Tech

National Nonpoint Source Monitoring Program

NOTES



September 2008

Donald W. Meals and Steven A. Dressing. 2008. Lag time in water quality
response to land treatment. Tech Notes 4, September 2008. Developed
for U.S. Environmental Protection Agency by Tetra Tech, Inc.,

Fairfax, VA, 16 p. Available online at https://www.epa.gov/polluted-rurioff-
nonpoint-source-pollution/nonpoint-source-monitoring-technical-notes.

Through the National Nonpoint Source Monitoring Program (NNPSMP),
states monitor and evaluate a subset of watershed projects funded by the
Clean Water Act Section 319 Nonpoint Source Control Program.

The program has two major objectives:

1.	To scientifically evaluate the effectiveness of watershed technologies
designed to control nonpoint source pollution

2.	To improve our understanding of nonpoint source pollution

NNPSMP Tech Notes is a series of publications that shares this unique
research and monitoring effort. It offers guidance on data collection,
implementation of pollution control technologies, and monitoring design,
as well as case studies that illustrate principles in action.

Lag Time in Water Quality Response to
Land Treatment

Introduction

Over the past three decades, some watershed land treatment projects
have reported little or no improvement in water quality after extensive
implementation of best management practices (BMPs) in the watershed.

Factors contributing to such failure to achieve water quality objectives are
nearly as numerous as the projects themselves—insufficient landowner
participation, uncooperative weather, improper selection of BMPs, mistakes
in understanding of pollution sources, poor experimental design, inadequate
level of treatment, etc.

Another important reason watershed projects may fail to meet our water quality
expectations is lag time. Lag time is an inherent characteristic of the natural systems
under study that may be generally defined as the amount of time between an action and
the response to that action. In this case, we define lag time as the time elapsed between
installation or adoption of land treatment at a level projected to reduce nonpoint source
pollution and the first measurable improvement in water quality in the target water body.
Installation refers to the completion of the construction phase for structural practices;
some vegetative practices will still need to mature over time. Adoption refers to the full
use of an installed physical practice or management practice such as nutrient management.
Land treatment-water quality monitoring projects—even those designed to be "long-
term"—may not show definitive results if the lag time exceeds the monitoring period.

Why Does Lag Time Occur?

There are both time and measurement components of lag time (Figure 1), and any or all
of them may come into play in a watershed project.

Lag time is the time
elapsed betiveen
installation or adoption
of land treatment and
improvement of water
quality.

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Planning and
implementation
process

f Measurement
V Components

LAG
TIME

Figure 1. Components of lag time experienced in land treatment—water quality projects.

One factor that affects the perceived delay between the decision to act and the result is
the time needed for planning and implementation. Although a project may be funded
today, it will be some time—perhaps years—before that project will be planned and
implementation begins. Even for point source control, it takes several years from the time
a wastewater treatment plant upgrade is approved to when it is designed, constructed, and
functioning. The lag time from planning to implementation of nonpoint source control
practices can be even greater, considering the time required to identify pollution sources
and critical areas, design management measures, engage landowner participation, and
integrate new practices into cropping and land management cycles. While not a true time
component of lag time as defined here, stakeholders—especially the general public -will
experience the planning and implementation process as part of the wait for results. The
planning and implementation process is, however, extremely critical for success in water
quality restoration; following a logical and comprehensive watershed planning process
(e.g., USEPA 2005) will help make the wait worthwhile.

Time Components

Time Required for Installed or Adopted Practice to
Produce Effect

Practices are installed in watersheds to provide a wide range of effects, including:

•	Reduce dissolved pollutant concentration or load

•	Reduce particulate/adsorbed pollutant concentration or load

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•	Improve vegetative habitat

•	Improve physical habitat

•	Restock desirable species

The time required to produce these effects will vary depending upon the degree of
impairment and the practices selected, as well as the nature of the effects themselves.

BMP Development. Once built, concrete and steel treatment works may begin to
function almost at the flip of a switch, with little time lag before pollutant load is
reduced. Some nonpoint source control measures may also take effect quickly (Figure 2).
For example, in the Lake Champlain Basin Watersheds (VT) NNPSMP Project
(1993-2000), implementation ot livestock exclusion fencing over a three month period
in the summer of 1997 resulted in significant nutrient concentration and load reductions
and reductions of fecal bacteria counts in two study streams in the first post-treatment
year (Meals 2001). This response probably resulted from the immediate prevention of
new manure deposition in the stream and riparian zone and the availability of su fficient
st ream flow to flush residual manure through the system.

