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Methane flux model for wetlands of the Prairie Pothole Region of North America: Model input data and programming code

Dates

Publication Date
Start Date
2003-05-21
End Date
2016-11-17

Citation

Bansal, S., and Tangen, B.A., 2022, Methane flux model for wetlands of the Prairie Pothole Region of North America: Model input data and programming code: U.S. Geological Survey data release, https://doi.org/10.5066/P9PKI29C.

Summary

This data release presents input data for plot- and landscape-scale models of Prairie Pothole Region wetland methane emissions as a function of explanatory variables and remotely sensed predictors. Field data for the plot- and landscape-scale models span the years 2003-2016 and 2005-2016, respectively. The data release also includes R programming code to run the generalized additive model (GAM; plot scale) and random forest (RF; landscape scale) model of methane flux rates. Input data were extracted and modified from existing sources, and combined to facilitate model development, as well as six scenario-based model runs (two historical, four future). Briefly, a bottom-up approach was used to develop a spatially explicit, temporally [...]

Contacts

Attached Files

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Chamber_GAM_model_data.csv 1.82 MB text/csv
Landscape_RF_model_data.csv 528.2 KB text/csv
Temp_data.csv 1.56 MB text/csv
CH4_model_data_release_code.txt 13.22 KB text/plain

Purpose

The primary goals of this study were to (1) develop a plot-scale methane flux model, (2) identify and characterize the functional relationships between methane flux and mechanistically relevant explanatory variables in PPR wetlands, (3) develop a spatially explicit landscape-scale model of methane flux for PPR wetlands, and (4) estimate future methane emissions from PPR wetlands. This data release provides data that were extracted from existing sources and combined to support model development, as well as to inform scenario-based model runs.

Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9PKI29C

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