Use of probability-based sampling of water-quality indicators in supporting development of quality criteria
Use of probability-based sampling of water-quality indicators in supporting development of quality
criteria. - ICES Journal of Marine Science
Use of probability-based sampling of water-quality indicators in supporting development of quality criteria
Walter G. Nelson
Cheryl A. Brown
Intensive, site-based data are typically used to establish protective water-quality criteria, but may only exist for few systems in a region. We examine whether or not water-quality indicator data collected from large-scale, probability-based assessments can support the development of regional quality criteria. Because such indicators may be subject to high natural variation over short time-scales, a key question is whether survey values will be sufficiently similar to site-based sampling to merit use in extrapolating quality criteria spatially. Median values for dissolved inorganic nitrogen, phosphorus, and Chl a for dry-season data collected within Yaquina Bay (OR, USA) over a 7-year period were compared with dry-season datasets collected from two studies comprising 6 and 14 Oregon estuaries, respectively. A second, reduced dataset (August - September only) was compared with data from 38 estuaries within the same ecoregion. All comparisons were made for marine and riverine salinity zones. Medians for Yaquina Bay were higher than those from the comparison surveys. Stochastic variation of coastal upwelling during sampling appears to cause the contrasts. Further work is required to define upwelling-based adjustments for regional, probability-based survey data before they can be used in regulatory applications. However, even without adjustment, these data may help in determining the appropriate regional context for quality criteria.
National Coastal Assessment; probability-based sampling; regional water quality; upwelling; water quality criteria; water quality indicators
In the USA, states and authorized tribes define designated uses for
their waters and must adopt water-quality criteria to protect these
designated uses. Although estuaries such as Chesapeake Bay have
been extensively studied and support comprehensive datasets
that can assist in determining appropriate criteria, few data exist
for many other estuarine systems. Therefore, the question of
whether or not numeric water-quality criteria developed in
systems with adequate data can be extrapolated to other estuaries
within the jurisdictional boundaries of the state or tribe, or even
across broader areas, is an important one. Relatively limited data
exist for most estuaries on the outer coast of the Pacific
Northwest region of the USA
(Bricker et al., 2007)
One important source of recent data for a suite of water-quality
indicators is the National Coastal Assessment (NCA) of the
Environmental Protection Agency
(US EPA, 2004; Nelson et al.,
2005; Hayslip et al., 2006)
. The NCA programme was instituted
with the goals of providing the scientific basis as well as increasing
state and tribal capabilities to monitor the status and trends in the
condition of coastal ecosystems. A key operational objective has
been to collect comparable data to report on the nationwide
condition of coastal resources. The programme uses suites of
environmental indicators to quantify habitat characteristics, stressor levels,
and biological condition
(US EPA, 2004)
, including five measures
of water quality: dissolved inorganic nitrogen (DIN), dissolved
inorganic phosphorus (DIP), Chl a, dissolved oxygen, and water
clarity (kd or Secchi disc depth). The indicators measured
represent those recommended in guidance to the states as parameters
that can be used as numeric water-quality criteria
(US EPA, 2001)
In contrast to many other monitoring programmes, NCA
utilizes a probability-based sampling design that selects sampling
stations randomly within the total extent of the resource being
assessed (e.g. all US west coast estuaries). Although this approach
has been optimized for producing quantitative, regional-, and
national-scale assessments of environmental condition, aspects
of the programme design may limit the utility of these data in
informing authorities about the development of criteria protective
of waters for which they are responsible. All sampling takes place
only during summer, and comprises one-time water samples
taken at the surface, in midwater, and near the bottom, irrespective
of the tidal stage. For cost and logistical reasons, sampling may be
spread over multiple years.
The availability of an extensive dataset from Yaquina Bay (OR,
USA) allows a comparison of the median values of key indicators
with data collected at larger scales but with shorter temporal extent
to determine the potential for using such studies in establishing
water-quality criteria. A first set of comparisons was made with
datasets restricted to Oregon, because criteria are usually set
by the states. Second, Yaquina Bay data were compared with a
set compiled from the coastline between the outer coast of
Washington State and central California
(EPA Level III-Ecoregion
1; Omernik, 1987)
. EPA has previously proposed setting
freshwater, but not yet estuarine, criteria at the ecoregional level.
