A Nonlinear Dynamics Approach for Urban Water Resources Demand Forecasting and Planning

Abstract

Over the past decades, controversial and conflict-laden water allocation issues among competing domestic, industrial and agricultural water use as well as urban environmental flows have raised increasing concerns (Huang & Chang, 2003); Particularly, Such competition has been exacerbated by the growing population, rapidly economic growth, deteriorating quality of water resources, and shrinking water availability due to a number of natural and human-induced impacts. A sounding strategy for water resources allocation and management can help to reduce or avoid the losses which are caused by water resources scarcity. However, in the water management system, many components and their interactions are uncertain. Such uncertainties could be multiplied not only by fasting changes of socioeconomic boundary conditions but also by unpredictable extreme weather events which caused by climate change. Thus, water resources management should be able to deal with all challenges above. Therefore, an effective integrated approach is desired for urban water adaptive management. Many methods, such as stochastic, fuzzy, and interval-parameter programming techniques, have been employed to counteract uncertainties in different fields of water management and have made great progresses in managing uncertainties in model scale. Water resource is an integral part of the socio-economic-environmental (SEE) system, which is a complex system dominated by human. In order to reach a sounding decision, it is necessary for decision-makers to obtain a better understanding of the significant factors that shape the urban and the way the water resources system reacting to certain policy. Therefore, study of sustainable water resource management should be based on general system theory that addresses dynamic interactions amongst the related social-economic, environmental, and institutional factors as well as non-linearity and multi-loop feedbacks. System dynamics (SD) aims at solving of complex systems problems by simulating development trends of the system and identifying the interrelations of each factor in the system. This will help to explore the hidden mechanism and thus improve the performance of the whole system. Hence, after proposed by W. Forrester (Forrester, 1968), SD model has been widely used in global, national, and regional scales for sustainability assessment and system development programme (Meadows 1973; Mashayekhi, 1990; Saeed, 1994). Due to