system dynamics: systemic feedback modeling for ...

SYSTEM DYNAMICS: SYSTEMIC FEEDBACK MODELING FOR WATER RESOURCES MANAGEMENT

Ali Kerem SAYSEL Boğaziçi University, Institute of Environmental Sciences, 34342, Bebek, İstanbul [email protected] web.boun.edu.tr/ali.saysel

ABSTRACT

Water resources planning and management requires analytic methods to guide decision making on development, use and consumption of water resources. Many econometric and operational research methods are prescriptive in nature, i.e. they evaluate the optimum or best outcomes achievable under rigid assumptions and based on specific preferences of the decision making authorities. However, apart from the complexity of hydrodynamic processes at a watershed, multiple stakeholder involvement and their often conflicting goals and values limit the usefulness of these analytic approaches. Systems methodologies can provide a flexible framework and a holistic perspective for learning about water resource systems and management of water resource problems. Particularly, System Dynamics (Systemic Feedback Modeling) offers appropriate principles and methods for long term policy analysis and design. Along with the hydrodynamic processes, the social environment and the decision rules (policies) of the stakeholders are modeled. The interactions between the natural and the human environment are identified. Model structures and behaviors (time dependent outputs) are scrutinized in group processes. Simulation games and interactive learning environments are developed to analyze the future behavior of systems under different scenarios and policies. The review presented in this article introduces the method and explores the potential of System Dynamics in policy design for sustainable water management. Keywords: Systems approach, descriptive models, computer simulation, deliberative processes, I. INTRODUCTION

Systems analysis gained enormous popularity in analysis and management of large scale water systems especially after 1960s and by invent of modern desktop computers and user friendly software, computation and simulation based ap-

PRACTICES ON RIVER BASIN MANAGEMENT

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proaches had considerably spread across administrative bodies responsible for water resources development and management. Nowadays, the ready-cut, custom-tailored software package is an indispensable part of engineer-manager’s everyday toolbox. These instruments are mainly concerned in the issues of water scarcity and abundance to either provide a required amount of water basically for municipal and agricultural use and for hydropower production, or protect from floods and overflows. It can be said that, for such conventional problems of water resources planning and management, rather short-term oriented and compartmental analytic perspectives are adopted, which are essentially prescriptive in nature. A brief look at standard textbooks, for example, at Mays and Tung [1992] would clarify the point. Water resources engineer-manager is typically educated through cost-benefit analysis for the economics of hydro-systems, a methodology based on the concepts of utility (its maximization), discounting (the future) and rational analysis of all potential (known) impacts. In case of large-scale planning and management problems, it is assumed, the utility of the beneficiaries (the community) is known, their preferences on discounting the future generations are fixed and all future impacts are certain. In forecasting for future demand, regression methods are preferred, which in terms of consumption patterns and population growth, takes the past behavior of the system or the behaviors of other systems under similar settings as a clue for the future behavior under concern. Reservoir operating rules are identified, that prescribes how water is going to be appropriated during subsequent periods (usually months) to achieve minimum desired or minimum required flows required to be met at selected points of the system. Then, the management practice assumes that, those prescriptions are understood by the organizational structure and complied by multiple stakeholders acting on a complex water supply and distribution network. Above examples illustrate that, a large body of analytic methods and procedures under the acronym “systems analysis in water resources planning and management” serve for expert values and expert opinion but may significantly lack the complexities in water issues emerging from ecological, human and social agency. Besides, the ready-cut, custom-tailored instruments on the engineer-manager’s desktop implement such methods and procedures and that is one reason why their outputs must be held with caution in implementation. On the contrary, many water resources problems are large-boundary and longterm in nature and involves complex human agency. Such problems are largeboundary not only in the sense that they involve large physical units such as watersheds, national territories or the globe but also many ecological, human and social elements that depend on and affect water. They are often long-term in nature, because the effect of water resources development projects and hydro-systems are not

