scenarios. The results of the peak flow scenario with the regression method generally did not
perform well in comparison to the “true” loading estimates, and, thus, are presented only in
Appendix D. Loadings for the integration and regression scenarios were calculated annually
and compared to the “true” loading estimates as a percent error by year. For most sites, years
1995 through 1999 are presented. For sites BO090 and NC060, only results for years 1996
through 1999 are presented, since sampling at these two sites did not begin until October 1995.
Results of estimated annual loads were somewhat mixed as to what might be the best
sampling method at a given site. For PO4-P, measuring the first four storm events along with
biweekly grab samples with the integration approach gave the best overall results (Table 3).
Using the regression method for estimating loadings, a set stage at 95 percent of average daily
flow along with biweekly grab samples gave somewhat reasonable average results, although
with much more variability between years than the integration method. At sites BO040 and
BO070, all six methods seemed to give fairly reasonable results, while at BO090, the three
regression method approaches greatly overestimated loadings. For PO4-P and TP (Tables 3
and 4), use of the integration method with just biweekly grab samples generally
underestimated annual loadings, except at site BO040 where loadings were slightly
overestimated. The close proximity of the Stephenville wastewater treatment plant discharge
to site BO040 makes water quality at this site slightly higher in concentration of PO4-P and TP
at low flow, when routine grab samples are more likely to be collected, than during storm
events (Adams and McFarland, 2001).
Results for TP were similar to the results for PO4-P (Table 4). Overall the best loading
estimation method appeared to use the integration approach with biweekly grab samples
supplemented with flow-weighted EMC data from the first four storm events in a year to
represent the concentration for elevated flows. On average across sites, using the first four
storm events generally underestimated or overestimated loadings within 20 percent of “true
loadings”, although variability in percent error for specific years and sites ranged greatly (see
Appendix E). The regression approach using set stage or storm chasing to represent
concentrations at elevated flows showed some promise as potential loading estimation
methods, although both these scenarios greatly overestimated loadings at site BO090
compared to “true” loads, and the variability in loadings between years at NF020 was
probably outside the margin of acceptable error.
44
Chapter 6
How Often to Monitor
Table 3 Annual average percent error in PO4-P loading estimates compared to “true” loadings
using regression and integration approaches.
Integration Approach
Regression Approach
First Four Storms + Quarterly Storms
Biweekly Grabs Biweekly Grabs + Biweekly Grabs
Biweekly Grabs
Set Stage +
Biweekly Grabs
Storm Chasing +
Biweekly Grabs
51%
66%
-1%
26%
3%
17%
17%
29%
-3%
24%
-8%
25%
3%
22%
25%
23%
14%
18%
-24%
20%
-16%
46%
-28%
44%
285%
297%
111%
150%
204%
223%
Average
Std. Dev.
-32%
27%
-3%
8%
-9%
46%
5%
57%
14%
65%
18%
72%
GC100
95-99
Average
Std. Dev.
-18%
37%
8%
92%
-24%
36%
213%
132%
23%
62%
128%
118%
NF020
95-99
Average
Std. Dev.
-42%
47%
-11%
12%
-17%
19%
69%
111%
-27%
11%
28%
70%
SF020
95-99
Average
Std. Dev.
-24%
65%
-15%
44%
22%
57%
164%
163%
65%
48%
18%
38%
Site & Years
PO4-P
BO040
95-99
Average
Std. Dev.
26%
28%
-7%
25%
BO070
95-99
Average
Std. Dev.
0%
18%
BO090
96-99
Average
Std. Dev.
NC060
96-99
Table 4 Annual average percent error in TP loading estimates compared to “true” loadings
using regression and integration approaches.
Integration Method
Regression Method
Biweekly Grabs First Four Storms + Quarterly Storms
Biweekly Grabs + Biweekly Grabs
Biweekly Grabs
Set Stage +
Biweekly Grabs
Storm Chasing +
Biweekly Grabs
17%
26%
-1%
22%
-3%
13%
18%
22%
-10%
32%
-24%
13%
-17%
9%
15%
20%
9%
18%
-49%
33%
2%
28%
-36%
43%
36%
150%
123%
254%
118%
228%
Average
Std. Dev.
-71%
13%
-14%
30%
-40%
45%
-72%
9%
-5%
32%
6%
45%
GC100
95-99
Average
Std. Dev.
-52%
20%
-18%
52%
-21%
39%
-7%
24%
-4%
23%
3%
23%
NF020
95-99
Average
Std. Dev.
-48%
39%
5%
13%
-10%
21%
27%
87%
-2%
49%
23%
74%
SF020
95-99
Average
Std. Dev.
-57%
34%
-22%
32%
-20%
18%
22%
73%
39%
82%
3%
52%
Site & Years
TP
BO040
95-99
Average
Std. Dev.
2%
21%
-11%
27%
BO070
95-99
Average
Std. Dev.
-22%
19%
BO090
96-99
Average
Std. Dev.
NC060
96-99
45
Stategies for Monitoring Nonpoint Source Runoff
For TSS, no one method consistently estimated loadings with reasonable accuracy, although as
with PO4-P and TP, integration using biweekly grab samples supplemented with an EMC
from the first four storm events gave the most promising results (Table 5). These results
generally underestimated TSS loadings, except at NF020 where TSS loadings were generally
overestimated. Under- or overestimation of TSS loadings by 50 percent or more often occurred
for individual years. These loading results emphasize the great variability in TSS
concentrations that can occur as illustrated in Figure 11. If an accurate estimate of TSS loadings
is necessary, very intensive monitoring of storm events and base flow maybe necessary.
Factors complicating the estimation of TSS loadings may be contributions from bank sluffing
and internal contributions, which may be relatively large in some streams or stream reaches.
Such complications occurred during large storm events at the North Bosque River site BO090
near Clifton (McFarland et al., 2001).
Table 5 Annual average percent error in TSS loading estimates compared to “true” loadings
using regression and integration approaches.
Integration Method
Regression Method
First Four Storms + Quarterly Storms
Biweekly Grabs Biweekly Grabs + Biweekly Grabs
Biweekly Grabs
Set Stage +
Biweekly Grabs
Storm Chasing +
Biweekly Grabs
-52%
41%
-16%
29%
20%
80%
137%
172%
-32%
49%
-73%
12%
-32%
39%
-19%
9%
157%
157%
-76%
30%
10%
42%
-54%
64%
502%
1110%
2379%
4512%
1540%
3011%
Average
Std. Dev.
-95%
6%
-21%
59%
-53%
80%
-95%
2%
211%
341%
123%
227%
GC100
95-99
Average
Std. Dev.
-91%
8%
-37%
57%
-42%
53%
-87%
2%
-54%
11%
-78%
3%
NF020
95-99
Average
Std. Dev.
-93%
11%
83%
143%
76%
269%
-94%
5%
762%
1154%
22%
126%
SF020
95-99
Average
Std. Dev.
-86%
17%
-41%
47%
-61%
25%
41%
214%
1832%
2538%
175%
375%
Site & Years
TSS
BO040
95-99
Average
Std. Dev.
-74%
19%
-42%
37%
BO070
95-99
Average
Std. Dev.
-70%
35%
BO090
96-99
Average
Std. Dev.
NC060
96-99
For loadings, storm sampling using automated samplers is recommended to complement
manual sampling. PO4-P, TP, and TSS loadings were generally underestimated by manual
sampling alone for sites where PO4-P, TP, and TSS was transported predominately by
nonpoint source runoff. An integration approach using biweekly grab sampling and a flowweighted EMC from the first four storm events at a site for elevated flows appeared to give
the best loading estimations on an annual basis compared to “true” loadings of the scenarios
evaluated. For reference, a comparison of these six methods by year is presented in Appendix
D. While annual loadings and discharge varied greatly among years and sites, these factors
did not seem to impose a pattern on the percent error indicated by the various loading
estimation methods.
46
CHAPTER 7
How Long to Monitor
The necessary duration of sampling can vary greatly depending on the purpose of the
monitoring program. For reconnaissance monitoring or synoptic monitoring where the
purpose is to provide a “snapshot” of current conditions for comparison between a number of
sites, monitoring may occur in one day. To evaluate increasing or decreasing trends in water
quality, many years of data are needed along with ancillary information on discharge and
weather conditions. When monitoring has the purpose of measuring water quality
improvement after a change in management practices, establishment of baseline (or impaired)
conditions should first occur, so the effectiveness of management control practices can be
determined. The duration of sampling for water quality improvement may take many years
depending on the level of impairment, the ability of the water body to recycle assimilated
pollutants, the effectiveness of control measures implemented, and the source of the
pollutants.
In determining the duration of a monitoring program, some fairly simple formulas are
available, although these formulas assume a fairly sophisticated knowledge of the system
being monitored. A key to these formulas is an estimate of the variance expected in water
quality data for a given constituent without the variance due to any change or trend.
Preliminary monitoring may be necessary to obtain an estimate of this variance. These
formulas allow an estimation of the time needed for sampling to statistically detect potential
trends or changes in water quality. Minimizing both the time and sampling effort involved can
be very important, not only in minimizing cost of sampling, but also in determining whether
or not implemented control practices are sufficient to meet target water quality goals. If
implemented control practices are insufficient, it is better to find out earlier in an
implementation program than later.
For example using a paired watershed design with sampling sites SF020 and NF020, it might
be desirable to know how much improvement in PO4-P and TP concentrations at site NF020
would be needed to detect a statistical change after the implementation of phosphorus control
practices over a three-year period. Sites NF020 and SF020 have intermittent flow and are
generally dry between storm events, so storm event mean concentrations (EMCs) are used for
comparison. Between 1993 and 1999, 42 paired events were monitored at NF020 and SF020 or
an average of 7 storms per year. Using the following formula as presented by Clausen and
Spooner (1993), we can obtain an estimate of the smallest difference that would be statistically
meaningful based on one, two, and three years of monitoring data.
ì
2)
ü
n1 n2
ï
ï
MSE
1
------------- = ----------------- ´ í --------------------------------------------ý
2
n1 + n2 ï æ
F
d
F 1 + --------------------------ö ï
î è
n 1 + n 2 – 2ø þ
47
Strategies for Monitoring Nonpoint Source Runoff
where MSE equals the mean square error from the regression analysis between paired
watershed sites prior to treatment, d2 equals the square of the smallest worthwhile difference
between pre- and posttreatment dependent mean concentrations, n1 equals the number of
storms monitored during the pretreatment period, n2 equals the number of storms monitored
during the posttreatment period, and F equals the F-table value for n1 and n2 using a = 0.05.
The equation can be rearranged to solve for d to indicate the percent decrease needed to see a
significant difference in water quality given only a certain length of monitoring, i.e., one, two,
or three years.
With EMCs from SF020 as the independent variable and EMCs from NF020 as the dependent
variable, our regression analysis for the pretreatment period indicates a MSE of 0.470 and a
mean for the dependent variable of 0.05 for PO4-P concentrations based on a natural log
transformation of the data. For TP concentrations, a MSE of 0.294 and a mean of the
dependent variable of 0.64 was obtained also using a natural log transformation. Assuming 7
storm events per year, in year one a 41 percent change in PO4-P concentrations would be
needed to measure a statistically meaningful difference in water quality due to improvement
practices (Table 6). By year three, only a 23 percent change in EMCs of PO4-P would be
needed, because as the number of posttreatment storm events monitored increases, our ability
to measure smaller statistical differences increases, assuming that the inherent variability in
the data does not change. For TP, only a 19 percent change in EMCs at NF020 would be
needed to statistically measure improvement after three years of storm data collection. Based
on this formula, fairly large improvements in water quality would be necessary to show
statistically significant improvement in storm event phosphorus concentrations with less than
three years of monitoring data.
