2
Adaptive Management Theories, Frameworks, and Practices

INTRODUCTION

Formal development of adaptive management as an approach for natural resources management can be traced back to the 1970s and to research conducted at the International Institute for Systems Analysis (IIASA) in Laxenburg, Austria (see Holling, 1978). As mentioned in the previous chapter, adaptive management draws from concepts within many different disciplines. Part of adaptive management’s philosophical foundations, for example, lie within the field of industrial operations theory (Johnson, 1999; see also Everett and Ebert, 1986). Although Holling’s seminal 1978 volume emphasizes ecosystem dynamics, it includes references to macroeconomics (Hafele and Burk, 1976), decision theory (Keeney, 1977), organizational behavior (Cyert and March, 1963), and policy analysis (Brewer, 1975). Thus, even in its articulation by ecological scientists in the late 1970s, adaptive management possessed strong interdisciplinary roots. Adaptive management seeks insights into the behavior of ecosystems that are utilized by humans, and it draws upon theories from ecosystem sciences, economics and social sciences, engineering, and other disciplines. Adaptive management incorporates and integrates concepts such as social learning, operations research, economic values, and political differences with ecosystem monitoring, models, and science. Applications of adaptive management principles within the Corps of Engineers to date have focused on aquatic and hydrologic systems. Although this report encourages the Corps to consider ways in which adaptive management principles could be applied in other parts of its work program, as applications within the agency to date have focused on ecosystem restoration, these experiences are emphasized within this report.

Adaptive management does not postpone actions until “enough” is known about a managed ecosystem (Lee, 1999), but rather is designed to support action in the face of the limitations of scientific knowledge and the complexities and stochastic behavior of large ecosystems (Holling,



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 19
Adaptive Management for Water Resources Project Planning 2 Adaptive Management Theories, Frameworks, and Practices INTRODUCTION Formal development of adaptive management as an approach for natural resources management can be traced back to the 1970s and to research conducted at the International Institute for Systems Analysis (IIASA) in Laxenburg, Austria (see Holling, 1978). As mentioned in the previous chapter, adaptive management draws from concepts within many different disciplines. Part of adaptive management’s philosophical foundations, for example, lie within the field of industrial operations theory (Johnson, 1999; see also Everett and Ebert, 1986). Although Holling’s seminal 1978 volume emphasizes ecosystem dynamics, it includes references to macroeconomics (Hafele and Burk, 1976), decision theory (Keeney, 1977), organizational behavior (Cyert and March, 1963), and policy analysis (Brewer, 1975). Thus, even in its articulation by ecological scientists in the late 1970s, adaptive management possessed strong interdisciplinary roots. Adaptive management seeks insights into the behavior of ecosystems that are utilized by humans, and it draws upon theories from ecosystem sciences, economics and social sciences, engineering, and other disciplines. Adaptive management incorporates and integrates concepts such as social learning, operations research, economic values, and political differences with ecosystem monitoring, models, and science. Applications of adaptive management principles within the Corps of Engineers to date have focused on aquatic and hydrologic systems. Although this report encourages the Corps to consider ways in which adaptive management principles could be applied in other parts of its work program, as applications within the agency to date have focused on ecosystem restoration, these experiences are emphasized within this report. Adaptive management does not postpone actions until “enough” is known about a managed ecosystem (Lee, 1999), but rather is designed to support action in the face of the limitations of scientific knowledge and the complexities and stochastic behavior of large ecosystems (Holling,