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INTERNATIONAL CONGRESS ON RIVER BASIN MANAGEMENT

limited to the lifetime of a single generation but it affects future generations as well. Large scale water diversion, groundwater exploitation, big dams, hydropower production and irrigation are excellent examples of water development whose impacts cross the boundaries of physical, ecological and social environments and span over the lifetime of several generations. In case of big dam constructions, for example, economic evaluation cannot be based on prescriptive cost-benefit measures, mainly because of the plurality of the values and concerns of multiple stakeholders that should be taken into account while deciding on the feasibility and legitimacy of alternative solutions for the targeted problems. Indeed, Munda [2000] provides a comprehensive criticism of reductionistic approaches to economic evaluation under the condition of plural perspectives and uncertain impacts. If a dam is going to be constructed, whose objectives (and utility) should be taken into account? The economic sectors that will benefit from the hydropower, or the farmers? Among farmers, the new large landholders, or the many small farmers that are going to be displaced by reservoir impoundment? Among the displaced, the compensated male, or the women? Shall we value more the increase in yields of cash-crops under monocultures, or threatened bio-diverse endemic crops? The list can almost indefinitely be extended to argue for the involved plurality of values and uncertainty that dwarfs the capability of so called rational cost-benefit approaches in the engineer-managers’ toolbox. Again, in case of big dam construction, while forecasting for future irrigation water demand to evaluate the capacity of irrigation networks, neither past trends observed in a similar geography under comparable conditions nor the prescribed (suggested) crop selections and their associated acreages can confidently be relied on. Because, all factors affecting water demand, such as population, crop selection and unit water consumption are prone to change due to several institutional and technological factors. See for example, Sterman [1991] on econometric forecasting methodologies and on why they are weak instruments of policy analysis especially for rather long term problems. How can one forecast for future population? Over longer time horizons, births and life expectancy should not be treated as exogenous inputs. Factors such as nutrition, access to heath care, material standard of living, pollution, and crowding all depend on the size and wealth of population and in turn affect the births and life expectancy. Crop selections depend on market prices smoothed over time, information and technology available to farmers, and marketing networks and organizations. Irrigation water consumption depends not only on crop’s theoretical requirements but on how the authorities and farmers value the water through pricing, marketing and cooperation or competition.


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Lastly, the prescribed reservoir operation policies can be impossible to follow due to several institutional barriers, pressures and coercion. How do the authorities respond to increasing demand for hydropower production? Do they simply follow the preset rules and regulations or they do adjust for increasing production over firm energy yield? Similarly, how do they respond to changing demand for irrigation? When crop selections, water consumption norms and irrigation on new acreages favor increased water consumption, do they adjust their operating policies? Do they build new capacity to further exploit available water resources? See for example Fernandez and Selma [2004] on how increasing demand for irrigation (also for golf courses) trigger overexploitation in the long term above preset water harvesting standards in Southeast Spain. Past experience in water resources management show that, many water development projects built on engineering expertise and hydrosystems analysis concepts had failed due to human-social agency and plus ecological impacts and uncertainty. If not “bad intentioned” from the very start -i.e. it is deliberately blind to multiple values and objectives and irreversible ecological impacts- many engineering projects suffered from lack of a conceptual framework and understanding of such social and ecological elements. Just to give a few dramatic examples among many, a global review of 52 large dams by World Commission on Dams reveals that many hydropower dams show an overall tendency to fall short of power generation goals; large dams designed to deliver irrigation services have typically fallen short of physical targets; and one fifth of irrigated land worldwide is affected by water-logging and salinity which often means severe, long-term and often permanent impacts on land, agriculture and livelihoods [IRN, 2002]. In Harran lowlands in Southeast Turkey, dominantly irrigated for cotton crop, evidence of waterlogging and salinization had once spread over 3400 hectares [Anadolu Ajansı, 6 Ocak 2007]. Around the towns of Hasankeyf, Hakkari and Tunceli, large communities are faced with the risk of loss of their culture and livelihoods [Ronayne, 2005]. In Punjab-India, on going conflict over sharing of the river waters had triggered revolts in 1980s that lead to more than 15.000 deaths [Shiva, 2002]. Faced with such disappointing facts of water resources development and management, some authors came to argue that, all quantitative analyses and modeling approaches in the engineer-manager’s toolbox are inherently blind to complex elements of real systems and therefore, in policy analysis, qualitative and deliberative social science methods must be adopted. The point in this article is that, the fundamental distinction should not be between quantitative and qualitative approaches but it should be between prescriptive and descriptive approaches in modeling and between prescriptive and deliberative attitudes in policy analysis. The requirement is

