intrinsic in information. Such uncertainty is commonly accounted for by conservative

parameter estimations, factors of safety, or statistical design.

 The general formulation of the engineering design problems and therefore models

consists of two main parts; the objection function and constraints (Heaney, unpublished

manuscript, 2006). Heaney (unpublished manuscript, 2006) defines the parts, where

decision variables are one-time parameter decisions and/or operating rules, as:

* Objective function: Maximize or minimize some stated objectives) by selecting
 the best values of the decision variables.

* Constraints: Physical, chemical, and/or biological process relationships and/or
 operational and regulatory constraints on the variables.

 Traditionally design relies on constraining the system to separate the design into

manageable and domain specific parts. Traditional design leads to a reductionism

approach, where the outcome of the design is a function of the constraints. The systems

engineering approach divides the design into disciplinary models, which are incorporated

into a whole system model. This leads to a design that is less focused on constraints and

may produce more optimal designs (Hazelrigg 1996).

 Lee et al. (2005) document an approach to optimize the design urban stormwater

storage-release systems. Urban approaches may also be applied to reservoir modeling as

they are fundamentally both storage-release systems. Lee et al. approach uses cost as the

objective for a design based on continuous simulation of water quantity and quality.

Spreadsheets are utilized to link powerful optimization tools to transparent process

models. Unlike traditional approaches, design may be optimized for 3 or more

parameters.