OCR for page 19
Adaptive Management for Water Resources Project Planning 1978). Adaptive management aims to enhance scientific knowledge and thereby reduce uncertainties. Such uncertainties may stem from natural variability and stochastic behavior of ecosystems and the interpretation of incomplete data (Parma et al., 1998; Regan et al., 2002), as well as social and economic changes and events (e.g., demographic shifts, changes in prices and consumer demands) that affect natural resources systems. Adaptive management aims to create policies that can help organizations, managers, and other stakeholders respond to, and even take advantage of, unanticipated events (Holling, 1978; Walters, 1986). Instead of seeking precise predictions of future conditions, adaptive management recognizes the uncertainties associated with forecasting future outcomes, and calls for consideration of a range of possible future outcomes (Walters, 1986). Management policies are designed to be flexible and are subject to adjustment in an iterative, social learning process (Lee, 1999). Adaptive management is intended to increase the ability to fashion timely responses in the face of new information and in a setting of varied stakeholder objectives and preferences. It encourages stakeholders to bound disputes and discuss them in an orderly fashion while environmental uncertainties are being investigated and better understood. Management decisions are often difficult to change because managers are subject to ordinary human failings, including a tendency to resist recognizing and learning from their own errors. In a bureaucracy, this tendency may be amplified. Adaptive management can help reduce decision-making gridlock by making it clear that decisions are provisional, that there is often no “right” or “wrong” management decision, and that modifications are expected. Adaptive management should help stakeholders, managers, and elected officials and other decision makers recognize the limits of knowledge and the need to act on imperfect information. Some of the disappointments with past efforts in implementing adaptive management can be traced to confusion surrounding definitions. There are many dimensions of adaptive management, and the ambiguities inherent in adaptive management can result in policymakers, managers, and stakeholders developing unique definitions and expectations. The term is complex and multidisciplinary, and participants in adaptive management programs should strive to become familiar with the broad literature on the topic. It should also be recognized that adaptive management is an evolving theory and practice and that experiences to date are limited (Lee, 1999). The richness and potential of the concept, however, have drawn a great deal of attention, and its prospects for redress-

OCR for page 19
Adaptive Management for Water Resources Project Planning ing complex public policy problems have generated a great deal of interest. Complex natural resources management problems, including many of those in which the Corps of Engineers is involved, defy simple solutions, and some of the case studies examined in this report may require an approach like adaptive management to reach agreeable, long-term solutions. A SPECTRUM OF ADAPTIVE MANAGEMENT PRACTICES Scientific inquiry can be approached and knowledge can be gained in many different ways. If these various means of inquiry are placed along a spectrum, formal, laboratory experimentation lies near one of this spectrum, and unmonitored, unstructured learning lies near the other end (Table 2.1). Table 2.1 lists examples of different modes of learning, and shows that learning by adaptive management lies somewhere between TABLE 2.1 Examples of Learning Modes Each mode of learning makes observations… and combines them… to inform activities… …that accumulate into usable knowledge Laboratory Expeirmentation Controlled observation to infer cause replicated to assure reliable knowledge enabling prediction, design, control theory (it works, but range of applicability may be narrow) Adaptive Management (quasi-experiments in the field) systematic monitoring to detect surprise integrated as-sessment to build system knowledge informing model-building to structure debate strong inference (but learning may not produce timely prediction or control) Trial and Error problem-oriented observation extended to analogous instances to solve or mitigate particular problems empirical knowledge (it works but may be inconsistent and surprising) Unmonitored Experience casual observation applied anecdotally to identify plausible solutions to intractable problems models of reality (test is political, not practical, feasibility)   SOURCE: Modified from Lee (1999).

OCR for page 19
Adaptive Management for Water Resources Project Planning formal laboratory science and a “trial and error” mode of learning. Adaptive management is not simply a “trial and error” process, but rather represents a more systematic “learning while doing” process (Lee, 1999). Some degree of learning is inevitable in almost any management approach; adaptive management is structured to make that learning more systematic and efficient, although this is questioned by some (Gunderson, 1999; see McLain and Lee, 1996). A distinction is often made between adaptive management approaches that are “passive” and those that are “active.” Within “passive” adaptive management, a single, preferred course of action, based on existing information and understanding, is selected. Outcomes of management actions are monitored, and subsequent decisions are adjusted based on the outcomes. This approach contributes to learning and to more effective management, but it is limited in its ability to enhance scientific and management capabilities for conditions that go beyond the course of action selected. By contrast, an “active” adaptive management approach reviews information before management actions are taken. A range of competing, alternative system models of ecosystem and related responses (e.g. demographic changes; recreational uses), rather than a single model, is then developed. Management options are then chosen based upon evaluations of these alternative models (Table 2.2 provides greater detail on “passive” and “active” adaptive management approaches). All modes of adaptive management require outcomes of management actions to be monitored. Learning is achieved by observing system responses to management actions. A lack of concordance between observation and expectation should lead to a revised model(s) of how the system functions and, ideally, to revised future management options and actions. ELEMENTS OF ADAPTIVE MANAGEMENT The theories and concepts of adaptive management themselves represent a work in progress. As mentioned, there is no accepted, clearly-defined course of adaptive management, and there may be instances in which adaptive strategies or a formal program may be inappropriate (e.g., protracted political disputes; see Lee, 1999). There is no prototype for its implementation, and no “cookbook”-type set of steps or building blocks that will immediately constitute an adaptive management program. It is context-specific, it involves feedback and learning between