Table 6 Minimal detectable change in storm water quality with varying years of monitoring
using equation 2. Analysis based on an a = 0.05.
Number of Pre-BMP Number of Post-BMP
Storms Sampled
Storms Sampled
Geometric Mean
Pretreatment
Concentration
(mg/L)
Minimum Mean P
Posttreatment
Concentration
Needed to Detect
Statistical
Differences on
Normal Scale
(mg/L)
Percent Decrease
Needed
Constituent
Years of Monitoring
PO4-P
1
2
3
42
42
42
7
14
21
1.06
1.06
1.06
0.62
0.76
0.81
41%
28%
23%
TP
1
2
3
42
42
42
7
14
21
1.89
1.89
1.89
1.24
1.46
1.54
34%
23%
19%
The above example assumes an average of seven paired storm events per year. From 1993
through 1999, the following number of paired events actually occurred:
48
•
1993 - 6 events
•
1994 - 11 events
•
1995 - 4 events
•
1996 - 3 events
•
1997 - 10 events
•
1998 - 5 events
•
1999 - 4 events
Chapter 7
How Long to Monitor
The year-to-year variability in paired storm events indicates that more than three years of
monitoring may be necessary to obtain an acceptable degree of statistical confidence that a
change in water quality has occurred. Drought or wet weather conditions can dramatically
influence the duration of monitoring needed to evaluate a change in water quality. Equation 2,
and others like it, should, thus, be considered only as a guide in estimating the number of
samples needed (or the duration of sampling) for evaluating trends or changes in water
quality.
An additional factor that should be considered in evaluating duration of sampling with regard
to monitoring a change in water quality between paired sites is the range in size of storm
events and EMCs measured during the pre- and post-BMP periods. Ideally, the same range of
EMCs and storm event sizes should be represented in the posttreatment period as measured
in the pretreatment period. This factor may extend the duration of monitoring needed if
drought conditions or very wet conditions, which impact runoff volume and concentrations,
dominate either the pretreatment or posttreatment period and not the other.
For evaluating a trend at a single site, the problem of estimating the duration of sampling
needed actually becomes even more difficult. Potential impacts on trend analysis as identified
by Gilbert (1987) include the following:
•
Cyclic patterns—Various cyclic patterns, such as seasonal patterns, can easily be mistaken
for trends depending on what time frame is used in the data analysis.
•
Changes in field or laboratory procedures—These changes may indicate a false step trend
in water quality data taken over time, if values are not adjusted for these changes.
•
Correlated data—Data taken close in time are likely to be correlated and most statistical
tests require uncorrelated data.
•
Relationships of water quality with flow—Adjustments to the data may be needed before
assessing trends if flow and water quality are related.
•
Missing values—Some statistical tests for trend or time series analysis work best if values
are evenly spaced. If there are missing values in a data set representing routine
monitoring, the performance of the statistical test will depend on the number and location
of missing values.
•
Method detection limits (MDLs)—Values below the method detection limit and changing
MDLs throughout a time series of water quality data may mask potential trends. Care
should be taken that the MDL used by the laboratory is low enough so that actual trends
in the data can be measured.
While it is best to consider the above factors prior to starting out on a monitoring program
with the goal of measuring long-term trends, often historical data sets must be used that may
have been collected for other reasons. Particular care should be used in evaluating historical
data, particularly if field or laboratory methods for the data are not clearly indicated. Reviews
of statistical methods for evaluating trends are presented by Gilbert (1987) and Esterby (1996),
which consider adjustments to help account for most of the factors outlined above.
49
Strategies for Monitoring Nonpoint Source Runoff
50
CHAPTER 8
What Resources are Available for
Monitoring
The strategies used for monitoring generally involve a comparison of cost with confidence in
the data collected. Unnecessary monitoring is wasteful, while insufficient monitoring may
lead to false conclusions or an inability to meet desired monitoring objectives. Sampling has
fixed and variable costs. Fixed costs are generally associated with such things as sampling
equipment. Variable costs are commonly a direct function of the intensity of the monitoring
effort. The more samples collected means more trips to a sampling site, an increased
consumption of field and laboratory supplies, increased labor costs for sample collection, and
an increase in the number of laboratory analyses. A direct relationship often exists between
sampling frequency and the confidence level in the information obtained from the data (as an
example see Figure 11 on p. 38). As more samples are collected, our confidence in the data and
the analysis results increases, and the sampling program is more likely to reflect actual
conditions, but this increased confidence must be balanced by the reality of costs and budgets.
Questions to answer in weighing the cost of sampling should relate directly to the objectives
of the monitoring program. For example:
•
Will the frequency of sampling be sufficient to meet monitoring objectives within
expected confidence limits?
•
Will the precision or method detection limit used by the laboratory allow monitoring
objectives to be met?
•
Will the constituents and ancillary variables being measured meet monitoring objectives?
•
Will fixed temporal sampling be sufficient or is automated sampling necessary to meet
monitoring objectives?
This last question is very important, because automated sampling has its own set of expenses.
The inclusion of automated sampling in a monitoring program greatly enhances the chances
of collecting samples at elevated flows, which is often necessary to adequately characterize
wet weather or storm water contributions associated with nonpoint source pollution. It is
often difficult or unsafe to have field personnel manually collecting stream samples during
elevated flows. Automated sampling also allows more control of when and at what stream
level samples are collected. As shown in Tables 3-5 on pp. 45-46, annual loading estimates are
generally underestimated by strictly grab sampling programs. Even when set on a strict
biweekly schedule, routine grab samples in the North Bosque River watershed were more
likely to represent base flow rather than storm flow conditions.
If evaluating the contributions from nonpoint sources is an important part of a monitoring
program, then supplementing a routine grab sampling program with storm sampling at some
level is recommended.Tables 3-5 indicate that it may not be necessary to monitor every storm
event. For sites in the North Bosque River watershed, intensively monitoring the first four
51
Strategies for Monitoring
storm events in a year appeared to give a fairly good estimate of the event mean concentration
for elevated flows throughout a year. While leading to a larger margin of error in calculated
loadings (Tables 3-5), storm chasing may also be an acceptable option for some monitoring
programs, if automated sampling equipment is outside the range of the program’s budget.
Additional costs associated with automated sampling can include:
•
Automated sampler and housing for equipment
•
Level meter for stream stage recordings
•
Solar recharge panel and battery
•
Training of personnel on sampler programming and sample retrieval
•
Increased labor in equipment installation and maintenance
•
Increased laboratory costs for analyses on an increased number of samples (compositing,
preferably by flow-weighting, may alleviate part of the increased laboratory costs)
•
Increased data storage and data analysis costs with increased data collection
Another important consideration is whether a stage-discharge relationship must be developed
for a site. A stage-discharge relationship provides information on stream flow which is
necessary for calculating loadings. If the sampling site is located at a culvert or another control
structure, establishing a stage-discharge relationship may be fairly simple task using
established hydrologic equations. If manual measurements are needed to develop a stagedischarge relationship, this can be a very costly and time consuming effort that is often at the
whimsy of the weather. Ideally, a number of manual measurements need to be obtained across
the range of potential stream stage levels. For highly elevated flows, this may be unsafe or
impossible, unless the site is at a bridge. When determining sampling site locations for which
flow measurements are necessary, placing a site near or at the location of U.S. Geological
Survey (USGS) stream flow station may be an important consideration. With cost saving
measures being implemented by almost all government agencies, the USGS has had to reduce
the number of sites it monitors and the types of data collected at some sampling sites. For
example, at some sites, flow at all stream levels is recorded, while at some stations only
“peak” flows are recorded. The USGS is very willing to partner with cooperators and can be
approached to assist in obtaining flow data for specific stream sites.
Another sampling cost consideration includes site location within the watershed. In practical
terms, as the location of sampling sites becomes more spread out and further from
headquarters and analytical laboratories, sampling costs increase and sample holding times
may become a constraint. Travel times from sampling crew headquarters to sites, travel times
between sites, and travel times from sites to delivery at the analytical laboratory need to be
considered in relation to project budgets and acceptable holding times for the analysis of
different constituents. Shorter holding times and/or increased travel distances can increase
the number of personnel needed to retrieve samples in a timely fashion, and also increase the
potential need to pay overtime for extended work days or work on weekends or holidays. As
the number of field personnel increase, overall salaries and equipment associated with each
field crew member also increase as well as the need for more vehicles to transport these people
and the maintenance of these vehicles. Wet weather sampling can be quite hard on vehicles,
particularly when they need to be taken off road to retrieve samples from more secluded
areas.
Further cost considerations in developing a monitoring strategy include the resources needed
to properly document, store, and analyze collected data. Data management can be a very
expensive part of a monitoring program, particularly if very intensive data are collected.
Careful attention must be paid to data entry or electronic data transfer from data loggers to
52
Chapter 8
What Resources are Available for Monitoring
make sure the collected measurements are as close to error free as possible. If data does not get
into the database correctly, subsequent analyses will be flawed and wrong conclusions may be
reached. It is important to have properly trained people on staff for data entry, data
management, and data analysis.
The objectives of most monitoring projects do not end with data collection and laboratory
analysis. If data are appropriately collected, properly analyzed in the laboratory, and carefully
stored in a database, but then forgotten, they are of little use to anyone. Considering what data
are needed to answer the questions associated with the objectives monitoring should be one of
the first considerations in developing a monitoring program. Data must be processed into
information which hopefully answers the objectives of the monitoring program. Often that
information must be published and distributed. Data storage, data analysis, report writing,
and publication all have costs that must be considered. The costs for data storage and analysis,
again, generally increase with the intensity of the monitoring program and the amount of data
collected. The costs associated with report writing, publication, and distribution will depend
largely on the audience that must be reached with the water quality information.
In addition, many state and federal programs require a quality assurance project plan (QAPP).
A QAPP specifies information on standard operating procedures from data collection to data
analysis and management. A detailed QAPP is an invaluable document, because it provides
clear and concise documentation of procedures used to control the quality of the data
collected and helps ensure that monitoring will meet the stated objectives. This
documentation can be used to ensure consistency in methods over time, and also allows
others to evaluate and use the data with a thorough knowledge of what the data represents.
Although the QAPP document should be considered indispensable, it represents a cost in time
and money that must be considered up front in the planning process of a monitoring project.
53
Strategies for Monitoring
54
CHAPTER 9
Summary & Conclusions
Defining appropriate water quality monitoring strategies for nonpoint source pollution can be
quite challenging. Nonpoint source pollution is inherently dynamic occurring largely during
rainfall-runoff events and will generally only be quantifiable through intensive storm water
(or wet-weather) monitoring. Routine grab sampling, which is often used for assessing
ambient or base flow conditions, will be inadequate to characterize nonpoint source pollution
in most instances.
