Progress on Nonpoint Pollution: - IA de Investigación especializada en el MIT

Progresos en materia de contaminación difusa:
Barriers & Opportunities

Adena R. Rissman & Stephen R. Carpintero

Abstracto: Nonpoint source pollution is the runoff of pollutants (including soil and nutrients) from agri-
cultural, urban, and other lands (as opposed to point source pollution, which comes directly from one outlet).
Many efforts have been made to combat both types of pollution, so why are we making so little progress in
improving water quality by reducing runoff of soil and nutrients into lakes and rivers? This essay exam-
ines the challenges inherent in: 1) producing science to predict and assess nonpoint management and pol-
icy effectiveness; y 2) using science for management and policy-making. Barriers to dem onstrating
causality include few experimental designs, different spatial scales for behaviors and measured outcomes,
and lags between when policies are enacted and when their effects are seen. Primary obstacles to using sci-
ence as evidence in nonpoint policy include disagreements about values and preferences, disputes over validi-
ty of assumptions, and institutional barriers to reconciling the supply and demand for science. We will illus-
trate some of these challenges and present possible solutions using examples from the Yahara Watershed in
Wisconsin. Overcoming the barriers to nonpoint-pollution prevention may require policy-makers to gain a
better understanding of existing scienti½c knowledge and act to protect public values in the face of
remaining scienti½c uncertainty.

ADENA R. RISSMAN is an Assis-
tant Professor of the Human Di –
mensions of Ecosystem Manage-
ment in the Department of Forest
and Wildlife Ecology at the Uni-
versity of Wisconsin–Madison.
STEPHEN R. CARPENTER, a Fel-
low of the American Academy since
2006, is the Stephen Alfred Forbes
Professor of Zoology and the Di –
rector of the Center for Limnology
at the University of Wisconsin–
Madison.

(*See endnotes for complete contributor
biographies.)

Water is an important, dwindling resource. Water

and aquatic ecosystems support industry, agricul-
tura, outdoor recreation, aesthetic pleasure, aquatic
food sources, and livelihoods. Massive, expensive
efforts have been made to improve water quality
and “repair what has been impaired.”1 These ef forts
have led to some important gains, but water quality
is still poor in many rivers, lakes, and coastal oceans.
Runoff of soil, nutrients, and other chemicals from
agricultural, urban, and other lands is called non-
point source pollution. In contrast, point source
pollution comes directly from a pipe, such as at an
industrial or municipal facility. Runoff of phospho-
rus–also called nonpoint phosphorus pollution–
is a major cause of toxic algae blooms, oxygen de –
pletion, and ½sh kills in streams, lakes, and reser-
voirs.2 Why are we not making progress on nonpoint
source pollution in water quality? What are the chal –

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Progress on
Nonpoint
Pollution:
Barriers &
Opportu –
niidades

lenges of producing science to predict and
assess nonpoint management and policy
eficacia, and of using this science in
management and political decisions?
Finalmente, what changes are needed to im –
prove water quality?

A major scienti½c enterprise is devoted
to producing scienti½c knowledge to in –
form nonpoint policy and management
through long-term monitoring, estadístico
análisis, and modeling. But is scienti½c
knowledge actually reducing uncertainty
about the causes of water-quality impair-
ment and the effectiveness of control mea –
sures? Researchers are increasingly vocal
about the challenges facing nonpoint-
pollution science on sediment, phospho-
rus, nitrogen, and other pollutants.3 For
instancia, it is well-established that end-
of-pipe mitigation of phosphorus im proves
water quality, but proving the ef fectiveness
of actions to control nonpoint-source
phosphorus is challenging. It is ex tremely
dif½cult to demonstrate causality when
con necting water-quality conditions to pol –
icies and the behaviors of agricultural and
urban residents. An increase in knowledge
and data has therefore not always trans-
lated to more effective policy.