OCR for page 19
Adaptive Management for Water Resources Project Planning TABLE 2.2 Passive and Active Adaptive Management   PASSIVE Adaptive Management ACTIVE Adaptive Management Characteristics Related tot eh Nature of The Ecological Problem Ongoing monitoring Essential Essential Decision points Multiple Multiple Choice at decisions points Best apparent management choice is selected at each decision point. A range of management choices is explored through modeling. Inferences are made and the best apparent alternative chosen and applied later. Characteristics Related to Internal Organization Chart Analytic requirements Moderate to high; reliability depends on quality of monitoring and time-series analysis. High, including experimental design and statistical analysis at end of experiment when inferences are made. Social organization required of the decision makers Continuity of oversight; timeframe may exceed manager’s professional tenure. Organizations should nurture curiosity, credit and checking. Timeframe may exceed manager’s professional tenure. Characteristics Related to External Social Context Goals and objectives Goals and objectives should be clearly defined. Goals will include a balance between management goals and learning. Hypotheses to be tested must relate to those goals. Uncertainty and learning Learning is a goal, but information at later decision points may be unreliable, owing to possible confounding factors. Learning is a goal, and good experimental design should produce reliable new information for later decisions.   SOURCE: Adapted from Anderson et al. (2003).

OCR for page 19
Adaptive Management for Water Resources Project Planning scientists, managers, and stakeholders, and it is likely to entail a mix of progress and setbacks. But there is a rich literature on the topic, informed by ecological and social scientists alike, and there have been some efforts toward its implementation in the U.S. and abroad. Elements of adaptive management that have been identified in theories and practice are: Management objectives that are regularly revisited and accordingly revised. Political differences among stakeholders, or competing paradigms among cooperating scientists, are inherent and unavoidable. Recognition and discussion of such differences should be part of adaptive management and its learning processes. But adaptive management participants must have some level of agreement if adaptive management is to be useful; a setting in which there is no agreement on goals, or modes of progress, is likely to render potential adaptive management applications ineffective. As Lee (1999) explained, “Unbounded conflict can tear apart the social fabric, thwarting learning.” Participants in adaptive management programs must at least agree upon key research questions or lines of inquiry to be pursued by an adaptive approach (ibid.). Some agreement on larger objectives could help better define program direction; but if full agreement on ecosystem management goals existed (an unusual condition), there would be a reduced need for adaptive approaches. Adaptive management is a means for bounding and addressing disputes and differences. As adaptive management proceeds, not only will ecosystem understanding by participants increase, but social and political preferences are likely to evolve, and environmental and social surprises may occur. Key questions, paths of inquiry, and programmatic objectives should be regularly reviewed in an iterative process to help participants maintain a focus on objectives and appropriate revisions to them. A model(s) of the system being managed. An explicit baseline understanding of and assumptions about the system being managed will help provide a foundation for learning (Holling, 1978; Lee, 1999; Walters, 1986). A system model(s) helps explain responses to management actions and helps identify gaps in and the limits of scientific and other knowledge (Box 2.1 discusses model construction and applications). Model sophistication and complexity should be tailored to the decision at hand. Active adaptive management employs multiple, quantitative models to generate hypotheses about the system (Walters, 1986; Walters and Holling, 1990). These models contain clearly-defined variables that characterize the state of the system and its rates and directions of change.

