Durighetto Nicola, Noto Simone, Tauro Flavia, Grimaldi Salvatore, Botter Gianluca
Department of Civil, Environmental and Architectural Engineering, University of Padua, 35131 Padua (Padua), Italy.
Department of Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, 01100 Viterbo (Viterbo), Italy.
iScience. 2023 Jul 23;26(8):107417. doi: 10.1016/j.isci.2023.107417. eCollection 2023 Aug 18.
The study of non-perennial streams requires extensive experimental data on the temporal evolution of surface flow presence across different nodes of channel networks. However, the consistency and homogeneity of available datasets is threatened by the empirical burden required to map stream network expansions and contractions. Here, we developed a data-driven, graph-theory framework aimed at representing the hierarchical structuring of channel network dynamics (i.e., the order of node activation/deactivation during network expansion/retraction) through a directed acyclic graph. The method enables the estimation of the configuration of the active portion of the network based on a limited number of observed nodes, and can be utilized to combine datasets with different temporal resolutions and spatial coverage. A proof-of-concept application to a seasonally-dry catchment in central Italy demonstrated the ability of the approach to reduce the empirical effort required for monitoring network dynamics and efficiently extrapolate experimental observations in space and time.
对非常年性溪流的研究需要关于不同河网节点处地表水流存在的时间演变的大量实验数据。然而，可用数据集的一致性和同质性受到绘制河网扩张和收缩所需的经验负担的威胁。在此，我们开发了一个数据驱动的图论框架，旨在通过有向无环图来表示河网动态的层次结构（即网络扩张/收缩期间节点激活/停用的顺序）。该方法能够基于有限数量的观测节点估计网络活跃部分的配置，并可用于组合具有不同时间分辨率和空间覆盖范围的数据集。对意大利中部一个季节性干旱集水区的概念验证应用表明，该方法能够减少监测网络动态所需的经验工作量，并有效地在空间和时间上外推实验观测结果。