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not to abandon the conventional prescriptive analyses in hydrosystems engineering built on hydrology, econometrics and operational research, but to make use of those prescriptions in a descriptive and deliberative framework. For this purpose, the principles, methods and applications of system dynamics, “systemic feedback modeling for policy analysis” is introduced. It is argued that, being a causal-descriptive method of modeling and policy analysis, the uses of system dynamics in a deliberative framework can help in designing better policies that takes into account the complexity of social and ecological environments and the plurality of perspectives. 2. SYSTEM DYNAMICS: SYSTEMIC FEEDBACK MODELING FOR POLICY ANALYSIS

System Dynamics is a discipline that has emerged in the late 1950s, as an attempt to address dynamically complex long term policy issues in the public and private domain [Barlas, 2002]. It is grounded in the theory of nonlinear dynamics and feedback control developed in mathematics, physics and engineering. Because these tools are applied to the behavior of human as well as physical and technical systems, system dynamics draws on cognitive and social psychology, economics and other social sciences as well [Sterman, 2000]. Since its early application such as Urban Dynamics [Forrester, 1969], World Dynamics [Forrester, 1971] and Limits to Growth [Meadows et al., 1972], applications on environmental and resource management and water in particular had constantly increased. A textbook in the field of environment came out in 1999 [Ford, 1999], the Environmental Special Interest Group of the International System Dynamics Society (www.systemdynamics.org) was established in 2002 and the special issue of System Dynamics Review on “Environmental and Resource Systems” was published in 2004. In the following sections, the problem characteristics, significance of structure behavior distinction, elements of dynamic complexity and simulation modeling in system dynamics are introduced. Dynamic Feedback Problems Dynamic problems are characterized by variables that undergo significant change in time. Administrators and citizens face increasing levels of water pollution, declining water yields; they are concerned with decreasing species diversity etc. In each one of these cases, there are one or more patterns of dynamic behavior that must be controlled, altered or even reversed. Yet, the defining property of a dynamic problem is not merely the variables being dynamic. More critically, in a system dynamics problem, the dynamics of the variables must be closely associated with the


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operation of the internal structure of some identifiable system [Barlas 2002]. It is said that the dynamics is essentially caused by the internal feedback structure of the system. Like, a mass-spring system oscillates not because it is disturbed from outside, but because of the characteristics of its potentially identifiable internal structure. A child grows not because s/he eats, but because of the characteristics of her/his potentially identifiable internal nature. In both cases, although the external elements (disturbing the mass-spring, feeding the child) are necessary to create the dynamics (oscillations in the first, exponential growth in the latter), they are not the causes of these behaviors. This argument is critical for policy analysis such that, if the dynamics are dictated by forces external to the system, there is not much possibility for managerial control and improvement. Like, if amount of irrigation water received by individual farmers is declining because a multinational company is constantly appropriating and exporting their water resources, there is not any “management” that the administrators or farmers can do. Hence, there is not any space for “policy design”. However, if, the amount of irrigation water is declining because of wrong institutional arrangements between the administrators and the farmers themselves, then probably, feedback loops exist between the human actions and various elements of the system. Then, identifying this structure (a feedback structure) and studying the dynamics created by this structure would help management and policy design. Structure and Behavior The “structure” of a system is the totality of the relationships that exist between system variables. The structure of a system operates over time so as to produce the dynamic behavior patterns of the system variables over time. For a real system, the structure is not exactly known. For a “model” of the real system, the structure is a representation of those aspects of the real structure that we hypothesize to be important for the problem of interest. In the above “irrigation water” example, probably, the model structure would include how water is stored, shipped, shared and appropriated by the farmers over a water distribution network. Later, we shall see that, in formal mathematical language, the structure corresponds to the system of equations, and the behavior corresponds to the solution of this system of equations. Any system (model) structure consists of causal relations among its variables. A causal relation means that, the input variable has a causal influence on the output variable. For example, the statement “an increase in total irrigated lands causes an increase in irrigation water requirement” reflects a cause-effect relationship and is illustrated in Figure 1. That is, other things being equal (ceteris paribus), if total irrigated lands increase, irrigation water requirement increases. Similarly, “an increase

