AVOIDING TRAIN WRECKS IN THE USE OF SCIENCE IN
ENVIRONMENTAL PROBLEM SOLVING
Lee E. Benda, N. LeRoy Poff, Christina Tague, Margaret A. Palmer, James Pizzuto, Nancy
Bockstael, Scott Cooper, Emily Stanley, Glenn Moglen
_______________________________________________________
Lee Benda (email LeeBenda@aol.com) is a senior scientist with the Earth Systems Institute, 1314 NE
43rd St., Suite 205, Seattle, WA 98105. LeRoy Poff (email: poff@lamar.colostate.edu) is an Assistant
Professor at Colorado State University, Department of Biology, and Ft. Collins, CO 80523.
Christina Tague (email: ctague@mail.sdsu.edu) is an Assistant Professor at the University of
California – San Diego, Department of Geography, San Diego CA 92182. Margaret Palmer
(mp3@umail.umd.edu) is a Professor at the University of Maryland, Department of Biology, College
Park, MD 20742. James Pizzuto (email: pizzuto@Udel.edu) is a Professor at the University of
Delaware, Department of Geology, Newark, DE 19716. Nancy Bockstael (email:
nancyb@arec.umd.edu) is a Professor at the University of Maryland, Department of Agricultural &
Resource Economics, College Park, MD 20742. Scott Cooper (email: scooper@lifesci.ucsb.edu_ is a
Professor at the University of California, Department of Ecology, Evolution, and Marine Biology,
Santa Barbara, CA 93106. Emily Stanley (email: ehstanley@facstaff.wisc.edu) is an Assistant
Professor at the University of Wisconsin, Department of Zoology, Madison, WI 53708. Glenn
Moglen (email: moglen@eng.umd.edu) is an Assistant Professor at the University of Maryland,
Department of Civil Engineering, College Park, MD 20742.
Abstract: Solving interdisciplinary problems requires, at the onset, an epistemological
analysis of the collaborating disciplines. Different forms of knowledge that are available
across disciplines are used to identify the scales and resolution of answers that are
possible, as well as, those that are not possible and those situations most likely to lead to
train wrecks.
1
INTRODUCTION
Natural ecological systems are being altered in diverse ways and at rapid rates by
human actions. Some activities produce sustained impacts on ecosystems that are readily
apparent, such as the effects of dam impoundments on river hydrology or the impacts of
exotic predators on communities and ecosystems (Mooney and Hobbs 2000). Other
anthropogenic influences such as the ecological impacts of global climate change
(Kareiva et al. 1993) or the synergistic effects of fishing and habitat alteration on fishery
stocks are more difficult to identify and involve many interacting factors. Finding
solutions to environmental problems is challenging because it requires an understanding
of complex scientific issues as well as how socio-political factors act to dictate feasible or
desirable courses of action.
Because of society’s interest in stemming environmental degradation, knowledge
from a range of scientific disciplines must often be focused on environmental problems.
Completing interdisciplinary analyses, however, is difficult for numerous reasons. A
fundamental problem is that science is inexorably shaped by the questions that it
addresses and by the scientific and socio-political context that determine these questions
(Kuhn 1970, Latour 1993, Pickett et al. 1994). Hence, existing scientific theories and
technologies may originate from questions that are unrelated to today's interdisciplinary
and complex environmental problems. This may lead to a shortfall between the concepts,
theories, analytical methods, and empirical relations available to explain and predict
phenomena and the knowledge and methodological requirements needed to answer
environmental questions (Ford 2000, NSB 2000, and many others). Moreover, it also can
2
be difficult to find a common language so that a diverse set of disciplinarians can
communicate (Wear 1999, Sarewitz et al. 2000).
In principle, the participation of individuals from a diverse set of disciplines
should enhance the success of problem solving, and it often does so. However,
addressing problems with multiple disciplines also may create difficulties owing to
mismatches in space and time scales, in forms of knowledge (e.g., qualitative vs.
quantitative), and in levels of resolution and accuracy addressed by different fields (e.g.,
Herrick 2000). These difficulties can lead to train wrecks1 defined here as failures to
solve environmental problems because of an inability to effectively communicate among
scientists, regulators, and the public, because current scientific understanding or
predictive capacity is inadequate in some fields, and/or because of disagreements about
problem solving strategies. We also recognize that there are other reasons for train
wrecks, often brought about by political realities or conflicts between private interests
and the interests of society, but we limit our analysis in this paper to scientific reasons.
The limitations and opportunities that arise from the diversity of scientific training,
approaches, and knowledge collectively embodied by an interdisciplinary team are often
misunderstood by the public, policy makers, or even by scientific practitioners
themselves.
Our goal in this paper is to propose a process for increasing the success of
interdisciplinary collaborations. The process begins with an awareness of the
epistemology of various disciplines, including an understanding of the origin, nature,
methods, and limits of knowledge. An epistemological analysis is used to identify
We borrow the term “train wrecks” from Fitzsimmons (1994) who referred more narrowly to problem
solving failures due to tension between economic growth and environmental preservation.
1
3
multiple pathways by which knowledge and approaches contributed by a diverse set of
disciplines can be applied to environmental questions, including identifying possible
solutions and the potential for train wrecks. Our general approach can be applied to a
broad range of interdisciplinary questions; however, we illustrate its use by focusing on
one general class of environmental questions, i.e. the effects of land use changes on
riverine ecosystems.
CREATING AN INTERDISCIPLINARY SOLUTION SPACE
All solutions to environmental problems begin with questions. When examining
the effects of land use changes on riverine ecosystems, we can pose specific questions:
Can we predict the effects of urbanization or logging on flow regimes and erosion, and
how do these factors, in turn, affect aquatic biota? Answers to this question will require
input from disciplines as diverse as economics, surface water hydrology, geomorphology,
and riverine ecology. These disciplines are hierarchically arranged to depict a cascading
sequence of hypothesized causes and effects (or inputs and outputs), representing
linkages between the questions and knowledge of different disciplines (Figure 1). Thus,
economic factors will drive urbanization or logging, and economists can forecast spatial
and temporal patterns in future land uses in a watershed. Hydrologists and
geomorphologists, in turn, use this information to predict how the storage and flux of
water, sediment and nutrients will change in stream channel networks draining the
watershed. Predicted changes in physical and chemical conditions in streams are then
used to evaluate the possible ecological consequences of the forecasted land use changes.
4
Central to the application of interdisciplinary approaches to successful problem
solving efforts is the recognition that each discipline produces knowledge (theories,
models, statistical relations, empirical descriptions) that has a particular history and
structure. The knowledge structures of disciplines (e.g., economics, surface water
hydrology, geomorphology, and riverine ecology) include the spatial and temporal scales
over which the knowledge applies, the qualitative vs. quantitative nature of knowledge,
and the levels of resolution and accuracy in predictions. By mapping each discipline’s
forms of knowledge on axes related to their space and time scales, resolution, and
accuracy, and by superimposing and linking the areas of knowledge produced by each
discipline, we produce a “solution space” (Figure 1). The solution space is used to
identify the feasible scales, accuracy, and resolution of solutions produced by an
interdisciplinary effort. We propose that such a solution space, structured around the
epistemological analysis of collaborating disciplines, would reveal a finite set of
“pathways” that lead from environmental questions to answers that identify both
opportunities and potential train wrecks of scientific investigations and applications.
Many attempts to answer interdisciplinary questions fail because participants
assume that there is a particular pathway to an answer at the outset, e.g., quantitative data
will be used to build quantitative models which predict outcomes with a high degree of
precision. In many cases, however, multiple solution pathways may be possible,
depending on the exact question and the knowledge structures of the disciplines applied
to the problem. We refer to these multiple answers to environmental questions as
“question – knowledge pathways” (Figure 2), and contend that explicit recognition of
these pathways would help avoid train wrecks in interdisciplinary research or problem
5
solving. The explicit recognition of the histories and knowledge structures of different
disciplines should advance interdisciplinary problem-solving efforts.
