Floor van der Hilst

Spatio-temporal uncertainty in Spatial Decision Support Systems: A case study of changing land availability for bioenergy crops in Mozambique

Abstract Spatial Decision Support Systems (SDSSs) often include models that can be used to assess the impact of possible decisions. These models usually simulate complex spatio-temporal phenomena, with input variables and parameters that are often hard to measure. The resulting model uncertainty is, however, rarely communicated to the user, so that current SDSSs yield clear, but therefore sometimes deceptively precise outputs. Inclusion of uncertainty in SDSSs requires modeling methods to calculate uncertainty and tools to visualize indicators of uncertainty that can be understood by its users, having mostly limited knowledge of spatial statistics. This research makes an important step towards a solution of this issue. It illustrates the construction of the PCRaster Land Use Change model (PLUC) that integrates simulation, uncertainty analysis and visualization. It uses the PCRaster Python framework, which comprises both a spatio-temporal modeling framework and a Monte Carlo analysis framework that together produce stochastic maps, which can be visualized with the Aguila software, included in the PCRaster Python distribution package. This is illustrated by a case study for Mozambique in which it is evaluated where bioenergy crops can be cultivated without endangering nature areas and food production now and in the near future, when population and food intake per capita will increase and thus arable land and pasture areas are likely to expand. It is shown how the uncertainty of the input variables and model parameters effects the model outcomes. Evaluation of spatio-temporal uncertainty patterns has provided new insights in the modeled land use system about, e.g., the shape of concentric rings around cities. In addition, the visualization modes give uncertainty information in an comprehensible way for users without specialist knowledge of statistics, for example by means of confidence intervals for potential bioenergy crop yields. The coupling of spatio-temporal uncertainty analysis to the simulation model is considered a major step forward in the exposure of uncertainty in SDSSs.

Identifying a land use change cellular automaton by Bayesian data assimilation

We present a Bayesian method that simultaneously identifies the model structure and calibrates the parameters of a cellular automaton (CA). The method entails sequential assimilation of observations, using a particle filter. It employs prior knowledge of experts to define which processes might be important in the system, and uses empirical information from observations to identify which ones really are and how these processes should be parameterized. In a case study for the Sao Paulo state in Brazil, we identify a land use change CA simulating sugarcane cropland expansion from 2003 to 2016. Eight annual observation maps of sugar cane cultivation are used, split over space and time for calibration and validation. It is shown that the identified CA can properly reproduce the observations, and has a minimum reduction factor of 3 in root mean square error compared to a Monte Carlo simulation without particle filter. In the part of the study area where no observational data are assimilated (validation area), there is little reduction in model performance compared to the part with observational data. So, incomplete datasets, regional land survey data, or clouded remote sensing images can still provide useful information for this particle filter method, which is an advantage because good quality land use maps are rare. Another advantage is that in our approach the output uncertainty encompasses errors from expert knowledge, model structure, parameters and observation (calibration) data. This can, in our opinion, be very useful for example to determine up to what future period the results are a secure basis for decisions and policy making.

What can and can't we say about indirect land-use change in Brazil using an integrated economic - land-use change model?

It is commonly recognized that large uncertainties exist in modelled biofuel‐induced indirect land‐use change, but until now, spatially explicit quantification of such uncertainties by means of error propagation modelling has never been performed. In this study, we demonstrate a general methodology to stochastically calculate direct and indirect land‐use change (dLUC and iLUC) caused by an increasing demand for biofuels, with an integrated economic – land‐use change model. We use the global Computable General Equilibrium model MAGNET, connected to the spatially explicit land‐use change model PLUC. We quantify important uncertainties in the modelling chain. Next, dLUC and iLUC projections for Brazil up to 2030 at different spatial scales and the uncertainty herein are assessed. Our results show that cell‐based (5 × 5 km2) probabilities of dLUC range from 0 to 0.77, and of iLUC from 0 to 0.43, indicating that it is difficult to project exactly where dLUC and iLUC will occur, with more difficulties for iLUC than for dLUC. At country level, dLUC area can be projected with high certainty, having a coefficient of variation (cv) of only 0.02, while iLUC area is still uncertain, having a cv of 0.72. The latter means that, considering the 95% confidence interval, the iLUC area in Brazil might be 2.4 times as high or as low as the projected mean. Because this confidence interval is so wide that it is likely to straddle any legislation threshold, our opinion is that threshold evaluation for iLUC indicators should not be implemented in legislation. For future studies, we emphasize the need for provision of quantitative uncertainty estimates together with the calculated LUC indicators, to allow users to evaluate the reliability of these indicators and the effects of their uncertainty on the impacts of land‐use change, such as greenhouse gas emissions.

