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

article i nfo 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 representbestandworstcaseconditions.Inthisstudy,theBayesiangeostatisticswasappliedtoassesstheuncer- tainty associated with the predictive models of SOC (top soils) in a large region — Florida, USA. Results showed thattheBayesianestimatesofmodelparameterswerecomparabletotheconventionalgeostatisticalmethodses- pecially the restricted maximum likelihood (REML). The Bayesian prediction uncertainty assessment was en- couragingly accurate based on the validation of 50 and 95% prediction intervals with the validation dataset. Generally,the widthof prediction intervalsincreasedwiththeposteriormeanSOCpredictions —largeprediction intervalswerefoundintheEvergladesAgricultural Area(Histosols)andthewetlandareasintheSuwannee 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 offactors impactingSOC prediction uncertainty suggest that effects that explained moreof the SOC variance con- tributed more uncertainty to the SOC prediction. These findings are critical to quantify SOC stocks in the south- eastern USA where a heterogeneous mosaic of high and low carbon in soils occurs ranging from 0.45 to 34.15 kg m −2