Limitations to interdisciplinary problem solving include the absence of critical
pieces of scientific knowledge or a question addressing dimensional scales that lie outside
of the available forms of knowledge (pathways 1, 2, 3, and 4, Figure 2). Opportunities
created by interdisciplinary problem-solving may include: 1) altering the environmental
question to more closely match the available forms of knowledge, which may require
omitting certain types of questions; 2) legitimizing qualitative, statistical, and other, new
forms of knowledge (Benda et al. 1998; Gaff 2000); and 3) identifying research or data
needed to answer environmental questions (pathways 5, 6, and 7; Figure 2). Acceptance
of one of these “opportunities” may save the problem-solving effort from failure.
APPLYING INTERDISCIPLINARY KNOWLEDGE TO ENVIRONMENTAL
QUESTIONS
The process that we used to construct the interdisciplinary solution space (Figure
1) and to identify the pathways leading from environmental questions to answers (Figure
2) is described in the remainder of this paper. Delineating and interpreting the solution
space requires identifying the knowledge structure of each relevant discipline, focusing
on the origins (i.e., history of questions and approaches), methods, and limits of
knowledge of each discipline. To begin, we map the spatial and temporal scales
addressed by the various forms of knowledge in each of the four disciplines relevant to
examining land use impacts on riverine ecosystems on a Stommel diagram (Stommel
1963) (Figure 3). The diagrams in Figure 3 are hierarchically arrayed in the solution
space in Figure 1.
6
The epistemological analysis recognizes that the history of a discipline strongly
influences its forms of knowledge (Kuhn 1970; Schaffer 1996), indicating that the
knowledge base of a discipline depends, in part, on the prevailing social and scientific
climate during the time the knowledge was generated (Feyerabend 1978). The research
traditions embodied in different disciplines often dictate what techniques are available,
what variables are measured, and how data are analyzed and interpreted. Some detail on
the traditions and bodies of knowledge in the four disciplines as they pertain to our focal
land use question are presented in Boxes 1- 4. Comparison of this information reveals
how knowledge incompatibilities may arise when disciplines interact. For example,
environmental economics has developed as a sub-discipline of economics during the last
few decades in direct response to the fact that environmental amenities and ecological
functions are valuable to people individually and to society as a whole, but can not be
traded on markets like other goods. This had led to analysis of the effects of land uses on
environmental goods and services, including air and water quality and landscape
aesthetics. The development of riverine ecology also has accelerated since the 1970s
during a time of increasing environmental awareness. In contrast, the development of
hydrology and, to some extent, geomorphology was driven by practical engineering
concerns in the late 19th and early to mid 20th centuries related to the protection of human
structures from floods and landslides, and the construction of stable, artificial waterways.
During that period and up to the present, geomorphology was also focused on landform
evolution. Until recently, hydrology and geomorphology were not directly concerned
with environmental issues.
7
Commonly, resource managers, regulators, and policy makers request scientific
predictions that are accurate and precise for large systems (i.e., large watersheds). The
expectation of precise, quantitative predictions arises from the predictive successes of
quantitative approaches in other scientific disciplines, such as chemistry and Newtonian
physics (Gell-Mann 1994). For instance, there is presently great concern about how
spatial patterns of urbanization will affect stream ecosystems (i.e., "Smart Growth"
issues, www.smartgrowth.com; Palmer et al. accepted). This expectation often exposes
one of the most common sources of incompatibilities among disciplines - differences in
spatial and temporal scales, a problem that can be recognized and possibly circumvented
by use of a solution space (Figure 1). For example, many economic theories about the
spatial arrangement of land use (e.g. Alonso 1964; Krugman 1996) characterize in a
qualitative way the expected nature of land use pattern around city centers. They are
broadly applicable to urban/suburban centers throughout the U.S. over decadal time
horizons, but provide little spatial detail and no precise quantitative predictions. In
contrast, empirical economic research that models spatially explicit land use change with
an aim of high levels of accuracy (e.g. Bockstael 1996; Landis 1995) is typically
organized around small-scale questions (Box 1, Figure 3), effectively precluding
forecasting over periods of decades and over broad regions. Even more critical, the
spatial scales used in economics are at the level of either neighborhoods or a land market
as characterized by a city center and surrounding suburbs. The relevant geographic scale
for detailed economic study is generally not compatible with the spatial scales that
govern the function of riverine ecosystems (entire large watersheds).
8
Spatially distributed hydrological models (Box 2, Figure 3) can be used to predict
relationships between land use patterns and flow for some small catchments, but data
limitations (i.e., precipitation and flow records) typically preclude the application of these
models to all the small streams draining a large basin. In large watersheds, fine-scale
variation in the hydrological behavior often can be ignored when estimating hydrological
responses to land use changes in the entire basin. Flow regimes at the outlet of the large
basin may be estimated with reasonable accuracy using relationships between total
discharge and average characteristics of the entire basin. These lumped approaches do
not provide fine-scale information on watershed responses and they do not incorporate
information on complex, continuous land use changes or the spatial distribution of these
changes (e.g., riparian vs. upland changes) (Moglen and Beighley submitted).
Geomorphology, like other areas of geology, examines natural systems that span
very large time and space scales (regional landscapes over millennia) where current
environmental conditions are strongly contingent on past events (i.e., geologic and
climatic history). As a result, geomorphic predictions are often imprecise, qualitative,
and have low accuracy compared to economics or to hydrology. Moreover, because each
geomorphic situation is somewhat unique and the large scale of focus precludes
experimental manipulations, opportunities for repeating observations and falsifying
hypotheses are few (Schumm 1991). In quantitative geomorphology, the precision of
predictions generally declines with increasing scale and the number of model parameters.
For example, geomorphological theories and concepts encompass a broad range of scales
(Box 3, Figure 3) and can be used to address questions at both small and large scales (i.e.,
sediment transport over meters during a single storm or in channel networks over
9
decades). However, theories and models addressing sediment transport produce
predictions that have low accuracy, even at small scales (Gomez and Church 1987). If
the effects of land use changes on sediment supply and transport at large scales are of
interest, then the number of parameters needed to predict these changes rises rapidly
(hillslope erosion and attendant changes in channel morphology must be considered).
Although this scale of analysis can lead to valuable insights into whether small or large
changes in channel state are anticipated (i.e., direction and general magnitude, e.g.,
Schumm 1977), the accuracy of predictions at small scales is quite low (e.g., channel
scour in reaches during individual storms).
Finally, the ecological knowledge required to address the effects of land use
changes on riverine ecosystems encompasses a large number of elements and parameters
(Box 4, Figure 3). The complexity of ecological systems include diversity of food web
components, linkages between them, influences of multiple environmental factors, and
the contingent role of history. Ecological patterns and processes are shaped by present
and past environmental conditions and by interactions among species within their
changing environments. The role of past conditions or events on river systems is difficult
to quantify; however, the impacts of historical conditions on current patterns can be
substantial (e.g., Sedell and Froggatt 1984). The great variation in environmental
conditions and species composition across riverine landscapes contributes to large spatial
and temporal variation in the structure and function of ecological systems. Consequently,
almost all of the general principles used in riverine ecology are conceptual and qualitative
(i.e., River Continuum Concept, Flood Pulse Concept, etc.) (Box 4, Figure 3).
10
Within qualitative, conceptual frameworks, however, riverine ecologists often use
quantitative, analytical approaches to express relationships between ecological variables
and environmental conditions. Both statistical and numerical modeling approaches are
now used widely in riverine ecology, yet their ability to make precise quantitative
predictions about ecological responses to land use change is often limited. Quantitative
predictions of land use effects on a riverine system require reach-scale data collected
under particular conditions. Usually, however, extrapolating local models to other
locations (where detailed data are lacking) is problematic, owing to the increased
complexity of larger systems and the role of history in producing spatially unique
patterns. On the other hand, statistical models can be developed for large scales to
express important relationships between dominant environmental variables at those scales
and ecological measures.
