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Volume 17
Issue 21
10.3390/w17213083
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Article

Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records

by
Amin Aghababaei
1,
Norman L. Jones
1,*,
Gustavious P. Williams
1,
Eniola Webster-Esho
2,
Ryan van der Heijden
3,
Xueyi Li
1,
T. Prabhakar Clement
2 and
Donna M. Rizzo
3
1
Department of Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA
2
Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
3
Department of Civil and Environmental Engineering, University of Vermont, Burlington, VT 05405, USA
*
Author to whom correspondence should be addressed.
Water 2025, 17(21), 3083; https://doi.org/10.3390/w17213083
Submission received: 23 September 2025 / Revised: 22 October 2025 / Accepted: 25 October 2025 / Published: 28 October 2025
(This article belongs to the Special Issue Advances in Research on Hydrology and Water Resources)
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Abstract

Accurately identifying baseflow-dominant (BFD) periods in streamflow records is crucial for evaluating low-flow conditions, groundwater interactions, and other water resource management issues. While baseflow separation methods are widespread, the definition and identification of BFD flows are relatively new areas. Here, we define BFD periods as flow conditions that occur with minimal contribution from quickflow, including periods dominated by bank flow, groundwater interaction, or residual flow routing through the system. We develop a comprehensive, expert-labeled dataset of BFD periods from 182 USGS stream gages across diverse hydrological settings in the continental United States as ground truth. Using this dataset, we evaluate various automated BFD identification methods, including three new approaches, a machine learning classifier, a gradient-based method, and a statistical method, as well as two established techniques: the BN77 and Strict Baseflow methods. Our results demonstrate that the machine learning model (RF-BFD) outperforms all other approaches, achieving an F1 score of 0.92 and 92% accuracy. This study characterizes challenges in identifying BDF periods and establishes benchmarks for improving BFD identification in large-scale hydrological studies. The findings offer a pathway toward more robust and scalable BFD identification techniques, enhancing low-flow forecasting and groundwater-surface water interaction assessments.
Keywords: baseflow; baseflow separation; baseflow dominant periods; machine learning baseflow; baseflow separation; baseflow dominant periods; machine learning

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MDPI and ACS Style

Aghababaei, A.; Jones, N.L.; Williams, G.P.; Webster-Esho, E.; van der Heijden, R.; Li, X.; Clement, T.P.; Rizzo, D.M. Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records. Water 2025, 17, 3083. https://doi.org/10.3390/w17213083

AMA Style

Aghababaei A, Jones NL, Williams GP, Webster-Esho E, van der Heijden R, Li X, Clement TP, Rizzo DM. Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records. Water. 2025; 17(21):3083. https://doi.org/10.3390/w17213083

Chicago/Turabian Style

Aghababaei, Amin, Norman L. Jones, Gustavious P. Williams, Eniola Webster-Esho, Ryan van der Heijden, Xueyi Li, T. Prabhakar Clement, and Donna M. Rizzo. 2025. "Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records" Water 17, no. 21: 3083. https://doi.org/10.3390/w17213083

APA Style

Aghababaei, A., Jones, N. L., Williams, G. P., Webster-Esho, E., van der Heijden, R., Li, X., Clement, T. P., & Rizzo, D. M. (2025). Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records. Water, 17(21), 3083. https://doi.org/10.3390/w17213083

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