论文标题
看到树木的森林：大湖水供应中能量的缩放分析
Seeing the Forest for the Trees: Scaling Analysis of Energy in Great Lakes Water Supplies
论文作者
论文摘要
资源规模量化水供应中的能源是供水系统的区域尺度治理以及对“水能发射” Nexus的国家和全球规模评估的基础。但是仍然缺乏基于身体的方法。在这里，应用最近出现的复杂系统原理，我们开发了“缩放分析”（SA），这是一种基于网络的复杂方法，用于量化资源或区域规模的水供应中的能量和能量尼克斯。 SA构思供水系统作为嵌入区域自我组织复杂系统中的网络，探索偏斜或缩放尺寸丰度的复杂系统定律（随着系统尺寸的增加而减小系统丰度），并将系统能量缩放（随着增加系统尺寸的增加）作为系统尺寸的系统尺寸和预测系统的能源的统一制度。克服跨层和无界系统大小分布以及能量数据稀缺的传统方法面临的挑战，SA代表基于物理的，数据驱动的预测。我们经验证明了SA并测试了其对大湖供水系统的预测，这是世界上地理上最广泛的水资源之一。
Resource-scale quantification of energy in water supplies is a basis for regional-scale governance of water systems and national- and global-scale assessments of the "water-energy-emission" nexus. But a physically based approach for this quantification remains lacking. Here, applying recently emerged complex system principles, we develop "scaling analysis" (SA), a complex network-based methodology for quantifying energies and energy-nexus properties in water supplies at the resource or regional scale. Conceiving water supply systems as networks embedded in regionally self-organizing complex systems, SA explores the complex system laws of skewed or scaled size abundance (decreasing system abundance with increasing system size) and allometric energy scaling (decreasing system energy intensity with increasing system size) as unifying formulation for profiling system size distribution and predicting system energies from system sizes. Overcoming challenges facing traditional approaches from transscale and unbounded system size distributions and scarcity of energy data, SA represents physically based, data-driven predictions. We empirically demonstrate SA and test its predictions for the water supply systems of the Great Lakes, one of the world's most geographically expansive water resources.