So-called black box models can be used to assess the probability and magnitude
of change at an individual site; however, the predicted outcomes are typically contingent
on many assumptions (about history; the validity of extrapolations to other sites, times, or
scales; the effects of unmeasured environmental variables; differences between largescale average vs. local responses). If these assumptions are violated, then quantitative
predictions become difficult and often inaccurate (Palmer et al. submitted). Of course,
limitations imposed by the effects of historical contingency and local conditions on
outcomes apply to many scientific disciplines (e.g., Ulanowicz 1997). Given these
considerations, riverine responses to land use change will often be couched as qualitative
predictions. For example, ecologists may be able to say that pollution tolerant taxa will
increase and sensitive taxa will decline if a forested watershed is developed; however,
11
accurately predicting the change in abundance for individual species or the change in
community diversity is not feasible.
In sum, the analysis of knowledge structures indicates a trend across the four
disciplines that begins with quantitative reasoning at small scales in the upper levels of
the solution space (Figure 1, economics and hydrology) and that evolves to increasingly
qualitative and contextual understanding at larger scales in the lower levels
(geomorphology and riverine ecology). This suggests that to successfully negotiate the
solution space in our example will require formulating tactics that recognize this dilemma
and that employ strategies that bridge barriers related to the different forms of
knowledge.
EXPLORING QUESTION – KNOWLEDGE PATHWAYS IN A SOLUTION
SPACE
Evaluating the structure of knowledge for each of the relevant disciplines (Boxes
1 through 4, Figure 3) leads to the interdisciplinary solution space shown in Figure 1.
The resulting set of general solution pathways summarized in Figure 2 is controlled by:
1) the presence or absence of scientific knowledge on a required topic; 2) qualitative
versus quantitative models of responses to change; 3) the accuracy and precision of
scientific understanding or predictions; 4) the availability of site-specific data for system
description, model calibration, and model tests; and 5) the ability to measure change in a
given variable in the environment (i.e., measurement precision).
We use examples to illustrate the seven solution pathways in Figure 2. A
scientist may be asked to address the question: what are the effects of altered hydrologic
regime on the biodiversity of aquatic invertebrates? Unfortunately, only a subset of those
12
invertebrates has been studied at the species level, and hence information on species
persistence and biodiversity as a function of flow regime may be unavailable (Palmer et
al. 1997). We may even lack precise data on how invertebrate communities respond to
the magnitude, frequency, and timing of floods. Hence, scientific information may be
unavailable within the general context of the question (Pathway #1, Figure 2). Other
questions about the ecological impacts of land use change require detailed predictions at
small, local scales, for example, predicting the effects of increasing flow on channel
scour at the reach scale (WDNR 1996). There are no widely-applicable models for
predicting scour even though geomorphologists understand that bed scour is governed by
a mixture of hydraulic forces and sediment supply (Pathway #2, Figure 2).
Another example originated from over two years in a working group at the
National Center for Ecological Analysis and Synthesis (NCEAS) in Santa Barbara, CA,
where the authors of this paper (among others) attempted to link quantitative models
through the four disciplines (listed in Figure 1) to forecast the ecological consequences of
land use change over a 20-year period. Because of differences in quantitative
information and models across the disciplines summarized in Boxes one through four,
and Figure 3, it was not feasible to obtain quantitative predictions across all disciplines
(Pathway #3, Figure 2); for further details, see Nilsson (submitted). To circumvent this
limitation, solution pathway #6 was adopted (by default) in a study of the effects of
urbanization in Maryland watersheds on stream communities (Palmer et al. accepted). In
this instance, a combination of quantitative economic and hydrologic models, semiquantitative geomorphic models, and ecological concepts and statistical models can be
used to predict ecological responses to projected land use changes in streams exposed to
13
increased urbanization. Another example of pathway#6 is the procedure referred to as
Watershed Analysis, which is a suite of measurement techniques developed and used by
federal and state agencies, and private industry, to address the effects of timber harvest on
fishery resources (WNDR 1996). The designers of Watershed Analysis understood at the
outset that although questions might be quantitative in nature (e.g., what is the effect of a
landslide on fish population size in a particular stream), answers would take a qualitative
form (i.e., high, medium, or low impacts).
For many environmental variables, including sediment, it is often not feasible to
predict the effects of changing land uses on water quality because of unresolved
background noise originating from either stochastic processes (e.g., landslides, floods)
(Benda 1995) or historical legacies (e.g., previous land use change). Hence, certain
environmental attributes, such as suspended sediment transport, cannot be interpreted or
predicted accurately, particularly at small spatial and temporal scales (Pathway #4, Figure
2). This technical limitation may confound certain regulatory protocols. For example,
the use of total maximum daily loads (TMDLs) of, for example, nitrates or suspended
sediments in fluvial systems, is used as a measure of regulatory compliance (USEPA
1997). Unresolved natural variability in the flux of materials at different time scales and,
in some cases, the lack of direct connections between land uses and contaminant flux
rates, cast doubt on the accuracy and applicability of TMDL standards (Boyd 2001).
Despite the problems represented by solution pathways #1 through #4, in certain
instances, multidisciplinary approaches to a quantitative question will yield quantitative
answers (Pathway #5, Figure 2). For example, examining the effects of a dam on river
discharge and temperature involves a relatively straightforward analysis and, when used
14
in conjunction with information on species-specific habitat needs (e.g., stream depths,
current speeds, thermal regimes, and/or substratum size) can be used to predict impacts
on the aquatic biota, at times with a high degree of accuracy. In addition, statistical
approaches can be used to directly link changes in land use (top level in Figure 1) to
ecological impacts (lowest level in Figure 1) without dealing with the intermediate causal
steps in hydrology and geomorphology (Strayer et al. submitted).
Finally, questions addressing complex issues, such as the synergistic effects of
logging and over-fishing on anadromous fish stocks over the last 50 years, have taken the
form of qualitative questions with qualitative and contextual answers (dealing with the
direction of change, Pathway #7, Figure 2) (Jensen et al. 2000). Recognizing the
difficulties in obtaining quantitative predictions for some problems has fueled a recent
call for applying qualitative forms of knowledge as a basis for adaptively managing and
solving complex environmental problems (Walters and Korman 1999). Future
environmental analyses may be more concerned with general patterns and qualitative
understanding, where scientific uncertainty is recognized as part of the analysis (Reid
1998).
AVOIDING TRAIN WRECKS: CREATING SUCCESSFUL PATHWAYS
Several of the seven pathways do not lead to satisfactory solutions (e.g., #1 and
#2 lead to dead ends and #3, #4, and possibly #5 lead to inappropriate or vague answers).
To circumvent these problems, the solution space can be manipulated to produce useful
solutions. First, a question can be altered to fit the available knowledge structure of all
relevant disciplines, or the epistemological analysis may indicate that a particular
question should not be asked. For instance, a quantitative question can be changed to a
15
qualitative one to circumvent the bottlenecks associated with the vague or qualitative
nature of knowledge in specific disciplines. Instead of asking how many centimeters of
channel bed scour will result from a 10% increase in peak flows, the question can be
rephrased: Will the forecasted increase in peak flows lead to small, moderate, or large
changes in channel morphology? Are gravel beds or cobble bed channels more
susceptible to change? Scientific limitations impart a legitimacy to these types of “soft”
questions. Alternatively, the question can be scrapped altogether and replaced with a
question about large-scale and persistent alternations to floodplains. The latter question
can be answered more easily and confidently with less geomorphological and ecological
ambiguity.
