Baseflow estimation based on a self-adaptive non-linear reservoir algorithm in a rainy watershed of eastern China

Area of Science:

• Hydrology
• Water Resources Management
• Environmental Engineering

Background:

• Accurate baseflow estimation is crucial for water resource management and pollution control.
• Traditional Nonlinear Reservoir Algorithm (NRA) shows limitations in rainy regions due to empirical functions and event disturbances.
• Existing methods struggle with baseflow separation during non-pure recession periods.

Purpose of the Study:

• To develop and validate a Self-Adaptive Nonlinear Reservoir Algorithm (SA-NRA) for improved baseflow separation in rainy watersheds.
• To address uncertainties in baseflow estimation caused by surface flow and rainfall disturbances.
• To enhance the reliability of baseflow prediction during complex hydrological events.

Main Methods:

• Development of SA-NRA integrating NRA with a self-adaptive parameter and Particle Swarm Optimization (PSO).
• Application and validation of SA-NRA in a representative rainy watershed in eastern China.
• Comparison of SA-NRA performance against traditional NRA and Eckhardt's two-parameter recursive digital filter (ERDF).

Main Results:

• SA-NRA demonstrated superior goodness-of-fit for baseflow recession behaviors in rainy regions.
• Traditional NRA and ERDF showed significant uncertainties during non-pure baseflow recession periods.
• SA-NRA effectively handles baseflow variations caused by surface flow and rainfall disturbances.

Conclusions:

• SA-NRA offers a more reliable approach for baseflow separation in challenging rainy environments.
• Baseflow separation uncertainties in non-pure recession periods require greater attention in hydrological studies.
• The developed SA-NRA provides a valuable tool for accurate water resource evaluation and management in humid regions.