Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG

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.

Automated summary

To address the incompatibility of conventional stream network metrics with non-perennial streams, the researchers treat non-perennial stream networks as directed acyclic graphs (DAGs). DAG metrics enable the summarization of important characteristics of non-perennial streams and tracking of these features as networks change. They introduce a new R package, streamDAG, which includes procedures and functions for handling water presence data and analyzing both unweighted and weighted stream DAGs. The package is demonstrated using two North American non-perennial streams.