Second, the solution pathway could be entered at a later disciplinary step in the
sequence to avoid unsolvable components (Figure 1). Similarly, the solution pathway
can be exited at an earlier step in the problem-solving sequence for the same reason. For
example, the environmental assessment protocol of Watershed Analysis (WDNR 1996)
enters the solution pathway at a later step (i.e., no forecasting of land use) and exits
earlier (i.e., biological effects were ignored) in the full problem solving sequence
depicted in Figure 1. In Watershed Analysis, land use impacts are evaluated as effects on
physical habitats under the simplifying assumption that physical habitat conditions are
tightly coupled to biological responses. Another alternative to disciplinary bottlenecks is
to develop statistical models that bypass specific disciplines used in the solution pathway
(Strayer et al. submitted).
Efforts to link disciplines that are focused on a particular environmental problem
might require new analytical strategies. For example, certain types of impacts of human
16
activities on naturally dynamic landscapes may only be discernible at large temporal and
spatial scales i.e., shifts in long-term probability distributions of processes such as flows
or erosion (Benda and Dunne 1997). Because long-term datasets are rare, simulation
models may be needed to identify large-scale and long-term patterns in human impacts
on natural landscapes, as well as the processes producing these patterns. Ideally, largescale models could be built on first principles derived from an understanding of the local
physical processes producing predicted patterns. There is a growing consensus, however,
that, at least in the near future, there will be theoretical and technical impediments to
developing large-scale environmental models based on an understanding and
mathematical formalization of small-scale processes, as well as problems in scaling up
from small-scale processes to large-scale results (Dooge 1986). To circumvent the
limitations associated with developing large scale models, investigators could ignore
some small-scale processes or subsume these processes within larger-scale
representations, a strategy commonly used by physicists and referred to as coarse graining (Gell-Mann 1994). In practice, this often requires combining mathematical
syntheses, computer simulations, empirical information, and theoretical logic available at
small scales to produce predictions and understanding at large scales. This approach will
not produce precise predictions about future states at individual sites, but rather will
produce new, testable hypotheses dealing with the large-scale aggregate responses of
various environmental attributes, including vegetation, erosion, channel conditions, etc.,
to environmental change (Benda et al., 1998). This aggregated, large-scale approach has
historical antecedents in particular large-scale problems addressed by a variety of
disciplines, including in economics (Mendelsohn, Nordhaus, and Shaw 1993), hydrology
17
(Blosch and Sivapalan 1995), geomorphology (Benda and Dunne 1997), and ecology
(Maurer 1999).
Finally, although we have focused here on single pathways leading from
questions to solutions (as in Figure 1), the diversity of knowledge types and levels of
accuracy brought to bear on a problem can yield a diversity of parallel, alternative
solution pathways. For example, economic and hydrological predictions may be
constrained in space and time but those predictions may produce broader interpretations
and predictions in other fields. The projection of increases in discharge due to logging,
for instance, can produce predictions of changing sediment transport at the reach scale or,
more broadly, alterations in erosion and flooding processes at larger scales (centuries
over entire watersheds or landscapes). Furthermore, these two scales of geomorphic
response may be addressed at several levels by riverine ecologists. For example, the
effects of changes in sediment transport on the aquatic biota at the reach scale could be
evaluated on short-term habitat structure (e.g., channel sedimentation) or longer-term
dynamics (e.g., changes in the probability distribution of bed scour). The diversity of
parallel, alternative pathways leading from questions to knowledge may prove useful for
circumventing limitations on the qualitative or quantitative accuracy of predictions
produced by “bottleneck” disciplines and may guide new approaches for solving
environmental problems.
AVOIDING PATHWAYS TO NOWHERE: TRAIN WRECKS
By not recognizing limitations to interdisciplinary problem solving (e.g.,
Pathways #1, 2, 3, and 4, Figure 2), we may excessively rely on science as the ultimate
18
arbiter in environmental debates. This can lead to unrealistic management practices and
policies and may elevate one form of knowledge over another, such as precise, numerical
solutions over imprecise, qualitative answers. Without a recognition of these limitations,
managers, policy-makers, and the public may harbor false expectations for solving
environmental problems using current science, or worse, a lack of appreciation of
constraints to interdisciplinary approaches may guarantee problem-solving failures.
In addition, if the absence of appropriate theory, models, and empiricisms in one
or more fields is ignored, inappropriate studies may continue with the intent of forcing an
answer to a complicated question. Existing theories and models, however, may be
inadequate to improve our understanding or predictive capabilities for a given problem,
and progress in addressing a question in one field (e.g., geomorphology) may be offset by
the increased complexity of the problem in other fields (e.g., ecology), particularly at
small scales (Dooge 1986; Walters and Korman 1999). This can lead to “smoke
screens”, defined as a call for additional studies which can delay policy decisions and
management practices, while allowing continued exploitation of natural resources.
Similarly, science can also be used as an unrealistic “litmus test”, defined as the forcing
of a question – knowledge pathway (typically a quantitative one) to justify a particular
environmental doctrine. For example, it is not feasible to predict with any degree of
precision the effects of particular levels or patterns of urbanization or logging on the
extinction of a species over a period of many decades to centuries. Hence, questions that
require knowledge that is not available, or that will not be forthcoming in the near future,
should not be placed in a pivotal and referee position.
19
CONCLUSIONS
Conducting an epistemological analysis of the scientific disciplines involved with
environmental problems may ease some of the difficulties, tensions, and train wrecks that
are often encountered. In particular, by defining solution pathways that identify both the
limits and opportunities in environmental problem solving, an interdisciplinary team can
help to establish realistic expectations about the role of science, the adequacy of
information, and the effectiveness of management policies in dealing with a particular
problem. Scientists can help guide and direct debates surrounding the use of scientific
knowledge in environmental risk assessment and in setting management policy. As a
case in point, there have been admissions regarding the efficacy of global climate
modeling in the environmental debate surrounding global warming. Although current
climatic models predict general weather patterns, detailed predictions at specific locations
remain out-of-reach, despite the great social need for such information (EOS 1999).
Similar admissions of scientific limitations (and opportunities) in the scientific disciplines
dealing with other interdisciplinary problems, such as those covered in this paper, are
urgently needed.
Delineating clear solution pathways by evaluating the structures of scientific
knowledge allows interdisciplinary teams to successfully identify options for solving
problems. These options may include changing the questions or measurement protocols,
rejecting certain types of questions, legitimizing qualitative and new forms of knowledge
and approaches, and identifying gaps in theories, models or data. In addition,
epistemological analyses can motivate necessary discussions about historical, contextual,
20
and qualitative understanding and how they can be integrated into risk assessment and
decision-making.
Ultimately, environmental goals and management are based on social values.
Science informs society, but is not the sole source of guidance for management policy,
especially where uncertainty is high, problems are complex, and solutions depend on
trade-offs among societal values. Science is an important contributor to the solution of
environmental problems but its contributions will depend on the ability of scientists to
clearly articulate the role, limitations, and potential of science for evaluating the
environmental consequences of human activity.
ACKNOWLEDGEMENTS
Funds from the National Science Foundation, the Environmental Protection Agency (Star
Grants) and the National Center for Ecological Analysis and Synthesis (NCEAS)
supported the authors’ activities, as well as a related team of economists, hydrologists,
geologists, and biologists working on hydrological forecasting at NCEAS. The U. S.
Bureau of Land Management (Portland, OR) supported the lead author in manuscript
preparation. Important insights and feedback were provided by Robert Naiman, Larry
Band, Ed Beighley, Lisa Thompson, Gordon Grant, Michael Hanemann, Tony Ladson,
David Maidment, Peter Moyle, Gilles Pinay, and Dave Strayer. Aaron Reeves aided with
graphic design.
References Cited
Alonso, W. 1964. Location and Land Use, Cambridge: Harvard University Press.