Yaquina Bay is a small (19 km2) estuary located on the coast of
central Oregon (Figure 1). The watershed (660 km2) is 82%
forested, 6% grasslands, ,1% highly developed land, with the
remainder a mixture of wetlands, open water, and parklands.
Population density is relatively low (12 km22). Yaquina Bay
(and the Pacific Northwest region in general) is characterized by
two distinct hydrological seasons. During the wet, winter season,
river flows are high and can be five times greater than during
the dry, summer season, and consequently the dominant (74%)
source of DIN into the estuary is the watershed
(Brown et al.,
. During summer, coastal upwelling is the major (82%)
source of nutrients into the bay
(Brown et al., 2007)
on an analysis of historical and recent water-quality data,
hydrodynamic modelling, and stable-isotope analyses of nitrogen
Brown et al. (2007)
divided Yaquina Bay into a marine
( 26 psu) and a riverine zone (,26 psu), which tend to be
dominated by differing sources of nutrients. The comparisons of
water-quality data were conducted using this criterion for zoning.
The Yaquina Bay data (Y1 set) spanned 7 years (1998 – 2004)
and were collected at 5 – 36 sites (spanning a 20 km reach of
the estuary) at a frequency ranging from weekly to monthly
during the dry season
(May – October; for details see Brown
et al., 2007)
. Station locations were selected primarily along the
longitudinal axis of the estuary, although in some cases, they
were selected randomly within a longitudinal segment.
DIN, DIP, and Chl a were selected from the NCA indicator
list for analysis. Water clarity was not examined because of
methodological differences that limited comparability among
studies. The complexities of dissolved-oxygen data will be
The Y1 set was compared with data from six estuaries sampled
as part of an EPA study to classify Oregon estuarine waters
Brown et al., 2007)
, and also with data from 14 Oregon estuaries
sampled for the NCA
(P1 set; for details see Nelson et al., 2004)
both carried out during the dry season. The number of samples
for each indicator and each dataset are provided in Table 1.
The C set is based on specific estuaries selected for sampling to
obtain a cross section of systems within Oregon estuarine
management categories, with stations lying at approximately equal
intervals along the longitudinal axis (or axes) of each estuary. For
this set (156 stations), fewer estuaries and more stations per
estuary (10 – 17; Figure 1) were sampled than for the P1 set, and
sampling time was tied to tidal stage (both low- and high-tide
cruises). The NCA study used a probability-based, stratified
random sampling design
(Diaz-Ramos et al., 1996; Olsen et al.,
, with station locations within a stratum established at
random, both in terms of which estuaries received sampling
stations and where within the estuary a station was located.
Details of the sampling design are provided in Nelson et al.
(2005). This P1 set (125 stations) spanned a larger geographic
extent and was random with respect to tidal stage, with number
of samples per estuary ranging from one for small systems to 67
for the large Columbia estuary (Figure 1). Except the Columbia
River, all estuaries sampled are similar to Yaquina Bay in that
they are relatively small (estuarine area ,55 km2), have similar
characteristics, e.g. are relatively shallow with strong tidal
forcing, and have similar local drivers, e.g. nitrogen-fixing red
alder, Alnus rubra, in the watershed; influenced by coastal
upwelling; and similar land-use characteristics. These watersheds are
primarily forested (60 – 98%), have a small percentage of developed
land area ( 2%), and have low human-population densities
(,25 km22; Brown et al., 2007)
. The watershed of the
Columbia River has less forest (56%), more development (low
to high intensity ¼ 6% of land area), and a higher population
density (90 km22).
A second comparison was made between the Yaquina Bay data
(Y2 set), including only samples collected during August –
September (NCA collection period), and the NCA data for 38
estuaries for the whole of Ecoregion 1
(P2 set; for details see Nelson
et al., 2005; Hayslip et al., 2006)
. This comparison was made
because ecoregions reflect many ecological factors related to the
landscape and watershed, e.g. climate and geology
, and have been used as an appropriate scale for
setting stream-nutrient criteria
(US EPA, 2000)
Because the data were not normally distributed, the
nonparametric Kruskal – Wallis one-way analysis of variance on
ranks (SigmaStat version 3.5, Systat Software, San Jose, CA,
USA) was used to test differences in the median values among
the Y1, C, and P1 sets, whereas Dunn’s test was used for pairwise
multiple comparisons. The distributions in the Y2 and P2 sets were
compared using the Mann – Whitney rank sum (M – W) test. All
tests used a significance level of p , 0.05.