Wet-weather monitoring will be a critical aspect of most watershed approaches concerned
with water quality impairments from nonpoint sources and is, most certainly, a linchpin of the
Planned Intervention Microwatershed Approach that was refined and demonstrated under
this project’s funding. Under such an approach, monitoring program objectives will typically
involve assessing present water quality conditions in a watershed (e.g., levels of impairment
and sources) and then measuring progress towards remediation of impairments once control
actions are implemented. Other objectives may also be pertinent to a particular watershed,
such as obtaining data for testing of computer models. The monitoring plan used to meet
these objectives can be defined through six primary points of consideration: 1) what to
monitor, 2) where to monitor, 3) what type of monitoring to implement, 4) how often to
monitor, 5) how long to monitor, and 6) what resources are available for monitoring. This
report reviewed these six considerations in developing a monitoring plan with particular
emphasis on wet-weather monitoring of nonpoint source contributions. Major points
associated with each consideration are summarized below:
•
What to monitor?
¤
In defining what to monitor, it is important to consider not only the primary variable
of interest, say the abundance of algae in an area with algal bloom problems, but also
ancillary variables, such as nutrients, that may be influencing the variable of interest.
Often water quality problems are in response to an imbalance caused by some other
factor, and these cause and effect relationships may need to be defined.
¤
Stream flow or discharge is a critical parameter for consideration in development of a
monitoring plan involved in measuring nonpoint source contributions. Accurate
stream flow measurement when obtaining a water quality sample, however, can
significantly increase the time and instrumentation required for sample collection.
Continuous stream flow measurement requires continuous measurement of water
level and either the development of a stage-discharge relationship or presence of a
control point (e.g., a flume or weir) for which the stage-discharge relationship is
known. Continuous stream flow data are necessary to calculate loadings for a
waterborne constituent. Stream flow at the time of water quality sampling also adds
important information for subsequent data interpretation, because concentrations of
most waterborne constituents respond to variations in stream flow.
55
Strategies for Monitoring
¤
•
56
If mathematical models are to be used to help meet objectives, model selection should
precede definition of the variables to be monitored. Many sophisticated water quality
models are available that can greatly aid in providing information to the watershed
planning process, but these models often require extensive watershed specific data.
Monitoring data may be needed to tune specific model coefficients and to provide
data for model calibration and verification.
Where to monitor?
¤
In determining sampling site locations, it is important to consider stream transport
and distance factors that may influence transformations of pollutants. The proximity
of a sampling site to a pollutant source will greatly influence the magnitude of
concentrations measured. As more distance occurs between the pollutant source and
the sampling location, not only is dilution of the pollutant expected by mixing with
other waters, but also there is a greater travel time, which allows for physical and
biochemical processes to transform pollutants.
¤
To define sources of nonpoint source pollution it may be impractical to monitor
individual sources, because even small watersheds generally represent a mix of
different land uses. When monitoring heterogeneous land-use watersheds care should
be taken in attributing sources, because relatively small land areas may
disproportionately contribute pollutants. Also, individual sources will typically be
indistinguishable once they have reached a stream. Edge of field monitoring may be
required in some situations and in other situations statistical regression analysis of
results from sampling several heterogeneous land-use watersheds may be
appropriate wherein land uses above each sampling site become the independent
variables (e.g., McFarland and Hauck, 1999). Uncertainty and statistical validity
should be considered, and mathematical models may be needed to support findings.
Though not discussed in this report, pollutant source identification can make use of
technologies such as nitrogen isotope analyses, DNA techniques for microorganisms,
and monitoring of surrogate constituents, such as various antibiotics uniquely
associated with animal feeding operations.
¤
Adequate distance should be allowed for complete mixing when locating sampling
sites below point source discharges and in a similar fashion, below the merging of two
streams. In these situations, a mixing zone will exist for some distance downstream
before the separate waters completely mix, and sampling in a mixing zone should
generally be avoided.
¤
The possibility of spatial variations in constituent concentrations within a water body
should be contemplated when deciding where to sample. A few relevant examples of
when a simple grab sample may not prove to be representative are provided. During
storm events, constituents associated with sediment will often be unequally
distributed through the stream cross section with greater concentrations found at
greater depths and near the center of flow. Estuarine and tidal rivers often experience
salinity variations with depth, which can also cause sufficient density stratification to
induce vertical variation in other water quality constituents. Deeper lakes and
reservoirs will experience seasonal temperature stratification, which also induces
vertical stratification and water quality variation. Lakes often have a longitudinal
water quality gradient from inlet of streams to the main body of the lake.
Chapter 9
¤
•
•
Summary & Conclusions
The upstream-downstream approach and paired watershed approach are two
statistical designs recommended to evaluate effectiveness of nonpoint source
management practices. Both approaches use a control-sampling site and a treatmentsampling site in conjunction with pretreatment and posttreatment sampling periods
to isolate water quality changes from an area receiving improvements in management
practices. Proper location of sampling sites is critical to the success of both approaches
as described in guidance documents (e.g., Spooner et al., 1985; Spooner and Line,
1993).
What type of monitoring to implement?
¤
In many stream systems flow is dominated by surface flow originating from rainfall
and runoff, and groundwater contributions are of secondary importance. To
characterize nonpoint source pollution in these streams, wet weather sampling will
typically be a necessity. The North Bosque River exemplifies these surface runoff
dominated systems found in many parts of our nation.
¤
The use of automated sampling equipment is recommended for characterizing storm
events. Water quality can be highly variable both within an individual storm event
and between different storm events. Consequently, accurate characterization of water
quality from storm events will require multiple samples collected during each event
and the sampling of more than one event. Automated sampling is also more reliable
and safer than manual grab sampling during periods of elevated flows.
¤
If it is necessary to composite storm samples, flow weighting rather than time
weighting should be used to obtain a more accurate characterization of overall storm
concentrations. Compositing samples is a systematic method of combining a number
of individual samples into one representative sample and thereby reducing analytical
(laboratory) costs. Flow-weighted samples can also be used in loading calculations,
while time-weighted samples are not related to stream discharge and, thus, have no
intrinsic relationship to loading.
¤
The equipment for real-time continuous monitoring is rapidly improving and may be
an important consideration for aiding wet-weather sampling or for evaluating
changes in water quality over time.
How often to monitor?
¤
In general, confidence in results will increase as the frequency of sampling increases.
¤
In comparison to biweekly sampling, percent errors in the calculation of annual
average stream concentrations increased greatly when monthly or quarterly sampling
was considered.
¤
For storm sampling, it may be possible to monitor a subset of storm events during the
year rather than every storm event. For selected sampling sites in the North Bosque
River watershed where on average about 16 storm events occur per year, monitoring
of 4 to 6 events per year provided a percent error of about 20 percent in the calculation
of annual storm concentrations of PO4-P, TP, and TSS.
¤
For calculating annual loadings, some storm monitoring will be necessary in areas
where nonpoint source contributions are important. An integration approach using an
overall flow-weighted mean of the first four storm events to complement biweekly
grab sampling appeared to give the best loading estimations, on an annual basis,
compared to "true" loadings of the sampling schemes evaluated for sites in the North
Bosque River watershed.
57
Strategies for Monitoring
•
•
58
How long to monitor?
¤
To reasonably characterize the water quality of a system with important nonpoint
source pollution contributions will usually require a minimum of two to three years of
monitoring and perhaps even more time if the period sampled is atypically wet or
dry. The inherent variability of rainfall runoff will typically necessitate such a
commitment if meaningful results are to be obtained.
¤
It may take several years to measure change or improvement in water quality
associated with implementation of control practices. Estimates of the needed duration
of sampling can be calculated if an estimate of the expected variance in the constituent
of interest can be obtained. This variance may be estimated from preliminary or
pretreatment monitoring or obtained from other monitoring programs.
¤
The duration of posttreatment monitoring is dependent on the percent change in
water quality expected from control practices. In general, the larger the impact of the
control practice, the shorter the duration of monitoring that should be needed to
measure significant improvements in water quality.
¤
Year-to-year variability in weather conditions can greatly influence the duration of
monitoring needed to statistically evaluate significant trends or improvements in
water quality with control practices. Care should be taken to make sure that changes
in water quality are actually associated with the control practices being implemented
and are not due to changes in weather conditions or other factors between the pre and
posttreatment monitoring periods.
What resources are available for monitoring?
¤
The resources needed to meet monitoring objectives will need to be balanced with
constraints imposed by budgets and available equipment, instruments, and
personnel. Monitoring resources are directly related to the frequency of sampling
needed, the precision with which data must be analyzed, the number of ancillary
variables that need to be monitored, and whether manual or automated sampling
needs to be implemented. Automated sampling, while expensive, greatly enhances
the chances of collecting samples at elevated flows, which may be necessary to
properly characterize storm water contributions associated with nonpoint source
pollution.
¤
For ease in obtaining stage-discharge information, efforts should be made to locate
sampling sites at control structures (e.g., road culverts) or established USGS
monitoring sites. If this is not possible, time and resources will need to be allocated for
manual measurements of flow to establish site-specific, stage-discharge relationships,
if discharge data are required.
¤
Travel times between sampling sites as well as from headquarters to sampling sites
and to the analytical laboratory should be considered in relation to holding times for
the constituents of interest.
¤
Resources available for monitoring should consider data storage, analysis, and
publication as well as the resources needed for sample collection and laboratory
analysis. While project dependent, these items may represent significant investment
of resources.
¤
A quality assurance project plan (QAPP) should be a mandatory resource available as
part of all monitoring programs to help ensure that the sampling strategies used will
meet the objectives of monitoring and aid in the watershed planning process.
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Thomann, R.V. and J.A. Mueller. 1987. Principles of Surface Water Quality Modeling and Control. Harper
and Row Publishers, Inc., New York, New York.
TNRCC, Texas Natural Resource Conservation Commission. 2001. Two Total Maximum Daily Loads for
Phosphorus in the North Bosque River for Segments 1226 and 1255. Strategic Assessment Division,
TMDL Team, TNRCC, Austin, Texas (February 2001).
TNRCC, Texas Natural Resource Conservation Commission. 1999. Surface Water Quality Monitoring
Procedures Manual. TNRCC, Austin, Texas, GI-252 (June 1999).
USEPA, United States Environmental Protection Agency. 2000a. Nutrient Criteria Technical Guidance
Manual: Rivers and Streams. Office of Water and Office of Science and Technology, USEPA, Washington, D.C., EPA-822-B-00-002 (July 2000).
USEPA, United States Environmental Protection Agency. 2000b. Nutrient Criteria Technical Guidance
Manual: Lakes and Reservoirs. Office of Water and Office of Science and Technology, USEPA, Washington, D.C., EPA-822-B-00-001 (April 2000).
USEPA, United States Environmental Protection Agency. 1990. Biological Criteria: National Program
Guidance for Surface Waters. Office of Water, Washington, D.C. EPA-440-5-90-004.
USEPA, United States Environmental Protection Agency. 1983. Methods for Chemical Analysis of Water
and Wastes. Environmental Monitoring and Support Laboratory, Office of Research and Development, USEPA, Cincinnati, OH. EPA-600/4-79-020, Revised March 1983.
USGS, United States Geological Survey. 1999. National Field Manual for the Collection of Water-Quality
Data. U.S. Geological Survey Techniques of Water-Resources Investigations.
Ward, R.C., J.C. Loftis, H.P. DeLong and H.F. Bell. 1988. Groundwater quality: A data analysis protocol. Journal of the Water Pollution Control Federation 60:1938-1945.
62
APPENDIX A
Sampling Sites, Weather, and
Monitoring Protocols
Sampling Sites
For this report, data analyses evaluating monitoring strategies focus specifically on seven
stream sites representing the diversity of land uses and flow conditions within the watershed
of the North Bosque River (Tables A-1 and A-2). These sites are BO040 on the North Bosque
River below Stephenville, BO070 on the North Bosque River above Hico, BO090 on the North
Bosque River near Clifton, GC100 on Green Creek, NC060 on Neils Creek, NF020 on the North
Fork of the North Bosque River, and SF020 on the South Fork of the North Bosque River (see
Figure 1, p. 14).