Anas, A, Kim, I. 1996. ‘General equilibrium models of polycentric urban land use with
endogenous congestion and job agglomeration,’ Journal of Urban Economics, 40(2),
232-256.
ASCE Task Committee on River Width Adjustment, 1998. Processes and
21
mechanisms. Journal of Hydraulic Engineering, 124:881-902.
Band, LE, Tague, CL, Brun, SE, Tenenbaum, DE, Fernandes, RA. 2000. “Modeling
watersheds as spatial object hierarchies: Structure and Dynamics”, Transactions in GIS,
181-196.
Baird, A. 1999. Modelling, in Eco-hydrology: Plants and water in terrestrial and aquatic
environments, Baird, A.J. and Wilby, R.L. (eds), Routledge, 300-345.
Bedient, PB, Huber, WC. 1988 Hydrology and Floodplain Analysis, Addison-Wesley
Publishing Company, Reading, MA.
Benda, L. 1995 Stochastic geomorphology: implications for monitoring and interpreting
erosion and sediment yields in mountain drainage basins. In: Symposium on the Effects
of Scale on Interpretation and Management of Sediment and Water Quality; International
Association of Hydrological Sciences, Boulder, Co.
Benda, L, Dunne, T. 1997 Stochastic forcing of sediment supply to the channel network
from landsliding and debris flow, Water Resources Research, Vol.. 33, No.12, 2849-2863
Benda, L, Miller, D, Dunne, T, Agee, J, Reeves, G. (1998) Dynamic Landscape Systems,
Pages 261 – 288 in Naiman, R, Bilby, R (eds) River Ecology and Management: Lessons
from the Pacific Coastal Ecoregion, Springer-Verlag.
22
Blosch, G, Sivapalan, M. 1995. Scale issues in hydrological modelling: A review, Pages
9 – 46 in Kalma, J.D., Sivapalan, M. (eds) Scale Issues in Hydrological Modelling, John
Wiley and Sons.
Bosch JM , Hewlett. 1982 A Review of catchment experiments to determine the effect of
vegetation changes on water yield and evapotranspiration. Journal of Hydrology, 55: 323.
Bockstael, N E. 1996. ‘Modeling economics and ecology: the importance of a spatial
perspective’, American Journal of Agricultural Economics, 78(5), 1168-80.
Boyd,R, Gasper, P, Trout J. D. (eds.) 1991. The philosophy of science. Massachusetts
Institute of Technology Press, Cambridge, MA.
Fitzsimmons, AK. 1994. Federal ecosystem management: a “train wreck” in the making.
Policy Analysis 217.
Donigan, ASD, Bicknell, BR, Imhoff, JC. 1995. `Hydrological Simulation Program Fortran (HSPF)'. In: Singh, V.P. (ed.), Computer Models of Watershed Hydrology, Water
Resources Publications, Highlands Ranch, Colorado, US, pp 395-442.
23
Dooge, JCI. 1986. Looking for hydrologic laws. Water Resources Research 22(9), 46S –
58S.
duBoys, MP, 1879. `Etudes du regime et l`action exercee par les eaux sur un lit a fond de
graviers indefinement affouilable. Annals des Ponts et Chaussees, Series 5, 18, pp.141195.
Dunne, T, Leopold, LB. 1978. Water in Environmental Planning, W.H.Freeman and
Company.
EOS 1999. Transactions, American Geophysical Union, V 80, No 39, September (Position
statement on climate change and greenhouse gases).
Feyerabend, P. 1978. Science in a Free Society. Verso, London
Fisher, SG. 1997. Creativity, idea generation, and the functional morphology of streams.
Journal of the North American Benthological Society 16: 305-318.
Ford, ED. 2000. Scientific methods for ecological research. Cambridge University Press,
Cambridge.
24
Gaff, H., DeAngelis, DL, Gross, LJ, Salinas, R, Shorrosh, M. 2000. A dynamic landscape
model for fish in the Everglades and its application to restoration. Ecological Modeling
127, 33 – 52.
Gell-Man, M. 1994. The Quark and the Jaguar: Adventures in the Simple and Complex.
W. H. Freeman and Company, New York. 329pp.
Gomez, B, Church, M, 1989. An assessment of bed load sediment transport formulae for
gravel-bed rivers, Water Resources Research, 25,1161-1186.
Hammer, TR. 1972. Stream channel enlargement due to urbanization. Water
Resources Research, v. 8, 1530-1540.
Herrick, C. 2000. Predictive modeling
of acid rain: obstacles to generating useful
information. Pp. 251 – 268 IN: Sarewitz, D., R. A. Pielke, Jr., and R. Byerly, Jr. (eds)
Prediction: science, decision making, and the future of nature. Island Press. Washington,
D.C.
Hewlett, JD, Hibbert, AR. 1967. Factors affecting the response of small watersheds to
precipitation in humid regions, in Forest hydrology, Sopper, W.E., and Lull, H.W. (eds),
Pergamon Press, 275-290.
25
Irwin, E, Bockstael, N. 2001. “Interacting agents, spatial externalitities and the evolution
of residential land use patterns,” under review at Economic Geography.
Jakeman, AJ, Hornberger, GM, Littlewood, IG, Whitehead, PG, Harvey, JW. 1992. `A
systematic approach to modelling the dynamic linkage of climate, physical catchment
descriptors and hydrologic response components'. Mathematics and Computers in
Simulation, 33:359-366.
Jensen, ME, Reynolds, K, Andreasen, J, Goodman, I A. 2000. A knowledge-based
approach to the assessment of watershed condition.
Environmental Monitoring and
Assessment 64 (1), 271-283.
Junk, WJ, Bayley, PB, Sparks, R E. 1989. The flood pulse concept in river-floodplain
systems. IN: D. P. Dodge (ed.), Proceedings of the International Large River Symposium.
Canadian Special Publications in Fisheries and Aquatic Sciences. 106.
Kareiva, PM, Kingsolver, JG, Huey, RB (eds). 1993. Biotic interactions and global
change. Sinauer Associates, Sunderland, MA. [Either eliminate or include the number of
pages for all other books.]
Klemes, V. 1988. Conceptualization and scale in hydrology, Journal of Hydrology, 65, 123.
26
Krugman, P. 1996.The Self-Organizing Economy, Cambridge, MA: Blackwell
Publishers.
Kuhn, TS. 1970. The Structure of Scientific Revolutions, second edition (University of
Chicago Press: Chicago).
Landis, J. 1995. ‘Imagining land use futures. Applying the California Urban Futures
Model,’ Journal of the American Planning Association, 61(4), 438-457.
Latour, B. 1993. We have never been modern. Harvard University Press, Cambridge MA
Mooney, H.A., and R.J. Hobbs, editors. 2000. Invasive species in a changing world.
Island Press, Washington D.C. 480 pp
Leopold, LB, Wolman, WG, Miller, JP. 1964. Fluvial Processes in Geomorphology. W.
H. Freeman, San Francisco, 522pp.
Maurer, BA. 1999. Untangling Ecological Complexity. University of Chicago Press.
Chicago, IL. 251pp.
Minshall, GW. 1984. Aquatic insect-substratum relationships. Pgs. 358-400 In V. H.
Resh and D. M. Rosenberg (edtors), The Ecology of Aquatic Insects. Praeger. New York.
27
Moglen, GE, Beighley, RE. Submitted. Spatially explicit hydrologic modeling of land use
change. Journal American Water Resources Association.
Muth R. 1969. Cities and Housing. Chicago: University of Chicago Press.
Mooney, HA, Hobbs, RJ. (eds) 2000. Invasive species in a changing world. Island Press,
Washington D.C.
Naiman, RJ. 1999. A perspective on interdisciplinary research. Ecosystems 2: 292-295.
National Science Board. 2000. Environmental science and engineering for the 21st
century. National Science Foundation Press, Washington, D.C.