OCR for page 19
Adaptive Management for Water Resources Project Planning BOX 2.1 Models And Adaptive Management The adaptive management literature refers frequently to “models” for use in scientific investigations and as aids in decision making. A model represents and simplifies reality by showing relationships between the objects of a theory, causal interactions, and the states of a system (Pickett et al., 1994). Models are useful abstractions of the dynamics of more-or-less complex systems and may be verbal, physical, graphical, or quantitative. Scientists and engineers often construct models to test hypotheses of how a process or a system functions, as there are limits to testing hypotheses on actual objects or structures. For example, the Corps of Engineers has long used physical models to test assumptions regarding river hydraulics, sediment transport, and environmental impacts of barge passage. Models are, however, are simplifications of reality and can rarely perfectly simulate real world conditions. The ecosystem models referred to in this report are mainly numerical models, in which elements and processes of a given ecosystem (e.g., river corridor, or a stand of trees) are quantitatively expressed in algorithms contained within a computer program. They offer scientists the opportunity to evaluate multiple ideas and hypotheses about disturbances, diseases, and other impacts on a given species or multiple species, but are not a substitute for empirical tests of hypotheses. Numerical models provide an opportunity to see how ecosystems might respond to a variety of human actions, and the better the model is able to simulate reality, the greater its credibility. Numerical models of ecosystems are useful for adaptive management applications and programs, as they allow scientists and stakeholders to observe how impacts vary across multiple management actions. The value of numerical models should be tempered with a clear understanding of model limitations and uncertainties in model projections, as the lack of communication or lack of common understanding between model builders and users may result in confusion and misinterpretation of model results. Mathematical models of the managed system are often developed to help understand systems behavior. But in poorly understood systems, or when the scale or risks of the actions being considered do not justify the expense of rigorous models, simple schematic diagrams can serve as useful conceptual models. Adaptive management recognizes the need for action in the face of uncertainty, and complete or perfect ecosystem models (which are not likely to be perfected in any case) do not need to be crafted in order to support decisions (Walters, 1997). Simple models

OCR for page 19
Adaptive Management for Water Resources Project Planning can educate decision makers and participants by organizing information, highlighting missing information that might be acquired by monitoring, providing a framework for comparing alternatives, and forcing managers to consider their understanding and assumptions of the system (Salafsky et al., 2001). The focus should be on learning, not on getting ready to learn (Lee, 1999). No matter what the setting or types of models used, it is important that adaptive management participants understand model assumptions and limits so that model results are not equated with reality. A range of management choices. Even when an objective is agreed upon, uncertainties about the ability of possible management actions to achieve that objective are common. That is, existing data rarely point to a single “best” management policy. For each decision, the range of possible management choices is considered at the outset in light of stated objectives and the model(s) of system dynamics. This evaluation takes into account the likelihood of achieving management objectives and the extent to which each alternative will generate new information or foreclose future choices. When possible, simultaneously implementing two or more carefully monitored actions can allow for rapid discrimination among competing models. Within the field of water resources planning and management, Gilbert White for decades strongly encouraged water managers and organizations to consider a broad range of alternatives for addressing water resources problems and opportunities (White, 1961). Monitoring and evaluation of outcomes. Adaptive management requires some mechanism for comparing outcomes of management decisions. The gathering and evaluation of data allow for the testing of alternative hypotheses, and are central to improving knowledge of ecological, economic, and other systems. Monitoring should focus on significant and detectable indicators of progress toward management objectives. Monitoring should also help distinguish between natural perturbations and perturbations caused by management actions. Monitoring, in and of itself, however, does not ensure progress, and monitoring should not be equated with adaptive management. Monitoring programs and results should be designed to improve understanding of environmental and economic systems and models, to evaluate the outcomes of management decisions, and to provide a basis for better decision making (ideally, independent estimates of the value of monitoring information and programs will be periodically conducted). Monitoring systems should be an integral part of program design at the outset and not simply added post hoc after implementation (Holling, 1978). A mechanism(s) for incorporating learning into future decisions.

