Dataset Information

A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability.

ABSTRACT: The soil CO2 emission is recognized as one of the largest fluxes in the global carbon cycle. Small errors in its estimation can result in large uncertainties and have important consequences for climate model predictions. Monte Carlo approach is efficient for estimating and reducing spatial scale sampling errors. However, that has not been used in soil CO2 emission studies. Here, soil respiration data from 51 PVC collars were measured within farmland cultivated by maize covering 25 km(2) during the growing season. Based on Monte Carlo approach, optimal sample sizes of soil temperature, soil moisture, and soil CO2 emission were determined. And models of soil respiration can be effectively assessed: Soil temperature model is the most effective model to increasing accuracy among three models. The study demonstrated that Monte Carlo approach may improve soil respiration accuracy with limited sample size. That will be valuable for reducing uncertainties of global carbon cycle.

SUBMITTER: Shi WY

PROVIDER: S-EPMC4667816 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability.

Shi Wei-Yu WY Su Li-Jun LJ Song Yi Y Ma Ming-Guo MG Du Sheng S

Ecology and evolution 20150923 19

The soil CO2 emission is recognized as one of the largest fluxes in the global carbon cycle. Small errors in its estimation can result in large uncertainties and have important consequences for climate model predictions. Monte Carlo approach is efficient for estimating and reducing spatial scale sampling errors. However, that has not been used in soil CO2 emission studies. Here, soil respiration data from 51 PVC collars were measured within farmland cultivated by maize covering 25 km(2) during t ...[more]

PMID: 26664693

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Project description:There is substantial evidence suggesting climate change is having an adverse impact on the world's water resources. One must remember, however, that climate change is beset by uncertainty. It is therefore meaningful for climate change impact assessments to be conducted with stochastic-based frameworks. The degree of uncertainty about the nature of a stochastic phenomenon may differ from one another. Deep uncertainty refers to a situation in which the parameters governing intervening probability distributions of the stochastic phenomenon are themselves subjected to some degree of uncertainty. In most climatic studies, however, the assessment of the role of deep-uncertain nature of climate change has been limited. This work contributes to fill this knowledge gap by developing a Markov Chain Monte Carlo (MCMC) analysis involving Bayes' theorem that merges the stochastic patterns of historical data (i.e., the prior distribution) and the regional climate models' (RCMs') generated climate scenarios (i.e., the likelihood function) to redefine the stochastic behavior of a non-conditional climatic variable under climate change conditions (i.e., the posterior distribution). This study accounts for the deep-uncertainty effect by evaluating the stochastic pattern of the central tendency measure of the posterior distributions through regenerating the MCMCs. The Karkheh River Basin, Iran, is chosen to evaluate the proposed method. The reason for selecting this case study was twofold. First, this basin has a central role in ensuring the region's water, food, and energy security. The other reason is the diverse topographic profile of the basin, which imposes predictive challenges for most RCMs. Our results indicate that, while in most seasons, with the notable exception of summer, one can expect a slight drop in the temperature in the near future, the average temperature would continue to rise until eventually surpassing the historically recorded values. The results also revealed that the 95% confidence interval of the central tendency measure of computed posterior probability distributions varies between 0.1 and 0.3 °C. The results suggest exercising caution when employing the RCMs' raw projections, especially in topographically diverse terrain.

Dataset Information

A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability.

Publications

A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability.

Similar Datasets

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