Once scienti½c knowledge is produced,
why is it so dif½cult to use it as evidence in
nonpoint pollution–related policy-mak-
ing and management? Science does not
de termine public interests and values, pero
it can serve important purposes in policy-
making and resource management.4 It can
identify problems, prioritize the location
or type of interventions, identify the likely
effects of actions before they are taken (en –
cluding anticipating unintended ef fects),
and evaluate the effects of actions after
they are taken.5 Science and society affect
each other deep ly.6 It is important to un der –
stand how sci enti½c evidence, modelos, y –
certeza, and risk enter into the decisions
of actors such as the Environmental Pro-
tection Agency (epa), county conserva-

tionists, farmers, ur ban homeowners, y
lake managers. We will illustrate how sci-
enti½c information has been created and
used to improve water quality in Wiscon-
sin’s Yahara Watershed, fo cusing on wa –
tershed nonpoint-pollution reduction and
in-lake biomanip ulation.

Water pollution is typically viewed as
an externality that does not directly sub-
tract from the productivity of those re –
sponsible for the pollution, except indi-
rectly or through social limits. This means
that producers of pollution are not inher-
ently incentivized to remedy it; the issue
of assigning responsibility becomes even
more dif½cult with the diffuse nature of
nonpoint source pollution. The dif½cult is –
sue of nonpoint source pollution has led
to a proliferation of blended regulatory,
in centive, and collaborative efforts to en –
gage homeowners, municipal stormwater
sistemas, and farmers in reducing nu tri –
ent and sediment runoff.7

Building scienti½c evidence for nonpoint
pollution is long, slow, and scale-depend-
ent. Given the rapid changes taking place
in ecological and social systems, is the base –
line moving faster than we can learn? Nosotros
suggest that, in addition to science, po liti –
cal will and public val ue should play a great –
er role in decision- making to improve en –
vironmental outcomes.

There are a number of dif½culties in her –

ent in producing knowledge about non-
punto- pollution control. Primero, a growing
number of studies from around the world
show that it is extremely dif½cult to de –
ter mine the ef½cacy of interventions aim –
ing to reduce nutrient runoff from wa ter –
sheds. In many cases, freshwater qual ity
has not been found to have recovered even
after decades of nutrient management,8
and the divergent explanations for lack of
suc cess reflect the complexity of water-
sheds as social-ecological systems.9 De –
spite the urgent need for management in –

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terventions to protect freshwaters, hay
a high level of uncertainty about the ef½ –
cacy of methods; en efecto, there may be
fun da men tal limits to our knowledge of
this subject. It is not clear whether water-
shed man agement is making progress on
un cer tain ty; for now, the success or fail-
ure of policies may be a matter of luck rath –
er than knowledge. For this reason, it is
im por tant to consider the barriers to the
production of knowledge about nu tri ent
pol icy and man agement and the op por tu –
nities to im prove scienti½c under stand ing
in this area. We will explore the reasons for
the dif½cul ty of demonstrating causal ef –
fects of nu trient-management pol icies in
large water sheds, incluido: long time lags
between intervention and response, spa-
tial hetero geneity (eso es, a solution that
works in one site may not work in an –
otro), simultaneous changes in multiple
pollution driv ers, and lack of monitoring.
Nonpoint pollution–management pro-
grams involve large areas with multiple
nutrient sources; many individual land
managers; spatially heterogeneous topog –
raphy, soils, and ecosystems; y diverso
streams and lakes. Speci½c practices for
ameliorating pollution–such as buffer
tiras, cover crops, tillage practices, y
wetland restoration–are usually tested on
relatively homogenous sites at scales of a
few hectares for a few years. While these
methods are effective in short-term, pequeño-
scale ½eld trials, little is known about how
they scale up to whole watersheds.10 At
the watershed scale, new sources or sinks
for phosphorus and new interactions
along flowpaths could emerge and lead to
surprising outcomes. It is plausible that
spatial interactions (such as movement of
soil from one area to another) contribute
to the observed failures of large-scale non –
point-pollution management.

