A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil

Wang, Gangsheng; Chen, Shulin

doi:10.1016/j.geoderma.2011.11.009

Cited by 57 publications

(37 citation statements)

References 73 publications

(95 reference statements)

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“…15–39 in Supporting Information Table S3). When multiple response variables (e.g., Sediment, TP, TN,

{NO}_{3}^{-}

{NO}_{2}^{-}

) were considered in model calibration, we calculated the overall objective function as the weighted average of individual objective function of respective response variable (Wang & Chen, ).…”

Section: Methodsmentioning

confidence: 99%

Hydrologic and water quality responses to biomass production in the Tennessee river basin

et al. 2018

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Reducing dependence on fossil-based energy has raised interest in biofuels as a potential energy source, but concerns have been raised about potential implications for water quality. These effects may vary regionally depending on the biomass feedstocks and changes in land management. Here, we focused on the Tennessee River Basin (TRB), USA. According to the recent 2016 Billion-Ton Report (BT16) by the US Department of Energy, under two future scenarios (base-case and high-yield), three perennial feedstocks show high potential for growing profitably in the TRB: switchgrass (Panicum virgatum), miscanthus (Miscanthus × giganteus), and willow (Salix spp.). We used the Soil & Water Assessment Tool (SWAT) to compare hydrology and water quality for a current landscape with those simulated for two future BT16 landscapes. We combined publicly available temporal and geospatial datasets with local land and water management information to realistically represent physical characteristics of the watershed. We developed a new autocalibration tool (SWATopt) to calibrate and evaluate SWAT in the TRB with reservoir operations, including comparison against synthetic and intermediate response variables derived from gage measurements. Our spatiotemporal evaluation enables to more realistically simulate the current situation, which gives us more confidence to project the effects of land-use changes on water quality. Under both future BT16 scenarios, simulated nitrate and total nitrogen loadings and concentrations were greatly reduced relative to the current landscape, whereas runoff, sediment, and phosphorus showed only small changes. Difference between simulated water results for the two future scenarios was small. The influence of biomass production on water quantity and quality depended on the crop, area planted, and management practices, as well as on site-specific characteristics. These results offer hope that bioenergy production in the TRB could help to protect the

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“…15–39 in Supporting Information Table S3). When multiple response variables (e.g., Sediment, TP, TN,

{NO}_{3}^{-}

{NO}_{2}^{-}

) were considered in model calibration, we calculated the overall objective function as the weighted average of individual objective function of respective response variable (Wang & Chen, ).…”

Section: Methodsmentioning

confidence: 99%

Hydrologic and water quality responses to biomass production in the Tennessee river basin

et al. 2018

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“…that is computed as the weighted average of multiple single-objectives (Wang and Chen, 2012) where m denotes the number of objectives and w i is the weighting factor for the i th (i ¼ 1, 2,y, m) objective (J i ). In this study, the total objective function (J) consists of four single-objectives (that is, m ¼ 4) in terms of four response variables: J 1 for total cumulative respiration (denoted by C-CO 2 ), J 2 for cumulative respired 14 C normalized by added 14 C (denoted by % 14 C-CO 2 ), J 3 for MBC and J 4 for DOC.…”

Section: Model Parameter Optimizationmentioning

confidence: 99%

“…When observations are available for various response variables, the strategy of multi-objective calibration is essential in order to reduce uncertainty in model parameterization (Yapo et al, 1998;Wang and Chen, 2012). We conducted model parameterization aiming at minimizing a synthesized objective function that consists of four objectives in terms of C-CO 2 , % 14 C-CO 2 , DOC and MBC.…”

Section: Model Parameterizationmentioning

confidence: 99%

Microbial dormancy improves development and experimental validation of ecosystem model

et al. 2014

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Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life-history traits and functions may be necessary to predict climate feedbacks owing to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here we developed the microbial enzyme-mediated decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, that is, MEND with dormancy (MEND) and MEND without dormancy (MEND_wod), against long-term (270 days) carbon decomposition data from laboratory incubations of four soils with isotopically labeled substrates. MEND_wod adequately fitted multiple observations (total C-CO 2 and 14 C-CO 2 respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. MEND improved estimates of microbial biomass by 20-71% over MEND_wod. We also quantified uncertainties in parameters and model simulations using the Critical Objective Function Index method, which is based on a global stochastic optimization algorithm, as well as model complexity and observational data availability. Together our model extrapolations of the incubation study show that long-term soil incubations with experimental data for multiple carbon pools are conducive to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field-and globalscale simulations, and enable more confident predictions of feedbacks between environmental change and carbon cycling.

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“…Different from the above single‐point (i.e., medians) analysis, MPSA assesses the parameter sensitivity in the entire parameter space based on the Monte Carlo simulations (Wang et al ., ). The procedure of MPSA is summarized as follows (Wang & Chen, ): (1) Select the parameters and determine their value ranges/distributions. (2) Randomly generate a series of parameter values from certain probability distributions within their ranges.…”

Section: Overall Maintenance Coefficient and Sensitivity Analysismentioning

confidence: 99%

A theoretical reassessment of microbial maintenance and implications for microbial ecology modeling

Wang

Post

2012

FEMS Microbiol Ecol

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We attempted to reconcile three microbial maintenance models (Herbert, Pirt, and Compromise) through a theoretical reassessment. We provided a rigorous proof that the true growth yield coefficient (Y(G)) is the ratio of the specific maintenance rate (a in Herbert) to the maintenance coefficient (m in Pirt). Other findings from this study include: (1) the Compromise model is identical to the Herbert for computing microbial growth and substrate consumption, but it expresses the dependence of maintenance on both microbial biomass and substrate; (2) the maximum specific growth rate in the Herbert (μ(max,H)) is higher than those in the other two models (μ(max,P) and μ(max,C)), and the difference is the physiological maintenance factor (m(q) = a); and (3) the overall maintenance coefficient (m(T)) is more sensitive to m(q) than to the specific growth rate (μ(G)) and Y(G). Our critical reassessment of microbial maintenance provides a new approach for quantifying some important components in soil microbial ecology models.

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A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil

Cited by 57 publications

References 73 publications

Hydrologic and water quality responses to biomass production in the Tennessee river basin

Hydrologic and water quality responses to biomass production in the Tennessee river basin

Microbial dormancy improves development and experimental validation of ecosystem model

A theoretical reassessment of microbial maintenance and implications for microbial ecology modeling

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