Table A–1
Site
Estimated land uses for the drainage area above selected sampling sites.
Wood/Range
(%)
Pasture
(%)
Dairy Waste
Application Fields
(%)
Urban
(%)
Other
(%)
Area
(hectares)
8.4
6.5
8.9
11.7
7.2
3.7
3.8
1.7
1.5
1.2
1.0
0.2
25,719
93,248
253,740
7.2
12.8
6.9
0.0
0.7
0.0
0.7
0.2
26,165
35,147
6.8
1.0
45.4
0.0
0.0
0.0
0.5
0.2
791
848
Cropland
(%)
Sites on the North Bosque River
BO040
51.1
23.8
BO070
68.2
15.4
BO090
71.9
13.8
Site on Major Tributaries to the North Bosque River
GC100
71.2
13.3
NC060
76.5
10.5
Sites on Microwatershed Stream Segments
NF020
38.8
8.5
SF020
96.1
2.7
Sites BO040, BO070, and BO090 represent sites with predominately perennial flow along the
North Bosque River. Site BO040 is located about 0.4 kilometers (0.25 miles) below the
Stephenville wastewater treatment plant (WWTP) discharge. The water quality at BO040,
thus, reflects the point source discharge from the WWTP at base flow mixed with nonpoint
sources during storm events. Site BO070, near Hico, is located above the Hico WWTP
discharge, thus, making the Stephenville WWTP discharge the only point source discharge
permitted above this site. Site BO090 is located above the Clifton WWTP discharge, but
integrates permitted point source discharges from the WWTPs of the cities of Stephenville,
Hico, Iredell, and Meridian. Particularly during the dry summer when evaporation is high,
flows at BO040, BO070, and BO090, if they occur, are dominated by discharge contributions
from the WWTPs located along the river.
Neils Creek and Green Creek are major tributaries to the North Bosque River. Site NC060 on
Neils Creek is considered a least impacted site and used as a reference site within the
63
Strategies for Monitoring Nonpoint Source Runoff
Table A–2
Daily streamflow statistics by year.
Site
Year
Average
(m3/s)
Median
(m3/s)
Minimum
(m3/s)
Maximum
(m3/s)
Total Volume
(m3)
BO040
BO040
BO040
BO040
BO040
1995
1996
1997
1998
1999
0.89
1.02
1.24
0.80
0.23
0.22
0.32
0.15
0.18
0.13
0.04
0.04
0.02
0.05
0.04
31.5
13.8
55.7
50.8
2.85
28,100,000
32,200,000
39,200,00
25,100,000
7,300,000
BO070
BO070
BO070
BO070
BO070
1995
1996
1997
1998
1999
4.53
2.44
7.46
4.08
0.91
2.40
0.63
2.65
2.17
1.10
0.10
0.00
1.19
0.38
0.00
116
74.9
197
159
9.03
143,000,000
77,000,000
235,000,000
129,000,000
29,700,000
BO090
BO090
BO090
BO090
BO090
1995
1996
1997
1998
1999
14.31
4.22
19.95
8.95
1.45
5.26
0.93
4.41
1.47
0.38
0.01
0.07
0.39
0.02
0.01
461
183
677
1420
186
451,000,000
133,000,000
629,000,000
282,000,000
45,800,000
GC100
GC100
GC100
GC100
GC100
1995
1996
1997
1998
1999
1.27
0.48
2.55
0.69
0.02
0.45
0.17
0.36
0.00
0.00
0.00
0.00
0.00
0.00
0.00
35.0
29.1
84.6
44.9
4.15
39,900,000
15,200,000
80,400,000
21,600,000
713,000
NC060
NC060
NC060
NC060
NC060
1995
1996
1997
1998
1999
naa
0.94
5.46
2.73
0.29
na
0.14
1.06
0.74
0.20
na
0.00
0.20
0.00
0.00
na
100
252
474
11.9
na
29,900,000
172,000,000
86,200,000
9,050,000
NF020
NF020
NF020
NF020
NF020
1995
1996
1997
1998
1999
0.03
0.03
0.07
0.04
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.83
1.15
6.49
5.40
0.07
809,000
852,000
2,100,000
1,110,000
14,800
SF020
SF020
SF020
SF020
SF020
1995
1996
1997
1998
1999
0.03
0.04
0.05
0.02
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.97
2.34
4.68
3.05
0.08
1,080,000
1,250,000
1,610,000
552,000
14,000
a. na, not applicable. Discharge data collection was not initiated at site NC060 until October 5, 1995.
watershed (Coan and Hauck, 1996), while the drainage area above GC100 contains a moderate
number of dairies and land area associated with dairy waste application. Green Creek is
considered intermittent with perennial pools, while Neils Creek is considered to have
perennial flow (D. Petrick, personal communication, 2001).
The North Fork and South Fork of the North Bosque River are considered microwatershed
sites. A microwatershed, in this context, is defined as a drainage area small enough to deal
with all landowners within the drainage area when intervention is needed for water quality
improvement (Rottler et al., 1999). The drainage area above NF020 is heavily impacted by
dairy operations, while the drainage area above SF020 has very limited impacts associated
with intensive agriculture and is primarily wood and range land. Both NF020 and SF020 are
intermittent and often dry between rainfall runoff events.
64
Appendix A Sampling Sites, Weather, and Monitoring Protocols
Precipitation and Hydrologic Conditions
Most of the monitoring data presented were collected between January 1, 1995 and December
31, 1999. During that time weather conditions and, thus, hydrologic conditions varied
considerably from month to month (Figure A–1). Precipitation was punctuated by several
months of well above average rainfall often followed by periods of drought. Notably high
precipitation occurred in August 1995, August 1996, and February 1997 at both the
Stephenville and Waco Dam sites, indicating that above average precipitation was probably
occurring throughout the watershed (Figure A–1). The period December 1997 through March
1998 was also abnormally wet, although extended periods of below average precipitation
occurred October 1995 through June 1996, July 1997 through November 1997, April 1998
through August 1998, and through much of 1999.
Figure A–1 Monthly precipitation and departure from normal for Stephenville and Waco Dam
National Weather Service observer sites. Long-term average or normal monthly precipitation calculated
for 1967-1996.
65
Strategies for Monitoring Nonpoint Source Runoff
Annual stream-flow statistics for sampling sites BO040, BO070, BO090, GC100, NC060, NF020,
and SF020 indicate the lowest flow occurring in 1999 across sites and the highest flows
occurring in 1997 (Table A-2). Distinct wet and dry periods occur between 1995 and 2000,
which should be considered in evaluating any water quality data set. Average daily discharge
for each site is, thus, graphically presented in Appendix B for reference.
Data Collection Methods and Procedures
Data collection at sites BO040, BO070, BO090, GC100, NC060, NF020, and SF020 consists of
routine grab samples collected on a biweekly basis and storm samples collected by a
programmed automated sampler. Routine grab samples consist of a single, representative
sample taken at 0.1 to 0.3 meters (0.25 to 1.0 ft.) below the surface of the water. Routine
sampling occurs on a set biweekly schedule and, thus, does not necessarily represent only
base flow conditions. A grab sample is not taken during routine monitoring if a site is dry or if
water is pooled at a site and not flowing. During low flow conditions, riffle areas near each
sampling site are observed to determine the presence or absence of flow.
Storm samples are collected using automated sampling equipment consisting of an ISCO 3230
or 4230 bubbler-type flow meter and an ISCO 3700 automated sampler enclosed in a sheet
metal shelter. The flow meter measures the pressure required to force an air bubble through a
3-mm (0.125-in) polypropylene tube (bubbler line) and records this pressure as water level.
The flow meters are programmed to record stream water level (stage) and are used to initiate
sample retrieval by the automated samplers when a certain stream level is obtained. Each flow
meter is programmed to record water level at five-minute intervals and typically actuates
storm sampling when a stream rise of 4-cm (1.5 in) above the bubbler datum is registered. The
actuation level was selected by trial and error as the lowest level at which the sampler would
actuate for rainfall-runoff events while avoiding undesired actuation from nonrainfall event
causes such as wave action. Electrical power is provided to the flow meter and sampler by
marine deep-cycle batteries with recharge provided by solar cells.
Most storm samples were collected using a sequential sampling sequence. The sampling
sequence for sites NF020 and SF020 included an initial sample, three samples taken at onehour intervals, two samples taken at two-hour intervals, and all remaining samples taken at
eight-hour intervals. At sites BO040, BO070, BO090, GC100, and NC060 the sampling
sequence was set to retrieve an initial sample, then a single sample at one-hour, two-hour,
three-hour, four-hour, and six-hour intervals with all remaining samples taken at eight-hour
intervals. A more frequent collection sequence was used at sites NF020 and SF020, because
these two sites are in the headwaters and respond much more quickly to rainfall-runoff
events. These sampling sequences allow for more frequent sample collection during the
typical rapid hydrograph rise and peak periods following sampler actuation and less frequent
sample collection during the longer, receding portion of a storm hydrograph.
In the laboratory, most samples collected between 1995 and 1999 were analyzed separately as
individual samples. In January 1995 through July 1995, flow-weighting at the sampler was
implemented at sites BO040, BO070, and GC100 by programming the sampler to take a
sample after a given amount of flow had passed by. This procedure was problematic in
determining a flow volume for sampling that would capture both small and large storm
events. If the flow volume between samples was sufficiently small to capture small runoff
events, there would be inadequate sample bottles (24 bottles in a sampler) to capture large
events, If the flow volume was set to allow characterization of large events, no sample would
be collected for small events. In mid-1997, daily flow-weighting of sequential samples was
initiated again at sites BO040, GC100, and NF020 to help decrease the number of samples
66
Appendix A Sampling Sites, Weather, and Monitoring Protocols
analyzed. This time samples were collected using the sequential sequence, but before being
processed in the laboratory, samples collected on a given day were combined into a single
flow-weighted sample based on continuous five-minute discharge information using a
midpoint rectangular integration method between collection times.
Water level data was converted to discharge using site specific stage-discharge relationships.
For stream sites BO040, GC100, NC060, NF020, and SF020, stage-discharge relationships were
developed from manual measurements of flow at various stream levels. At site BO070, the
USGS stage-discharge relationship for site 08094800 was used in conjunction with measured
level data to calculate flow. At site BO090, fifteen-minute discharge data from corresponding
USGS site 08095000 were obtained for use in this report.
Routine grab and storm water samples were analyzed for soluble ammonia-nitrogen (NH3N), soluble nitrite-nitrogen plus nitrate-nitrogen (NO2-N+NO3-N), total Kjeldahl nitrogen
(TKN), soluble orthophosphate phosphorus (PO4-P), total phosphorus (TP), and total
suspended solids (TSS). In addition, conductivity, dissolved oxygen (DO), pH, and water
temperature values were measured about one-foot below the surface at stream sites when
routine grab samples were collected using a Hydrolab RecorderTM Water Quality Multiprobe
Logger connected to a Scout®2 Display (Hydrolab Corporation, Austin, TX). Samples
analyzed for chlorophyll-a (CHLA) were also collected with routine grab samples but on a
monthly rather than biweekly basis. Data analyses for this report focus primarily on PO4-P, TP,
and TSS, because excessive phosphorus is considered the major water quality problem in the
North Bosque River watershed associated with excessive algal growth (TNRCC, 2001). All
sampling and analyses were conducted under EPA or TNRCC approved quality assurance
project plans (e.g., TIAER, 1998). Methods of laboratory analysis are outlined in Table A-3.
