Authors: Xueyi Li, Amin Aghababaei, Norm Jones, and Gustavious Williams – Brigham Young University; Eniola Webster-Esho, Prabhakar Clement – The University of Alabama; Ryan Van Der Heijden, Donna Rizzo – University of Vermont
Presentation Type: Poster
Title: BASEFLOW: A Python package for digital baseflow separation and analysis
Abstract: Accurate baseflow estimation from streamflow data is critical for hydrological applications and water resource management in low flow and drought conditions. We present a Python package that provides a comprehensive set of digital filtering and graphical methods from the literature for baseflow separation. The package implements widely used algorithms with both one-parameter and two-parameter digital filters (USGS HYSEP, Eckhardt), as well as graphically-based techniques such as the local minimum method or recursive digital filters.
This package uses a common API, which provides users with the flexibility to easily explore and compare different baseflow estimation approaches. Users can adjust algorithm parameters, apply filters to specific periods, and visualize separated baseflow and quickflow components. The package offers some visualization tools for exploratory analysis, generating publication-quality hydrographs with baseflow/quickflow and recession curve analysis, but returns standard numpy arrays for use with other python tools.
This open-source python package provides hydrologists and researchers with a powerful tool to explore different baseflow estimation and analysis methods, contributing to improved understanding and management of water resources.