Figure 2. Fencing immediately excludes livestock from a stream (left, VT NNPSMP project),

while a forested riparian buffer may take years to develop (right, PA NNPSMP project)

However, other nonpoint source management measures may take years to become fully
effective. This is especially true of vegetative practices where plant communities need
time to become established. For example, in the Stroud Preserve (PA) NNPSMP Project
(1992-2007), it took nearly ten years to achieve reforestation of a riparian forest buffer and
significant reductions in ground water nitrate through the buffer did not occur until forest
growth had achieved a certain level (Szpir et al. 2005).

Source Behavior. Lag time between BMP implementation and reduction ot pollutant
losses at the edge-of-fi.eld scale varies by the pollutant type and source. Erosion controls
such as cover crops, contour farming, and water/sediment control basins tend to have a
fairly rapid effect on soil loss from a crop field as the forces contributing to detachment
and movement of soil particles are quickly and drastically reduced.

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However, the response time of runoff phosphorus (P) to nutrient management is likely to
be very different. Runoff losses of dissolved P are strongly controlled by soil P levels; very
high soil P levels promote high levels of dissolved P in surface runoff (Pote et al. 1996,
Sims et al. 2000). Where soil P levels are excessive, even if nutrient management reduces
P inputs to levels below crop removal rates, it may take years or decades to "mine" the P
out of the soil to the point where dissolved P in runoff is effectively reduced.

Time Required for Effect to Be Delivered to Water
Resource

Practice effects initially occur at or near the practice location, yet usually watershed
managers and stakeholders want and expect these effects to appear promptly in the water
resource of interest in the watershed. The time required to deliver an effect to a water
resource depends on a number of factors, including:

•	The route for delivering the effect

a.	Directly in (e.g., streambed restoration) or adjacent to (e.g., shade) the water
resource

b.	Overland flow (e.g., particulate pollutants)

c.	Overland and subsurface flow (e.g., dissolved pollutants)

d.	Infiltration to ground water (e.g., nitrate)

•	The path distance

•	The path travel rate

a.	Fast (e.g., ditches and artificial drainage outlets to surface waters)

b.	Moderate (e.g., overland and subsurface flow in porous soils)

c.	Slow (e.g., groundwater infiltration in absence of macropores)

d.	Very slow (e.g., transport in a regional aquifer)

•	Precipitation patterns during the study period

a.	Wet periods generally increase volume and rate of transport

b.	Dry periods generally decrease volume and rate of transport

Once in a stream, dissolved pollutants like nitrogen and phosphorus can move rapidly
downstream with flowing water to reach a receiving body relatively quickly. Even
accounting for repeated uptake and release of nutrients by sediments, plants, or animals
during downstream transport (i.e., nutrient spiraling, Newbold et al. 1981), dissolved
nutrients are unlikely to be retained in a river or stream system for an extended period of
time. Research in Vermont observed, for example, that despite active cycling of dissolved
P between water, sediment, and plants in a river system, P inputs to the river were unlikely
to be held back from Lake Champlain by internal cycling for much more than a year
(Wang et al. 1999).

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Figure 3. Dissolved pollutants usually move downstream rapidly, while sediment and attached
pollutants can take years to be transported as particles are deposited, resuspended,
and redeposited.

However, sediment and attached pollutants (e.g., P and some synthetic chemicals) can
take years to move downstream as particles are repeatedly deposited, resuspended, and
redeposited within the drainage network by episodic high flow events (Figure 3). This
process can delay sediment and P transport (when P adsorbed to sediment particles
constitutes a large fraction of the P load) from headwaters to outlet by years or even
decades. This means that substantial lag time could occur between reductions of sediment
and P delivery into the headwaters and those reductions being measured at the watershed
outlet.

Pollutants delivered predominantly in ground water such as nitrate N or some synthetic
chemicals move at the rate of ground water flow, typically much more slowly than the
rate of surface water flow. About 40 percent of all N reaching the Chesapeake Bay travels
through ground water before reaching the Bay. Relatively slow ground water transport
introduces substantial lag time between reductions of N loading to groundwater and
reductions in N loads to the Bay (STAC 2005).