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in total irrigated lands causes a decrease in water delivered to farmlands” reflects another cause-effect relationship presented in Figure 1. That is, other things being equal, if total irrigated lands increase, water delivered to farmlands decrease. Note that the arrows in Figure 1 are the causal links representing the direction and the polarity of causality (positive or negative in character).

+

total irrigated lands -

irrigation water requirement water delivered to farmlands

Figure 1. Illustration of direct causalities.

However, identifying unidirectional (open loop) causality is rarely sufficient to conceptualize an adequate model structure that can generate insights about the dynamic behavior patterns of system variables. Therefore, circular (feedback) causalities are discovered, identified and represented. For example, “an increase in water delivered to farmlands causes an increase in average water availability”, i.e. crop irrigation requirements are better satisfied. “An increase in average water availability causes an increase in transformation from rain-fed to irrigated lands” and “that causes an increase in irrigated lands”. Therefore irrigation water requirement increases. This feedback causality is presented in Figure 2. Feedback loops are either positive or negative in polarity. Positive (reinforcing) loop means, any change in one variable embedded in the loop is triggered through succession of causalities. Negative (balancing) loop means, any change in one variable embedded in the loop is balanced through succession of causalities. Figure 2 illustrates a negative feedback loop adopted from Saysel [2006]. land abandonment + irrigated lands total

land transformation +

average water availability +

+ irrigation water requirement

water delivered to farmlands

Figure 2. Illustration of circular (feedback) causality.


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System structure that creates the behavior of interest (the model structure) is conceptualized by identifying the relevant variables, causalities and feedback loops. At the end, the constructed model structure typically consists of multiple, non-linear feedback loops with time lags and exhibits the characteristics of dynamic complexity. Dynamic Complexity One reason for the requirement for simulation based analysis of dynamic socioenvironmental problems is the dynamic complexity of the real world. It is said, the real world is a multi-loop, multi-state, non-linear feedback system that reacts the decision makers’ actions in ways both anticipated and unanticipated [Sterman, 2000]. That is, the effects of our actions can appear at a distant point in time and space and even with unintended consequences. The elements of dynamic complexity that dwarfs our individual and organizational decision making skills are typically classified as: feedback, non-linearity and time delays. In systems operating with multiple feedback loops, it is not easy to predict which way the system will move. The system’s states continuously determine the path in which the system will change. Prediction requires mental simulation of interaction of several loops simultaneously. Almost all multi-loop systems are non-linear. That is, the cause-effect relations between variables are not linearly proportional. An effect observed at a certain range of its cause (and of other variables) may not be valid in another range of the same variables. Time delays between taking a decision and its effects on the state of the system are common. Delays make it particularly difficult to learn from outcome feedback and properly adjust for the desired system state. For example, in a groundwater appropriation problem, obviously there are multiple feedbacks between the system state (groundwater levels) and the extraction decisions based on observed or assumed state. Furthermore, feedbacks can exist between different physical elements of the system (like, in between interdependent groundwater reservoirs). The change in groundwater levels can be disproportional to total extraction efforts at different ranges of the variables (like it can be depleted much severely after running below a certain threshold). There may be considerable time delays between water extraction efforts and their ultimate effects on distant reservoir levels. Dynamic (Simulation) Models A dynamic system model distinguishes between stock and flow variables. Stock variables are the accumulations that represent the system state. They are the memories of systems, that is, stocks persist at an instant in time (represented by rectangular

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box in Figure 3). Flow variables are the rate of change of stocks that represent the change in system state. While stocks persist, flows disappear when time is frozen (represented by pipes in Figure 3). Stocks can change values only via their flows. If the summation of values of all flows connected to a stock is zero at a point in time, then its value does not change at that point in time. If it is larger than zero, it increases; if it is less than zero, it decreases. This stock-flow dynamics peculiar to all dynamic phenomena is well illustrated by the bathtub analogy in Figure 3.

