Assessing uncertainty in soil organic carbon modeling across a highly heterogeneous landscape

To understand if soil carbon acts as a sink or source in the global carbon cycle it is not only important to make reliable estimates, but also determine upper and lower prediction bounds through uncertainty analysis that represent best and worst case conditions. In this study, the Bayesian geostatistics was applied to assess the uncertainty associated with the predictive models of SOC (top soils) in a large region - Florida, USA. Results showed that the Bayesian estimates of model parameters were comparable to the conventional geostatistical methods especially the restricted maximum likelihood (REML). The Bayesian prediction uncertainty assessment was encouragingly accurate based on the validation of 50 and 95% prediction intervals with the validation dataset. Generally, the width of prediction intervals increased with the posterior mean SOC predictions - large prediction intervals were found in the Everglades Agricultural Area (Histosols) and the wetland areas in the Suwannee River Basin. The Bayesian constant mean model (high model inadequacy) had marked prediction uncertainty which was reduced by accounting for the effects of environmental covariates in the Bayesian linear trend model (low model inadequacy), indicating that model inadequacy had a negative impact on prediction uncertainty. Analyses of factors impacting SOC prediction uncertainty suggest that effects that explained more of the SOC variance contributed more uncertainty to the SOC prediction. These findings are critical to quantify SOC stocks in the southeastern USA where a heterogeneous mosaic of high and low carbon in soils occurs ranging from 0.45 to 34.15 kg m(-2). Although this study considers only the topsoil, the results are valuable for global carbon cycling research. The uncertainty of SOC predictions not only enables identification of hot and cold spots in a landscape to mitigate and adapt to global climate change, but also informs scenario assessment to imagine possible carbon- rich, -neutral, and -poor futures. (C) 2015 Elsevier B.V. All rights reserved.