"Resilience Thinking" is a relatively new paradigm that maybe used in the evaluation and management of complex systems. Up to now this conceptual framework was mostly used for the management of social-ecological systems. The goal of managing for a resilient complex system is finding ways to maintain the ability of the system to absorb stochastic and human induced perturbations and eventually return to the stable domain of the current state. Or, as the question goes, understanding what happens to the complex system behavior if it is improperly managed and a tipping point is reached and the system is kicked out of the stability domain of the current state. The likelihood of such a change may yield the

Multimedia Environmental Simulations Laboratory, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA e-mail: [email protected]

following possibilities: (i) the system crosses a threshold and enters into an alternate stability domain where the resilience of the overall system may increase or decrease; and, (ii) the system may not find another stability domain and the outcome is unsustainable chaotic behavior of the complex system. This is a concept which is extremely useful especially for those cases where we anticipate that the command and control approach of classical analysis of environmental systems may not work.

More and more we are recognizing that the surroundings in which we live in, function and enjoy life as human beings can only be described in terms of the principles embedded in the complex system theory [1, 2]. Extracting some parts of this complex system and only incorporating those parts into our analysis renders the overall analysis to be fragmented and inadequate. For the overall assessment to be more representative and inclusive, more coherence to the complex nature of the environment studied is needed and in this analysis the integration of various components of the complex system we live in (environmental and non-environmental stressors) must be represented. Computational aspects of this analysis must integrate deterministic and stochastic approaches. The treatment of uncertainties must be based on sound scientific procedures that represent probabilistic and heuristic uncertainty that are inherent in the data used.

Current environmental models and accompanying methods of analysis are mostly compartmentalized, that is they use reduced dimensionality in both conceptual and quantitative level. This analysis is mostly based on information that is obtained from environmental models that are designed to control change in systems that are assumed to be stable. The aim in "control based" approach is to evaluate the capacity of the system to cope with, adapt to, and shape the change that will be imposed. This has been the case for most water resources, environmental management models and applications. However, there are risks with this "command-and-control" analysis principle. Stochastic or human induced changes or stresses may change the state of the system with serious impacts on the outcome. Perception of the stability of human-environmental systems and the concept of "change is possible to control," has proven to be false in many occasions in the past. Today we know that human-environmental systems do not respond to change in a smooth and predictable manner. Rather, a stressed system can suddenly shift from a seemingly steady state to a state that is difficult to reverse [3, 4]. As a result, proper system evaluation methodologies are becoming increasingly complex and it is beneficial to reflect this in climate change analysis as well. Taking this complexity seriously has fundamental consequences for our understanding of what we are defining here to be a resilience based analysis of climate change effects on water quality and other state variables such as health effects.

Basic concepts of this conceptual framework will be covered in what follows and some insight will be provided for the quantification of this analysis which may prove to be useful in the application of this methodology in this field and also in other related fields.

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