OCR for page 19
Adaptive Management for Water Resources Project Planning Adaptive management aims to achieve better management decisions through an active learning process. Objectives, models, consideration of alternatives, and formal evaluation of outcomes all facilitate learning. But there should be one or more mechanisms for feeding information gained back into the management process. The political will to act upon that information must also exist. Without a mechanism to integrate knowledge gained in monitoring into management actions, and without a parallel commitment and the political will to act upon knowledge gained from monitoring—which will not eliminate all uncertainties—monitoring and learning will not result in better management decisions and policies. In addition, adaptive management organizations must likewise have some degree of flexibility in order to adjust operations in light of new information, environmental changes, and shifting social and economic conditions and preferences (Gunderson, 1999). A collaborative structure for stakeholder participation and learning. The inclusion of parties affected by ecosystem management actions in decision making is becoming a broadly-accepted management tenet of natural resources management programs in the U.S. and around the world (WCD, 2000). The Corps of Engineers, for example, has long supported this notion, and stakeholder outreach is a part of Corps planning studies in many locales. Achieving meaningful stakeholder involvement that includes give and take, active learning (through cooperation with scientists), and some level of agreement among participants, represents a challenge, but is essential to adaptive management. This implies that some of the onus for adaptive management goes beyond managers, decisions makers, and scientists, and rests upon interest groups and even the general public. As mentioned, even though differences between stakeholders are inevitable, some agreement upon key questions and areas of research is essential to adaptive management of public projects (Lee, 1999). Stakeholders may also need to exhibit flexibility and some willingness to compromise in order for adaptive management to be implemented effectively. As one expert in the field has noted, “In a nutshell, if there is no resilience in the ecological system, or flexibility among stakeholders in the coupled social system, then one simply cannot manage adaptively” (Gunderson, 1999, emphasis added). ADAPTIVE MANAGEMENT OF OTHER ENTERPRISES The concept of adaptive management is not restricted to natural resources or ecosystem management, as similar concepts have been applied

OCR for page 19
Adaptive Management for Water Resources Project Planning in fields such as business, education (Dewey, 1938), engineering (de Neufville, 2000; de Neufville and Odoni, 2003), geography (White, 1988), and public administration (Lindblom, 1979). Related concepts and practices from these other disciplines include “learning by experience,” “ex post audits,” and “muddling through,” and designers of engineering systems may refer to “flexible planning” rather than “adaptive management.” Alternative forms of this concept have been and are being applied in the broade area of strategic planning, which often emphasizes adaptability in system development and management. The traditional approach to developing infrastructure systems often centers on a “master plan,” or a linear path to a selected, well-defined endpoint (NRC, 2003a). In practice, developers first engage in a planning exercise in which they lay out a desired end state for the system (the master plan, i.e., objectives). Within the context of rigidity that characterizes some traditional design practices, the view that designers should design and manage systems flexibly presents a challenge. But several concepts of flexible planning and engineering systems management have been developed that frame the planning process as a series of choices with indeterminate consequences (de Neufville, 2004). Flexible Management of Engineering Systems Practices for the planning, design, and management of large, complex engineering systems are evolving in fundamental ways. Professional practice is in the middle of a transition that is reshaping design, evaluation, and implementation of major civil engineering developments. Individually, experts do not share a consensus on exactly how to describe this evolution. Collectively, however, traditional norms of practice are often viewed as insufficient in current settings and given current knowledge (de Neufville and Odoni, 2003). Current and prospective Corps of Engineers practice should be sensitive to these changes. In broad terms, the evolution is from simplicity to complexity. Most major civil investments were traditionally designed and implemented primarily in terms of single investments, for single purposes, on the basis of a single forecast of future events, and with a narrow focus on construction (Table 2.3 summarizes practices that were fairly standard as of a generation ago, as well as ways in which those practices are evolving).

