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. •Directed acyclic graph (DAG) approaches can describe non-perennial stream networks.•A wide variety of DAG methods are codified in the new R package streamDAG.•Package functions provide both local and global summaries of stream networks.•Functions can be adjusted to stream water presence/absence data.•Weighted DAG approaches can be applied in streamDAG, including Bayesian models.