To evaluate the relative influence of variability in ocean
conditions on the estimated medians, we developed regression
relationships between water temperature, and NO3 þ NO2, and
PO4, using data from the inner continental shelf off Yaquina Bay
(Wetz et al., 2005)
. Nitrate is the primary form of DIN entering
the estuary from the ocean
(Brown et al., 2007)
. Previous studies
have demonstrated a high coherence in water temperature
fluctuations among locations along the Oregon shelf
(Hickey and Banas,
. In addition, flood-tide water temperatures from Yaquina
Bay are correlated with 40 h, low-pass-filtered water temperatures
on the inner shelf. Flood-tide water temperatures from Yaquina
Bay were assumed to represent ocean conditions along the
Oregon coast. To test this approach, the regressions between
nutrients and flood-tide water temperature were used to estimate
variability in oceanic conditions on the specific dates of estuarine
sampling for the Y1 and P1 datasets. Temperature data were
obtained from South Beach Station (Station: 9435380, 44.6258N
124.0438W; available online at http://co-ops.nos.noaa.gov), and
flood-tide values were extracted from the hourly data using
times of predicted high tides. Pearson product moment
correlations (r) were calculated between observed and predicted
nutrients. Differences between predicted and observed median values
for each dataset and differences between predicted nutrients for
the Y1 and P1 datasets were tested using the M – W test.
Plots of the NCA data (P2 set) for DIN and DIP measured across
the Level III-Ecoregion 1 vs. surface salinity at the sample sites
demonstrate two distinct patterns (Figure 2). DIN exhibited a
bimodal pattern with highest values at either end of the salinity
distribution, although these data did not yield a statistically
significant fit to a regression model (second order polynomial,
p ¼ 0.18). In contrast, DIP had a significant linear relationship
with surface salinity (PO4 ¼ 0.4 þ 0.024 salinity; r2 ¼ 0.44).
Figure 3 summarizes the various comparisons between the
medians of the three indicators for the two zones separately.
Median DIN concentration in the Y1 set in the marine zone was
significantly greater than the median concentrations in both the
C and P1 sets, which did not differ significantly from each
other. For the riverine zone, the median DIN value in the Y1 set
did not differ from the C set, whereas both values were
significantly higher than in the P1 set, and the range of values observed
in the first two sets was much greater. The median DIP
concentration in the Y1 set in both the marine and riverine zones was
significantly greater than the medians of the other two sets, which did
not differ significantly from each other. Median Chl a
concentration in the Y1 set in the marine zone was significantly greater
than those for the other two sets, which did not differ significantly
from each other. In the riverine zone, the median concentration in
the Y1 set was significantly greater than in the P1 set, which in turn
had a significantly higher median than the C set.
Similar comparisons of median DIN and DIP-values between
the Y2 set and the P2 set gave generally the same pattern of
results. For both zones and parameters, Y2-set medians were
significantly higher. The median Chl a values for the marine
zone were not significantly different, whereas the median for
the riverine zone in the Y2 set was significantly higher than in
the P2 set.
On the inner Oregon shelf, nutrient (NO3 þ NO2, and PO4)
levels were significantly correlated with water temperature
(Figure 4). Oceanic nutrient concentrations (in mM) can be
predicted from water temperature (T in 8C) using the following
equations (presented in Figure 4):
NO3 þ NO2 ¼ 44:7
1 þ e ðT 8:29Þ=0:996 ðn ¼ 570; r2 ¼ 0:85Þ ð1Þ
PO4 ¼ 19:5 e T=3:67 ðn ¼ 432; r2 ¼ 0:77Þ:
These equations can be used to assess the influence of upwelling on
comparisons of medians among datasets. First, we compared the
modelled NO3 þ NO2 concentration from July through
September 1999 with the dates of the 1999 P1 set of the marine
zone (Figure 5), demonstrating that 82% of NCA-Oregon
sample dates occurred during periods of low upwelling activity.
We then used flood-tide water temperature at Yaquina and
Equations (1) and (2) to predict NO3 þ NO2, and PO4
concentrations for each observation in the marine zone of both the Y1
and P1 datasets. This approach provided an estimate of the
nutrient concentration expected if all input came from the nearshore.