OCR for page 19
Adaptive Management for Water Resources Project Planning TABLE 2.3 Trends in the Evolution of Civil Engineering Design Practice   Nature of Change Design Element From Traditional Broadening To Scope Project System of projects Purpose Single purpose Multiple and sometimes conflicting objectives Means Structural Nonstructural Focus Construction Long-term Management Risk Recognition Little Extensive Engineering design practices are today more sophisticated than in a previous era. Although its evolving nature precludes a precise definition, its main contours can generally be described. Most fundamentally, it is generally accepted that the interactions between projects are important, and that designs should consider any single project as part of a larger whole. These approaches are standard elements of today’s engineering design textbooks (e.g., de Neufville, 1990). An important aspect of evolving concepts of engineering practice is the way uncertainty is recognized and addressed. It is today widely appreciated that many consequences of civil engineering investments cannot be precisely forecasted. The example of the Florida Everglades (discussed in Chapter 4) is illustrative, as this project is set within a context of demographic and ecological changes that are rapidly changing southern Florida. In this region, the nation is attempting something never done before, in the midst of substantial scientific uncertainties about the effectiveness of solutions that have been proposed. At the very least, it must be recognized that even the best projections of future system performance will contain some uncertainties. Recognizing that the consequences of designs entail risks leads to an important feature of current best practice in the design of engineering systems. Promoters and developers of engineering and other projects are responsible for managing inevitable uncertainties associated with those projects. On the one hand, there is a need to take advantage of new opportunities for improving water resources systems performance through advances in engineering, biophysical sciences, and social sciences. On the other hand, proposed solutions should seek to minimize the potential for negative consequences and seek to keep development options open by, to the extent possible, proceeding incrementally and evaluating the results of design and planning decisions. Whether the objective is to take advantage of new opportunities or to insure against bad outcomes, the goal is to create the capacity to respond appropriately as new situations—which may include unforeseen surprises—develop. Flexibility

OCR for page 19
Adaptive Management for Water Resources Project Planning over the life of the project is essential to effective development and functioning of civil engineering systems. Adaptive Implementation, Staging, and Site Management Adaptive management approaches are increasingly being seen as useful in dealing with problems where the outcomes, and even goals, are uncertain. Recent reports of National Research Council committees have recommended the use of adaptive strategies and methods in addressing Total Maximum Daily Load (TMDL) approaches to water quality management (NRC, 2001a), in the staged development of geological repositories for high-level radioactive waste (NRC, 2003b), and in clean-up of hazardous waste sites at U.S. Navy facilities (NRC, 2003c). Under the Clean Water Act, the Environmental Protection Agency (EPA) is obligated to implement the TMDL program when ambient water quality standards are not attained through the National Pollutant Dis-charge Elimination System (NPDES) permit program. The maximum discharge loads that can be tolerated, consistent with attaining water quality standards, must be determined and allocated among sources. There are uncertainties throughout this process, which over time could result in changed requirements and even goals. Adaptive implementation of TMDL plans has therefore been recommended, which involves a cyclical process in which TMDL plans are periodically assessed for their achievement of water quality standards including designated uses (NRC, 2001a). If designated uses and goals are not being achieved after implementation, scientific data and information should be used to revise the plan, thus ensuring that the TMDL program is not halted but progresses while better information is collected and analyzed with the intent of improving on initial TMDL plans. Adaptive staging (as opposed to linear staging with a predetermined path to a well-defined endpoint) was recommended as a promising approach to the development of geological repositories for high-level radioactive wastes, such the Yucca Mountain Project (NRC, 2003b). Adaptive staging is a way to deal with uncertainties regarding not only environmental factors, but also programmatic, safety, security, institutional, regulatory, and societal variables throughout the construction, operation, closure and post-closure phases of repository development. Adaptive staging provides a flexible reference framework so that the ultimate path to success, and the endpoints themselves, are determined by knowledge and experience gathered along the way. Adaptive staging is a

