Baseflow estimation based on a self-adaptive non-linear reservoir algorithm in a rainy watershed of eastern China. (15th April 2023)

• Record Type: Journal Article
• Authors: He, Shengjia; Yan, Yan; Yu, Ke; Xin, Xiaoping; Guzman, Sandra M.; Lu, Jun; He, Zhenli
• Is Part Of: Journal of environmental management. Volume 332(2023)
• Publication Date: 2023-04-15
• Subjects: Baseflow -- Baseflow separation -- Baseflow index -- Nonlinear reservoir algorithm -- Digital filter; NRA Nonlinear reservoir algorithm -- SA-NRA Self-adaptive non-linear reservoir algorithm -- ERDF Eckhardt's two-parameter recursive digital filter -- PSO Particle Swarm Optimization algorithm -- FD Fréchet distances; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2023.117379
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Accurate baseflow estimation is critical for water resources evaluation and management, and non-point source pollution quantification. Nonlinear reservoir algorithm (NRA) has been increasingly applied to baseflow separation because of its good approximation to the real groundwater discharge (commonly dominated by the unconfined aquifer) in most watersheds. However, in the rainy regions, large uncertainties may remain in the traditional NRA-separated baseflow sequences due to its empirical transition function for the rising limb of discharge process, and the evident variations of baseflow recession in the initial period of the falling limb caused by the disturbance from surface flow or rainfall events. To improve the reliability of baseflow separation, a self-adaptive non-linear reservoir algorithm (SA-NRA) was developed in this study based on the NRA, a self-adaptive groundwater discharge modified parameter, and the Particle Swarm Optimization algorithm (PSO). The validation of SA-NRA in a rainy watershed of eastern China showed that SA-NRA could be the approach to provide a goodness-of-fit for baseflow recession behaviors in the rainy regions. The traditional NRA and Eckhardt's two-parameter recursive digital filter (ERDF), calibrated (or validated) only with the pure baseflow recession data, can hardly provide reliable baseflow predictions for the non-pure baseflow recession periods (including the rising limb and the falling limb with surface flow or rainfall disturbance) due to the apparent variations of baseflow recession behavior. Therefore, more attentions should be paid to the uncertainties of baseflow separation for the non-pure baseflow recession periods in the rainy regions. Highlights: Bseflow estimation uncertainty mainly remains in non-pure baseflow recession period. A self-adaptive nonlinear reservoir algorithm was proposed for baseflow separation. This approach reduced baseflow separation uncertainty apparently in a rainy watershed. Comparison results showed that this approach was superior to traditional approaches. This approach can be a good choice for baseflow estimation in the rainy regions.