Interventions to mitigate nutrient inputs
also have delayed effects because of the
slow response of nutrients in the environ –

ment.11 Time lags ranging from one to
more than ½fty years have been measured
between the initiation of a management
intervention and the observation of an
environmental response.12 Projections es –
timate that interventions to cut off phos-
phorus fertilization of soil will take two
hundred and ½fty years to produce a new,
low-phosphorus equilibrium in the agricul –
tural lands of a Wisconsin watershed.13
In a diverse set of watersheds, re sponse
times for nutrient interventions ranged
from less than one year to more than one
thousand.14 Such long time lags pose ser –
ious dif½culties for scienti½c in ference and
for sustaining the engagement of the pub –
lic and policy-makers.

Además, many factors that affect
water quality change simultaneously. Para
ejemplo, precipitation, land use, agricul-
tural management practices, and ecologi-
cal characteristics of lakes and streams are
always changing.15 Effects of management
interventions to improve water quality
must be discerned against this background
of multiple changing drivers, each of
which affects water quality. The lengthy
response time of the environment com-
pounds this dif½culty. Ecosystem scientists
generally employ an array of ap proaches,
including observing paired reference eco –
sistemas, to distinguish between the effect
of the intervention and that of other
changing drivers.16 However, these tools
of inference are rarely applied to non point
pollution–management programs.

Lack of monitoring is a common defect
in nonpoint pollution–control programs.
Without before-and-after observations
of nutrient loads and water quality, it is
impossible to determine an intervention’s
effectiveness in reducing nutrient runoff.
Because of the previously mentioned long
time lags, monitoring must be sustained
for years or decades. The monitoring of
nonpoint-pollution projects rarely employs
reference watersheds, which are common –

Adena R.
Rissman &
Stephen R.
Carpintero

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144 (3) Verano 2015

Progress on
Nonpoint
Pollution:
Barriers &
Opportu –
niidades

ly used in ecosystem experiments. Refer-
ence ecosystems help separate the effects
of other simultaneous changes from the
effects of the intervention. Two neighbor –
ing watersheds, one mitigated and the oth –
er not, may have similar biogeochemical
and hydrological characteristics and ex –
perience the same weather, but only the
mitigated watershed should show ef fects
of nutrient management. If it be comes
clear the actions are working, then the ref –
erence watershed can also be managed.

Nonpoint control programs are some-
times evaluated by enumerating the num –
ber and size of conservation practices
established instead of the nutrient char-
acteristics of lakes and streams. Mientras que la
number of conservation practices is im –
portant, reaching target nutrient loads and
water quality is the ultimate goal. Estos
metrics of water quality must be meas-
ured before and after the installation of
the mitigation practices in order to evalu-
ate the effects of the program.

Cómo, entonces, should nonpoint pollution
be addressed? Decision-makers and the
public should expect slow responses and
high uncertainty. Nonpoint-pollution
man agement plans will be easier to ex –
plain if they include explicit plans for
mea suring and managing uncertainty.
Sustained monitoring that includes mea –
surements of nutrient outcomes is essen-
tial. Simultaneous monitoring of multiple
subwatersheds (including a reference sub –
watershed) can reduce uncertainty by
accounting for the effects of changes in
clima, agricultural production, and de –
velopment.

Policies for nonpoint-pollution manage –
ment assume that outcomes will be pre-
dictable.17 Models used for nonpoint-pol-
lution planning tend to be complex com-
puter programs with large numbers of pa –
rameters, often exceeding the number of
observations from actual watersheds.
Such models support a culture of spurious

certainty that sets the stage for disappoint –
ment when freshwater ecosystem respons-
es turn out to be slow, variable, y gripe-
enced by multiple changing forces. En –
lugar, research is needed on the dynamics
of uncertainty itself. Por ejemplo, él
would be helpful to observe unmanaged
watersheds over the long term to under-
stand how baselines are moving.18 What
is the frequency distribution of extreme
nutrient loads and how is it changing?
How can we best use landscape heteroge –
neity to understand multiple drivers
through comparisons among subwater-
sheds? How can the planning process en –
gage a broad cross-section of society, make
the best use of science, and create realistic
expectations about response time, variar-
capacidad, e incertidumbre? Questions about
the nature and management of uncertain –
ty are moving to the foreground as society
grapples with the expanding impact of non –
point pollution on freshwaters.