Figure 3. Stocks and flows.

In the previous section, it is said that a model structure is identified by discovering relevant variables, causalities and feedback loops. A next step in model construction is to identify the stock and flow variables embedded in feedback loops. Figure 4 illustrates one stock-flow relationship identified on the illustration in Figure 2.

+

TOTAL IRRIGATED LANDS

Land Transformation +

average water availability +

-

Land Abandonment

+ irrigation water requirement

water delivered to farmlands

Figure 4. Stock-flows embedded in a feedback loop.

The stock-flow processes correspond to integral and differential equations illustrated below: t (1) TotalIrrigatedLands(t ) = ( LandTransformation − LandAbandonment)dt + TotalIrrigatedLands(t )

∫

t0

0

d (TotalIrrig atedLands ) / dt = LandTransf ormation (t ) − LandAbando nment (t )

(2)


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In sum, a systemic feedback analysis of a complex dynamic problem starts with identifying how the problem manifests itself over time and follows the model conceptualization (relevant variables, causalities and feedbacks) and model construction (stock-flow relationships, parameter values and equations) stages. Typically, the model is a high-order, non-linear system of differential (or difference) equations solved and analyzed by numeric simulation. Several simulation platforms such as Stella /Ithink [ISEE, 1988], Vensim [Ventana, 1996], Powersim [Powersim, 1996] and Anylogic [XJTechnologies, 2004] facilitate model building, simulation and analysis by user friendly software design. Even in very complicated modeling tasks, the modeler, developer and the analyst are essentially the same person or the same group of people. Further steps in modeling methodology include validation and analysis steps not discussed in this article. 3. APPLICATIONS ON WATER RESOURCES MANAGEMENT

System Dynamics has been applied to various large-boundary, long-term water policy problems. One illustrative example is Ford [1996] on a hundred years analysis of major developments in one of the largest rivers in Northwest USA, the Snake River. The unique feature of the model is its ease of use by a diverse group of individuals who are familiar with Snake River but may not be experts in computer simulation. Saysel et al. [2002] presents a multi-sector model designed for the analysis of long term environmental impacts of Southeastern Anatolian Project (GAP). The model represents the interactions of irrigation development with the agricultural environment and illustrates how various water distribution strategies interferes pesticide consumption and salinization processes as it stimulates different land use practices. Fernandez and Selma [2004] studies the overexploitation of local aquifers in the irrigated lands of Mazarron and Aguilas in Spain and shows how increased water harvesting stimulates growth in irrigated lands and creates further water deficits in the long term against conventional wisdom. Rajasekaram et al. [2003] develops a framework for water conflicts resolution that makes use of a model base management system. The system consists of tools for multipurpose reservoir operation, river flow routing, multi-criteria decision making and spatial data analysis. The list can be extended. Towards Deliberative Processes Systemic feedback modeling in water resources management is grounded in a holistic, systemic conception of nature-human interactions and on a causal descriptive approach towards model construction. The ultimate purpose is to analyze, how and why the observed (and simulated) dynamics occur, given the assumptions on the natural-physical processes and decision rules (policies) represented in the model

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structure. As a computer simulation methodology, system dynamics provides an experimental platform primarily for the analysts herself /himself to test and learn about the consequences of various policies and scenarios. However, if narrowly conceived as a computer simulation methodology, or as a computer based decision support instrument, the impact of system dynamics in policy would not be that different from any approach serving primarily for the expert opinion. So as to enhance the capacity of this approach to reflect plural perspectives, to enhance learning of multiple stakeholders, to provide a rational basis for the analysis of long term water problems, and finally, to increase the adoptability of generated insights, a deliberative framework should be sought for. In System Dynamics practice, model conceptualization and analysis in participatory group processes [Vennix, 1996; Van den Belt, 2004] is particularly oriented for these purposes. According to this participatory framework, the primary function of the expert is not to model, but to facilitate the group processes for problem identification, model construction and analysis. Within this framework, typically, an irrigation water distribution problem is analyzed by groups involving water distribution authorities, operators, conservation specialists, farmer unions, individual farmers and different gender and age groups. 4. CONCLUSION

