Water Resources Management

, Volume 22, Issue 6, pp 681–698

Interval-parameter Two-stage Stochastic Nonlinear Programming for Water Resources Management under Uncertainty


DOI: 10.1007/s11269-007-9186-8

Cite this article as:
Li, Y.P. & Huang, G.H. Water Resour Manage (2008) 22: 681. doi:10.1007/s11269-007-9186-8


Over the past decades, controversial and conflict-laden water allocation issues among competing interests have raised increasing concerns. In this research, an interval-parameter two-stage stochastic nonlinear programming (ITNP) method is developed for supporting decisions of water-resources allocation within a multi-reservoir system. The ITNP can handle uncertainties expressed as both probability distributions and discrete intervals. It can also be used for analyzing various policy scenarios that are associated with different levels of economic consequences when the promised allocation targets are violated. Moreover, it can deal with nonlinearities in the objective function such that the economies-of-scale effects in the stochastic program can be quantified. The proposed method is applied to a case study of water-resources allocation within a multi-user, multi-region and multi-reservoir context for demonstrating its applicability. The results indicate that reasonable solutions have been generated, which present as combined interval and distributional information. They provide desired water allocation plans with a maximized economic benefit and a minimized system-disruption risk. The results also demonstrate that a proper policy for water allocation can help not only mitigate the penalty due to insufficient supply but also reduce the waste of water resources.


Decision making Inexact optimization Policy analysis Two-stage programming Stochastic Uncertainty Water resources management 

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Environmental Systems Engineering ProgramUniversity of ReginaReginaCanada
  2. 2.Chinese Research Academy of Environmental ScienceBeijingChina

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