research paper

Progress on Nonpoint Pollution: Barriers & Opportunities

Adena R. Rissman & Stephen R. Carpenter

ADENAR. 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.

STEPHENR. 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- ture, 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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© 2015 by the American Academy of Arts & Sciences doi:10.1162/DAED_a_00340

Abstract: 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; and 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.

lenges of producing science to predict and assess nonpoint management and policy effectiveness, and of using this science in management and political decisions? Finally, 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, statistical analysis, 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 instance, 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, but 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 (in - 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, models, un - certainty, and risk enter into the decisions of actors such as the Environmental Pro- tection Agency (epa), county conserva-

tionists, farmers, ur ban homeowners, and 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 systems, 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? We 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- point- pollution control. First, 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, there is a high level of uncertainty about the ef½ - cacy of methods; indeed, 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, including: long time lags between intervention and response, spa- tial hetero geneity (that is, a solution that works in one site may not work in an - other), 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; and diverse streams and lakes. Speci½c practices for ameliorating pollution–such as buffer strips, cover crops, tillage practices, and 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, small- 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.

Furthermore, many factors that affect water quality change simultaneously. For example, 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 - systems, 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 -

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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. While the number of conservation practices is im - portant, reaching target nutrient loads and water quality is the ultimate goal. These 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.

How, then, 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 weather, 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, and influ- enced by multiple changing forces. In - stead, research is needed on the dynamics of uncertainty itself. For example, it 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, vari- ability, and uncertainty? 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? Our 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 - periment, 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- tion, regulators can turn off the pollutant flow at the end of the pipe. In the cele- brated case of Lake Washington, water 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.

In contrast, 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 years, 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, have 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; and 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. First, un - derlying disagreements about public val- ues and preferences influ ence how science is interpret ed and used. Second, there are many disputes over the assumptions used to create models and the validity of their results. Institutional barriers such as com - plex reg ulatory environments can slow the uptake of new information.29 In terms of solutions, 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- making, 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 food, 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

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use has a privileged status in environmen- tal policy- making, 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, organized, and funded than pollution- producing in - dustries, although business in terests can also be powerful allies for clean water. In 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- tions, 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, since both politics and scienti½c development are iterative, ongoing pro cesses. Further,

sci ence influences goal-setting itself, since 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.38 Agenda-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 (the 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 question; 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- ment, bacteria, nutrients, and other chem - icals in the water. Positive qualitative in - dicators such as ½shability and swimma- bility (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. Unfortunately, many large-scale moni- toring efforts have not yielded informa- tion that ½ts the needs of managers and policy- makers. For instance, 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- tion (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 - ing. In the nonpoint-source arena, models can estimate the sources of pollution, pre - dict the ef½cacy of different types of solu- tions, prioritize spatial locations for man - agement, and determine compliance with regulation.41 Implementation often differs from modeled plans in unpredict able ways: for instance, 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. Sometimes 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 average. As Carl Walters, a biologist and quantitative mod eler, has concluded, “We cannot assure policy-makers that our mod -

els will give accurate predictions: they are 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

R egardless of these shortcomings, mod - els of nonpoint source pollution can and do play critical roles predicting the ef fects of incentive and regulatory programs. For instance, 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, [and] 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 water, 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. In this case, 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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sources. For instance, 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, however, 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 recognized; 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, mod - 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 taken. Several barriers can impede as sess - ment, 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, only 43 percent of lakes, 37 percent of estuaries, 28 percent of rivers, and 1 percent of wet- lands had been assessed.54

In practice, 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. How - ever, 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 changes (for example, edge-of-½eld mon- itoring on farms).

Evaluation is also a political process. Even when scientists demonstrate an effect (or 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, or - ganizations may promote their programs as successful even without substan tial in - formation about their effectiveness. Even the question of who has access to in for - mation is dependent on political and per- sonal values. For instance, conservationists may wish to obtain farm- and ½eld- scale 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- ing. 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. Although in a broad sense, clean water, agricultural production, 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 decision-making, 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. Furthermore, 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, and 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 use. Yet “thinking practitioners” have suc - cessfully improved wa ter quality and used scienti½c knowledge to inform manage- ment, policy, and governance despite these many barriers.59 There is no denying that science plays critical roles in goal-setting, planning, 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, policy- making, and governance reveals the copro - duction of science, modeling, 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.

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endnotes * Contributor Biographies: ADENAR. 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.

STEPHENR. CARPENTER, a Fellow of the American Academy since 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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2 Stephen R. Carpenter, Nina F. Caraco, David L. Correll, Robert W. Howarth, Andrew N. Sharpley, and Val H. Smith, “Nonpoint Pollution of Surface Waters with Phosphorus and Nitrogen,” Ecological Applications 8 (3) (1998): 559–568.

3 Graham P. Harris and A. Louise Heathwaite, “Why is Achieving Good Ecological Outcomes in Rivers so Dif½cult?” Freshwater Biology 57 (1) (2012): 91–107.

4 Daniel Sarewitz and Roger A. Pielke, Jr., “The Neglected Heart of Science Policy: Reconcil- ing Supply of and Demand for Science,” Environmental Science & Policy 10 (1) (2007): 5–16.

5 Kenneth Prewitt, Thomas A. Schwandt, and Miron L. Straf, Using Science as Evidence in Public Policy (Washington, D.C.: National Academies Press, 2012).

6 Sheila Jasanoff, ed., States of Knowledge: The Co-Production of Science and the Social Order (London: Routledge, 2004): 317.