Newbold, JD, Elwood, JW, Oneill, RV, Sheldon, AL. 1983. Phosphorus Dynamics in a
Woodland Stream Ecosystem - a Study of Nutrient Spiralling. Ecology 64: 1249-1265.
Nilsson, CJ, Pizzuto, GE, Moglen, GE, Palmer, MA, Stanley, EH, Bockstael, NE,
Thompson, LC. (submitted) Ecological forecasting and running-water systems:
challenges for economists, spatial analysts, hydrologists, geomorphologists, and
ecologists. Ecological Applications.
28
Palmer, MA, Covich, AP, Finlay, BJ, Gibert, J, Hyde, KD, Johnson, RK, Kairesalo, T,
Lake, S, Naiman, RJ, Ricci, C, Sabater, F, Strayer, D. 1997. Biodiversity and ecosystem
processes in freshwater sediments. Ambio 26: 571-577.
Palmer, MA, Covich, AP, Lake, S, Biro, P, Brooks, JJ, Cole, J, Dahm, D, Gibert, J,
Goedkoop, W, Martens, K, Verhoeven, J, van de Bund, WJ. 2000. Linkages between
aquatic sediment biota and life above sediments: potential drivers of biodiversity and
ecological processes? BioScience 50:1062-1075.
Palmer, MA, Moglen, GE, Bockstael, NE, Brooks, S, Pizzuto, PE, Wiegand, C, Vanness,
K. Accepted. The Ecological consequences of changing land use for running waters: the
surburban Maryland case. Bulletin of the Yale School of Forestry and Environmental
Studies. Vol __ (October 2001 issue)
Pickett, STA, Burch, WRJr, Grove, JM. 1999. Interdisciplinary research: maintaining the
constructive impulse in a culture of criticism. Ecosystems 2: 302- 307.
Pickett, STA., Kolasa, J, and Jones, CG. 1994. Ecological understanding: the nature of
theory and the theory of nature. San Diego: Academic Press.
Poff, NL, Allan, JD, Bain, MB, Karr, JR, Prestegaard, KL, Richter, BD, Sparks, RE,
Stromberg, JC. 1997. The natural flow regime: a paradigm for river conservation and
restoration. BioScience 47: 769-784.
29
Poff, NL, Ward, JV. 1990. Physical habitat template of lotic systems: recovery in the
context
of
historical
patterns
of
spatiotemporal
heterogeneity.
Environmenal
Management 14: 629-645.
Reid, LM. 1998. Cumulative watershed effects and watershed analysis. Pages 476 – 501
in Chapter 19 in Robert Naiman and Robert Bilby (eds) River Ecology and Management:
Lessons from the Pacific Coastal Ecoregion, Springer.
Rodriquez-Iturbe, I, Valdes, JB. 1979. The geomorphic structure of hydrologic response,
Water Resources Research, 15: 1409-1420.
Sarewitz, D, Pielke, RA Jr, Byerly, R Jr. (eds). 2000. Prediction: science, decision
making, and the future of nature. Island Press. Washington, D.C.
Schumm, SA. 1977. The Fluvial System. John Wiley and Sons, New York. 338pp.
Schumm, SA. 1991. To Interpret the Earth: Ten Ways to be Wrong. Cambridge
University Press,132pp.
Schaffer, S. 1996, Contextualizing the canon. Pp. 207-230 IN: Galison, P. and D.J.
Stump. The disunity of science: boundaries, contexts, and power. Stanford University
Press, Stanford, CA.
30
Schlosser, IJ. 1987. A conceptual framework for fish communities in a small warmwater
stream. Pgs. 17-24 In
W. J. Matthews and D. C. Heins (edtors), Community and
Evolutionary Ecology of North American Stream Fishes. University of Oklahoma Press.
Norman.
Sedell, JR, Froggatt, JL. 1984. Importance of streamside forests to large rivers: the
isolation of the Willamette River, Oregon from its floodplain by snagging and streamside
forest removal. Verhandlungen-Internationale Vereinigung fuer Theorelifche und
Angewandte Limnologie (International Association of Theoretical and Applied
Limnology). 22: 1828-1834.
Sepkoski, J, Rex, MA. 1974. Distribution of freshwater mussels: coastal rivers as
biogeographic islands. Systematic Zoology 23: 165-188.
Strayer, DL, Beighley, RE, Thompson, LC, Brooks, S, Nilsson, C., Pinay, G, Naiman,
RJ. (submitted) The ecological consequences of altered hydrological regimes. Ecological
Applications.
Terzahi, K, 1936. Stability of slopes in natural clay. Proceedings First International
Conference of Soil Mechanics. Foundation Engineering #1. Pp.161-165.
31
Townsend, CR 1989. The patch dynamics concept of stream community ecology. Journal
of the North American Benthological Society 8: 36-50.
Ulanowicz, RE. 1997. Ecology, the ascendent perspective. Columbia University Press.
New York.
USEPA 1997. Compendium of tools for watershed assessment and TMDL development.
EPA 841-13-97-006. U. S. Environmental Protection Agency, WA DC.
Vannote, RL, Minshall, GW, Cummins, KW, Sedell, JR, Cushing, CE. 1980. The river
continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37: 130-137.
Vaughn, CC. 1997. Regional patterns of mussel species distributions in North American
rivers. Ecography 20: 107-115.
Walters, C, Korman, J. 1999. Cross-scale modeling of riparian ecosystem responses to
hydrologic management. Ecosystems 2(5):411-421.
Washington Department of Natural Resources 1997. Standard Methodology for
Conducting Watershed Analysis. Version 4.0. Washington Forest Practices Board.
Wear, DN. 1999. Challenges to interdisciplinary discourse. Ecosystems 2: 299-301.
32
Wigmosta, MS, Vail, LW, Lettenmaier, DP. 1994. `A distributed hydrology-vegetation
model for complex terrain'. Water Resources Research 30:6:1665-1679.
33
BOX 1: Economics
Economics is the study of how society combines natural resources, human and man-made
capital and transforms them into outputs, as well as how these outputs are distributed
across, and valued by, members of society. It involves understanding human behavior as
it relates to the allocation of society’s scarce resources both in the context of markets (for
land, labor, goods, services, capital) and outside conventional markets (with regard to
such things as public goods, environmental amenities, pollution, etc.). Finally, it deals
with how behavior changes in the face of regulatory policy. In attempting to forecast
what is likely to happen to land use patterns in the watershed, a study of economics can
provide a translation between policy actions and regional economic forces on the one
hand and local land use outcomes on the other. At times, the behavior of human agents
as they contribute to and respond to markets signals is modified by regulation.
As with other sciences, economics research generates theories of qualitative relationships
that are derived from first principles and which provide useful insights. For example,
will increases in real incomes lead to more or less dense development? Would changing
the terms of agricultural preservation programs lead to a more or less fragmented
landscape? Early theories describe land use patterns relative to exogenous city centers
(e.g. the bid-rent model of Alonso 1964; Muth 1969) and ones that are more recent take
interactions among other exogenous agents into account (Krugman 1996). Similar to
other sciences, economic research produces quantitative answers by measuring and
statistically analyzing existing data to estimate the parameters of the theoretical
relationships. Detailed simulation models of household and industry movements among
34
transportation zones have been conducted for a few specific cities (e.g. Anas and Kim
1996; Landis 1995). More detailed and statistically testable empirical models of
interaction are developing because of the increase in economic GIS data (Irwin and
Bockstael 2001).
Temporal scale is important in economic models because the response of economic
agents to exogenous stimuli will change as human agents have time to adjust. Typically
economists have had more success modeling and forecasting short run adjustments
(months), because the shorter the time period the more types of behavior can be held
constant (Figure 3). Given the length of time that it takes to accomplish a change in land
use, however, land use change would typically be modeled on an annual basis and
forecasted out 3 – 5 years. What plagues longer run forecasting is not only uncertainty
about behavioral adjustments, but also uncertainty about additional exogenous stimuli
that might further disturb the economic system (global markets, weather, social
movements, etc.).