How do management interventions af –

fect complex systems such as lakes? Nuestro
ability to draw conclusions depends in
part on experimental design and in part
on how immediately the environment re –
sponds to a given change. During the
1970s, ecologists demonstrated that phos –
phorus pollution was the underlying cause
of algae blooms in lakes.19 In one key ex –
perimento, a lake was divided in half and
enriched with carbon, nitrogen, and phos –
phorus on one side and only carbon and
nitrogen on the other. Algae bloomed only
on the side with phosphorus, clearly dem –
onstrating the importance of managing
phosphorous in lakes.20

In cases of point source–nutrient pollu-
ción, regulators can turn off the pollutant
flow at the end of the pipe. In the cele-
brated case of Lake Washington, agua
quality dramatically improved in a short
period of time after nutrient input from
sewage was diverted.21 The direct and im –

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mediate response of the ecosystem sup-
ported the belief that nutrient control was
the cause of water quality improvements.
Wisconsin’s Lake Mendota provides an
opportunity to compare fast and slow
responses to intervention and how they af –
fect subsequent management decisions.22
The lake’s food web was manipulated by
½sh stocking and mortality to increase the
abundance of Daphnia pulicaria, a highly
effective grazer. The rise of D. pulicaria sub –
stantially improved water clarity in less
than a year.23 Previously, whole-lake ex –
periments had compared manipulated and
unmanipulated lakes and determined
that food-web changes could improve wa –
ter clarity.24 Lake Mendota’s sharp re –
sponse to food-web manipulation corro –
borated these expectations.

A diferencia de, Lake Mendota’s response to
management of nonpoint phosphorus
inputs has been quite slow.25 There has
been no statistically discernible change in
lake water quality in more than thirty
años, despite extensive efforts to mitigate
nonpoint pollution entering the lake.
Gradual changes in the watershed phos-
phorus budget have likely contributed to
the lake’s slow response.26 Decades of
man agement have been frustrated by si –
multaneous increases in manure concen-
tration, precipitation, the number of large
rainstorms, and impervious surface area.27
These changes in phosphorus-pollution
drivers, occurring simultaneously with
changes in management practices, tener
allowed for conflicting interpretations of
the effects of management on the lake.
These interpretations are equally plausi-
ble, but each has starkly different implica –
tions for policy, complicating the jobs of
managers and policy-makers.

Efforts to use scienti½c information as

evidence to improve water quality face
many challenges. Greater attention has
been paid to the production of water qual –

ity science than to how that sci ence is sub-
sequently used as evidence in water-qual-
ity management and policy. Science has
three primary roles in the for mation of
water-quality policy: 1) identifying and de –
scribing problems; 2) predict ing the likely
effects of potential choices; y 3) evalu-
ating the effects of pri or ac tions.28 We will
identify the barriers to using science in
each of these three major arenas. Primero, y –
derlying disagreements about public val-
ues and preferences influ ence how science
is interpret ed and used. Segundo, hay
many disputes over the assumptions used
to create models and the validity of their
resultados. Institutional barriers such as com –
plex reg ulatory environments can slow the
uptake of new information.29 In terms of
soluciones, individuals and organizations
can learn and change their behavior or
rou tines and so cial networks can en –
hance learn ing and quicken the diffusion
of infor mation.30 Even if scienti½c infor-
mation informs in dividual and organiza-
tional learn ing and management choices,
it may not affect political decisions about
funding or legal environmental protec-
tion.31 Here we identify the roles that sci-
ence plays in non point policy and man –
age ment, de scribe the barriers and oppor –
tunities for use of science in decision-
haciendo, and sum mar ize the reasons it has
been so dif ½cult to reduce nonpoint source
pol lu tion.

