Why aren’t we making more progress on improving water quality in our lakes and rivers, despite decades of effort? One solution we often hear is the need for more science. However, we should also ask what kind of science is needed, and how can we use scientific knowledge to make better decisions for improving water quality.
A new essay by UW-Madison Water Sustainability & Climate researchers Adena Rissman and Steve Carpenter analyzes why it is difficult to improve water quality and highlights some opportunities for improving the use of scientific information. Their essay is featured in the newest issue of Daedalus, the journal of the American Academy of Arts and Sciences (AAAS), which contains a collection of essays on opportunities for addressing humanity’s ever-increasing demands on water, a dwindling resource.
Rissman and Carpenter focus specifically on the challenges associated with reducing nonpoint source pollution, which describes the soil and nutrients that rain and snowmelt carry from many different places across a landscape and into our lakes and rivers (in contrast, point source pollution comes directly from one outlet, such as a factory or sewage treatment plant). Some key sources of nonpoint pollutants include sediments from poorly managed construction sites, lawn fertilizers, and nutrient runoff from agricultural areas.
The widespread nature of nonpoint source pollution is among the reasons it is so hard to manage. Other reasons lie in the difficulties of gaining scientific knowledge about the behavior of pollution and its remedies, and in the barriers to using this knowledge in policymaking.
Below Rissman explains some of the key points from their essay.
Jenny Seifert (JS): How does policy rely on science to improve water quality, and vice versa?
Adena Rissman (AR): Science has three primary roles in water quality policy: 1) identifying and describing the problems affecting water quality; 2) predicting the possible intended and unintended effects of choices for addressing the problems; and 3) evaluating the effects—both intended and unintended—of the actions taken.
In other words, policymakers need reliable scientific information on the problems and the likely effects of solutions to design new programs. For example, scientific information is helpful for setting goals, such as how many nutrients to remove from the system, and targeting the most important pollution sources. Policymakers and the public also rely on science to measure success and identify when efforts are not reaching their goals.
In our essay, we also tell the story about how policymakers’ needs shape the kinds of scientific models that scientists create. Models play important roles in linking science to decision making. Scientists create and use models to predict the likely outcomes of options for managing land and water, and water quality regulations have necessitated the use of models.
For instance, a model called the Soil and Water Assessment Tool (SWAT) helps scientists and managers predict the impacts of different land management practices over time. This model was an outcome of the Clean Water Act, which prompted the Agricultural Research Service to develop water quality models starting in the early 1970s.
JS: You outline several challenges to using science to inform policies to reduce nonpoint source pollution. Which do you think are the most important to address?
AR: That’s true—there are many challenges. For instance, underlying disagreements about public values and preferences influence how policymakers use and interpret science. There are disputes over the assumptions used to create models and the validity of their results. Also, institutional barriers, such as the complexity of regulatory environments, can slow the uptake of new information.
One important issue is to give meaning to scientific information. The large amount of data generated by water quality monitoring does not by itself meaningfully inform water quality management. Rather, for the science to be truly useful, this data must be interpreted and aligned with public values. We need to continue to invest in the human and technological capacity to make links between what can be measured and what people care about.
JS: What can both policymakers and scientists do to overcome these challenges?
AR: Scientists can do a better job of communicating the uncertainty inherent in model predictions and science in general. Policymakers, on the other hand, can do a better job of acting in the face of uncertainty.
JS: What is your take-home message?
AR: Barriers to demonstrating causality in water quality problems and interventions include few experimental designs, different spatial scales for behaviors and measured outcomes, and the long time lags between when policies are enacted and when their effects are seen.
Primary obstacles to using science as evidence in nonpoint policy include disagreements about public values and preferences, disputes over the validity of the assumptions made in models, and institutional barriers to reconciling the supply and demand for science.
We argue that decision makers should learn from scientific information when making decisions, and—if water quality is important—to be prepared to act to protect public values, even when scientific uncertainties exist.
Want to learn more about the ideas featured in this issue of Daedalus? AAAS partnered with WGBH, Boston-based public broadcasting, to explore the demands water faces in a series called Water Pressure.
This post originally appeared in Yahara in Situ on July 13, 2015.