Bayesian Data—Model Integration in Plant Physiological and Ecosystem Ecology

Ogle, Kiona; Barber, Jarrett J.

doi:10.1007/978-3-540-72954-9_12

Cited by 62 publications

(85 citation statements)

References 79 publications

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“…Methods for data-model integration have been recently created, for example, Kalman filter (Gao et al, 2011), Bayesian (Ogle and Barber, 2008;Ricciuto et al, 2008;Schleip et al, 2009;Van Oijen et al, 2005), and Markov chain Monte Carlo (Casella and Robert, 2005). However, no studies have evaluated these methods for integrating CH 4 data with models.…”

Section: Data-model Integrationmentioning

confidence: 99%

Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems

et al. 2016

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Abstract. Over the past 4 decades, a number of numerical models have been developed to quantify the magnitude, investigate the spatial and temporal variations, and understand the underlying mechanisms and environmental controls of methane (CH 4 ) fluxes within terrestrial ecosystems. These CH 4 models are also used for integrating multi-scale CH 4 data, such as laboratory-based incubation and molecular analysis, field observational experiments, remote sensing, and aircraft-based measurements across a variety of terrestrial ecosystems. Here we summarize 40 terrestrial CH 4 models to characterize their strengths and weaknesses and to suggest a roadmap for future model improvement and application. Our key findings are that (1) the focus of CH 4 models has shifted from theoretical to site-and regional-level applications over the past 4 decades, (2) large discrepancies exist among models in terms of representing CH 4 processes and their environmental controls, and (3) significant datamodel and model-model mismatches are partially attributed to different representations of landscape characterization and inundation dynamics. Three areas for future improvements and applications of terrestrial CH 4 models are that (1) CH 4 models should more explicitly represent the mechanisms underlying land-atmosphere CH 4 exchange, with an emphasis on improving and validating individual CH 4 processes over depth and horizontal space, (2) models should be developed that are capable of simulating CH 4 emissions across highly heterogeneous spatial and temporal scales, particularly hot moments and hotspots, and (3) efforts should be invested to develop model benchmarking frameworks that can easily be used for model improvement, evaluation, and integration with data from molecular to global scales. These improvements in CH 4 models would be beneficial for the Earth system models and further simulation of climate-carbon cycle feedbacks.

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Section: Data-model Integrationmentioning

confidence: 99%

Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems

et al. 2016

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“…For example, a particular study may yield multiple types of data representing different biological processes operating at diverse temporal and spatial scales. As Ogle and Barber (2008) note, however, the data are often treated in a piece-wise fashion where different components of the data are analyzed independent of each other despite the fact that all/most data arise from interconnected processes. Moreover, data analysis tends to proceed via simple analysis of variance (ANOVA) or regression methods (Cottingham et al 2005, Hobbs and) that assume linearity and normality of responses (e.g., data) and parameters (e.g., treatment effects, regression coefficients).…”

Section: Experimental Vs Modeling Approachesmentioning

confidence: 99%

“…Fruitful interactions between ecologists and statisticians have spawned dialogue and specific examples demonstrating the utility of such modeling approaches in ecology (e.g., Wikle 2003, Clark and Gelfand 2006a, b, Ogle and Barber 2008, and I applaud Cressie et al for introducing ecologists to some of the fundamental statistical and probability concepts underlying hierarchical statistical modeling. Readers are also referred to Ogle and Barber (2008) for a more in-depth treatment of the hierarchical modeling framework, fundamental probability results, and examples that illustrate the advantages of this approach in plant physiological and ecosystem ecology.…”

Section: Introductionmentioning

confidence: 99%

“…However, application of these approaches requires appropriate training, but training opportunities in hierarchical modeling methods that integrate experimental and/or observational data with models are lacking , Little 2006. Thus, overview papers such as those by and Ogle and Barber (2008) are expected to stimulate interests and motivate new curriculums that deliver training in modern, modelbased approaches to data analysis. Cressie et al discuss several strengths and limitations of hierarchical statistical modeling, but no single topic is treated in great detail.…”

Section: Introductionmentioning

confidence: 99%

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Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models

Luo¹,

Weng²,

Wu³

et al. 2009

Ecological Applications

125

100

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“…uncertainties into the model, and (4) robust estimates of uncertainty and quantification of most likely solutions in an underdetermined system (number of sources greater than number of isotopes plus one). The Bayesian mixing model described here is similar to those used in other studies involving 205 stable isotope data (Ogle and Barber, 2008;Parnell et al, 2010;Cable et al, 2011;Mailloux et al, 2014). For our analysis, we first define the likelihood of the source isotope data.…”

Section: Bayesian Mixing Model and Statistical Analysesmentioning

confidence: 99%

Isotopic reconnaissance of urban water supply system dynamics

Jameel¹,

Brewer²,

Fiorella³

et al. 2018

Preprint

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Abstract. Public water supply systems (PWSS) are critical infrastructure that are vulnerable to 10 contamination and physical disruption. Exploring susceptibility of PWSS to such perturbations requires detailed knowledge of supply system structure and operation. The physical structure of the distribution system (i.e., pipeline connections) and basic information on sources are documented for most industrialized metropolises. Yet, most information on PWSS function comes from hydrodynamic models that are seldom validated using observational data. In 15 developing regions, the issue may be exasperated as information regarding the physical structure of the PWSS may be incorrect, incomplete, undocumented, or difficult to obtain in many cities.Here, we present a novel application of stable isotopes in water (SIW) to quantify the contribution of different water sources, identify "static" and "dynamic" regions (e.g., regions supplied chiefly by one source vs. those experiencing active mixing between multiple sources), 20 and reconstruct basic flow patterns in a large, complex PWSS. Our analysis, based on a Bayesian mixing model framework, uses basic information on the SIW and production volumes of sources but requires no information on pipeline connections in the system. Our work highlights the ability of SIW to analyze PWSS and document aspects of supply system structure and operation

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Bayesian Data—Model Integration in Plant Physiological and Ecosystem Ecology

Cited by 62 publications

References 79 publications

Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems

Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems

Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models

Isotopic reconnaissance of urban water supply system dynamics

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