NetConUS Stream Dataset

Stream classification is essential for understanding ecosystem diversity and guiding conservation efforts, especially given the increasing impacts of land and riverscape modification in the United States. Existing stream classification does not utilize the network connectivity of streams, while network connectivity directly affects the movement of water, nutrients, sediments, and aquatic species throughout a watershed. Hence, accurately classifying streams based on the connectivity of the stream networks is critical for effective management and biodiversity protection. To address this problem, we developed a robust framework and dataset that incorporates a Network Connectivity-based stream classification system for the conterminous United States (NetConUS), utilizing the National Hydrography Dataset Plus version 2 (NHD). We have validated the developed stream classification using Bayesian Neural Networks (BNNs) to account for uncertainty in the Network connectivity classes. The dataset captures the structural roles of stream segments within their watersheds, facilitating a deeper understanding of stream dynamics for improved estimation of population-, community-, and ecosystem-level processes throughout the conterminous US riverine networks.