Improvement of global litter turnover rate predictions using a Bayesian MCMC approach

Oleksandra Hararuk, Yiqi Luo

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Global terrestrial carbon (C) cycle has a strong influence on atmospheric CO2 concentrations and temperatures. Litter mass is relatively small in comparison to soil and plant pools but its turnover rate is fast. Litter dynamics is important part of the global terrestrial carbon cycle as it is a critical stage in the soil organic matter formation and nutrient mineralization. Litter turnover rates have been observed on site, regional, and global levels, however little effort has been put into validating and calibrating litter decay models against the observations. In this study, we used a Bayesian Markov Chain Monte Carlo data assimilation technique and globally observed leaf litter turnover rates to calibrate a first order litter decay model with different assumptions about litter quality limitations of decomposition. The first order decay model with original parameters and a commonly-used litter quality limitation function explained 15% of the spatial variation in the observed leaf litter turnover rates, and parameter calibration increased the explained variation in the observations to 44%. When litter quality limitation of decomposition was determined by litter lignin-to-nitrogen ratio rather than structural lignin content the performance of the calibrated first order decay model was further improved, explaining 62% of variation in the observations. Litter feedbacks to changing climate differed between the original and best-fitting models: original model predicted a 16% decrease in leaf litter pool after 95 years of climate change (2006-2100), whereas the best-fitting model predicted a 2% increase. Furthermore, assuming that litter quality decreased with increasing CO2 concentrations resulted in a 28% decrease in leaf litter pool predicted by the original model, and a 15% increase predicted by the best-fitting model. Thus, assimilating observed leaf litter turnover rates into a first-order decay model improved model fit and reversed leaf litter feedbacks to changing climate.

Original languageEnglish (US)
Article number163
JournalEcosphere
Volume5
Issue number12
DOIs
StatePublished - Dec 1 2014
Externally publishedYes

Keywords

  • Carbon dynamics
  • Model calibration

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology

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