Time Required for Water Body to Respond to Effect

Another key factor is the speed with which the water resource responds to the effect
produced by and delivered from the practice. For example, it may take a few years for
algae production in a lake to decrease in response to reduced nutrient loading because of a
lengthy flushing rate. If the response to be measured is fish populations rather than algae
production, then even more time will be needed because fish need time to fill the newly
improved habitat.

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Nature Of the Indicator/Impairment. Lag time in water quality response may depend
on the indicator used or the impairment involved, especially it the focus is on biological
water quality If E. coli is the pollutant of concern, a relatively short lag time would
often be expected between reductions of bacteria inputs and reduction in bacteria levels
in the receiving waters because the bacteria generally do not tend to persist long in the
environment compared to heavy metals or synthetic organic chemicals. Even where
indicator bacteria may survive in aquatic sediments, without continual replenishment this
stock would t end to be exhausted in a matter of months. The quantity in the receiving
water could therefore reflect the incoming supply fairly quickly. Such response has been
demonstrated in estuarine systems where bacterial contamination of shellfish beds has
been reduced or eliminated through improved waste management on the land over a short
period of time.

However, significant lag times have been observed in the response of benthic invertebrates
and fish to land treatment. In the Middle Fork Holston River project (VA), Index of
Biotic Integrity (IBI, a measure of the stream fish community) scores and Ephemeroptera-
Plecoptera-Trichoptera (EPT, a measure of the benthic macroinvertebrate community)
scores did not improve, even though the project resulted in a substantial reduction in
the sediment, nitrogen, and phosphorus loadings there (Virginia Dept. Cons, and Rec.
1996). Altough undetected stressors could play a role, the lack of increase in the biological
indicator scores indicates a system lag time between the actual BMP implementation and
positive changes in the biological community structure (Figure 4).

Figure 4. Significant lag time may occur between improvements in physical habitat and changes in the
biological community.

Exceptions to such lag in response of stream biota can occur where in -stream restoration
is the BMP applied. The Waukegan River (IL) NNPSMP project installed vegetative
and structural stabilization and habitat structures including a series of pool-and-riffle
complexes using stone weirs to help restore the habitat functions within a channelized
stream reach. Significant improvement in habitat, macroinvertebrate communities, and in
the number and abundance of fish species were documented in the study reach.

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In the Lake Champlain Basin Watersheds (VT) NNPSMP Project (1993-2000),
the benthic invertebrate community did improve in response to reductions of sediment,
nutrient, and organic matter inputs from the land within three years of treatment (Meals
2001). However, despite observed improvement in stream habitat and water temperature,
no improvements in the fish community were documented. The project attributed this at
least partially to a lag time in community response exceeding the monitoring period.

Receiving Water Response, Even when reductions of tributary pollutant loads are
observed in a short time, the variable response times of receiving water bodies may
introduce a significant lag time between treatment and restoration of impaired uses. In
some cases, this lag time may be relatively short. For example, researchers anticipate
that the Chesapeake Bay will respond fairly rapidly to reductions in nutrient loading,
as incoming nutrients are quickly buried by sediment or exported to the atmosphere
or the ocean. Even beds of submerged aquatic vegetation (SAV), critical to the Bay's
aquatic ecosystem, can return within a few years after improvements in water clarity
(STAC 2005). In the Totten and Eld Inlets (WA) NNPSMP Project (1993-2002),
bacteriological water quality in shellfish beds in the estuaries improved rapidly in response
to improved animal waste management in the drainage area, but unfortunately also
deteriorated equally rapidly when animal waste management on the land deteriorated
(Szpir et at. 2005).

However, St. Albans Bay (VT) in Lake Champlain tells a different story (Figure 5).

From 1980 through 1991, a combination of wastewater treatment upgrades and intensive
implementation of dairy waste management BMPs through the Rural Clean Water
Program (RCWP) brought about a reduction of phosphorus loads to this eutrophic bay.
Nonetheless, water quality in the bay did not improve significantly; this pattern was
attributed to internal loading from sediments highly enriched in phosphorus from decades
of point and nonpoint source inputs (Meals 1992).

Although researchers at that time believed that the
sediment P would begin to decline over time as the
internal supply was depleted, subsequent monitoring
has shown that phosphorus levels have not declined
over the years as expected. Recent research has
confirmed that a substantial reservoir of phosphorus
continues to exist in the sediments that can be
transferred into the water under certain chemical
conditions and nourish algae blooms for many
years to come (Druschel et al. 2005). In effect, this
internal loading has become another source of P, one
that cannot be addressed by treatment on the land.