The nature of nonpoint management it –
self presents challenges for policy and gov –
ernance, in turn influencing the poten tial
roles for scienti½c information.32 Nearly
all economic development and re source
use–including primary production of
alimento, ½ber, and minerals and secondary
processing into consumer goods and
built infrastructure–produces some water
pollution. Nonpoint-pollution sources
are numerous and often well-organized,
and each contributes only a small propor-
tion of the pollution. Agricultural land

Adena R.
Rissman &
Stephen R.
Carpintero

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144 (3) Verano 2015

Progress on
Nonpoint
Pollution:
Barriers &
Opportu –
niidades

use has a privileged status in environmen-
tal policy- haciendo, which makes regulat-
ing agriculture dif½cult politically.33 The
ben e½ciaries of clean lakes and rivers for
½shing, swim ming, the habitat, and aes-
thetic pleasure are less cohesive, organizado,
and funded than pollution- producing in –
Industrias, although business in terests can
also be powerful allies for clean water. En
many ways, producers of pol lution have a
powerful sociopolitical presence, and this
influences how scienti½c information is
used for water-quality management.

Use of scienti½c information is one tool
for improving decision-making, but sci-
ence does not speak for itself. Scienti½c in –
formation becomes evidence in the minds
and hands of actors with different posi-
ciones, incentives, and viewpoints. The so –
cial-psychology theory of motivated rea-
soning suggests that people interpret in –
formation in light of existing beliefs.34 At
an organizational level, information that
supports agency missions is more likely
to be used–and also more likely to be
fund ed in the ½rst place–while other po –
tential research is left undone.35 Scaling
up to whole watersheds, the diversity of
stakeholder objectives and worldviews
means that disagreements about the mean –
ing of scienti½c information, such as mod –
eled predictions, are inevitable.

Disagreements about values and goals
often underlie disagreements about science
in decision-making.36 Once a goal has been
established, scienti½c information can be
used to help reach it. But if political actors
are unable to agree upon values or goals,
then the tendency is to shift the debate to
technical disagreements over mod els and
data sources.37 However, it is not simple or
realistic to wait to reach po litical agree-
ment before beginning a mod eling process
to determine how to reach that goal, desde
both politics and scienti½c development
are iterative, ongoing pro cesses. Más,

sci ence influences goal-setting itself, desde
scienti½c information is often used to iden –
tify problems for ac tion. Information about
environmental con ditions and trends must
be translated into evidence of a problem if
it is to in form a policy or management
agenda.38Agenda-setting and problem iden –
ti½cation are inherently sociopolitical pro –
cesses that involve the framing and social
construction of information.

De½ning water-quality problems has
been a long-term goal of water-quality
monitoring and research. Water-quality
laws, such as the Federal Water Pollution
Control Act in the United States (el
Clean Water Act, or cwa), established pro –
cesses for setting water-quality standards
for water bodies. The de½nition of how
much pollution constitutes a problem de –
pends on the uses of the water body in
pregunta; stakeholder-based de½nitions of
water-quality problems vary widely. Typ-
ical indicators of water-quality problems
include poor water clarity levels and high
concentrations and total loads of sedi-
mento, bacteria, nutrients, and other chem –
icals in the water. Positive qualitative in –
dicators such as ½shability and swimma-
habilidad (absence of algae blooms or ½sh kills)
are also taken into account.

