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Complex adaptive systems simulation-optimization framework for adaptive urban water resources management.

机译：用于城市水资源自适应管理的复杂自适应系统仿真优化框架。

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摘要

Population growth, urbanization and climate change threaten urban water systems. The rise of demands caused by growing urban areas and the potential decrease of water availability caused by the increase of frequency and severity of droughts challenge the continued well-being of society. Due to increasing environmental and financial constraints, water management paradigms have shifted from supply augmentation to demand management, and water conservation initiatives may efficiently decrease water demands to more sustainable levels. To provide reliable assessment of the efficiencies of different demand management strategies, new modeling techniques are needed that can simulate decentralized decisions of consumers and their interactions with the water system. An integrated simulation-optimization framework, based on the paradigm of Complex Adaptive Systems, is developed here to model dynamic interactions and adaptations within social, built, and natural components of urban water systems. The framework goes beyond tradition engineering simulations by incorporating decentralized, heterogeneous and autonomous agents, and by simulating dynamic feedback loops among modeling components. The framework uses modeling techniques including System Dynamics, Cellular Automata, and Agent-based Modeling to simulate housing and population growth, a land use change, residential water consumption, the hydrologic cycle, reservoir operation, and a policy/decision maker. This research demonstrates the applicability of the proposed framework through a series of studies applied to a water supply system of a large metropolitan region that is located in a semi-arid region and suffers recurrently from severe droughts. A set of adaptive demand management strategies, that apply contingency restrictions, land use planning, and water conservation technologies, such as rainwater harvesting systems, are evaluated. A multiobjective Evolutionary Algorithm is coupled with the CAS simulation framework to identify optimal strategies and explore conflicting objectives within a water system. The results demonstrate the benefits of adaptive management by updating management decisions to changing conditions. This research develops a new hydrologic sustainability metric, developed to quantify the stormwater impacts of urbanization. The Hydrologic Footprint Residence captures temporal and spatial hydrologic characteristics of a flood wave passing through a stream segment and is used to assess stormwater management scenarios, including Best Management Practices and Low Impact Development.
机译：人口增长，城市化和气候变化威胁着城市供水系统。不断增长的城市地区引起的需求增加以及干旱频率和严重程度的增加导致水资源的潜在减少，对社会的持续福祉提出了挑战。由于越来越多的环境和财务约束，水管理范式已从增加供应转向需求管理，并且节水举措可能有效地将水需求降低到更可持续的水平。为了提供对不同需求管理策略效率的可靠评估，需要新的建模技术，该技术可以模拟消费者的分散决策及其与水系统的交互作用。这里开发了一个基于复杂自适应系统范式的集成模拟优化框架，以对城市供水系统的社会，建筑和自然组成部分之间的动态相互作用和适应进行建模。该框架通过结合分散，异构和自治的代理，并通过模拟建模组件之间的动态反馈循环，超越了传统的工程仿真。该框架使用建模技术，包括系统动力学，元胞自动机和基于代理的建模，以模拟住房和人口增长，土地用途变化，住宅用水量，水文循环，水库运行以及政策/决策制定者。这项研究通过对位于半干旱地区且经常遭受严重干旱困扰的大城市地区的供水系统进行的一系列研究，证明了拟议框架的适用性。评估了一组适应性需求管理策略，这些策略应用了应急限制，土地使用规划和节水技术（例如雨水收集系统）。多目标进化算法与CAS仿真框架结合在一起，可以确定最佳策略并探索水系统中相互冲突的目标。结果通过将管理决策更新为变化的条件来证明自适应管理的好处。这项研究开发了一种新的水文可持续性指标，旨在量化雨水对城市化的影响。水文足迹公寓可捕获流经河流段的洪水波的时空水文特征，并用于评估雨水管理方案，包括最佳管理实践和低影响开发。

著录项

  • 作者

    Giacomoni, Marcio Hofheinz.;

  • 作者单位

    Texas A&M University.;

  • 授予单位 Texas A&M University.;
  • 学科 Engineering Civil.;Water Resource Management.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 193 p.
  • 总页数 193
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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