DOI: 10.1002/2017JG004140
Scopus记录号: 2-s2.0-85042549488
论文题名: Overcoming Equifinality: Leveraging Long Time Series for Stream Metabolism Estimation
作者: Appling A.P. ; Hall R.O. ; Jr. ; Yackulic C.B. ; Arroita M.
刊名: Journal of Geophysical Research: Biogeosciences
ISSN: 21698953
出版年: 2018
卷: 123, 期: 2 起始页码: 624
结束页码: 645
语种: 英语
英文关键词: aquatic
; carbon
; metabolism
; oxygen
; photosynthesis
; respiration
英文摘要: The foundational ecosystem processes of gross primary production (GPP) and ecosystem respiration (ER) cannot be measured directly but can be modeled in aquatic ecosystems from subdaily patterns of oxygen (O2) concentrations. Because rivers and streams constantly exchange O2 with the atmosphere, models must either use empirical estimates of the gas exchange rate coefficient (K600) or solve for all three parameters (GPP, ER, and K600) simultaneously. Empirical measurements of K600 require substantial field work and can still be inaccurate. Three-parameter models have suffered from equifinality, where good fits to O2 data are achieved by many different parameter values, some unrealistic. We developed a new three-parameter, multiday model that ensures similar values for K600 among days with similar physical conditions (e.g., discharge). Our new model overcomes the equifinality problem by (1) flexibly relating K600 to discharge while permitting moderate daily deviations and (2) avoiding the oft-violated assumption that residuals in O2 predictions are uncorrelated. We implemented this hierarchical state-space model and several competitor models in an open-source R package, streamMetabolizer. We then tested the models against both simulated and field data. Our new model reduces error by as much as 70% in daily estimates of K600, GPP, and ER. Further, accuracy benefits of multiday data sets require as few as 3 days of data. This approach facilitates more accurate metabolism estimates for more streams and days, enabling researchers to better quantify carbon fluxes, compare streams by their metabolic regimes, and investigate controls on aquatic activity. ©2018. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/114593
Appears in Collections: 气候减缓与适应
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作者单位: U.S. Geological Survey, Integrated Information Dissemination Division, Data Science Branch, Tucson, AZ, United States; Flathead Lake Biological Station, University of Montana, Polson, MT, United States; U.S. Geological Survey, Grand Canyon Monitoring and Research Center, Flagstaff, AZ, United States; Department of Plant Biology and Ecology, University of the Basque Country, Bilbao, Spain
Recommended Citation:
Appling A.P.,Hall R.O.,Jr.,et al. Overcoming Equifinality: Leveraging Long Time Series for Stream Metabolism Estimation[J]. Journal of Geophysical Research: Biogeosciences,2018-01-01,123(2)