Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory

Nicola Durighetto; Simone Noto; Flavia Tauro; Salvatore Grimaldi; Gianluca Botter

doi:10.1016/j.isci.2023.107417

Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory

iScience. 2023 Jul 23;26(8):107417. doi: 10.1016/j.isci.2023.107417. eCollection 2023 Aug 18.

Authors

Nicola Durighetto¹, Simone Noto¹, Flavia Tauro², Salvatore Grimaldi², Gianluca Botter¹

Affiliations

¹ Department of Civil, Environmental and Architectural Engineering, University of Padua, 35131 Padua (Padua), Italy.
² Department of Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, 01100 Viterbo (Viterbo), Italy.

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.

Keywords: Earth sciences; Geomatics; Hydrology.