'T



u *§*

y

-

Figure 5. Aerial view of St. Albans Bay, Lake Champlain

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Measurement Components

Watershed project managers are routinely pressed for results by a wide range of
stakeholders. The fundamental time components of lag time control how long it will
take for a response to occur, but they do not address the effectiveness of measuring the
response. In other words, it is possible for a response to occur without anybody noticing,
unless the response is measurable and a suitable monitoring program is in place.

The magnitude of the potential effects produced by a watershed land treatment program
depends on the effectiveness of each unit of installed or adopted practices, the number of
practice units installed or adopted, the effectiveness with which the practices are targeted
to the correct pollutants and sources, and numerous other factors. While not all responses
can be measured, the design of the monitoring program is a major determinant of our
ability to discern a response against the background of the variability of natural systems.

In the context of lag time, sampling frequency with respect to background variability is
a key determinant of how long it will take to document change. In a given system taking
n samples per year, a certain statistical power exists to detect a trend. If the number
of samples per year is reduced, statistical power is reduced, and it may take longer to
document a significant trend or to state with confidence that a concentration has dropped
below a water quality standard. Simply stated, taking fewer samples a year introduces an
additional "statistical" lag time before a change can be effectively documented.

Magnitude of Lag Time

The magnitude of lag time is difficult to predict in specific cases and few generalizations
are possible. A few examples can, however, illustrate some possible time frames for several
categories of lag times.

The Stroud Preserve (PA) NNPSMP Project (1992-2007) is currently evaluating
the development and performance of a newly
established riparian forest buffer (Szpir et al.

2005). Reforestation of the riparian area took
about eight to twelve years (Figure 6), considerably
longer than anticipated due to drought and deer
damage. Preliminary analysis of groundwater
nitrate data indicate that, except for initial
reductions due to taking the buffer area out of
agriculture, significant nitrate removal from
groundwater flowing toward the stream did not
occur until a major increase in tree growth began
about ten years after tree planting. The results of

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Figure 6. Changes in basal area of trees in reforested riparian

buffer, Stroud Preserve National Monitoring Program
Project. (Newbold, 2005).

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the project so far suggest that water quality improvement from riparian reforestation may
take on the order of a decade or more to be measurable.

The rate of ground water movement and pollutant transport can be a major contributor to
lag time in water quality response to treatment. For example:

•	Delaware's Inland Bays, thirty square miles of estuary on the state's southern
Atlantic coast, suffer from excessive nutrient and sediment loading, resulting in
degraded communities of benthic organisms, submerged vegetation, and fish.
Nitrates delivered to the Bays in groundwater discharge from agricultural fields
and poultry operations and from septic-system effluent in the watershed is one of
the most severe stressors of the Inland Bays. Studies and efforts to reduce nitrate
loading have been underway for two decades, from state, university, and USGS
ground water studies in the 1970s to a USDA Hydrologic Unit Area (HUA)
project in the 1990s to aTMDL in 2004. Unfortunately, restoration efforts

are constrained by the 50 to 100 years required for ground water to travel from
agricultural land in the watershed to the Bays.

•	In the Pequea and Mill Creek Watershed (PA) NNPSMP Project (1994-2003),
changes in fertilizer applications to cropland did not result in changes in nitrogen
concentrations in streams due to lag time between applications and nutrients
reaching the stream channel. Ground water age dating conducted during the study
indicated that nitrogen applied to land reached springs in two to three years, but
ground water flow to the stream channel took 15 to 39 years (Galeone 2005).

•	The Big Spring Basin (IA) Water Quality Monitoring Program (1981-1998)
was one of the longest-running ground water monitoring projects in the U.S.
(Hallberg et al. 1989). The program was initiated in response to concerns about
rising nitrate contamination of the Galena Aquifer, primarily from agricultural
land use. In 1983, a national agricultural commodity program led to a 40 percent
decline in fertilizer nitrogen applications in the basin for that year. A dramatic
drop in groundwater nitrate was observed two years later, suggesting a two-year
lag time in the response to decreased inputs. However, ground water nitrate
concentrations did not respond to subsequent variations in nitrogen inputs
(either increases following the end of the commodity program or decreases due
to implementation of new BMPs) as much as they did to hydrologic variations
including both drought and high-water years. Despite lower inputs of nitrogen
to the agricultural system throughout the prior decade, the 1990s experienced
consistently high levels of groundwater nitrate, illustrating the importance of
climatic variation on lag time.

