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• W4385155134 abstract "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." @default.
• W4385155134 created "2023-07-23" @default.
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• W4385155134 date "2023-08-01" @default.
• W4385155134 modified "2023-10-08" @default.
• W4385155134 title "Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory" @default.
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• W4385155134 doi "https://doi.org/10.1016/j.isci.2023.107417" @default.
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