NetConUS_Data_Final.csv

Network connectivity of streams is crucial because it directly affects the movement of water, nutrients, sediments, and aquatic species throughout a watershed. 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. 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). The developed dataset was validated to evaluate statistical associations between the NetConUS stream classes and the physical stream classes based on size, gradient, bifurcation, and temperature, hydrology and valley confinement. We have also validated the developed stream classification using Bayesian Neural Networks (BNNs) to account for uncertainty, and a co-operative game theory-based approach was applied to interpret the influence of network metrics and physical attributes on the classification. 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.