The correlations between observed and predicted NO3 þ NO2
and PO4 in the marine zone were significant for both datasets
(Pearson product moment correlation r, 0.42 – 0.64, p , 0.05;
Table 2), suggesting that ocean conditions are influencing nutrient
levels in the marine zone. Although observed and predicted values
are significantly correlated, there are significant differences in the
observed and predicted median values for these parameters. For
the Y1 set, differences between observed and predicted median
values were significant, with predicted median values being 14%
lower than observed for NO3 þ NO2 and 5% higher than the
observed for PO4. For the P1 set, observed median NO3 þ NO2
values were significantly higher (66%) than the modelled values.
In contrast, there were no significant differences in PO4 between
modelled and observed values for the P1 set. The modelled
median values for both were significantly higher for the Y1 set
than for the P1 set, suggesting that differences in ocean conditions
may be the cause of the differences in observed medians between
these sets (Table 2).
The patterns of DIN and DIP values vs. salinity found across the
Level III-Ecoregion 1, determined from probability-based
sampling (Figure 2), were essentially the same as the patterns
Brown et al. (2007)
from an intensive study of Yaquina
Bay. Although in this study only the DIP relationship was
significant for the ecoregional data, both the bimodal DIN and linear
DIP relationships vs. salinity within Yaquina Bay were statistically
(Brown et al., 2007)
. For DIN, the bimodal pattern
reflects oceanic inputs from offshore upwelling at high salinities
and watershed inputs, even under low flow conditions associated
with nitrogen-fixing red alder within the riparian zone in the
(Brown et al., 2007)
. This similarity initially encouraged
the view that water-quality criteria determined from intensive
studies within a single estuary might be extrapolated to a
broader range of systems. However, of the six statistical
comparisons of median values conducted using data collected within
Oregon’s estuaries (Figure 3), in only one case (DIN, riverine
zone) was the median value from the Yaquina study not
significantly different from the two spatially broader datasets. Similar
results were found for statistical comparisons of the reduced
Yaquina dataset (Y2) that matched the ecoregional NCA set (P2)
in terms of sampling period (Figure 3), where, of six comparisons,
only one (marine zone, Chl a) did not differ significantly. In all
cases of significant differences, medians of parameters from
Yaquina Bay were higher.
Estuaries on the Pacific Northwest coast are next to the
California Current system, which exhibits strong interannual,
seasonal, and event-scale variability
(Hickey and Banas, 2003)
Wind-driven upwelling advects relatively cool, nutrient-rich
(NO3 and PO4) water to the surface, typically from April to
September. A greater amount of the short-term variability in
nutrient and Chl a concentrations in these estuaries is related to
import of oceanic water
(De Angelis and Gordon, 1985; Roegner
and Shanks, 2001; Brown et al., 2007)
. Nitrate near the mouth
of the Yaquina estuary can vary by as much as 30 mM over a
period of days in response to local windforcing
(Brown et al.,
. Because the bay is not being affected by large anthropogenic
inputs of nutrients
(Brown et al., 2007)
, the differences observed
compared with other datasets, particularly in the marine zone,
are likely to have resulted from variability in upwelling conditions
Great interannual variation in nutrient and Chl a levels on the
Oregon shelf also results from variability in upwelling conditions
(Corwith and Wheeler, 2002; Huyer et al., 2002; Thomas et al.,
2003; Wheeler et al., 2003)
, as well as from significant delays of
the upwelling season during some years (Barth et al., 2007).
Hickey and Banas (2003)
examined variations in temperature,
salinity, and alongshore windstress for three estuaries spanning
400 km along the Oregon and Washington coasts and
demonstrated similarity in the fluctuations of specific water properties
among these estuaries during summer, resulting from the
large-scale patterns of alongshelf windforcing.
The analysis using water temperature and modelled nutrient
relationships suggests that the differences between the
NCA-Oregon and Y1 datasets may be largely the result of
differences in ocean conditions at the times that samples were collected.
The water temperature and nutrient relationships performed
better at predicting nutrient levels in the marine zone for the
Y1 set than for the P1 set, which is not surprising because the
relationships were generated using data from the inner shelf near
the Yaquina estuary. The large discrepancy between the observed
and modelled NO3 þ NO2 levels suggests that the observations
from the marine zone of the P1 set may be influenced by
additional nitrogen sources. We conclude that the influence of
upwelling variability must be accounted for, if data on
waterquality indicators collected from short-term, large-scale studies
are to be used to extending the spatial scope of water-quality
criteria derived from long-term, site-specific studies. We suggest
that relationships between water temperature and nutrients
offer a potential means of generating correction factors for
probability-based data collection in the region. A future
refinement of this approach would be to develop temperature – nutrient
relationships, using data from multiple stations along the Oregon
shelf to predict ocean conditions at the time of sampling.
