A key issue in optimization model development is the selection of spatial and temporal scale representing the system. This study proposes a framework for reasoning about scale in this context, drawing on a review of studies applying multi-objective optimization for water management involving environmental flows. We suggest that scale is determined by the management problem, constrained by data availability, computational, and model capabilities. There is therefore an inherent trade-off between problem perception and available modelling capability, which can either be resolved by obtaining data needed or tailoring analysis to the data available. In the interest of fostering transparency in this trade-off process, this paper outlines phases of model development, associated decisions, and available options, and scale implications of each decision. The problem perception phase collects system information about objectives, limiting conditions, and management options. The problem formulation phase collects and uses data, information, and methods about system structure and behaviour.

优化模型开发中的关键问题是代表系统的时空尺度的选择。这项研究提出了一个在此背景下进行规模推理的框架，该研究借鉴了将多目标优化应用于涉及环境流量的水管理的研究综述。我们建议规模由管理问题确定，并受数据可用性，计算和模型功能的约束。因此，在问题感知和可用建模能力之间存在内在的权衡，可以通过获取所需数据或根据可用数据定制分析来解决。为了在此折衷过程中提高透明度，本文概述了模型开发，相关决策和可用选项的阶段，以及每个决策的规模含义。问题感知阶段收集有关目标，限制条件和管理选项的系统信息。问题制定阶段收集并使用有关系统结构和行为的数据，信息和方法。