Table A–3
Analysis methods for water quality constituents.
Constituent
Field Measurements
Conductivity
Dissolved Oxygen
pH
Water Temperature
Laboratory Measurements
Ammonia-Nitrogen
Chemical Oxygen Demand
Chlorophyll-a
Fecal Coliform
Nitrite-Nitrogen + Nitrate-Nitrogen
Total Kjeldahl Nitrogen
Orthophosphate-Phosphorus
Total Phosphorus
Total Suspended Solids
Method
Source
SM 2510B
EPA 360.1
EPA 150.1
EPA 170.1
APHA (1995)
USEPA (1983)
USEPA (1983)
USEPA (1983)
EPA 350.1
Hach 8000
SM 10200H
SM 9222D
EPA 353.2
EPA 351.2
EPA 365.2
EPA 365.4
EPA 160.2
USEPA (1983)
Hach (1991)
APHA (1995)
APHA (1995)
USEPA (1983)
USEPA (1983)
USEPA (1983)
USEPA (1983)
USEPA (1983)
In data management, left censored data (values measured below the laboratory method
detection limit) were entered into the water quality database as one-half the method detection
limit (MDL) as recommended by Gilliom and Helsel (1986) and Ward et al. (1988). MDLs were
evaluated about every six months by TIAER’s water quality laboratory (Table A-4).
67
Strategies for Monitoring Nonpoint Source Runoff
Table A–4
68
Laboratory method detection limits effective March 1996 through December 1999.
Effective Dates
NH3-N
(mg/L)
NO2-N+NO3-N
(mg/L)
PO4-P
(mg/L)
Total-P
(mg/L)
TKN
(mg/L)
COD
(mg/L)
TSS
(mg/L)
CHLA
(µg/L)
19 Sep 95 - 20 Mar 96
21 Mar 96–27 Oct 96
28 Oct 96–04 May 97
05 May 97–30 Nov 97
01 Dec 97–16 Dec 97
17 Dec 97–14 May 98
15 May 98–30 Nov 98
01 Dec 98–01 Jun 99
02 Jun 99–10 Dec 99
11 Dec 99–31 Dec 99
0.016
0.015
0.037
0.022
0.022
0.022
0.009
0.015
0.030
0.037
0.004
0.015
0.006
0.016
0.008
0.008
0.016
0.016
0.010
0.013
0.009
0.003
0.010
0.008
0.011
0.011
0.006
0.006
0.003
0.006
0.074
0.110
0.101
0.077
0.153
0.024
0.048
0.052
0.053
0.086
0.116
0.299
0.194
0.173
0.195
0.195
0.113
0.165
0.150
0.122
6
5
6
4
4
4
6
4
4
4
10
10
10
10
3
3
7
6
4
6
5.00
5.36
8.63
12.20
3.30
3.30
8.84
1.14
0.99
2.46
APPENDIX B
Average Daily Flow at Sampling
Sites
Figure B–1
Average daily flow at site BO040 on the North Bosque River below Stephenville.
BO040
10.00
1.00
0.10
Jan-00
Sep-99
May-99
Jan-99
Sep-98
May-98
Jan-98
Sep-97
May-97
Jan-97
Sep-96
May-96
Jan-96
Sep-95
May-95
0.01
Jan-95
Average Daily Flow (m3 /s)
100.00
69
Strategies for Monitoring
Figure B–2
Average daily flow at site BO070 on the North Bosque River at Hico.
Average Daily Flow (m3 /s)
1,000.00
BO070
100.00
10.00
1.00
0.10
Figure B–3
Jan-00
Sep-99
May-99
Jan-99
Sep-98
May-98
Jan-98
Sep-97
May-97
Jan-97
Sep-96
May-96
Jan-96
Sep-95
May-95
Jan-95
0.01
Average daily flow at site BO090 on the North Bosque River near Clifton.
Average Daily Flow (m3 /s)
10,000.00
BO090
1,000.00
100.00
10.00
1.00
0.10
70
Jan-00
Sep-99
May-99
Jan-99
Sep-98
May-98
Jan-98
Sep-97
May-97
Jan-97
Sep-96
May-96
Jan-96
Sep-95
May-95
Jan-95
0.01
Appendix B
Figure B–4
Average Daily Flow at Sampling Sites
Average daily flow at site GC100 on Green Creek.
Average Daily Flow (m3 /s)
100.00
GC100
10.00
1.00
0.10
May-98
Sep-98
Jan-99
May-99
Sep-99
Jan-00
Sep-98
Jan-99
May-99
Sep-99
Jan-00
Jan-98
Sep-97
May-97
Jan-97
Sep-96
May-96
Jan-96
May-98
Figure B–5
Sep-95
Jan-95
May-95
0.01
Average daily flow at site NC060 on Neils Creek.
NC060
100.00
10.00
1.00
0.10
Start date:
05Oct95
Jan-98
Sep-97
May-97
Jan-97
Sep-96
May-96
Jan-96
Sep-95
May-95
0.01
Jan-95
Average Daily Flow (m3 /s)
1,000.00
71
Strategies for Monitoring
Figure B–6
Average daily flow at site NF020 on the North Fork of the North Bosque River.
Average Daily Flow (m3 /s)
10.00
NF020
1.00
0.10
Figure B–7
Jan-00
Sep-99
May-99
Jan-99
Sep-98
May-98
Jan-98
Sep-97
May-97
Jan-97
Sep-96
May-96
Jan-96
Sep-95
May-95
Jan-95
0.01
Average daily flow at site SF020 on the South Fork of the North Bosque River.
Average Daily Flow (m3 /s)
10.00
SF020
1.00
0.10
72
Jan-00
Sep-99
May-99
Jan-99
Sep-98
May-98
Jan-98
Sep-97
May-97
Jan-97
Sep-96
May-96
Jan-96
Sep-95
May-95
Jan-95
0.01
APPENDIX C
Impact of Sampling Frequency on
Annual Average Concentration
Table C–1 Impact of sampling interval on average annual concentration at site BO040.
B represents biweekly sampling, M1 and M2 represent monthly sampling schemes, and Q1 through Q6
represent quarterly sampling schemes.
Year
B
M1
PO4-P (mg/L)
1995
1.247
1.380
1996
1.703
1.626
1997
0.889
1.013
1998
1.362
1.505
1999
1.861
1.879
TP (mg/L)
1995
1.50
1.54
1996
1.87
1.82
1997
0.98
1.15
1998
2.16
1.76
1999
2.02
2.04
TSS (mg/L)
1995
35
9
1996
15
15
1997
20
23
1998
11
12
1999
11
10
Number of Observations
1995
29
12
1996
26
12
1997
28
12
1998
25
12
1999
26
12
M2
Q1
Q2
Q3
Q4
Q5
Q6
1.201
1.649
0.772
1.270
1.794
0.963
1.483
0.940
1.678
2.005
1.913
1.830
0.383
1.443
1.773
1.045
1.553
0.438
0.988
1.903
1.280
2.003
1.740
1.380
1.908
1.070
1.195
1.215
1.190
1.340
1.108
2.220
0.625
1.475
2.068
1.53
1.77
0.85
2.70
1.93
1.05
1.68
1.20
1.91
2.12
2.12
2.05
0.50
1.81
2.00
1.61
1.79
0.62
1.17
2.06
1.69
2.09
1.75
1.54
2.16
1.30
1.32
1.20
4.90
1.54
1.24
2.35
0.72
1.71
2.13
61
16
22
9
12
13
21
19
13
14
5
5
8
11
9
157
20
43
6
12
10
12
40
12
7
40
27
12
9
11
15
7
11
11
8
12
12
12
12
12
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
73
Strategies for Monitoring
Table C–2 Impact of sampling interval on average annual concentration at site BO070.
B represents biweekly sampling, M1 and M2 represent monthly sampling schemes, and Q1 through Q6
represent quarterly sampling schemes.
Year
B
M1
PO4-P (mg/L)
1995
0.22
0.21
1996
0.31
0.32
1997
0.17
0.19
1998
0.14
0.14
1999
0.30
0.28
TP (mg/L)
1995
0.37
0.29
1996
0.40
0.41
1997
0.30
0.30
1998
0.30
0.31
1999
0.41
0.33
TSS (mg/L)
1995
71
14
1996
19
13
1997
16
23
1998
10
13
1999
12
6
Number of Observations
1995
27
12
1996
26
12
1997
27
12
1998
25
12
1999
17
8
M2
Q1
Q2
Q3
Q4
Q5
Q6
0.26
0.33
0.15
0.11
0.33
0.30
0.29
0.18
0.16
0.29
0.16
0.22
0.15
0.08
0.38
0.26
0.40
0.10
0.06
0.41
0.12
0.30
0.24
0.17
0.39
0.35
0.36
0.22
0.18
0.24
0.19
0.26
0.14
0.14
0.36
0.44
0.43
0.29
0.30
0.49
0.41
0.35
0.28
0.35
0.34
0.21
0.31
0.29
0.18
0.53
0.65
0.48
0.24
0.26
0.63
0.30
0.36
0.50
0.41
0.57
0.51
0.52
0.33
0.26
0.36
0.25
0.36
0.19
0.30
0.53
131
27
11
9
19
23
21
13
9
4
7
5
11
4
13
371
11
8
8
9
18
23
25
25
10
50
45
10
5
3
10
10
15
12
10
12
12
12
12
8
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
Table C–3 Impact of sampling interval on average annual concentration at site BO090.
B represents biweekly sampling, M1 and M2 represent monthly sampling schemes, and Q1 through Q6
represent quarterly sampling schemes.
Year
B
M1
PO4-P (mg/L)
1995
0.008
0.011
1996
0.033
0.038
1997
0.040
0.050
1998
0.034
0.043
1999
0.005
0.004
TP (mg/L)
1995
0.06
0.06
1996
0.08
0.07
1997
0.13
0.12
1998
0.13
0.17
1999
0.11
0.11
TSS (mg/L)
1995
16
19
1996
14
16
1997
40
40
1998
20
23
1999
9
9
Number of Observations
1995
8
4
1996
26
12
1997
27
12
1998
26
12
1999
27
12
74
M2
Q1
Q2
Q3
Q4
Q5
Q6
0.006
0.032
0.029
0.025
0.006
0.010
0.071
0.061
0.040
0.005
0.008
0.007
0.028
0.013
0.003
0.008
0.032
0.021
0.012
0.004
0.008
0.013
0.037
0.078
0.004
0.008
0.039
0.029
0.016
0.004
0.008
0.032
0.044
0.048
0.010
0.06
0.09
0.14
0.10
0.12
0.04
0.05
0.12
0.20
0.13
0.04
0.05
0.09
0.13
0.19
0.04
0.08
0.16
0.04
0.06
0.09
0.08
0.12
0.19
0.06
0.04
0.13
0.13
0.07
0.08
0.04
0.07
0.14
0.19
0.12
21
12
49
18
9
28
27
12
26
10
23
15
8
14
9
23
8
12
15
6
25
12
22
29
9
26
11
100
16
8
31
13
31
21
11
4
12
12
12
12
2
4
4
4
4
2
4
4
4
4
2
4
4
4
4
2
4
4
4
4
2
4
4
4
4
2
4
4
4
4
Appendix C Impact of Sampling Frequency on Annual Average Concentration
Table C–4 Impact of sampling interval on average annual concentration at site GC100.
