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Non-linear dynamics of United States streamflow dataset
Krzysztof Raczyński1, Katarzyna Grala1, John H Cartwright1
1Mississippi State University, Geosystems Research Institute, 2 Research Blvd, 39759, Starkville, MS, USA.
Data in Brief
|October 23, 2025
Summary
This study presents a comprehensive hydrological dataset of streamflow dynamics across the US. It includes fractal and chaos metrics to aid in understanding water resource variability and developing predictive models.
Area of Science:
- Hydrology
- Geophysics
- Data Science
Background:
- Streamflow data is crucial for water resource management and hydrological research.
- Understanding the complex dynamics of streamflow, including fractal and chaotic behaviors, is essential for accurate modeling and prediction.
- Existing datasets often lack comprehensive analyses of these complex dynamics across diverse spatial and temporal scales.
Purpose of the Study:
- To create a comprehensive hydrological dataset with streamflow time series and associated fractal and chaos metrics.
- To provide data for benchmarking streamflow dynamics, evaluating hydrological models, and supporting machine learning applications.
- To enable regional classification of hydrological behaviors based on dynamic properties.
Main Methods:
- Compiled daily, weekly, monthly, quarterly, and annual streamflow time series from 2899 US and Puerto Rican gauging stations (1970-2023).
- Calculated a suite of fractal and chaos metrics including Hurst exponents, DFA, multifractality, WTM, sample entropy, RQA, and Lyapunov exponents.
- Applied fuzzy C-means clustering to group gauges into three dynamic-behavior categories, generating membership probabilities and including station metadata.
Main Results:
- A rich dataset containing raw and processed streamflow time series, computed fractal and chaos metrics, cluster assignments, and geolocational metadata.
- Data covers three flow regimes (maximum, average, minimum) across five temporal resolutions with interpolated data for completeness.
- Identified three distinct dynamic-behavior groups of gauging stations based on hydrological complexity.
Conclusions:
- The dataset provides a valuable resource for researchers and water managers to analyze and benchmark streamflow dynamics.
- Facilitates the evaluation of hydrological models and the development of advanced water resource management strategies.
- Supports the advancement of hydrological science through data-driven insights and machine learning applications.
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