•	Recent research in the Chesapeake Bay Watershed has confirmed that a substantial
lag time between implementation of management practices and reductions in
nitrogen loading to the Bay is very likely (Phillips and Lindsey 2003, STAC 2005).
Ground water supplies a significant amount of water and nitrogen to streams in the
watershed and about half of the nitrogen load in streams in the Bay watershed was
transported through ground water. The age of ground water in shallow aquifers in

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the Chesapeake Bay watershed ranges from less than 1 year to more than 50 years.
The median age of all samples was 10 years, with 25 percent of the samples having
an age of 7 years or less and 75 percent of the samples having an age of up to 13
years. Based on this age as representative of time of travel, scientists estimated that
in a scenario of complete elimination of nitrogen applications in the watershed,
a 50 percent reduction in base flow nitrate concentrations would take about five
years, with equilibrium reached in about 2040.

Finally, some insight into lag time may be gained from modeling. Drawing from the
experience of the Rural Clean Water Program (RCWP), Clausen et al. (1992) used
a simple dynamic mass-balance model to evaluate lag time in water quality response
to nutrient management applied to agricultural land. The model predicted that even
following complete elimination of fertilizer P inputs to a field starting at an excessive
soil P level, 32 years would be required to reach 50 percent of the new equilibrium
P concentration in runoff, and over 100 years needed to reach 90 percent of the new
equilibrium. At a lower initial soil P level, the same reduction of P inputs would take 11
years to reach 50 percent of equilibrium, and 18 years to reach 90 percent of equilibrium.

A recent, more sophisticated P mass balance model of silage corn production in Vermont
(Meals et al. 2008) shows a similar picture (Figure 7). The model accounts for all inputs
and outputs of P, as well as the dynamics of soluble and particulate P runoff and leaching.
As shown in Figure 7, restriction of P inputs in manure and fertilizer to below crop
removal rate beginning in year 10 results in a downward trend in soil test P. However, 25
years elapse before soil test P declines below the "high" level (in year 35) and soil test P
does not decline below "optimum" levels until 40 years have elapsed.

Time (years)

Figure 7. Simulated changes in soil test P in response to nutrient management on silage corn and hay
in a Vermont agricultural watershed (Meals et al. 2008).

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In sum, at best only broad ranges of lag times can be generalized. In the Chesapeake Bay
watershed, where lag time issues have been examined closely, researchers have proposed
some general guidelines for considering lag times in the Bay restoration program:

Table 1. Factors affecting lag times, and implications for improvement in water quality in the Chesapeake Bay

Nutrient Source

Management
Practice
Implementation
Time

Watershed
Residence Time

Implications for Load Reductions in the Chesapeake Bay

Point Sources

(About 20 percent
of nutrient load
delivered to the Bay)

Several years

Hours to weeks

Would provide the most rapid improvement of water
quality due to immediate reduction of source.

Nonpoint Sources

(About 80 percent
of nutrient load
delivered to the Bay)

Dissolved nutrients

Several years

Hours to months
if associated with
runoff/soil water;
years to decades
if associated with
ground water, with
a median time of
10 years

Improvement in water quality would depend on the rate of
implementation of nonpoint-source reduction and amount
of nutrients still in soils. Once fully implemented, there
would be a fairly rapid reduction of the load associated
with runoff and soil water. Nitrogen load associated with
ground water would have a median time of 10 years for
water-quality improvements to be evident.

Nutrients
associated with
sediment

Several years

Decades or
longer depending
on location in
watershed

Load reductions would be greatly influenced by
streamflow variability. Storm events would deliver
sediment and associated nutrients contained on land
and in stream corridors. Loads to the Bay may not show
reductions for decades due to long residence times.

Source: Phillips and Lindsey 2005.

Dealing with Lag Time

In most situations, some lag time between land treatment and
water quality response is inevitable. Although it is nearly impossible
to predict the exact duration of the lag, in many cases the lag time
will probably exceed the length of the post-treatment monitoring
period, making it problematic to document a water quality response
to treatment. How can we deal with this unfortunate fact of life?

