Date of Award


Publication Type


Degree Name



Civil and Environmental Engineering


Above-Ground Biomass;Allometric Model;Climate Change;Forest Carbon;Model Uncertainty;Uncertainty Analysis


Rajeev Ruparathna


Jerald Lalman



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


In compliance with international commitments to address the increasingly urgent need to reduce greenhouse gas (GHG) emissions, countries prepare national GHG inventories (NGHGI). NGHGIs include annual estimates of anthropogenic GHG emissions and removals. Reliable data in NGHGIs are essential for creating effective climate change policies and mitigation strategies, determining compliance with internationally agreed-upon targets, and tracking the sources and trends of GHG emissions and reductions. The above-ground biomass (AGB) carbon pool from the forestry sector is expected to contribute largely to carbon reductions; however, data for this sector is highly uncertain due to quantification challenges. These uncertainties have adversely impacted the reliability of the climate mitigation strategies and policies based on this data. AGB is quantified mainly by employing a Tier 3 approach involving allometric models derived from forest inventory data. A review of published literature indicated a need for research on the methods used to quantify model uncertainties and the effects of these uncertainties on carbon estimates from AGB. This research employs a simulation-based uncertainty analysis to quantify model uncertainties in carbon estimates from AGB allometric models. The literature, manuals, and R software were used to develop the uncertainty analysis. Alternative uncertainty analysis approaches were proposed to determine their effects on the uncertainty estimates. Case studies were performed for study areas in Canada and Sweden to determine the feasibility of the uncertainty analysis method when used in different countries. The results of this study demonstrated how model uncertainties can be quantified using the proposed uncertainty analysis method, and how estimates can be adjusted for uncertainties. The uncertainty estimates did not differ significantly from using the alternative uncertainty analysis methods. The main causes of model uncertainty for both case studies were due to measurement uncertainty in the model input variables and residual uncertainty. Recommendations were made on how uncertainties can be reduced by prioritizing methodological and data collection improvements in these areas. The effects of uncertainties on climate change mitigation strategies and methods to incorporate uncertainty information into climate change policies were assessed.

Available for download on Thursday, September 26, 2024