Article ; Online: Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG
Environmental Modelling and Software. 2023 Sept., v. 167 p.105775-
2023
Abstract: Many conventional stream network metrics are poorly suited to non-perennial streams, which can vary substantially in space and time. To address this issue, we considered non-perennial stream networks as directed acyclic graphs (DAGs). DAG metrics allow: ... ...
| Abstract | Many conventional stream network metrics are poorly suited to non-perennial streams, which can vary substantially in space and time. To address this issue, we considered non-perennial stream networks as directed acyclic graphs (DAGs). DAG metrics allow: 1) summarization of important non-perennial stream characteristics (e.g., complexity, connectedness, and nestedness) from both local (individual segment) and global stream network perspectives, and 2) tracking of these features as networks expand and contract. We review a large number of graph theoretic metrics, and introduce a new R package, streamDAG that codifies approaches we feel are most useful. The streamDAG package contains procedures for handling water presence data, and functions for both local and global analyses of both unweighted and weighted stream DAGs. We demonstrate streamDAG using two North American non-perennial streams: Murphy Creek, a simple drainage system in the Owyhee Mountains of southwestern Idaho, and Konza Prairie, a relatively complex network in central Kansas. |
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| Keywords | computer software ; drainage systems ; nestedness ; space and time ; streams ; Idaho ; Kansas ; Non-perennial stream ; Graph theory ; Directed acyclic graph ; Network analysis ; Hydrologic connectivity ; R computational software |
| Language | English |
| Dates of publication | 2023-09 |
| Publishing place | Elsevier Ltd |
| Document type | Article ; Online |
| ISSN | 1364-8152 |
| DOI | 10.1016/j.envsoft.2023.105775 |
| Database | NAL-Catalogue (AGRICOLA) |
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