Mapping land use changes resulting from biofuel production and the effect of mitigation measures

Many of the sustainability concerns of bioenergy are related to direct or indirect land use change (LUC) resulting from bioenergy feedstock production. The environmental and socio‐economic impacts of LUC highly depend on the site‐specific biophysical and socio‐economic conditions. The objective of this study is to spatiotemporally assess the potential LUC dynamics resulting from an increased biofuel demand, the related greenhouse gas (GHG) emissions, and the potential effect of LUC mitigation measures. This assessment is demonstrated for LUC dynamics in Brazil towards 2030, considering an increase in the global demand for bioethanol as well as other agricultural commodities. The potential effects of three LUC mitigation measures (increased agricultural productivity, shift to second‐generation ethanol, and strict conservation policies) are evaluated by using a scenario approach. The novel modelling framework developed consists of the global Computable General Equilibrium model MAGNET, the spatiotemporal land use allocation model PLUC, and a GIS‐based carbon module. The modelling simulations illustrate where LUC as a result of an increased global ethanol demand (+26 × 109 L ethanol production in Brazil) is likely to occur. When no measures are taken, sugar cane production is projected to expand mostly at the expense of agricultural land which subsequently leads to the loss of natural vegetation (natural forest and grass and shrubland) in the Cerrado and Amazon. The related losses of above and below ground biomass and soil organic carbon result in the average emission of 26 g CO2‐eq/MJ bioethanol. All LUC mitigation measures show potential to reduce the loss of natural vegetation (18%–96%) as well as the LUC‐related GHG emissions (7%–60%). Although there are several uncertainties regarding the exact location and magnitude of LUC and related GHG emissions, this study shows that the implementation of LUC mitigation measures could have a substantial contribution to the reduction of LUC‐related emissions of bioethanol. However, an integrated approach targeting all land uses is required to obtain substantial and sustained LUC‐related GHG emission reductions in general.

Impact of increased wood pellet demand on biodiversity in the south-eastern United States

Increasing wood pellet exports from the United States are projected to lead to changes in land use and timberland management, including a shift from natural timberland to pine plantations. These projected changes may impact biodiversity. This study aims to quantify potential biodiversity impacts of increased wood pellet demand in the south‐eastern United States in a spatially explicit manner. We determined differences according to an index of potential species richness (for total, threatened and endemic species and four taxonomic groups) between scenarios of high and low demand for wood pellets, while taking into account potential developments in other wood markets and other land uses. Increased demand for wood pellets was projected to cause both positive and negative biodiversity impacts. Negative shifts in total potential species richness were projected for areas in Florida, coastal Virginia and North Carolina, and parts of the Gulf Coast. Positive shifts in total potential species richness were projected in parts of Oklahoma and Arkansas. In some locations, the direction of change differed per taxonomic group, highlighting the importance of analysing different taxonomic groups. Shifts in potential species richness due to increased wood pellet demand were considerably smaller compared to the changes due to other drivers, such as urbanization and increased timber demand. Biodiversity impacts due to wood pellet demand should therefore be considered in the context of other drivers of land‐use change and biodiversity loss. Our results provide information that allows policymakers, industry and NGOs to focus on areas of concern and take appropriate mitigation measures to limit negative biodiversity impacts and promote positive impacts. The spatially explicit approach presented in this study can be applied to different regions and drivers of land‐use change, to show how projected demand for an internationally traded commodity may lead to impacts on land use and biodiversity in the procurement region.

Low-ILUC-risk rapeseed biodiesel: potential and indirect GHG emission effects in Eastern Romania

ABSTRACT Indirect land-use change (ILUC) can have a severe impact on the greenhouse gas (GHG) balance of biofuels. Mitigating ILUC risk is important to avoid additional GHG emissions compared to fossil fuels. This is possible by making surplus land available through land demand reduction and using this for low-ILUC-risk biodiesel production. For a case study in Eastern Romania, we calculated the rapeseed biodiesel potential and the GHG emissions of four measures to make surplus land available in 2020. Four scenarios varying in assumptions on productivity and sustainability in the agricultural sector show the variation in the potential of these measures. We find that using surplus land to produce low-ILUC-risk rapeseed biodiesel has a potential of 3-64 PJ, 1-28% of the projected Romanian transport diesel consumption. The main contribution to this potential comes from yield improvements in crop and livestock production. Average GHG emissions of the ILUC mitigation measures are -11 to 22 g CO2-eq MJ−1 (maximum total lifecycle emissions are 34 g CO2-eq MJ−1; 60% reduction from fossil fuel reference). This means ILUC mitigation is possible without necessarily missing the GHG emission reduction target, provided that the entire agricultural sector is sustainably intensified, going beyond a focus on biofuel production alone.