Overcoming Equifinality: Leveraging Long Time Series for Stream Metabolism Estimation. Issue 2 (28th February 2018)

• Record Type: Journal Article
• Authors: Appling, Alison P.; Hall, Robert O.; Yackulic, Charles B.; Arroita, Maite
• Is Part Of: Journal of geophysical research. Volume 123:Issue 2(2018)
• Page Start: 624
• Page End: 645
• Publication Date: 2018-02-28
• Subjects: aquatic -- metabolism -- carbon -- oxygen -- photosynthesis -- respiration; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1002/2017JG004140
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: 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 ( K 600 ) or solve for all three parameters (GPP, ER, and K 600 ) simultaneously. Empirical measurements of K 600 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 K 600 among days with similar physical conditions (e.g., discharge). Our new model overcomes the equifinality problem by (1) flexibly relating K 600 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 K 600, 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. Key Points: We introduce a flexible tool to estimate metabolism from multiday time series by Bayesian inference Representing both process and observation error in metabolism models improves accuracy of estimates Hierarchical partial pooling of gas exchange rates reduces bias and noise in metabolism estimates