The voluminous data from water-quality
monitoring does not by itself meaning-
fully inform water-quality management:
these data must be interpreted and linked
with public values in order for the science
to be truly useful.39 Monitoring schemes
must be designed with the likely use of
the information in mind so that their samp –
ling is statistically relevant to those goals.
Desafortunadamente, many large-scale moni-
toring efforts have not yielded informa-
tion that ½ts the needs of managers and
política- fabricantes. Por ejemplo, the epa’s En –
vironmental Monitoring and Assessment
Program (emap) struggled because it was
viewed as out of touch with policy needs
and exhibited a lack of consideration of

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how values drive information interpreta-
ción (despite warnings from the National
Re search Council and the Science Adviso-
ry Board).40
Predicting the likely effects of potential

choices is also a challenge due to the limi –
tations of models and prediction. As man –
agers and policy-makers debate op tions,
they rely on conceptual and quantitative
models to make predictions about the in –
tended and unintended results of al terna –
tive courses of action. Debates over the val –
idity of model predictions are long – stand –
En g. In the nonpoint-source arena, modelos
can estimate the sources of pollution, pre –
dict the ef½cacy of different types of solu-
ciones, prioritize spatial locations for man –
agement, and determine compliance with
regulation.41 Implementation often differs
from modeled plans in unpredict able ways:
por ejemplo, reliance on volun tary farmer
participation means that plan ners typical-
ly cannot predict or control where agri –
cul tural conservation practices will be ap –
plied.42

Models are widely misunderstood as
“truth machines” in environmental poli-
cy.43 Because models are often poorly con –
strained and sometimes have large and un –
clear errors, stakeholders are able to mount
legitimate and signi½cant challenges to
the use and selection of models. A veces
doubt is sown deliberately to discredit un –
favorable data or model estimates.44 But
because models are better at estimating
average conditions in a large area than
assigning accurate estimates to particular
parcels of land, individuals may be justi –
½ed in their skepticism of the ½t of models
to their particular farms or residences. Peo –
ple generally have a tendency to think of
their own situation as exceptional and to
underestimate their risks compared to the
promedio. As Carl Walters, a biologist and
quantitative mod eler, has concluded, “We
cannot assure policy-makers that our mod –

els will give accurate predictions: ellos son
incomplete representations of managed
systems.”45 Critics suggest that models
emphasize quanti½able over dif½cult-to-
quantify ob jectives and shift the debate
from values to technical terms.46 To this
end, environ mental policy expert Daniel
Sarewitz has written, “The abandonment
of a political quest for de½nitive, predic-
tive knowledge ought to encourage, or at
least be compat ible with, more modest,
iterative, incremental approaches to deci –
sion making.”47
Regardless of these shortcomings, modificación –

els of nonpoint source pollution can and
do play critical roles predicting the ef fects
of incentive and regulatory programs. Para
instancia, the Soil and Water Assessment
Tool (swat) is a watershed-based mod el
that was “de veloped to predict the impact
of land management practices on water,
sediment and agricultural chemical yields
in large complex watersheds with varying
soils, [y] land use and management con –
ditions over long periods of time.” swat is
a “continuation of thirty years of non-
point source modeling” that simulates the
agua, sediment, and nutrient balance at
the land surface.48 Water-quality regula-
tions have necessitated these complex
mod els for es timating point and nonpoint-
source contributions to surface water pol –
lution. En este caso, the cwa prompted the
Agricultural Research Service to de vel op
the swat model in the early 1970s.49

Under the cwa, jurisdictions must de vel –
op a Total Maximum Daily Load (tmdl)
for impaired waters: a calculation of the
maximum amount of a pollutant that a
wa ter body can receive and still meet water-
quality standards. As of 2014, sixty- eight
thousand tmdls had been developed in
the United States. tmdls and their im –
plementation plans translate model re sults
into responsibilities split among point
sources and urban and rural nonpoint

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Rissman &
Stephen R.
Carpintero

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144 (3) Verano 2015

Progress on
Nonpoint
Pollution:
Barriers &
Opportu –
niidades

sources. Por ejemplo, swat provides the
basis for allocating necessary reductions
under the Rock River tmdl in Southern
Wisconsin. In the Yahara Watershed,
which flows into the Rock, modeling has
contributed to goal setting, prioritization,
and implementation.