B represents biweekly sampling, M1 and M2 represent monthly sampling schemes, and Q1 through Q6
represent quarterly sampling scheme.
Year
B
M1
PO4-P (mg/L)
1995
0.073
0.051
1996
0.038
0.024
1997
0.079
0.084
1998
0.120
0.126
1999
0.015
0.017
TP (mg/L)
1995
0.13
0.10
1996
0.09
0.10
1997
0.20
0.24
1998
0.28
0.30
1999
0.14
0.17
TSS (mg/L)
1995
21
17
1996
9
9
1997
17
12
1998
15
16
1999
11
11
Number of Observations
1995
26
12
1996
23
12
1997
27
12
1998
14
7
1999
9
5
M2
Q1
Q2
Q3
Q4
Q5
Q6
0.089
0.051
0.085
0.141
0.013
0.057
0.029
0.090
0.130
0.030
0.060
0.016
0.028
0.181
0.009
0.085
0.033
0.081
0.076
0.006
0.068
0.027
0.140
0.123
0.014
0.126
0.054
0.078
0.142
0.011
0.055
0.065
0.091
0.223
0.015
0.14
0.10
0.21
0.30
0.15
0.12
0.07
0.35
0.36
0.14
0.10
0.10
0.25
0.25
0.11
0.20
0.12
0.22
0.18
0.12
0.20
0.09
0.23
0.33
0.28
0.10
0.15
0.13
0.28
0.12
0.12
0.08
0.14
0.42
0.09
28
12
24
18
12
19
7
8
23
14
25
9
10
18
10
54
12
47
20
9
6
7
20
25
11
15
13
15
24
10
19
10
8
27
14
11
12
12
7
5
4
4
4
3
2
4
4
4
3
2
4
4
4
3
2
4
4
4
3
2
3
4
4
3
2
4
4
4
3
2
Table C–5 Impact of sampling interval on average annual concentration at site NC060.
B represents biweekly sampling, M1 and M2 represent monthly sampling schemes, and Q1 through Q6
represent quarterly sampling scheme.
Year
B
M1
PO4-P (mg/L)
1995
0.005
0.005
1996
0.010
0.008
1997
0.024
0.031
1998
0.012
0.013
1999
0.005
0.003
TP (mg/L)
1995
0.05
0.04
1996
0.07
0.09
1997
0.07
0.09
1998
0.08
0.10
1999
0.07
0.06
TSS (mg/L)
1995
5
5
1996
5
6
1997
12
9
1998
3
3
1999
3
3
Number of Observations
1995
8
4
1996
21
11
1997
27
12
1998
21
10
1999
16
8
M2
Q1
Q2
Q3
Q4
Q5
Q6
0.005
0.012
0.018
0.013
0.007
0.005
0.011
0.035
0.014
0.003
0.005
0.008
0.022
0.010
0.004
0.005
0.004
0.023
0.010
0.005
0.005
0.004
0.025
0.016
0.003
0.005
0.007
0.017
0.014
0.004
0.005
0.023
0.021
0.017
0.008
0.05
0.05
0.06
0.08
0.09
0.04
0.15
0.10
0.10
0.08
0.06
0.05
0.07
0.14
0.06
0.04
0.05
0.04
0.06
0.08
0.06
0.05
0.13
0.10
0.06
0.04
0.05
0.08
0.05
0.10
0.04
0.05
0.05
0.13
0.12
5
5
17
3
3
5
5
4
3
4
5
5
5
3
3
5
5
5
3
3
5
5
5
3
3
5
5
40
3
3
5
5
5
3
3
4
11
12
11
8
2
4
4
3
4
2
4
4
4
4
2
4
4
4
4
2
4
4
4
4
2
4
4
4
4
2
4
4
4
4
75
Strategies for Monitoring
Table C–6 Impact of sampling interval on average annual concentration at site NF020.
B represents biweekly sampling, M1 and M2 represent monthly sampling schemes, and Q1 through Q6
represent quarterly sampling scheme.
Year
B
M1
PO4-P (mg/L)
1995
0.819
0.686
1996
1.848
2.072
1997
1.002
1.151
1998
0.861
0.712
1999
na
na
TP (mg/L)
1995
1.39
1.03
1996
2.49
2.95
1997
1.40
1.41
1998
1.14
1.03
1999
na
na
TSS (mg/L)
1995
85
49
1996
35
31
1997
24
11
1998
17
12
1999
na
na
Number of Observations
1995
16
8
1996
10
6
1997
17
8
1998
9
5
1999
0
0
M2
Q1
Q2
Q3
Q4
Q5
Q6
0.789
1.683
1.090
0.906
na
1.290
1.890
1.555
0.285
na
0.300
2.203
0.995
0.495
na
0.457
2.033
1.575
0.540
na
1.043
1.493
1.338
1.015
na
1.670
1.603
1.113
0.455
na
0.650
1.397
1.073
1.200
na
1.70
1.77
1.48
1.14
na
1.62
2.29
1.65
0.57
na
0.76
3.59
1.52
0.69
na
1.26
1.92
2.27
0.65
na
1.47
1.81
2.12
1.42
na
2.53
1.94
1.02
0.62
na
1.26
1.20
1.52
1.59
na
146
31
39
20
na
23
35
8
9
na
89
31
7
40
na
310
12
64
37
na
38
16
10
16
na
37
25
8
10
na
139
37
9
38
na
8
6
8
5
0
3
3
4
2
0
3
3
4
2
0
3
3
4
2
0
3
3
4
2
0
3
3
4
2
0
3
3
4
2
0
Table C–7 Impact of sampling interval on average annual concentration at site SF020.
B represents biweekly sampling, M1 and M2 represent monthly sampling schemes, and Q1 through Q6
represent quarterly sampling scheme.
Year
B
M1
PO4-P (mg/L)
1995
0.041
0.031
1996
0.022
0.014
1997
0.044
0.046
1998
0.029
0.033
1999
na
na
TP (mg/L)
1995
0.14
0.11
1996
0.07
0.06
1997
0.13
0.15
1998
0.12
0.16
1999
na
na
TSS (mg/L)
1995
75
56
1996
9
5
1997
35
9
1998
9
6
1999
na
na
Number of Observations
1995
17
9
1996
10
6
1997
16
7
1998
6
4
1999
0
0
76
M2
Q1
Q2
Q3
Q4
Q5
Q6
0.050
0.028
0.049
0.024
na
0.033
0.019
0.057
0.030
na
0.012
0.008
0.020
0.030
na
0.073
0.014
0.019
0.030
na
0.021
0.009
0.057
0.028
na
0.057
0.013
0.040
0.028
na
0.011
0.041
0.035
0.030
na
0.10
0.08
0.13
0.09
na
0.11
0.07
0.16
0.17
na
0.06
0.06
0.09
0.08
na
0.13
0.05
0.14
0.08
na
0.15
0.05
0.20
0.11
na
0.27
0.07
0.13
0.11
na
0.02
0.08
0.09
0.08
na
82
11
70
11
na
60
5
11
6
na
22
17
5
16
na
160
17
5
16
na
52
17
139
7
na
59
5
23
7
na
24
17
5
16
na
9
6
7
4
0
3
3
3
2
0
3
3
3
2
0
3
3
3
2
0
3
3
3
2
0
3
3
3
2
0
3
3
3
2
0
APPENDIX D
Regression Estimation of
Concentrations and Loadings
Table D–1 Regression method results for site BO040.
Results based on data collected between January 1, 1995 to December 31, 1999.
Concentration Regression
Sampling Scheme
Monthly Loadings - MVUE
Residuals
Normala
R2
n
R2
1055
0.61
**b
no
0.93
168
0.58
**
yes
155
0.03
ns
158
135
0.69
0.50
995
Monthly Loadings - Smearing
Slope
Std Error
R2
**
1.02
0.04
0.93
0.91
**
0.81
0.03
yes
0.86
**
0.04
**
**
yes
yes
0.88
0.91
**
**
0.18
**
no
0.93
167
0.21
**
yes
156
0.29
**
155
134
0.24
0.25
1071
Slope
Std Error
**
0.41
0.01
0.91
**
0.87
0.04
0.00
0.87
**
0.04
0.00
4.21
1.11
0.20
0.05
0.88
0.90
**
**
4.44
1.27
0.22
0.05
**
1.05
0.04
0.93
**
0.46
0.02
0.88
**
1.01
0.05
0.88
**
1.02
0.05
yes
0.89
**
0.71
0.03
0.89
**
0.71
0.03
**
**
yes
yes
0.89
0.83
**
**
0.96
0.77
0.04
0.04
0.89
0.84
**
**
0.97
0.79
0.04
0.05
0.23
**
no
0.91
**
1.06
0.04
0.91
**
0.42
0.02
167
0.29
**
yes
0.89
**
1.17
0.05
0.89
**
1.18
0.06
156
0.22
**
yes
0.85
**
1.55
0.09
0.85
**
1.49
0.08
157
133
0.32
0.28
**
**
yes
yes
0.89
0.85
**
**
1.04
0.98
0.05
0.05
0.89
0.86
**
**
1.03
0.99
0.05
0.05
TSS
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
TP
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
PO4-P
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
a. Normality of residuals based on a Shapiro-Wilks test with an a = 0.05.
b. ** indicates significance at a = 0.01.
77
Strategies for Monitoring
Table D–2 Regression method results for site BO070.
Results based on data collected between January 1, 1995 to December 31, 1999.
Concentration Regression
Sampling Scheme
Monthly Loadings - MVUE
Residuals
Normala
R2
n
R2
1425
0.55
**b
no
0.92
156
0.57
**
no
151
0.09
*c
151
123
0.58
0.50
1378
Monthly Loadings - Smearing
Slope
Std Error
R2
**
1.80
0.07
0.92
0.88
**
1.71
0.08
no
0.71
**
0.03
**
**
no
no
0.88
0.92
**
**
0.10
**
no
0.98
154
0.08
ns
no
151
0.03
ns
149
122
0.16
0.05
1434
Slope
Std Error
**
0.62
0.02
0.88
**
2.20
0.11
0.00
0.73
**
0.04
0.00
4.55
1.07
0.22
0.04
0.87
0.91
**
**
6.32
1.44
0.32
0.06
**
1.13
0.02
0.98
**
0.41
0.01
0.98
**
0.96
0.02
0.98
**
0.94
0.02
no
0.95
**
0.54
0.02
0.95
**
0.51
0.02
**
ns
yes
no
0.97
0.98
**
**
1.43
0.79
0.03
0.02
0.97
0.98
**
**
1.40
0.80
0.03
0.02
0.03
**
no
0.96
**
1.18
0.03
0.96
**
0.41
0.01
155
0.08
ns
no
0.96
**
0.92
0.03
0.96
**
0.87
0.02
151
0.12
**
no
0.86
**
2.01
0.10
0.86
**
1.77
0.09
149
122
0.08
0.09
*
ns
no
no
0.94
0.93
**
**
1.05
0.87
0.04
0.03
0.94
0.94
**
**
0.98
0.88
0.03
0.03
TSS
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
TP
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
PO4-P
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
a. Normality of residuals based on a Shapiro-Wilks test with an a = 0.05.
b. ** indicates significance at a = 0.01.
c. * indicates significance at a = 0.05.
