US Geological Survey

 

Date of this Version

2016

Citation

Ecological Modelling 337 (2016), pp. 176–187, http://dx.doi.org/10.1016/j.ecolmodel.2016.07.002.

Comments

U.S. government work.

Abstract

tQuantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties acrossagriculture-dominated areas remains challenging for understanding regional carbon cycles. Character-istics of local land cover inputs could impact the regional carbon estimates but the effect has not beenfully evaluated in the past. Within the North American Carbon Program Mid-Continent Intensive (MCI)Campaign, three models were developed to estimate carbon fluxes on croplands: an inventory-basedmodel, the Environmental Policy Integrated Climate (EPIC) model, and the General Ensemble biogeo-chemical Modeling System (GEMS) model. They all provided estimates of three major carbon fluxes oncropland: net primary production (NPP), net ecosystem production (NEP), and soil organic carbon (SOC)change. Using data mining and spatial statistics, we studied the spatial distribution of the carbon fluxesuncertainties and the relationships between the uncertainties and the land cover characteristics. Resultsindicated that uncertainties for all three carbon fluxes were not randomly distributed, but instead formedmultiple clusters within the MCI region. We investigated the impacts of three land cover characteristicson the fluxes uncertainties: cropland percentage, cropland richness and cropland diversity. The resultsindicated that cropland percentage significantly influenced the uncertainties of NPP and NEP, but noton the uncertainties of SOC change. Greater uncertainties of NPP and NEP were found in counties withsmall cropland percentage than the counties with large cropland percentage. Cropland species richnessand diversity also showed negative correlations with the model uncertainties. Our study demonstratedthat the land cover characteristics contributed to the uncertainties of regional carbon fluxes estimates.The approaches we used in this study can be applied to other ecosystem models to identify the areaswith high uncertainties and where models can be improved to reduce overall uncertainties for regionalcarbon flux estimates.