Empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissions
Methane (CH4) emissions from freshwater ecosystems represent a major and dynamic component of the global greenhouse gas budget, yet their regulation by interacting physical, chemical, and biological processes remains inadequately understood, particularly under nonlinear and lagged system responses....
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25007216 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850082563462266880 |
|---|---|
| author | Liu YANG Zhe LI Dianchang WANG Kun SHAN Yan XIAO Lunhui LU Xinghua WU Chong LI |
| author_facet | Liu YANG Zhe LI Dianchang WANG Kun SHAN Yan XIAO Lunhui LU Xinghua WU Chong LI |
| author_sort | Liu YANG |
| collection | DOAJ |
| description | Methane (CH4) emissions from freshwater ecosystems represent a major and dynamic component of the global greenhouse gas budget, yet their regulation by interacting physical, chemical, and biological processes remains inadequately understood, particularly under nonlinear and lagged system responses. Here, we introduce a data-driven modeling framework that integrates Non-negative Latent Factor (NLF) imputation and Empirical Dynamic Modeling (EDM) to disentangle the nonlinear mechanisms driving freshwater CH4 emissions. Using 12 years of in-situ observations from a hydrologically regulated reservoir system, our results reveal that diffusive CH4 emissions are jointly driven by allochthonous organic carbon inputs and phytoplankton community dynamics. Dissolved organic carbon (DOC) dominates CH4 emissions, contributing ∼ 40 % under extreme reservoir inflows (>30,000 m3·s−1), while phytoplankton biomass surpasses DOC’s influence when total phosphorus exceeds 0.14 mg·L−1, highlighting eutrophication’s role in amplifying methanogenic pathways. A time lag of 1 to 2 weeks exists in the air–water CH4 flux response to environmental changes, aligning with phytoplankton growth cycles and underscoring the complexity of the ecosystem-level regulation for CH4 emissions. Dynamic path analysis and scenario simulations based on interaction strength trajectories demonstrate that the dominant regulation pathways of CH4 emissions shift with seasonality and environmental thresholds. This study advances a transferable framework for modeling nonlinear emission dynamics and feedback regulation in freshwater systems, underscoring the necessity of integrating hydrological and ecological controls into CH4 mitigations and must be balanced with CO2 management at the ecosystem scale. |
| format | Article |
| id | doaj-art-6f8d2835f2fe42e380423e842eb30b80 |
| institution | DOAJ |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-6f8d2835f2fe42e380423e842eb30b802025-08-20T02:44:29ZengElsevierEcological Indicators1470-160X2025-08-0117711379110.1016/j.ecolind.2025.113791Empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissionsLiu YANG0Zhe LI1Dianchang WANG2Kun SHAN3Yan XIAO4Lunhui LU5Xinghua WU6Chong LI7State Key Laboratory of Lake and Watershed Science for Water Security, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, ChinaState Key Laboratory of Lake and Watershed Science for Water Security, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China; Corresponding author at: State Key Laboratory of Lake and Watershed Science for Water Security, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.National Engineering Research Center of Eco-Environment in the Yangtze River Economic Belt, China Three Gorges Corporation, Wuhan 430010, ChinaState Key Laboratory of Lake and Watershed Science for Water Security, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, ChinaState Key Laboratory of Lake and Watershed Science for Water Security, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, ChinaState Key Laboratory of Lake and Watershed Science for Water Security, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, ChinaChina Three Gorges Corporation, Wuhan 430010, ChinaNational Engineering Research Center of Eco-Environment in the Yangtze River Economic Belt, China Three Gorges Corporation, Wuhan 430010, ChinaMethane (CH4) emissions from freshwater ecosystems represent a major and dynamic component of the global greenhouse gas budget, yet their regulation by interacting physical, chemical, and biological processes remains inadequately understood, particularly under nonlinear and lagged system responses. Here, we introduce a data-driven modeling framework that integrates Non-negative Latent Factor (NLF) imputation and Empirical Dynamic Modeling (EDM) to disentangle the nonlinear mechanisms driving freshwater CH4 emissions. Using 12 years of in-situ observations from a hydrologically regulated reservoir system, our results reveal that diffusive CH4 emissions are jointly driven by allochthonous organic carbon inputs and phytoplankton community dynamics. Dissolved organic carbon (DOC) dominates CH4 emissions, contributing ∼ 40 % under extreme reservoir inflows (>30,000 m3·s−1), while phytoplankton biomass surpasses DOC’s influence when total phosphorus exceeds 0.14 mg·L−1, highlighting eutrophication’s role in amplifying methanogenic pathways. A time lag of 1 to 2 weeks exists in the air–water CH4 flux response to environmental changes, aligning with phytoplankton growth cycles and underscoring the complexity of the ecosystem-level regulation for CH4 emissions. Dynamic path analysis and scenario simulations based on interaction strength trajectories demonstrate that the dominant regulation pathways of CH4 emissions shift with seasonality and environmental thresholds. This study advances a transferable framework for modeling nonlinear emission dynamics and feedback regulation in freshwater systems, underscoring the necessity of integrating hydrological and ecological controls into CH4 mitigations and must be balanced with CO2 management at the ecosystem scale.http://www.sciencedirect.com/science/article/pii/S1470160X25007216Methane emissionsNonlinear feedback loopsTime-lag responseEmpirical Dynamic Modeling (EDM)Freshwater ecosystem |
| spellingShingle | Liu YANG Zhe LI Dianchang WANG Kun SHAN Yan XIAO Lunhui LU Xinghua WU Chong LI Empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissions Ecological Indicators Methane emissions Nonlinear feedback loops Time-lag response Empirical Dynamic Modeling (EDM) Freshwater ecosystem |
| title | Empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissions |
| title_full | Empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissions |
| title_fullStr | Empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissions |
| title_full_unstemmed | Empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissions |
| title_short | Empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissions |
| title_sort | empirical dynamic modeling helps deciphering the nonlinear feedback loops governing freshwater methane emissions |
| topic | Methane emissions Nonlinear feedback loops Time-lag response Empirical Dynamic Modeling (EDM) Freshwater ecosystem |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25007216 |
| work_keys_str_mv | AT liuyang empiricaldynamicmodelinghelpsdecipheringthenonlinearfeedbackloopsgoverningfreshwatermethaneemissions AT zheli empiricaldynamicmodelinghelpsdecipheringthenonlinearfeedbackloopsgoverningfreshwatermethaneemissions AT dianchangwang empiricaldynamicmodelinghelpsdecipheringthenonlinearfeedbackloopsgoverningfreshwatermethaneemissions AT kunshan empiricaldynamicmodelinghelpsdecipheringthenonlinearfeedbackloopsgoverningfreshwatermethaneemissions AT yanxiao empiricaldynamicmodelinghelpsdecipheringthenonlinearfeedbackloopsgoverningfreshwatermethaneemissions AT lunhuilu empiricaldynamicmodelinghelpsdecipheringthenonlinearfeedbackloopsgoverningfreshwatermethaneemissions AT xinghuawu empiricaldynamicmodelinghelpsdecipheringthenonlinearfeedbackloopsgoverningfreshwatermethaneemissions AT chongli empiricaldynamicmodelinghelpsdecipheringthenonlinearfeedbackloopsgoverningfreshwatermethaneemissions |