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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #410712
Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Title: Streamflow characteristics and changes over the conterminous United States

Author
item WANG, Y - Former ARS Employee
item Zhang, Xuesong
item ZHAO, K - The Ohio State University
item SINGH, D - Oak Ridge National Laboratory

Submitted to: Scientific Data
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/5/2024
Publication Date: 7/17/2024
Citation: Wang, Y., Zhang, X., Zhao, K., Singh, D. 2024. Streamflow characteristics and changes over the conterminous United States. Scientific Data. https://doi.org/10.1038/s41597-024-03618-0.
DOI: https://doi.org/10.1038/s41597-024-03618-0

Interpretive Summary: Streamflow is an important indicator of water availability. Here, we compiled historical daily streamflow data at over 8000 hydrologic stations in the U.S. and derived streamflow characteristics and changes. In general, streamflow across the U.S. is subject to more frequent changes and experiences more extreme events from 1960-2020. The new dataset developed here helps understand how streamflow has changed in the past and provides useful information to support future research elucidating drivers of changes in streamflow in the U.S.

Technical Abstract: Long-term changes in streamflow play a crucial role in assessing water availability, anticipating floods, and addressing water scarcity. Additionally, it can aid in the detection of climatic and environmental changes. The lack of temporal streamflow change data at the continental scale of the U.S. represents a data gap in quantifying the underlying mechanisms and factors influencing the river water availability. To address this gap, our study endeavours to (1) detect temporal trends and change points of streamflow for over 8000 hydrologic stations across the Conterminous United States (CONUS) based on a Bayesian trend detection and change point identification algorithm, and (2) derive vital streamflow indicators that are biologically relevant statistics such as the duration and frequency of high or low streamflow events. These streamflow indices offer valuable information for future research for better understanding and quantification of changes in the hydrological cycle.