Here are a few suggested approaches:

• Recognize lag time and adjust expectations. Once a water
quality problem is recognized and action is taken, the public
and political system usually want and expect quick results.

Failure to meet such expectations may cause frustration,
pessimism, and a reluctance to pursue further action. It is up
to scientists, investigators, and project managers to recognize that some lag time
between treatment and response is likely and to explain the issue to all stakeholders
in realistic terms. It usually takes time for a water body to become impaired and it
will take time to accomplish the clean-up.

How to deal with lag time

•	Adjust expectations

•	Characterize the watershed

•	Select and site BMPs

•	Monitor small watersheds

•	Select indicators carefully

•	Design effective monitoring programs

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•	Characterize the watershed. Before designing a land treatment program and
an associated monitoring program, important watershed characteristics likely
to influence lag time should be investigated. Determining the time of travel
for ground water movement is an obvious
example. Watershed characterization is an
important step in the project planning process
(USEPA 2005) and such characterization
should especially address important aspects of
the hydrologic and geologic setting, as well as
documentation of nonpoint source pollution
sources and the nature of the water quality
impairment, all of which can influence
observed lag time in system response.

•	Consider lag time in selection and siting
of BMPs. Recognition of lag time may
require an adjustment of the approach to
targeting land treatment. When designing
a land treatment program, potential
BMPs should be evaluated to determine
which practices might provide the most
rapid improvement in water quality, given
watershed characteristics. For example,
practices affecting direct delivery of nutrients
into surface runoff and streamflow, such
as barnyard runoff management, may yield
more rapid reductions in nutrient loading to the receiving water than practices
that reduce nutrient leaching to ground water, when ground water time of travel
is measured in years. Fencing livestock out of streams may give immediate water
quality improvement, compared to waiting for riparian forest buffers to grow
in. Such considerations, combined with application of other criteria such as cost
effectiveness, can help determine priorities for land treatment programs in a
watershed project.

Lag time should also be considered in locating treatment within a watershed.
Where sediment and sediment-bound pollutants from cropland erosion are primary
concerns, for example, implementing practices that target the largest sediment
sources closest to the receiving water may provide a more rapid water quality
benefit than erosion controls in the upper reaches of the watershed.

It is important to point out that factoring lag time into BMP selection and
targeting is not to say that long-term management improvements like riparian
forest buffer restoration should be discarded or that upland sediment sources
should be ignored. Rather, it is suggested that planners and managers may want to
consider implementing BMPs and treating sources likely to exhibit short lag times
first to increase the probability of demonstrating some water quality improvement
as quickly as possible. "Quick-fix" practices with minimum lag time should not

Bay journal

Myriad reasons behind delays in cleanup results after
actions are taken

By Karl Blankenship

For anyone who expects a clean Chesapeake at the end of the
decade, a new report from a team of scientists offers a word of
advice: patience.

The report says that even if billions of dollars become available
to upgrade wastewater treatment plants, control farm runoff and
undertake myriad other cleanup actions that have been proposed
to meet cleanup goals, the Bay may not look significantly different
in 2010.

The reason is that most actions will take longer to show results,
for a host of reasons, than most people realize. "We want instant
gratification," said Kevin Sellner, of the Bay Program's Scientific and
Technical Advisory Committee, which produced the report. "But
we've taken 200 years to do what we've done. So if we put things
in and it takes five years to show results, we should be willing to
wait."

http://www.bayjournal.com/article.cfm?article=2503

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automatically replace practices implemented in locations that can ultimately yield
permanent reductions in soil loss.

•	Monitor small watersheds close to sources. In cases where
documentation of the effects of a treatment program on water quality
is a critical goal, lag time can sometimes be minimized by focusing
monitoring on small watersheds, close to pollution sources (Figure
8). Lag times introduced by transport phenomena (e.g., ground
water travel, sediment flux through stream networks) will likely be
shorter in small watersheds than in larger basins. In the extreme,
this principle implies monitoring at the edge of field or above/below
a limited treatment area, but small watersheds (e.g., less than 1500
ha) can also yield good results. In the NNPSMP, projects monitoring
land treatment in small watersheds (e.g., the Morro Bay Watershed
Project in California, the Jordan Cove Project in Connecticut, the
Pequea/Mill Creek Watershed Project in Pennsylvania, and the
Lake Champlain Basin Watersheds Project in Vermont) were more
successful in documenting improvements in water quality in response
to land treatment in the watershed than were projects that took
place in large watersheds (e.g., the Lightwood Knot Creek Project in
Alabama and the Sny Magill Watershed Project in Iowa) in the seven
to ten year time frame of the NNPSMP (Szpir et al. 2005).

