Temporal Information Partitioning Networks (TIPNets): A process network approach to infer ecohydrologic shifts
Temporal Information Partitioning Networks (TIPNets): A process network approach to infer ecohydrologic shifts
复制标题
DOI：
10.1002/2016wr020218
复制
发表时间：
2017-07-01
影响因子：
5.4
通讯作者：
Kumar, Praveen
中科院分区：
文献类型：
--
作者：
Goodwell, Allison E.;Kumar, Praveen
在一个生态水文系统中，大气、植被和根土子系统的成分在不同的时间尺度和强度上参与强迫和反馈相互作用。由于扰动或降雨、干旱或土地使用等条件的变化，这种复杂相互作用网络的结构在连通性、强度和时间尺度方面都有所不同。然而，这些相互作用的表征是困难的，由于在噪声，非线性和有限的数据存在的多变量和弱依赖性。我们介绍了一个框架的时间信息划分网络（TIPNets），其中时间序列变量被视为节点，滞后的多元互信息措施的链接。这些链接被划分为协同，独特和冗余的信息组件，其中协同是信息只提供联合，独特的信息只提供了一个单一的来源，和冗余是重叠的信息。我们从1分钟的气象站数据在几个小时的时间窗口构建TIPNet。从比较干燥，潮湿，多雨的条件下，我们发现，信息强度增加时，太阳辐射和表面水分，表面水分和风变率是冗余和协同的影响，分别。在一个生长季节，网络趋势揭示了随植被和降雨模式而变化的模式。这里提出的框架，使我们能够解释过程的连接在一个多变量的背景下，这可以导致更好的推断，由于生态水文系统的扰动行为的转变。这项工作有助于更全面地描述系统的行为，并有利于对复杂系统的各种研究。简明的语言摘要生态系统比其各部分的总和更大，因为许多单个过程的组合导致更大规模的行为，如对干旱，天气事件或人类影响的反应。在这项研究中，我们将生态系统视为一个网络，其中时间序列变量是在快速或慢速时间尺度上相互作用的节点。我们开发了一种方法来检测这些节点之间的链接，反映了各种类型的相互作用。这种方法的应用程序，称为时间信息划分网络（TIPNets），气象站的数据表明，网络特性不同的白天，夜间，多雨和干燥的时间段。在一个季节中，这些网络的属性与降雨和植被生长有关。这种方法有助于我们研究生态系统中共同发生的过程，并提高我们对相互作用网络如何导致不同生态系统对变化的反应的理解。
In an ecohydrologic system, components of atmospheric, vegetation, and root-soil subsystems participate in forcing and feedback interactions at varying time scales and intensities. The structure of this network of complex interactions varies in terms of connectivity, strength, and time scale due to perturbations or changing conditions such as rainfall, drought, or land use. However, characterization of these interactions is difficult due to multivariate and weak dependencies in the presence of noise, nonlinearities, and limited data. We introduce a framework for Temporal Information Partitioning Networks (TIPNets), in which time-series variables are viewed as nodes, and lagged multivariate mutual information measures are links. These links are partitioned into synergistic, unique, and redundant information components, where synergy is information provided only jointly, unique information is only provided by a single source, and redundancy is overlapping information. We construct TIPNets from 1 min weather station data over several hour time windows. From a comparison of dry, wet, and rainy conditions, we find that information strengths increase when solar radiation and surface moisture are present, and surface moisture and wind variability are redundant and synergistic influences, respectively. Over a growing season, network trends reveal patterns that vary with vegetation and rainfall patterns. The framework presented here enables us to interpret process connectivity in a multivariate context, which can lead to better inference of behavioral shifts due to perturbations in ecohydrologic systems. This work contributes to more holistic characterizations of system behavior, and can benefit a wide variety of studies of complex systems.Plain Language Summary An ecosystem is greater than the sum of its parts, in that a combination of many individual processes leads to larger scale behaviors such as responses to droughts, weather events, or human impacts. In this study, we view an ecosystem as a network, where time-series variables are nodes that interact with each other on fast or slow time scales. We develop a method to detect links between these nodes that reflect various types of interactions. An application of this method, called Temporal Information Partitioning Networks (TIPNets), to weather station data shows that network characteristics differ between day-time, night-time, rainy, and dry time periods. Over a season, properties of these networks are associated with rainfall and vegetation growth. This method helps us to study processes that occur within an ecosystem together versus separately, and improves our understanding of how a network of interactions leads to different ecosystem responses to changes.