7 Winston Harrington, Alan J. Krupnick, and Henry M. Peskin, “Policies for Nonpoint-Source Water Pollution Control,” Journal of Soil and Water Conservation 40 (1) (1985): 27–32; Paul A. Sabatier, Will Focht, Mark Lubell, Zev Trachtenberg, Arnold Vedlitz, and Marty Matlock, eds., Swimming Upstream: Collaborative Approaches to Watershed Management (Cambridge, Mass.: The mit Press, 2005): 327.

8 Donald W. Meals, Steven A. Dressing, and Thomas E. Davenport, “Lag Time in Water Qual- ity Response to Best Management Practices: A Review,” Journal of Environmental Quality 39 (1) (2010): 85–96, doi:10.2134/jeq2009.0108.

9 Harris and Heathwaite, “Why is Achieving Good Ecological Outcomes in Rivers So Dif½cult?”; and Helen P. Jarvie, Andrew N. Sharpley, Paul J. A. Withers, J. Thad Scott, Brian E. Haggard, and Colin Neal, “Phosphorus Mitigation to Control River Eutrophication: Murky Waters, Inconvenient Truths, and ‘Postnormal’ Science,” Journal of Environmental Quality 42 (2) (2013): 295–304, doi:10.2134/jeq2012.0085.

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11 Meals, Dressing, and Davenport, “Lag Time in Water Quality Response to Best Management Practices: A Review”; Stephen K. Hamilton, “Biogeochemical Time Lags May Delay Responses of Streams to Ecological Restoration,” Freshwater Biology 57 (2012): 43–57, doi:10.1111/ j.1365-2427.2011.02685.x; and Andrew Sharpley, Helen P. Jarvie, Anthony Buda, Linda May, Bryan Spears, and Peter Kleinman, “Phosphorus Legacy: Overcoming the Effects of Past Management Practices to Mitigate Future Water Quality Impairment,” Journal of Environ- mental Quality 42 (5) (2013): 1308–1326, doi:10.2134/jeq2013.03.0098.

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14 Hamilton, “Biogeochemical Time Lags May Delay Responses of Streams to Ecological Res - toration.”

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16 Stephen R. Carpenter, “The Need for Large-Scale Experiments to Assess and Predict the Response of Ecosystems to Perturbation,” in Successes, Limitations, and Frontiers in Ecosystem Science, ed. Michael L. Pace and Peter M. Groffman (New York: Springer, 1998): 287–312.

17 Harris and Heathwaite, “Why is Achieving Good Ecological Outcomes in Rivers so Dif½cult?” 18 P. C. D. Milly, Julio Betancourt, Malin Falkenmark, Robert M. Hirsch, Zbigniew W. Kund -

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19 Val H. Smith, Samantha B. Joye, and Robert W. Howarth, “Eutrophication of Freshwater and Marine Ecosystems,” Limnology and Oceanography 51 (1) (2006): 351–355; and David W. Schindler, “The Dilemma of Controlling Cultural Eutrophication of Lakes,” Proceedings of the Royal Society B: Biological Sciences 279 (1746) (2012), doi:10.1098/rspb.2012.1032.

20 Schindler, “The Dilemma of Controlling Cultural Eutrophication of Lakes.” 21 W. T. Edmondson, The Uses of Ecology: Lake Washington and Beyond (Seattle: University of

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24 Stephen R. Carpenter and James F. Kitchell, eds., The Trophic Cascade in Lakes (Cambridge: Cam - bridge University Press, 1993): 385.

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26 Emily Kara, Chad Heimerl, Tess Killpack, Matthew Van de Bogert, Hiroko Yoshida, and Stephen Carpenter, “Assessing a Decade of Phosphorus Management in the Lake Mendota, Wisconsin Watershed and Scenarios for Enhanced Phosphorus Management,” Aquatic Sciences–Research Across Boundaries (2011): 1–13, doi:10.1007/s00027-011-0215-6.

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48 Texas Water Resources Institute, Soil and Water Assessment Tool: Theoretical Documentation, Version 2009 (College Station, Tex.: Texas Water Resources Institute, 2011).

49 Ibid. 50 Lathrop and Carpenter, “Water Quality Implications from Three Decades of Phosphorus

Loads and Trophic Dynamics in the Yahara Chain of Lakes.” 51 Kathleen B. Boomer, Donald E. Weller, and Thomas E. Jordan, “Empirical Models Based on

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52 P. A. Vadas, C. H. Bolster, and L. W. Good, “Critical Evaluation of Models Used to Study Agri cul - tural Phosphorus and Water Quality,” Soil Use and Management 29 (S1) (2013): 36–44.

53 Katharine Jacobs and Lester Snow, “Adaptation in the Water Sector: Science and Institutions,” Dædalus 144 (3) (2015).

54 Environmental Protection Agency, “Watershed Assessment, Tracking & Environmental Re - sults.”

55 Donald P. Moynihan, The Dynamics of Performance Management: Constructing Information and Reform (Washington, D.C.: Georgetown University Press, 2008).

56 Daniel Sarewitz and Roger A. Pielke, Jr., “The Neglected Heart of Science Policy: Reconcil- ing Supply of and Demand for Science,” Environmental Science & Policy 10 (1) (2007): 5–16; and Elizabeth C. McNie, “Reconciling the Supply of Scienti½c Information with User Demands: An Analysis of the Problem and Review of the Literature,” Environmental Science & Policy 10 (1) (2007): 17–38.

57 Prewitt, Schwandt, and Straf, Using Science as Evidence in Public Policy. 58 Pahl-Wostl, “A Conceptual Framework for Analysing Adaptive Capacity and Multi-Level

Learn ing Processes in Resource Governance Regimes,” Global Environmental Change 19 (3) (2009): 354–365.

59 John Briscoe, “Water Security in a Changing World,” Dædalus 144 (3) (2015): 27–34.