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Modeling approaches used to estimate above‐ and belowground stocks of wetland carbon can be partitioned to diagnostic and prognostic approaches. Diagnostic modeling tends to rely more heavily on observational data, such as remote‐sensing derived vegetation distributions, to initialize and update model states over time. In contrast, prognostic modeling relies on “first principles,” where fundamental relationships linking biophysical information, carbon uptake, carbon allocation, and carbon turnover determine vegetation and soil carbon stocks. Many examples of diagnostic and prognostic wetland modeling comparisons exist in the literature and the uncertainties between and among these approaches remains high (Fisher et al., 2015).

To some extent, all the estimates of above‐ and belowground carbon stocks require empirical or process‐based models either to scale site‐level measurement, or convert volumetric to mass‐based estimates, or to attribute changes over time due to climate and land use. However, the relationship of the models to underlying data reflects a gradient of data‐constrained to process‐based representation of how estimates are made. The estimates provided in this chapter span this gradient, with some emphasizing empirical approaches, where carbon density is multiplied by wetland area, others using semi‐empirical methods, where the carbon densities are spatially constrained by remote sensing observations, or where prognostic models are used to assess long‐term historical or future climate feedbacks on carbon turnover and respiration losses.