Demographics, land, and housing stocks vary dramatically from one region to another.
While qualitative models may generalize over regions, quantitative models will not. The
geographic scale of any quantitative study is likely to be the scale of the housing market –
a city (major employment center) and its environs (e.g. 1000-2000 sq. miles), which does
not generally align itself with a watershed (Figure 3). Restricting the economic analysis
to a watershed is inappropriate because within a housing market events or regulations can
easily deflect development from one watershed to another. This is especially likely to
35
happen where land markets span several political jurisdictions and growth management
policies vary over these jurisdictions. If the study is careful and incorporates more
recently available spatially explicit (GIS data), it can provide more locally specific
forecasts of land use change but these will be of a probabilistic nature.
BOX 2: Surface Water Hydrology
Historically hydrology emerged as an engineering discipline focused on the construction
and maintenance of urban water supply and irrigation systems, on flood protection, and,
to a lesser degree, on the maintenance of navigable channels. Traditional engineering
approaches use empirical relationships and quantitative techniques (i.e. flow forecasting)
primarily for designing structures rather than for understanding natural processes
(Klemes 1988). In the last few decades, however, there has been mounting scientific
interest in understanding hydrologic processes which are an integral part of ecosystems,
leading to the development of conceptual, lumped, and spatially distributed hydrologic
models (reviewed in Baird, 1999). These models extended the focus of hydrology from
the engineering analysis of streamflow patterns to an analysis of hydrological processes
within catchments (i.e., evaporation, interception, infiltration, etc.) that influence both
water quality and water quantity. Of the four disciplines considered here, hydrology is
the most quantitative in an engineering sense, although general theories, such as variable
source areas (Hewlett and Hibbert, (1967), the geomorphic unit hydrograph (RodriquezIturbe and Vales, 1979) and the hydrologic cycle provide important foundations for the
quantitative approaches.
36
Empirical analyses of discharge data (e.g., flood frequency analysis) can be conducted
across a broad range of spatial scales (101 to 106 km2). Because stream flow integrates a
diverse set of watershed characteristics, including many affected by land use, flood
frequency analysis generally is not used to predict hydrological changes due to land use
changes. However, empirical comparisons of discharge data from paired catchments
(i.e., disturbed vs. control basins) can be used to examine the effects of land use changes
on hydrological regimes (i.e. Bosch and Hewlett, 1982), but natural variability in
precipitation, topography, land use, and channel structure limit the degree to which data
from these field experiments can be extrapolated to other sites. Similarly, the
development of empirical relationships between discharge and land use (i.e. the rational
method) have also been widely used, however, simplifying assumptions (i.e. such as a
linear relationship between land use and runoff) result in considerable uncertainty and
limited applicability (Dunne and Leopold, 1978).
Modeling approaches have been developed as an alternative to these empirical
approaches. One approach applies rainfall-runoff measurements to parameterize models
that predict streamflow outputs for a collection of land-use, soil, and topographic
conditions, often incorporated into a GIS framework at the 101 to 103 km2 scale, a
strategy referred to as ‘lumped modeling’ (IHACRES, Jakeman et al., 1992; HSPF,
Donigan et al., 1995). Lumped models, however, ignore the role of the spatial
organization of surface and subsurface flow paths as well as non-linearities in
relationships among vegetation, soil moisture, and the atmosphere. These omissions
reduce the accuracy of these models for predicting the hydrological impact of land use
37
changes particularly when dealing with small-scale watershed hydrologic responses or
with the estimation of water quality impacts, and with predicting hydrological responses
under altered climatic regimes. Physically -based spatially distributed models (i.e.
DHSVM (Wigmosta et al., 1994, RHESSys (Band et al., 2000)) attempt to overcome
these limitations by incorporating the spatial distribution of detailed hydrologic processes
(e.g., infiltration, evapotranspiration) across a landscape. Although very realistic and
more representative of physical processes, these spatially distributed models require
detailed data for parameterization and calibration which limits either the scale of their
application to small watersheds (101 to 103 km2) or the certainty associated with
forecasts.
At larger scales, hydrologic response becomes increasingly dependent on stream channel
characteristics, which can also be subject to human modifications (i.e. construction of
dams, straightening and diking of channels). There is a similar suite of hydrodynamic or
hydrologic routing models that relate channel characteristics with flow (reviewed in
Bedient and Huber, 1988). Examining land use impacts, of course, must consider both
effects to the land surface and to the channel and thus consider both surface hydrology
and channel routing.
All of the hydrological approaches outlined above assume that topography, including
channel morphology, remains constant through time, thus limiting their application to
time scales where this assumption is valid (channel morphology may change over a
single storm). In addition, lumped and spatially distributed models rely on precipitation
data that are highly variable in both time and space. Because the average and spatial
38
accuracy of precipitation data are limited by the spatial coverage of rain gauges, model
outputs for regions with low rain gauge density (the typical case) or regions with modeled
climatic conditions (i.e., Global Climate Models) contain appreciable uncertainty (low
accuracy and precision), particularly in small watersheds.
BOX 3: Fluvial Geomorphology
The discipline of geomorphology originated in qualitative theories of landscape evolution
proposed by physical geographers in the 19th and early part of the twentieth centuries. In the mid
to latter half of the twentieth century, geomorphologists began to emphasize quantitative
knowledge of landform evolution using field estimates of erosion and deposition, mathematical
models, and remote-sensed imagery. Engineering has also made major contributions to
quantitative approaches to geomorphology by addressing problems related to sediment transport
(duBoys 1879) and landslides (Terzagi 1936).
Sediment transport theory provides a quantitative foundation for addressing issues related
to the entrainment, movement, and deposition of individual sediment grains, representing
the smallest spatial and temporal scales addressed by fluvial geomorphology in the
context of land use change (Figure 3). Unfortunately, knowledge of sediment transport
processes in stream channels cannot be scaled up easily to channel networks or whole
watersheds because of data limitations, limiting the value of this knowledge for assessing
the impacts of land use change.
39
Sediment transport models also are applied over length scales of reaches to segments (104
to 101 km) to predict sediment flux over periods of years to decades (Figure 3). Such
models provide quantitative predictions with relatively low precision (+/- 100 - 1000%),
even when calibrated using empirical data from the study site. Channel morphology is
typically treated as constant over time because bank erosion, changes in channel
morphology, and sediment storage on the floodplain are not included in the models.
The channel cross-section represents the next largest scale of analysis. Many studies
have attempted to explain the width, depth, and slope of rivers either empirically using
“hydraulic geometry” regression equations (e.g., Leopold et al., 1964) or theoretically
using principles of sediment transport mechanics (ASCE 1998). Because temporal
variability in flow regimes and channel form are not included in any of these approaches,
they cannot readily be used to study the evolution of channel form following changes in
land use. The concept of channel metamorphosis (Schumm 1977) represents a useful,
although qualitative application of hydraulic geometry equations. These may be used to
evaluate the effects of increases or decreases in sediment and water discharge on channel
form (general magnitude and direction of change).
Case studies documenting changes in channel morphology linked to a record of land use
are primarily descriptive and have been conducted at scales of reaches to small
watersheds over decades to centuries (Figure 3) (Hammer, 1972). A large number of
descriptive studies of land use impacts on the geomorphological characteristics of
streams and watershed have been conducted in diverse landscapes, resulting in a number
40
of general, qualitative principles that can provide highly accurate predictions that are
primarily descriptive. For example, increased storm runoff due to increased impervious
areas in watersheds that have been converted to urban uses often leads to significant
channel enlargement (Hammer, 1972).