Although major difficulties appear to arise when directly
extrapolating indicator data from probability-based surveys to systems
where data are limited, the use of all available data in a regional
context for setting criteria that help to protect the environment
remains an important issue. For example, the current Oregon
Chl a criterion for estuaries that must be satisfied is that the
average value (based on a minimum of three samples collected
over any three consecutive months) must be ,15 mg l21.
However, this value is rarely exceeded within either marine or
riverine zones for a system examined over multiple years (Yaquina
Bay) or for multiple systems across Oregon, or even across the
entire ecoregion. Median Chl a levels in all Oregon estuaries
sampled were generally in the low ( 5 mg l21) category as
Bricker et al. (2003)
, in terms of eutrophication
status. The current criterion is 3 – 7 times higher than median
Chl a values observed in any of the datasets examined. If
3-monthly average concentrations of estuaries were to approach
the current criterion value routinely, it is likely that trophic
status would have altered. Therefore, we suggest that the current
criterion may not be adequate to prevent further anthropogenic
eutrophication of estuaries in the region and may need to be
A second example of the relevance of regional data to
waterquality standards relates to DIP. In both estuarine zones, the
median values of all three sets of observations fell between the
concentration limits (0.32 – 3.2 mM) proposed by
Bricker et al.
as representing a medium eutrophication status. For the
larger ecoregion, .75% of the DIP observations in all datasets
examined exceeded 0.32 mM. Upwelling appears to be an
important source of DIP into the estuaries, suggesting that the
reference level for Pacific Northwest estuaries needs to be adjusted
upwards to reflect this natural influence.
Probability-based sampling for water-quality indicators
appears adequate for defining spatial patterns of nutrient
distribution across the estuarine salinity gradient at an ecoregional
scale. However, median indicator values in short-term surveys
appear to be strongly influenced by temporal variation in
upwelling activity extending across the region. This stochastic variability
currently hinders the ability to use data derived from
probabilitybased sampling studies for extrapolating water-quality criteria
beyond the systems for which they have been developed.
Although the use of modelled nutrient and water temperature
relationships to correct for upwelling variability may ultimately
be useful in improving the use of regional-scale assessment data
in a regulatory context, more rigorous analyses are required to
reach this goal. The acquisition of additional years of large-scale
survey data, as currently in progress, as well as the development
of modelled nutrient and water temperature relationships for
different parts of the ecoregion, may also help to clarify these
Extremely helpful comments and editorial suggestions were
provided by Claude Belpaire, Stephen Cotterell, Niels Daan, and Jan
Kurtz. Colleagues at EPA and other state and federal agencies,
too many to name, contributed to the collection of the field data
presented, and we thank them for their efforts. Maps were
produced by Pat Clinton of EPA. The information in this document
has been funded by the US Environmental Protection Agency. It
has been subjected to review by the National Health and
Environmental Effects Research Laboratory’s Western Ecology
Division and approved for publication. Approval does not
signify that the contents reflect the views of the Agency nor does
mention of trade names or commercial products constitute
endorsement or recommendation for use.
Barth , J. A. , Menge , B. A. , Lubchenco , J. , Chan , F. , Bane , J. M. , Kirincich , A. R. , McManus , M. A. , et al. 2007 . Delayed upwelling alters nearshore coastal ocean ecosystems in the northern California Current . Proceedings of the National Academy of Sciences , 104 : 3719 - 3724 .
Bricker , S. B. , Ferreira , J. G. , and Simas , T. 2003 . An integrated methodology for assessment of estuarine trophic status . Ecological Modeling , 169 : 39 - 60 .
Bricker , S. , Longstaf , B. , Dennison , W. , Jones , A. , Boicourt , K. , Wicks , C. , and Woerner , J. 2007 . Effects of nutrient enrichment in the nation's estuaries: a decade of change . NOAA Coastal Ocean Program Decision Analysis Series, 26. National Centers for Coastal Ocean Science , Silver Spring, MD. 328 pp.