78
APPENDIX D Regression Estimation of Concentrations and Loadings
Table D–3 Regression method results for site BO090.
Results based on data collected between October 10, 1995 to December 31, 1999.
Concentration Regression
Sampling Scheme
Monthly Loadings - MVUE
Residuals
Normala
R2
n
R2
998
0.56
**b
no
0.57
136
0.63
**
no
134
0.05
ns
130
113
0.49
0.44
958
Monthly Loadings - Smearing
Slope
Std Error
R2
**
1.51
0.19
0.57
0.54
**
2.15
0.28
no
0.66
**
0.02
**
**
yes
no
0.56
0.65
**
**
0.31
**
no
0.91
137
0.27
**
yes
134
0.10
*c
130
113
0.19
0.13
1011
Slope
Std Error
**
0.57
0.07
0.53
**
2.64
0.35
0.00
0.78
**
0.01
0.00
1.28
0.43
0.16
0.04
0.53
0.61
**
**
1.59
0.52
0.21
0.06
**
1.06
0.05
0.91
**
0.45
0.02
0.90
**
1.03
0.05
0.89
**
1.10
0.06
no
0.94
**
0.31
0.01
0.94
**
0.32
0.01
**
*
yes
yes
0.90
0.94
**
**
1.17
0.62
0.06
0.02
0.86
0.91
**
**
1.39
0.76
0.08
0.03
0.52
**
no
0.71
**
1.97
0.18
0.71
**
0.80
0.07
137
0.60
**
yes
0.72
**
1.20
0.11
0.69
**
1.26
0.12
134
0.41
**
no
0.69
**
5.46
0.52
0.57
**
9.04
1.10
130
113
0.53
0.58
**
**
yes
yes
0.65
0.61
**
**
1.96
2.30
0.21
0.26
0.57
0.53
**
**
2.45
2.90
0.30
0.39
TSS
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
TP
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
PO4-P
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
a. Normality of residuals based on a Shapiro-Wilks test with an a = 0.05.
b. ** indicates significance at a = 0.01.
c. * indicates significance at a = 0.05.
79
Strategies for Monitoring
Table D–4 Regression method results for site GC100.
Results based on data collected from January 1, 1995 to December 31, 1999.
Concentration Regression
Sampling Scheme
Monthly Loadings - MVUE
Residuals
Normala
R2
n
R2
734
0.38
**b
no
0.81
121
0.55
**
yes
114
0.08
ns
114
99
0.41
0.35
677
Monthly Loadings - Smearing
Slope
Std Error
R2
**
0.45
0.03
0.82
0.81
**
0.43
0.03
no
0.65
**
0.03
**
**
yes
yes
0.89
0.85
**
**
0.28
**
yes
0.93
120
0.34
**
yes
114
0.15
**
114
100
0.29
0.24
741
Slope
Std Error
**
0.15
0.01
0.81
**
0.45
0.03
0.00
0.66
**
0.02
0.00
0.21
0.11
0.01
0.01
0.89
0.85
**
**
0.24
0.12
0.01
0.01
**
0.92
0.03
0.93
**
0.42
0.02
0.94
**
1.01
0.03
0.94
**
0.99
0.03
yes
0.90
**
0.56
0.02
0.90
**
0.53
0.02
**
**
yes
yes
0.93
0.91
**
**
1.13
1.04
0.04
0.04
0.93
0.91
**
**
1.13
1.06
0.04
0.04
0.41
**
no
0.90
**
1.16
0.05
0.90
**
0.53
0.02
121
0.26
**
yes
0.87
**
0.87
0.04
0.87
**
0.84
0.04
115
0.45
**
yes
0.80
**
4.67
0.30
0.80
**
4.76
0.31
115
100
0.39
0.39
**
**
no
yes
0.85
0.83
**
**
1.64
2.65
0.09
0.16
0.85
0.82
**
**
1.64
2.79
0.09
0.17
TSS
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
TP
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
PO4-P
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
a. Normality of residuals based on a Shapiro-Wilks test with an a = 0.05.
b. ** indicates significance at a = 0.01.
80
APPENDIX D Regression Estimation of Concentrations and Loadings
Table D–5 Regression method results for site NC060.
Results based on data collected from October 5, 1995 to December 31, 1999.
Concentration Regression
Sampling Scheme
Monthly Loadings - MVUE
Residuals
Normala
R2
n
R2
847
0.47
**b
no
0.73
119
0.65
**
no
114
0.17
**
111
92
0.67
0.41
809
Monthly Loadings - Smearing
Slope
Std Error
R2
**
1.53
0.13
0.72
0.65
**
4.51
0.46
no
0.47
**
0.00
**
**
no
no
0.70
0.78
**
**
0.14
**
no
0.97
121
0.30
**
no
115
0.13
*
113
93
0.30
0.06
862
Slope
Std Error
**
0.52
0.04
0.65
**
6.20
0.64
0.00
0.49
**
0.00
0.00
3.23
0.05
0.30
0.00
0.69
0.78
**
**
3.98
0.05
0.37
0.00
**
0.93
0.02
0.97
**
0.38
0.01
0.98
**
1.29
0.02
0.98
**
1.34
0.03
no
0.77
**
0.12
0.01
0.78
**
0.12
0.01
**
ns
no
no
0.98
0.89
**
**
1.56
0.21
0.03
0.01
0.98
0.91
**
**
1.62
0.24
0.03
0.01
0.35
**
no
0.77
**
1.01
0.08
0.77
**
0.36
0.03
121
0.46
**
yes
0.80
**
0.66
0.05
0.79
**
0.66
0.05
115
0.49
**
no
0.42
**
17.69
2.91
0.41
**
20.22
3.45
113
93
0.48
0.46
**
**
yes
yes
0.78
0.79
**
**
0.69
0.57
0.05
0.04
0.78
0.77
**
**
0.69
0.61
0.05
0.05
TSS
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
TP
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
PO4-P
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
a. Normality of residuals based on a Shapiro-Wilks test with an a = 0.05.
b. ** indicates significance at a = 0.01.
81
Strategies for Monitoring
Table D–6 Regression method results for site NF020.
Results based on data collected from January 1, 1995 to December 31, 1999.
Concentration Regression
Sampling Scheme
Monthly Loadings - MVUE
Residuals
Normala
R2
n
R2
519
0.36
**b
no
0.69
85
0.56
**
yes
63
0.21
*
68
53
0.49
0.22
440
Monthly Loadings - Smearing
Slope
Std Error
R2
**
1.92
0.17
0.69
0.36
**
9.30
1.61
yes
0.63
**
0.01
**
ns
yes
yes
0.49
0.68
**
**
0.14
**
no
0.95
84
0.27
**
yes
61
0.14
ns
66
51
0.33
0.24
520
Slope
Std Error
**
1.43
0.13
0.37
**
9.52
1.63
0.00
0.64
**
0.01
0.00
1.33
0.00
0.18
0.00
0.46
0.65
**
**
1.52
0.08
0.21
0.01
**
0.85
0.02
0.95
**
0.32
0.01
0.93
**
0.90
0.03
0.93
**
0.89
0.03
yes
0.89
**
0.59
0.03
0.89
**
0.59
0.03
**
*c
yes
yes
0.89
0.82
**
**
1.10
1.01
0.05
0.06
0.88
0.81
**
**
1.12
1.16
0.05
0.07
0.14
**
no
0.96
**
1.09
0.03
0.96
**
0.33
0.01
85
0.10
ns
no
0.97
**
0.66
0.02
0.97
**
0.57
0.01
62
0.14
ns
no
0.88
**
0.81
0.04
0.88
**
0.74
0.03
67
52
0.25
0.20
**
ns
no
no
0.88
0.82
**
**
1.31
1.61
0.06
0.10
0.88
0.81
**
**
1.20
1.85
0.06
0.12
TSS
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
TP
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
PO4-P
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
a. Normality of residuals based on a Shapiro-Wilks test with an a = 0.05.
b. ** indicates significance at a = 0.01.
c. * indicates significance at a = 0.05.
82
APPENDIX D Regression Estimation of Concentrations and Loadings
Table D–7 Regression method results for site SF020.
Results based on data collected from January 1, 1995 to December 31, 1999.
Concentration Regression
Sampling Scheme
Monthly Loadings - MVUE
Residuals
Normala
R2
n
R2
868
0.22
**b
no
0.65
89
0.45
**
yes
74
0.13
ns
71
50
0.46
0.39
775
Monthly Loadings - Smearing
Slope
Std Error
R2
**
1.37
0.13
0.64
0.37
**
11.57
1.98
yes
0.41
**
0.02
**
**
no
no
0.34
0.28
**
**
0.08
**
no
0.93
88
0.40
**
yes
73
0.04
ns
70
49
0.16
0.30
876
Slope
Std Error
**
0.32
0.03
0.34
**
11.60
2.11
0.00
0.44
**
0.02
0.00
1.52
0.70
0.27
0.14
0.31
0.24
**
**
1.84
1.12
0.36
0.26
**
0.89
0.03
0.93
**
0.25
0.01
0.79
**
1.33
0.09
0.79
**
1.35
0.09
yes
0.87
**
0.35
0.02
0.90
**
0.34
0.01
ns
*c
no
yes
0.93
0.86
**
**
1.10
1.26
0.04
0.07
0.92
0.86
**
**
1.53
1.72
0.06
0.09
0.12
**
no
0.68
**
1.31
0.12
0.68
**
0.31
0.03
88
0.13
ns
yes
0.74
**
1.74
0.13
0.72
**
1.76
0.14
73
0.25
**
yes
0.72
**
5.66
0.46
0.71
**
6.84
0.57
70
49
0.14
0.27
ns
*
yes
yes
0.78
0.71
**
**
1.22
3.02
0.08
0.25
0.78
0.67
**
**
1.24
4.64
0.09
0.43
TSS
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
TP
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
PO4-P
Full data set
Set stage + biweekly
grabs
Peak flow + biweekly
grabs
Storm chasing +
biweekly grabs
Biweekly grabs
a. Normality of residuals based on a Shapiro-Wilks test with an a = 0.05.
b. ** indicates significance at a = 0.01.
c. * indicates significance at a = 0.05.
83
Strategies for Monitoring
84
APPENDIX E
Comparison of Annual Loading
Estimation Methods
85
Strategies for Monitoring
Table E–1 Annual percent error in PO4-P loading estimates compared to “true” loadings
using regression and integration approaches.