In the Yahara Watershed, sin embargo, swat
did not provide reliable estimates for phos –
phorus loads from agricultural subwater-
sheds when compared with measure ments
from U.S. Geological Survey streamga –
ges.50 swat substantially underpredict ed
agricultural phosphorus loading from agri –
cultural subwatersheds, in part be cause it
was not yet modeling late-winter runoff of
manure and sediment on frozen ground.
Farmers are also often skeptical of model
re sults; a representative of the Wisconsin
Farmers Union claimed that “landowner
lack of trust in models” was a repeated is –
sue. This mistrust is deepened by discrep –
ancies between model estimates averaged
over space and time and farmer experi-
ences of individual ½elds.

Model limitations are becoming better
Reconocido; some have suggested that their
failure to predict measured outcomes
makes swat and other similar soil ero-
sion–based models “unsuitable for mak-
ing management decisions.”51 swat and
other models are based on techniques that
have been minimally updated since the
mid-1980s despite advances in under-
standing of soil phosphorus availability
and transport, leading to a situation in
which “the quality of commonly used
models may now lag behind the demand
for reliable predictions to make policy
and management decisions.”52 However,
analysis of model ½ndings continues to re –
veal some of their limitations and lead to
updates. Despite their imperfections, modificación –
els are critical for regulatory policy and
will continue to be used and im proved in
the absence of alternatives.

A third major role of science is to assess

the effects of actions after they have been
tomado. Several barriers can impede as sess –
mento, including limited information, limits
of causal inferences, and conflicting inter –
pretations based on values and political
pref erences. After a course of action has
been selected and implemented, long-term
monitoring can indicate changes in con-
ditions, but evaluations compared to a ref –
erence site are needed to make caus al in –
ferences about the effects of an action.
Fur thermore, information about “what
works” often cannot be translated from
one local context to another.53

One barrier to assessment is the frag-
mentary nature of water-quality data in
the United States. The National Water
Qual ity Inventory under the Clean Water
Act requires states to report water quality
assessment to the epa. As of 2014, solo 43
percent of lakes, 37 percent of estuaries,
28 percent of rivers, y 1 percent of wet-
lands had been assessed.54

En la práctica, the evaluation of compliance
with new policies for enforcement purpos-
es is typically based on behavioral changes,
not on measured water-quality outcomes.
Agen cies often evaluate their effectiveness
by re lying on the same models that were
used to pre dict the effects of interventions;
there fore, if behaviors do not actually
result in de sired environmental changes,
there would be no data to show this. Cómo –
alguna vez, a limited number of policy-makers are
ex periment ing with performance-based
management, which eval uates measured
environmental outcomes rather than mea –
surements of tech nology or behavioral
cambios (Por ejemplo, edge-of-½eld mon-
itoring on farms).

Evaluation is also a political process. Incluso
when scientists demonstrate an effect (o
lack thereof ), it might not become the
dominant narrative about a policy or pro-
gram. Evaluations and performance in for –
mation are constructed by actors to ad –

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vance their interests.55 For instance, o –
ganizations may promote their programs
as successful even without substan tial in –
formation about their effectiveness. Incluso
the question of who has access to in for –
mation is dependent on political and per-
sonal values. Por ejemplo, conservationists
may wish to obtain farm- and ½eld- escala
information on soil phosphorus and land-
use practices, but farmers may be reluc-
tant to share those data, since they could be
used to assign blame or intensify water-
quality requirements.

Signi½cant barriers face efforts to im –

prove the use of science in decision-mak-
En g. These include matching the supply
and demand for science and communicat –
ing between the cultures and incentive-
structures of scientists and managers.56
Deeper issues challenge us to rethink how
we use science. Perhaps we should not con –
sider better use of science to be the ulti-
mate objective, but rather better deci-
sions.57 Asking a question about better
deci sion-making requires a normative
view of what is socially desirable. A pesar de
in a broad sense, clean water, agricultural
producción, and thriving cities are all so –
cially desirable, making tough decisions
about trade-offs between these goals will
require compromise and continual rene-
gotiation. Social scientists ex amine the
roles of science through multiple lenses,
including discourse analysis of the social
construction of information, psychologi-
cal study of evidence and persuasion in
Toma de decisiones, and systems models that
examine the change in both social and
ecological components of wa tersheds.