Simulation models have been constructed to examine geomorphological changes at large
spatial and temporal scales (i.e., 101 to 103 years, 101 to 103 km2; Figure 3). Such models
incorporate simplified descriptions of key processes (Benda and Dunne, 1997). These
models couple hillslope erosion with fluvial processes and have the potential to track the
spatial and temporal evolution of landscapes and channel networks in response to natural
and land use disturbances (i.e., wildfire or logging). However, they are not designed to
make precise geomorphological predictions at small scales, but rather generate a
theoretical understanding of interrelationships among climate, topography, lithology, land
cover, and sediment supply and transport.
BOX 4: Riverine Ecology
The field of riverine ecology developed rapidly during the 1970s. This field is
closely aligned with, and reflects advances in, general ecology, particularly
advances in population and community ecological theory, biogeography, and
biogeochemistry. Because running-water ecosystems are intimately associated
with their catchments via fluxes of water, sediment, elements, and organic matter,
riverine ecological theory is also closely linked to concepts from hydrology and
geomorphology. Riverine ecology has focused largely on the recognition of
41
ecological patterns, the identification of environmental problems, and
phenomenology (Fisher 1997). Qualitative concepts dealing with ecological
organization are abundant whereas theoretical advances that allow for quantitative
predictions are few. Of the dominant concepts used by riverine ecologists, some
are specific to particular space and time scales, whereas others may be applied at
multiple scales (Figure 3).
Probably the most well known “theory” in riverine ecology is the River
Continuum Concept that posits that downstream changes in channel geometry,
riparian influences, and organic matter result in predictable ecological changes
(Vannote et al., 1980). Because the RCC is an equilibrium concept and does not
consider temporal changes in ecological responses, such as changes over the
seasons, across years with different weather conditions, or after disturbances, it
addresses a narrow space – time domain (Figure 3). As a result, the concept is
applied very qualitatively to short time scales (seasonal). The Habitat Template
Concept (Schlosser 1987, Poff and Ward 1990) is based on niche theory that is
applied across broad space-time scales (watersheds over 102 yrs). The concept is
used to examine species distributions across river landscapes with varying
physical, chemical, and biological conditions. Patch Dynamics Theory also views
the riverine landscape as a mosaic of patches with different environmental
conditions and emphasizes the roles of disturbance and succession in driving
spatial patterns in biological characteristics through time (Townsend 1989). Only
rarely can these concepts or theories be applied to the large spatial scales
42
examined by hydrologists and geomorphologists (e.g., drainage networks);
however, they can be used to examine the local distributions of sedentary
organisms or the basin-wide distributions of mobile, long-lived species, such as
migratory fish.
Qualitative models of species distributions at long time and large spatial scales
has benefited from Metapopulation Theory and Island Biogeography Theory. The
former body of theory deals with how local populations blink on and off owing to
local extinction and re-colonization from other populations, facilitating
predictions of population’s persistence in habitats fragmented by human activity.
Although Metapopulation Theory has provided conceptual insights into the
dynamics of stream species, quantitative predictions have proven elusive because
there are insufficient data available to calibrate models (e.g., Vaughn 1997).
Island Biogeography has been applied to various kinds of virtual islands in
streams, ranging from individual rocks (e.g., Minshall 1984 to basins (Sepkowski
and Rex 1974).
Concepts that have been used to understand the distribution of types of organisms
(e.g., functional groups) include models that deal with responses of organisms to
disturbance. For example, the Flood Pulse Concept (Junk et al. 1989) deals with
ecological responses to hydrological changes over seasons in river floodplains,
and relies on a comprehensive description of the routing of water and sediment
(Figure 3). The Natural Flow Regime Concept (Poff et al. 1997) is a regional
43
model couched in terms of hydrological regimes that are set by natural climatic
and topographic conditions. The Patch Dynamics Concept, addressed above,
deals with ecological responses to disturbance at more local scales. Concepts are
generally qualitative because they predict directional changes in ecological
responses to increased disturbance (e.g., diversity will increase or production will
decline as natural or anthropogenic disturbances increase).
Quantitative models in riverine ecology usually deal with small space and time scales.
The Nutrient Spiraling Concept was developed specifically to examine material cycling
and nutrient uptake and release within reaches (Newbold et al. 1983). Because nutrient
transport and uptake depend on flow and biological conditions, and channel morphology,
the mathematical form of the Nutrient Spiraling Concept incorporates hydrologic
variables and applies to precise hydro-chemical measurements. At slightly larger scales,
the In-stream Flow Incremental Methodology (IFIM) attempts to quantify how changes
in flow regimes will affect fish populations by coupling empirical relationships between
fish distributions and physical conditions (current velocity, substratum size, depth) with
forecasted hydrological changes. IFIM, however, is only useful over a narrow range of
spatial scales, requires large empirical databases, may ignore important variables
affecting fish distributions (e.g., food supplies), and may produce predictions with low
accuracy and precision.
44
Figure Captions
Figure 1. An interdisciplinary solution space is created when the knowledge structures of
each contributing discipline (Figure 3) are compared, essentially an epistemological
exercise. The mapping of the knowledge structures according to the space and time
scales at which their knowledge applies is illustrated in the series of surfaces labeled
“knowledge structures”. The shaded areas on each of the four surfaces indicate an
increasing range of space-time scales from economics through ecology that corresponds
to the knowledge structures in Figure 3. Superimposing different forms of knowledge
available in each discipline identifies commensurable scales and accuracy associated with
possible solutions, and the potential for train wrecks. A finite set of question –
knowledge pathways can be identified (Figure 2), as well as other strategies, such as
avoiding certain types of questions, legitimizing qualitative and new forms of knowledge,
and identifying gaps in theories, models, and data.
Figure 2. The set of question – knowledge pathways that were identified using the
solution space in Figure 1.
Figure 3. An epistemological analysis addresses the origin, nature, methods, and limits of
knowledge within each contributing discipline. Mapping that analysis for each of the
disciplines in our example, namely environmental economics, surface water hydrology,
geomorphology, and riverine ecology, is used to identify differences in space and time
scales, levels of accuracy, and resolutions (quantitative vs. quantitative). This process
45
creates the solution space in Figure 1. The similarities and differences among the
knowledge structures yields a finite set of question – knowledge pathways listed in
Figure 2.
46
(enter later)
Disciplinary Sequence
Question
Economics
Knowledge
Temporal Scale
Hydrology
Knowledge
Temporal Scale
Geomorphology
(exit early)
Knowledge
Temporal Scale
Riverine
Ecology
Knowledge
Answer
Temporal Scale
Interdisciplinary Solution Space
Figure 1.
47
Figure 2.
OUTCOMES FOR
INTERDISCIPLINARY PROBLEM SOLVING
(1) Science is unavailable
(2) Science is available
but not at the scale required
(3) Quantitative question does
not yield quantitative answer
Question posed requires knlowedge not available from
theories, models, or empirical studies; may
require entering and leaving array at different levels
Question is posed at spatial and temporal scales
that lie outside the range of scales addressed
by available theor ies, models, and empiricisms
Due to unequal quantitative abilities across
disciplines there is a weak link in the chain
of knowlege
(4) Quantitative question produces
quantitative answer that cannot be
measured empiriically
Due to limitations in measurement technologies or
because of high spatial variability in
the environmental attribute
(5) Quantitative question produces
quantitative answer with varying
precision acorss disciplines
Quantitative knolwedge in place to answer question
in numerical form; however, precision and accuracy
highly variable among disciplines and usually low
for complex ecosystem questions
(6) Quantitative question answered
in quantitative terms
(7) Qualitative question answered
in qualitative terms
Knowledge structure of relevant disciplines
precludes quantitative solutions at all levels
and a qualitative outcome is chosen
Question cannot be posed in quantitative terms,
or is chosen not to or knowledge structure of
disciplines dictates a qualitative solution
48