Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe use of projections of greenhouse gas emissions (GHG) estimates are fundamental to design appropriate policies to combat climate change, but the inherent complex nature of the climate system results in projections with a significant degree of uncertainty. An important source of uncertainty in GHG emissions estimates refers to land use changes (LUC) due to the complexity of the land system. As the land domain plays a relevant role in climate change mitigation, understanding the effects of uncertainty on projections of LUC-related GHG emissions estimates is crucial to better support the process of decision making. Based on a case study conducted by van der Hilst et al. (2018), this thesis evaluates the effects of uncertainty on the projections of LUC-related GHG emissions in Brazil towards 2030, given an expected increase in the global biofuel demand and distinct scenarios of LUC mitigation measures. With the use of Monte Carlo simulation technique, we developed a spatially explicit, stochastic model in Python programming language to perform the uncertainty analysis. As uncertainty can be derived from many sources, we focused on adding uncertainty in the model input data to assess its effects on the LUC-related GHG emissions estimates resulting from an increase in the global biofuel demand. As van der Hilst et al. (2018) performed an analysis of the same case study, but without uncertainty analysis, this thesis compares the stochastic results of the deterministic results. The comparison of the results obtained between the deterministic and the stochastic approach provides valuable insights about the effects of uncertainty in the final estimates of emissions. We run the model for six distinct LUC scenarios and computed the LUC-related GHG emission estimates given the changes in soil organic carbon (SOC) and biomass stocks, resulting in estimates with an associated uncertainty. We performed a statistical test to verify the existence of significant differences in the emission estimates between the scenarios and we run a sensitivity analysis to evaluate the contribution of the model components in the overall uncertainty of the emission estimates. The outcomes allows saying that adding uncertainty in the input data results in estimates with great uncertainty, specially in the emissions resulting from the changes in SOC stocks. The emission estimates obtained in this thesis have similar values when comparing to results of the deterministic approach of van der Hilst et al. (2018). The statistical test allows saying that the LUC-related GHG emission estimates resulting from an additional ethanol demand are significantly different between all scenarios, therefore the emission estimates could be used to support decision making e.g. to define or prioritize the implementation of a new LUC mitigation measure in Brazil

Comparing the structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages

Research on the uncertainty of Land Use Cover Change (LUCC) models is still limited. Through this paper, we aim to globally characterize the structural uncertainty of four common software packages (CA_Markov, Dinamica EGO, Land Change Modeler, Metronamica) and analyse the options that they offer for uncertainty management. The models have been compared qualitatively, based on their structures and tools, and quantitatively, through a study case for the city of Cape Town. Results proved how each model conceptualised the modelled system in a different way, which led to different outputs. Statistical or automatic approaches did not provide higher repeatability or validation scores than user-driven approaches. The available options for uncertainty management vary depending on the model. Communication of uncertainties is poor across all models.Spanish GovernmentEuropean Commission INCERTIMAPS PGC2018-100770-B-100Spanish Ministry of Economy and Competitiveness and the European Social Fund [Ayudas para contratos predoctorales para la formacion de doctores 2014]University of Granada [Contratos Puente 2018]Spanish Ministry of Science and Innovation [Ayudas para contratos Juan de la Cierva-for-macion] 2019-FJC2019-040043University of Cape Town (Centre for Transport Studies

A Time Monte Carlo method for addressing uncertainty in land-use change models

One of the main objectives of land-use change models is to explore future land-use patterns. Therefore, the issue of addressing uncertainty in land-use forecasting has received an increasing attention in recent years. Many current models consider uncertainty by including a randomness component in their structure. In this paper, we present a novel approach for tuning uncertainty over time, which we refer to as the Time Monte Carlo (TMC) method. The TMC uses a specific range of randomness to allocate new land uses. This range is associated with the transition probabilities from one land use to another. The range of randomness is increased over time so that the degree of uncertainty increases over time. We compare the TMC to the randomness components used in previous models, through a coupled logistic regression-cellular automata model applied for Wallonia (Belgium) as a case study. Our analysis reveals that the TMC produces results comparable with existing methods over the short-term validation period (2000–2010). Furthermore, the TMC can tune uncertainty on longer-term time horizons, which is an essential feature of our method to account for greater uncertainty in the distant future