System Dynamics provides a formal causal-descriptive framework and a computer simulation method for the analysis of dynamically complex socio-economic problems, water resource problems in particular. Deliberative use of this approach in group processes, together with other participatory methods is useful for an informed analysis of water development options and management strategies that take into account multiple stakeholder view and environmental uncertainty. Water resources development and management practices in Turkey would considerably benefit from these efforts, if initiated in collaboration with relevant organizations such as universities, public agencies, non governmental organizations and citizen groups. 5. REFERENCES

Barlas, Y., System Dynamics: Systemic Feedback Modeling for Policy Analysis, 2002, Encyclopedia for Life Support Systems. Y. Barlas. Paris-Oxford, UNESCO Publishing. Fernandez, J. M. and Selma, M. A. E., "The dynamics of water scarcity on irrigated landscapes: Mazarron and Aguilas in southeastern Spain." 2004, System Dynamics Review 20(2): 117-139. Ford, A., "Testing the Snake River Explorer." 1996, System Dynamics Review 12(4): 305-329. Ford, A., Modeling the Environment: An Introduction to System Dynamics Modeling of Environmental Systems, 1999, Washington, D. C., USA, Island Press.


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Forrester, J. W., Urban Dynamics, 1969, Cambridge, Massachusets, M.I.T. Press. Forrester, J. W. (1971). World Dynamics. Cambridge, Massachusets, Wright-Allen Press. IRN, Citizen's Guide to the World Commission on Dams, 2002, Berkeley, CA, International Rivers Network. ISEE, STELLA: Systems Thinking for Education and Research, 1988, NH, USA. Mays, L. W. and Tung, Y.K., Hydrosystems Engineering and Management, 1992, McGraw-Hill, Inc. Meadows, D. H., D. L. Meadows, et al., The Limits to Growth, 1972, New York, Universe Books. Munda, G., Conceptualising and Responding to Complexity, 2000, Environmental Valuation in Europe. C. L. Spash and C. Carter, Cambridge Research for the Environment (CRE): 1-18. Powersim, 1996, Powersim Constructor and Powersim Studio. Bergen, Norway. Rajasekaram, V., Simonovic, S.P., et al., "Computer Support for Implementation of a Systemic Approach to Water Conflict Resolution." 2003, Water International 28(4): 454-466. Ronayne, M., The Cultural and Environmental Impact of Large Dams in Southeast Turkey, 2005, Great Britain, National University of Ireland, Galway Kurdish Human Rights Project: 162. Saysel, A.K., Irrigation Projects, Agricultural Dynamics and the Environment. Encyclopedia of Life Support Systems. 2006, Y. Barlas, Paris-Oxford, UNESCO. Saysel, A. K., Barlas, Y., et al., "Environmental sustainability in an agricultural development project: a system dynamics approach." 2002, Journal of Environmental Management 64: 247-260. Shiva, V., Water Wars: Privatization, Pollution and Profit. 2002, Cambridge, MA, South End Press. Sterman, J.D., A Skeptic's Guide to Computer Models. Managing a Nation: The Microcomputer Software Catalog, 1991, G. O. Barney. Boulder, Westview Press: 209229. Sterman, J.D., Business Dynamics: Systems Thinking and Modeling for a Complex World, 2000, McGraw-Hill. Van den Belt, M., Mediated Modeling: A system Dynamics Approach to Environmental Consensus Building, 2004, Island Press. Vennix, J. A. M., Group Model Building: Facilitating Team Learning Using System Dynamics, 1996, John Wiley & Sons. Ventana, 1996, Vensim: The Ventana Simulation Environment. MA, USA. XJTechnologies, 2004, AnyLogic Software. Saint Petersburg, Russia. ACKNOWLEDGEMENT

Financial support provided by the Boğaziçi University Research Fund (BAP), İstanbul, Turkey, Project No: 06HY102D is gratefully acknowledged.