Integration Method
86
Regression Method
Biweekly
Grabs
First Four
Storms +
Biweekly
Grabs
Quarterly
Storms +
Biweekly
Grabs
Biweekly
Grabs
Storm
Set Stage +
Chasing +
Biweekly
Biweekly
Grabs
Grabs
Measured Annual
Discharge
(m3)
"True" Annual
PO4-P Loading
(kg)
1995
1996
1997
1998
1999
28,088,179
32,234,746
39,158,673
25,072,194
7,031,493
Average
Std
17,231
24,899
21,292
17,475
10,250
44%
64%
-4%
22%
5%
26%
28%
16%
-48%
-2%
9%
-11%
-7%
25%
160%
47%
2%
50%
-5%
51%
66%
43%
-12%
-26%
-10%
2%
-1%
26%
27%
-5%
-10%
16%
-13%
3%
18%
65%
-3%
-2%
25%
-1%
17%
29%
BO070
1995
1996
1997
1998
1999
142,731,373
77,026,469
235,271,714
128,608,684
28,700,086
Average
Std
27,031
18,900
59,019
26,930
8,447
8%
26%
-7%
-22%
-3%
0%
18%
-32%
28%
13%
-12%
-14%
-3%
24%
-31%
26%
-31%
7%
-10%
-8%
25%
38%
-13%
-15%
12%
-5%
3%
22%
66%
14%
6%
19%
20%
25%
24%
42%
1%
-4%
11%
18%
14%
18%
BO090
1996
1997
1998
1999
133,342,938
629,252,789
282,320,839
45,781,829
Average
Std
13,919
87,735
22,049
225
-35%
-44%
3%
-21%
-24%
20%
-83%
21%
3%
-5%
-16%
46%
-70%
-61%
9%
12%
-28%
44%
-1%
58%
543%
539%
285%
297%
-22%
-7%
192%
283%
111%
150%
-14%
40%
422%
368%
204%
223%
NC060
1996
1997
1998
1999
29,871,838
172,206,270
86,236,844
9,048,835
Average
Std
697
9,644
1,533
77
-21%
-65%
-41%
-2%
-32%
27%
-13%
0%
5%
-5%
-3%
8%
-45%
-53%
37%
25%
-9%
47%
-14%
-43%
87%
-12%
5%
57%
-10%
-35%
110%
-8%
14%
65%
-5%
-33%
125%
-14%
18%
72%
GC100
1995
1996
1997
1998
1999
39,920,190
15,212,549
80,386,270
21,645,543
677,454
Average
Std
4,795
1,507
13,059
6,328
28
27%
-63%
7%
-45%
-18%
-18%
37%
-55%
169%
-12%
-39%
-25%
8%
92%
-37%
21%
-4%
-27%
-75%
-24%
36%
159%
259%
213%
38%
398%
213%
132%
1%
38%
9%
-50%
118%
23%
62%
104%
151%
94%
-16%
309%
128%
118%
NF020
1995
1996
1997
1998
1999
809,418
852,261
2,095,411
1,098,122
14,849
Average
Std
987
1,657
3,908
1,646
6
-37%
29%
-38%
-65%
-100%
-42%
47%
-3%
-29%
1%
-6%
-17%
-11%
12%
-13%
-46%
-19%
8%
-17%
-17%
19%
191%
175%
45%
-2%
-64%
69%
111%
-40%
-10%
-30%
-31%
-25%
-27%
11%
104%
93%
21%
-18%
-60%
28%
71%
SF020
1995
1996
1997
1998
1999
1,081,900
1,246,557
1,605,089
551,534
14,046
Average
Std
35
51
65
22
1
20%
-76%
57%
-18%
-100%
-24%
65%
-63%
53%
-35%
-27%
0%
-15%
44%
0%
-10%
-40%
59%
100%
22%
57%
412%
166%
136%
151%
-43%
164%
162%
121%
53%
70%
90%
-7%
65%
48%
50%
29%
38%
22%
-48%
18%
38%
Site
Year
BO040
APPENDIX E Comparison of Annual Loading Estimation Methods
Table E–2 Annual percent error in TP loading estimates compared to “true” loadings
using regression and integration approaches.
Integration Method
Regression Method
Biweekly
Grabs
First Four
Storms +
Biweekly
Grabs
Quarterly
Storms +
Biweekly
Grabs
Biweekly
Grabs
Storm
Set Stage +
Chasing +
Biweekly
Biweekly
Grabs
Grabs
Measured Annual
Discharge
(m3)
"True" Annual TP
Loading
(kg)
1995
1996
1997
1998
1999
28,088,179
32,234,746
39,158,673
25,072,194
7,031,493
Average
Std
31,012
36,907
36,446
30,867
11,519
15%
25%
-31%
-4%
3%
2%
21%
10%
-57%
1%
1%
-11%
-11%
27%
60%
14%
0%
16%
-4%
17%
25%
10%
-1%
-26%
-18%
29%
-1%
22%
14%
-20%
5%
-11%
-1%
-3%
13%
29%
-11%
45%
22%
4%
18%
22%
BO070
1995
1996
1997
1998
1999
142,731,373
77,026,469
235,271,714
128,608,684
28,700,086
Average
Std
74,423
43,476
124,058
74,397
12,293
6%
-29%
-34%
-41%
-12%
-22%
19%
-59%
22%
13%
-10%
-14%
-10%
32%
-41%
-21%
-34%
-13%
-11%
-24%
13%
-13%
-30%
-12%
-21%
-6%
-17%
9%
40%
13%
-5%
-4%
29%
15%
20%
29%
4%
-8%
-6%
26%
9%
18%
BO090
1996
1997
1998
1999
133,342,938
629,252,789
282,320,839
45,781,829
Average
Std
56,128
295,036
197,197
2,878
-71%
-53%
-72%
-2%
-49%
33%
-10%
28%
-32%
21%
2%
28%
-73%
-54%
-41%
26%
-36%
43%
-53%
-35%
-27%
261%
36%
150%
-33%
-8%
30%
503%
123%
255%
-35%
2%
48%
456%
118%
228%
NC060
1996
1997
1998
1999
29,871,838
172,206,270
86,236,844
9,048,835
Average
Std
9,904
38,937
30,707
1,315
-76%
-66%
-85%
-55%
-71%
13%
-27%
4%
-50%
16%
-14%
30%
-76%
-47%
-62%
24%
-40%
44%
-78%
-67%
-80%
-61%
-72%
9%
-21%
25%
18%
-43%
-5%
32%
-22%
47%
43%
-43%
6%
46%
GC100
1995
1996
1997
1998
1999
39,920,190
15,212,549
80,386,270
21,645,543
677,454
Average
Std
17,065
6,085
29,869
12,533
382
-39%
-79%
-33%
-41%
-68%
-52%
20%
-68%
58%
-11%
-5%
-65%
-18%
52%
-47%
30%
-6%
-12%
-70%
-21%
39%
-36%
-19%
26%
-10%
5%
-7%
24%
-24%
-18%
20%
-18%
22%
-4%
23%
-21%
-12%
33%
-8%
23%
3%
24%
NF020
1995
1996
1997
1998
1999
809,418
852,261
2,095,411
1,098,122
14,849
Average
Std
1,623
2,535
7,402
2,660
16
-22%
1%
-54%
-66%
-100%
-48%
39%
10%
-3%
0%
-8%
25%
5%
13%
-26%
-4%
-36%
18%
0%
-10%
22%
156%
71%
-9%
-16%
-65%
27%
87%
61%
29%
-14%
-20%
-65%
-2%
48%
130%
59%
-1%
-9%
-65%
23%
74%
SF020
1995
1996
1997
1998
1999
1,081,900
1,246,557
1,605,089
551,534
14,046
Average
Std
199
308
298
93
6
-52%
-83%
-30%
-22%
-100%
-57%
34%
-60%
0%
15%
-15%
-50%
-22%
32%
-44%
7%
-27%
-23%
-17%
-20%
18%
123%
34%
21%
14%
-82%
22%
73%
162%
68%
28%
-6%
-54%
39%
82%
30%
-1%
38%
33%
-85%
3%
52%
Site
Year
BO040
87
Strategies for Monitoring
Table E–3 Annual percent error in TSS loading estimates compared to “true” loadings
using regression and integration approaches.
Integration Method
88
Regression Method
Biweekly
Grabs
First Four
Storms +
Biweekly
Grabs
Quarterly
Storms +
Biweekly
Grabs
Biweekly
Grabs
Storm
Set Stage +
Chasing +
Biweekly
Biweekly
Grabs
Grabs
Measured Annual
Discharge
(m3)
"True" Annual
TSS Loading
(kg)
1995
1996
1997
1998
1999
28,088,179
32,234,746
39,158,673
25,072,194
7,031,493
Average
Std
10,179,417
6,305,332
12,675,136
7,133,848
181,164
-46%
-86%
-81%
-93%
-62%
-74%
19%
-74%
-73%
16%
-38%
-42%
-42%
37%
-93%
-85%
9%
-47%
-42%
-52%
41%
5%
-49%
-5%
12%
-45%
-16%
29%
132%
-31%
73%
-17%
-57%
20%
79%
205%
-7%
370%
172%
-55%
137%
172%
BO070
1995
1996
1997
1998
1999
142,731,373
77,026,469
235,271,714
128,608,684
28,700,086
Average
Std
53,364,308
19,072,767
81,007,695
50,255,136
783,833
-10%
-89%
-88%
-95%
-70%
-70%
35%
-95%
5%
28%
-51%
-48%
-32%
49%
-79%
-90%
-71%
-66%
-58%
-73%
12%
4%
-63%
13%
-41%
-73%
-32%
39%
-6%
-29%
-25%
-20%
-13%
-19%
9%
224%
16%
247%
330%
-32%
157%
157%
BO090
1996
1997
1998
1999
133,342,938
629,252,789
282,320,839
45,781,829
Average
Std
68,023,763
507,143,617
266,662,904
429,205
-95%
-80%
-96%
-32%
-76%
30%
29%
34%
-53%
28%
10%
42%
-94%
-85%
-80%
42%
-54%
64%
-70%
-73%
-15%
2166%
502%
1110%
-1%
0%
376%
9142%
2379%
4512%
-32%
-38%
176%
6055%
1540%
3011%
NC060
1996
1997
1998
1999
29,871,838
172,206,270
86,236,844
9,048,835
Average
Std
38,062,104
60,771,744
38,026,491
692,694
-100%
-87%
-99%
-96%
-95%
6%
-65%
6%
-74%
50%
-21%
59%
-99%
-88%
-92%
67%
-53%
80%
-98%
-94%
-94%
-95%
-95%
2%
6%
225%
686%
-73%
211%
341%
8%
126%
439%
-81%
123%
227%
GC100
1995
1996
1997
1998
1999
39,920,190
15,212,549
80,386,270
21,645,543
677,454
Average
Std
17,065
6,085
29,869
6,443,191
317,707
-77%
-95%
-90%
-95%
-97%
-91%
8%
-92%
36%
-6%
-26%
-95%
-37%
57%
-92%
28%
-11%
-41%
-95%
-42%
53%
-84%
-89%
-86%
-88%
-87%
-87%
2%
-36%
-60%
-53%
-65%
-54%
-54%
11%
-74%
-80%
-75%
-78%
-80%
-78%
3%
NF020
1995
1996
1997
1998
1999
809,418
852,261
2,095,411
1,098,122
14,849
Average
Std
688,232
1,072,651
1,590,056
511,007
10,667
-73%
-98%
-96%
-97%
-100%
-93%
11%
41%
334%
1%
-13%
51%
83%
143%
-56%
556%
-27%
-63%
-28%
76%
269%
-85%
-95%
-94%
-95%
-99%
-94%
5%
2772%
271%
649%
220%
-100%
762%
1154%
234%
-37%
8%
6%
-99%
22%
126%
SF020
1995
1996
1997
1998
1999
1,081,900
1,246,557
1,605,089
551,534
14,046
Average
Std
185,252
656,897
259,883
102,074
3,263
-66%
-98%
-68%
-98%
-100%
-86%
17%
-92%
-33%
32%
-71%
-39%
-41%
47%
-54%
-43%
-76%
-98%
-38%
-61%
25%
420%
-38%
-29%
-50%
-98%
41%
214%
6306%
934%
975%
973%
-29%
1832%
2538%
838%
29%
48%
45%
-86%
175%
375%
Site
Year
BO040