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.