Organizational learning systems have
been designed to advance the use of infor –
mation in decision-making. Research on
learning organizations examines how
organizations learn and change their rou-
tines based on new information. Scenar-
ios are one strategy that organizations can

deploy to examine uncertainties and al –
ternative future trajectories. Además,
organizations can learn about how to
learn more effectively and develop new
institutional structures and informal net-
works to facilitate learning.58 However,
efforts to build learning organizations may
be impeded by institutional fragmentation;
limited capacity; organizational culture;
the different timelines and incentives of
sci entists, managers, and policy-makers;
and the command-and-control paradigm
(top-down management).

Nonpoint pollution challenges our abil-

ity to measure, predict, and regulate. Sci-
enti½c information is limited by few ex –
perimental designs, complex causality, y
the dif½culty of creating solutions to ½t
het erogeneous spatial and temporal scales.
Barriers to using the scienti½c in formation
we do have arise in part from the conflict
over values and goals for water and land
usar. Yet “thinking practitioners” have suc –
cessfully improved wa ter quality and used
scienti½c knowledge to inform manage-
mento, política, and governance despite these
many barriers.59 There is no denying that
science plays critical roles in goal-setting,
planificación, and evaluation. In the conten –
tious pro cess to extend Clean Water Act
regulation to agricultural and urban non-
point sources, models are cast in starring
roles to prioritize implementation and as –
sign responsibility. An examination of the
use of science in management, política-
haciendo, and governance reveals the copro –
duction of science, modelado, and non-
point control systems. Overcoming the
bar riers to non point-pollution prevention
requires that stakeholders and policy-
makers renew their commitment to learn –
ing from scien ti½c information and at times
act in the face of uncertainty.

Adena R.
Rissman &
Stephen R.
Carpintero

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144 (3) Verano 2015

Progress on
Nonpoint
Pollution:
Barriers &
Opportu –
niidades

notas finales
* Contributor Biographies: ADENA R. RISSMAN is an Assistant Professor of the Human Dimen-
sions of Ecosystem Management in the Department of Forest and Wildlife Ecology at the Uni-
versity of Wisconsin–Madison. Her research has appeared in such journals as Conservation Let-
ters, Journal of Environmental Management, Environmental Science and Policy, and Landscape and
Urban Planning.
STEPHEN R. CARPENTER, miembro de la Academia Americana desde 2006, is the Stephen Alfred
Forbes Professor of Zoology and the Director of the Center for Limnology at the University of
Wisconsin–Madison. He is the author of Princeton Guide to Ecology (with S. A. Levin et al., 2009)
and Regime Shifts in Lake Ecosystems: Patterns and Variation (2003). His research has ap peared in
such journals as Ecology, Sustainability, and Science.

Authors’ Note: We thank the Water Sustainability and Climate Team, which is funded by
National Science Foundation DEB-1038759.

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12 Meals, Dressing, and Davenport, “Lag Time in Water Quality Response to Best Management

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Adena R.
Rissman &
Stephen R.
Carpintero

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16 Stephen R. Carpintero, “The Need for Large-Scale Experiments to Assess and Predict the
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19 Val H. Herrero, Samantha B. Joye, and Robert W. Howarth, “Eutrophication of Freshwater and
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20 Schindler, “The Dilemma of Controlling Cultural Eutrophication of Lakes.”
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23 R. C. Lathrop, B. METRO. Johnson, t. B. Johnson, METRO. t. Vogelsang, S. R. Carpintero, t. R. Hrabik,
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26 Emily Kara, Chad Heimerl, Tess Killpack, Matthew Van de Bogert, Hiroko Yoshida, and Stephen
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144 (3) Verano 2015

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Progress on
Nonpoint
Pollution:
Barriers &
Opportu –
niidades

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Adena R.
Rissman &
Stephen R.
Carpintero

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