OCR for page 19
Adaptive Management for Water Resources Project Planning deliberate, incremental decision-making and management process, fully consistent with good engineering practices. It should reflect seven attributes: commitment to systematic learning, flexibility, reversibility, auditability, transparency, integrity, and responsiveness. Adaptive site management is seen as a more effective alternative to the traditional paradigm for hazardous site restoration, which involved a linear, unidirectional path from site investigation, to remedial actions, and eventually to site closure (NRC, 2003c). Adaptive site management is viewed as: applicable to a variety of sites and stages of restoration, providing for optimization of remedial measures, formalizing the use of monitoring, incorporating public participation, dealing with uncertainty, and stimulating development of innovative technologies (ibid.). COMMENTARY Although concepts of formal adaptive management for ecosystem restoration date from the late 1970s, the concept evolved, broadened, and gained currency in the 1990s, as natural resources policy makers and managers alike began to embrace adaptive management. Planners, engineers, and decision makers in other sectors and disciplines have also advocated approaches that seek to keep options open, that monitor and evaluate outcomes, and that incorporate lessons learned into future decisions. A lesson in the development and application of adaptive management-type elements in these fields beyond natural resources management is that scientists, engineers, and managers in engineering sectors are increasingly recognizing the limits of linear, deterministic approaches and predictive models. Adaptive management concepts and practices represent innovative, current thinking on resolving conflicting demands and adjusting to changing social preferences and priorities. Adaptive management can be applied in various forms, ranging from less to more formal practices. Adaptive management entails a set of core principles, each of which can be applied in more or less elaborate forms. In deciding whether to adopt an adaptive management approach and principles, or whether more “active” or “passive” approaches should be adopted, decision makers should compare the costs and benefits of learning. Decision makers should weigh the likelihood of obtaining useful results from monitoring and the costs of obtaining them. Although adaptive approaches may be complex and may thereby frustrate some participants, many contemporary public policy problems—including many faced by the Corps—may require sophisticated approaches like adaptive

OCR for page 19
Adaptive Management for Water Resources Project Planning management if lasting solutions are to be identified and established. Adaptive management may entail resistance from some stakeholders, management agencies, and elected officials. Stakeholders may be concerned with the ambiguities of the approach or with possible threats to existing structures and values, management agencies may feel that their decisions and judgment are being second-guessed, and legislators may be concerned with the costs of what may appear to be open-ended science programs. The value of adaptive management will ultimately be gauged by its ability to improve decision making by being responsive to environmental and social changes, thereby enhancing environmental and economic benefits. Adaptive management may entail a variety of detailed and useful scientific and learning exercises (e.g., development of alternative ecological and engineering models; scenario investigations by participants) and administrative processes (e.g., meetings of stakeholders). Maintaining a focus on economic and environmental goals and objectives is important to helping coordinate scientific inquiry with management decisions and stakeholder discussions and learning. The Corps has traditionally constructed its civil works projects based on engineering principles founded upon a deterministic planning framework. Over time, however, the Corps’ mission has shifted from the construction of engineering projects to managing an existing infrastructure and distributing benefits (e.g., stream flows and their associated benefits) among multiple stakeholders. Successful execution of this latter mission will require less reliance upon concepts related to linear, stable systems, and greater reliance upon expertise in ecosystem dynamics and modeling, as well as economics and other social sciences. Over time, the limits of a deterministic planning paradigm have been revealed, as have many unanticipated consequences of Corps projects. The Corps has different degrees of experience in the six elements of adaptive management identified in this chapter, and adaptive management would thus build upon some existing concepts and practices. Yet the Corps has only limited experience in integrating them within a formal adaptive management framework. A Corps of Engineers 2003 draft report prepared in connection with its Upper Mississippi River feasibility study, for example, demonstrates an understanding of adaptive management principles and challenges regarding its implementation (Lubinski and Barko, 2003). As the following chapter explains, constraints remain on the Corps’ ability to implement adaptive management. Some of these constraints come from Corps planning guidance and organizational traditions, others from factors beyond the Corps, such as the influence of stakeholder groups and guidance from the administration and the Congress.