Brown , C. A. , Nelson , W. G. , Boese , B. L. , DeWitt , T. H., Eldridge , P. M. , Kaldy , J. E. , Power , J. H. , et al. 2007 . An approach to developing nutrient criteria for Pacific Northwest estuaries: a case study of Yaquina Estuary, Oregon . EPA/600/R-07 /046. US EPA Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, OR. 169 pp.
Corwith , H. L. , and Wheeler , P. A. 2002 . El Nin˜o related variations in nutrient and chlorophyll distributions off Oregon . Progress in Oceanography, 54 : 361 - 380 .
De Angelis , M. A. , and Gordon , L. I. 1985 . Upwelling and river runoff as sources of dissolved nitrous oxide to the Alsea estuary , Oregon. Estuarine, Coastal and Shelf Science , 20 : 375 - 386 .
Diaz-Ramos , S. , Stevens , D. L. , and Olsen , A. R. 1996 . EMAP Statistical Methods Manual . EPA-620 -R- 96-002.
Hayslip , G. , Edmond , L. , Partridge , V. , Nelson , W. , Lee , H. , Cole , F. , Lamberson , J. , et al. 2006 . Ecological condition of the estuaries of Oregon and Washington . EPA 910 -R- 06 - 001 . US Environmental Protection Agency, Office of Environmental Assessment, Region 10 , Seattle, WA.
Hickey , B. M. , and Banas , N. 2003 . Oceanography of the US Pacific Northwest coastal ocean and estuaries with application to coastal ecology . Estuaries , 26 : 1010 - 1031 .
Huyer , A. , Smith , R. L. , and Fleischbein , J. 2002 . The coastal ocean off Oregon and northern California during the 1997 - 8 El Nin˜o . Progress in Oceanography, 54 : 311 - 341 .
Nelson , W. G. , Lee , H. , Lamberson , J. O. , Engle , V. , Harwell , L. , and Smith , L. M. 2004 . Condition of estuaries of Western United States for 1999: a statistical summary . EPA/620/R-04/200. Office of Research and Development, National Health and Environmental Effects Research Laboratory.
Nelson , W. G. , Lee , H. , and Lamberson , J. O. 2005 . Condition of estuaries of California for 1999: a statistical summary . EPA 620 -R- 05 -004. Office of Research and Development, National Health and Environmental Effects Research Laboratory.
Olsen , A. R. , Sedransk , J. , Edwards , D. , Gotway , C. A. , Liggett , W. , Rathbun , S. , Reckhow , K. H. , et al. 1999 . Statistical issues for monitoring ecological and natural resources in the United States . Environmental Monitoring and Assessment , 54 : 1 - 45 .
Omernik , J. M. 1987 . Ecoregions of the conterminous United States . Map (scale 1:7 , 500 ,000). Annals of the Association of American Geographers , 77 : 118 - 125 .
Omernik , J. M. 1995 . Ecoregions: a spatial framework for environmental management . In Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making , pp. 49 - 62 . Ed. by W. S. Davis , and T. P. Simon . Lewis Publishers, Boca Raton, FL.
Roegner , G. , and Shanks , A. 2001 . Import of coastally-derived chlorophyll a to South Slough, Oregon . Estuaries, 24 : 224 - 256 .
Thomas , A. C. , Strub , P. T. , Brickley , P. , and James , C. 2003 . Anomalous satellite-measured chlorophyll concentrations in the northern California Current in 2001 - 2002 . Geophysical Research Letters, 30 : Art. No. 8022 .
Wetz , J. J. , Hill , J. , Corwith , H. , and Wheeler , P. A. 2005 . Nutrient and extracted chlorophyll data from the GLOBEC Long-Term Observation Program, 1997 - 2004 . Data Report 193, COAS Reference 2004-1. College of Oceanic and Atmospheric Sciences (COAS) , Oregon State University, Corvallis, OR.
Wheeler , P. A. , Huyer , J. , and Fleischbein , J. 2003 . Cold halocline, increased nutrients and higher productivity off Oregon in 2002 . Geophysical Research Letters, 30 : Art. No. 8021 .
US EPA . 2000 . Ambient Water Quality Criteria Recommendations. Information Supporting the Development of State and Tribal Nutrient Criteria for Rivers and Streams in Nutrient Ecoregion II . EPA 822 -B- 00-015.
US EPA . 2001 . Nutrient Criteria Technical Guidance Manual. Estuarine and Coastal Waters . EPA-822 -B- 01-003.
US EPA . 2004 . National Coastal Condition Report II. EPA-620-R03-002.