The monitoring program itself can be designed to get a better handle on lag
time issues. Monitoring indicators at all points along the pathway from source to
response or conducting periodic synoptic surveys over the course of a project will
identify changes as they occur and document progress toward the end response.
Special studies of sediment transport, soil P levels, ground water dynamics, or
receiving water behavior can shed light on phenomena that affect lag time in water
quality response. For example, the Long Creek Watershed (NC) NNPSMP
Project (1993-2002) conducted special studies of the effects of a wetland on PAH
concentrations in an urban stream, the use of microbial indicators to assess land
use impacts, and interactions between P and stream sediments to better explain the
temporal and spatial water quality response to land treatment (Line and Jennings
2002). Supplementing a stream monitoring program with special studies can help
project managers understand and predict potential lag times and can help explain
delays in water quality improvement to stakeholders.

•	Select indicators carefully. Some water quality variables can be expected to
change more quickly than others in response to land treatment. As documented
in the Jordan Cove (CT) NNPSMP Project (1996-2005), peak storm flows from
a developing watershed can be reduced quickly through application of stormwater
infiltration practices (Clausen 2004). Reductions in nutrient loads in surface waters
might be expected to occur promptly in response to a ban on winter application

of animal waste in northern states. NNPSMP projects in CA, NC, PA, and V T
(Szpir et al. 2005) demonstrated rapid reductions in nutrients and bacteria by
reducing direct deposition of livestock waste in surface waters through fencing

Figure 8. Monitoring small
watersheds, close to
pollution sources can
minimize lag time between
land treatment and water
quality response.

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livestock out of streams. However, improvements in stream biota appear to come
much more slowly, beyond the time frame of many monitoring efforts. Where
restoration of biological integrity is a goal, this may argue for a more sustained
monitoring effort to document a biological response to land treatment. Failing that,
however, selection of indicators that have relatively short lag times where possible
will make it easier (and quicker) to demonstrate success.

• Design monitoring programs to detect change effectively. Monitor at locations
and at a frequency sufficient to detect change with reasonable sensitivity. As soon
as background variability is assessed (ideally before the project begins), conduct a
minimum detectable change analysis (Spooner et al. 1987, Richards and Grabow
2003) to determine a sampling frequency sufficient to document the anticipated
magnitude of change with statistical confidence. If the monitoring program is
intended to detect trends, evaluate statistical power to determine the best sampling
frequency for the project.

Lag time between implementation of land treatment and water quality response is an
unfortunate fact of life in many circumstances. Unless it is recognized and dealt with,
the existence of lag time will frequently confound our ability to successfully document
improved water quality resulting from treatment of nonpoint sources and may discourage
vital restoration efforts. While ongoing and future research may provide us with better
tools to predict and account for lag time, it is essential that watershed monitoring
programs today recognize and grapple with this issue.

Clausen, J.C., D.W. Meals, and E.A. Cassell. 1992. Estimation of lag time for water
quality response to BMPs. Pages 173-180 in The National Rural Clean Water
Program Symposium, September, 1992, Orlando, FL, US EPA ORD,
EPA/625/R-92/006.

Clausen, J.C. 2004. Jordan Cover Urban Watershed Section 319 National Monitoring
Program Project. Annual Report. College of Agriculture and Natural Resources,
University of Connecticut, Storrs, CT.

Druschel, G., A. Hartmann, R. Lomonaco, and K. Oldrid. 2005. Determination
of sediment phosphorus concentrations in St. Albans Bay, Lake Champlain:
Assessment of internal loading and seasonal variations of phosphorus sediment-water
column cycling. Final Report prepared for Vermont Agency of Natural Resources,
Department of Geology, University of Vermont, Burlington.

Galeone, D.G. 2005. Pequea and Mill Creek Watersheds Section 319 NMP Project:
Effects of Streambank Fencing on Surface-water Quality. NWQEPNotes 118:
August, 2005. NC State University, Raleigh, NC.

Conclusions

References

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Hallberg, G.R., R.D. Libra, D.J. Quade, J. Littke, and B. Nations. 1989. Groundwater
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