5.4 Regional modeling
Given that methods to verify carbon offsets commonly rely on models and
encounter substantial uncertainty (Smith et al., 2020), the three models
that we used estimated floodplain carbon stock exhibited reasonable
performance. Of the three models, the Random Forest model performed the
best. Although the linear mixed model results aligned well with the
measured carbon and the other model results, this model relies on
information about the specific sites to account for the study design and
therefore would be more laborious to use in a predictive setting in
contrast to the estimation setting used here to evaluate the models. In
general, our goal was to create a model that uses climate and landscape
variables that are easily obtainable, such as remote sensing data, to
generate a first-order estimate of carbon stock. Remote indices exist
that can be used to estimate carbon stock (e.g., Angelopoulou et al.,
2019) but are commonly developed on barren or agricultural soils that do
not contain the same level of complexity as river corridors. Future
steps for the application of these models would be to test or
incorporate validation data from outside of our study areas.