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He S, Yan Y, Yu K, Xin X, Guzman SM, Lu J, He Z. Baseflow estimation based on a self-adaptive non-linear reservoir algorithm in a rainy watershed of eastern China. J Environ Manage 2023; 332:117379. [PMID: 36724598 DOI: 10.1016/j.jenvman.2023.117379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/11/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
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.
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Affiliation(s)
- Shengjia He
- School of Environmental and Resource Sciences, Zhejiang A & F University, Lin'an, Hangzhou, 311300, China; University of Florida-IFAS, Indian River Research and Education Center, Fort Pierce, FL, 34945, USA.
| | - Yan Yan
- School of Environmental and Resource Sciences, Zhejiang A & F University, Lin'an, Hangzhou, 311300, China
| | - Ke Yu
- School of Environmental and Resource Sciences, Zhejiang A & F University, Lin'an, Hangzhou, 311300, China
| | - Xiaoping Xin
- University of Florida-IFAS, Indian River Research and Education Center, Fort Pierce, FL, 34945, USA
| | - Sandra M Guzman
- University of Florida-IFAS, Indian River Research and Education Center, Fort Pierce, FL, 34945, USA
| | - Jun Lu
- China Ministry of Education Key Lab of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou, 310058, China
| | - Zhenli He
- University of Florida-IFAS, Indian River Research and Education Center, Fort Pierce, FL, 34945, USA
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