U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Full-Flow-Regime Storage-Streamflow Correlation Patterns Provide Insights into Hydrologic Functioning over the Continental U.S.
Published: September 23, 2017
Posted: January 23, 2018

New study examines storage-streamflow correlations using data mining approaches to reveal fundamental controls of hydrologic responses.

The Science
Researchers examine the connections between streamflow and water storage and used data mining to form hypotheses about what controls system behaviors.

The Impact
The research demonstrates the importance of subsurface properties and groundwater flow in capturing flood and drought and define a “storage-streamflow correlation spectrum” which may be used in future studies.

Summary
“Characteristic hydrologic behavior” or system “signatures” refer to system behaviors that are consistently observed, and they serve as excellent benchmarks or evaluation metrics for model behaviors. In this study, DOE-funded authors from Penn State University studied streamflow and water storage, and formed hypotheses about significant controls on these systems. Inter-annual changes in low, median and high regimes of streamflow have important implications for flood control, irrigation, and ecologic and human health. The Gravity Recovery and Climate Experiment (GRACE) satellites record global terrestrial water storage anomalies (TWSA), providing an opportunity to observe, interpret, and potentially utilize the complex relationships between storage and full-flow-regime streamflow. Here we show that utilizable storage-streamflow correlations exist throughout vastly different climates in the continental U.S. (CONUS) across low to high flow regimes. A panoramic framework, the storage-streamflow correlation spectrum (SSCS), is proposed to examine macroscopic gradients in these relationships. SSCS helps form, corroborate or reject hypotheses about basin hydrologic behaviors. SSCS patterns vary greatly over CONUS with climate, land surface and geologic conditions. Data mining analysis suggests that for catchments with hydrologic settings that favor storage over runoff, e.g., a large fraction of precipitation as snow, thick and highly permeable soil, SSCS values tend to be high. Based on our results, we form the hypotheses that groundwater flow dominates streamflows in Southeastern CONUS and Great Plains, while thin soils in a belt along the Appalachian Mountains impose a limit on water storage. SSCS also suggests shallow water table caused by high-bulk density soil and flat terrain induces rapid runoff in several regions, and indicate the importance of subsurface properties and groundwater behavior to determine flood or drought potential. The team proposes that SSCS can be used as a fundamental hydrologic signature to constrain models and to provide insights that lead us to better understand hydrologic functioning.

Contacts (BER PMs)
Dorothy Koch
Earth System Modeling
Dorothy.Koch@science.doe.gov

PI Contact
Chaopeng Shen
Penn State University
Shen.Chaopeng@gmail.com

Funding
The U.S. Department of Energy Office of Science, Biological and Environmental Research, Earth System Modeling Program.

Publication
Fang, K. and C. Shen. “Full-flow-regime storage-streamflow correlation patterns provide insights into hydrologic functioning over the continental U.S.” Water Resources Research (2017). [DOI: 10.1002/2016WR020283]

Related Links
Reference link

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

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