Land-use transition for bioenergy and climate stabilization: model comparison of drivers,impacts and interactions with other land use based mitigation options

In this article, we evaluate and compare results from three integrated assessment models (GCAM, IMAGE, and ReMIND/MAgPIE) regarding the drivers and impacts of bioenergy production on the global land system. The considered model frameworks employ linked energy, economy, climate and land use modules. By the help of these linkages the direct competition of bioenergy with other energy technology options for greenhouse gas (GHG) mitigation, based on economic costs and GHG emissions from bioenergy production, has been taken into account. Our results indicate that dedicated bioenergy crops and biomass residues form a potentially important and cost-effective input into the energy system. At the same time, however, the results differ strongly in terms of deployment rates, feedstock composition and land-use and greenhouse gas implications. The current paper adds to earlier work by specific looking into model differences with respect to the land-use component that could contribute to the noted differences in results, including land cover allocation, land use constraints, energy crop yields, and non-bioenergy land mitigation options modeled. In scenarios without climate change mitigation, bioenergy cropland represents 10–18 % of total cropland by 2100 across the different models, and boosts cropland expansion at the expense of carbon richer ecosystems. Therefore, associated emissions from land-use change and agricultural intensification as a result of bio-energy use range from 14 and 113 Gt CO2-eq cumulatively through 2100. Under climate policy, bioenergy cropland increases to 24–36 % of total cropland by 2100.     

Orientation and nature of active crustal stresses determined by electromagnetic measurements in the Patagonian segment of the South America Plate

Based on electromagnetic measurements we determined the current stress directions in the uppermost continental crust of Patagonia between the active plate margin of the Chilean Pacific coast and the Argentinean passive Atlantic margin. Regional variations of the observed stress pattern are giving details onto the acting tectonic processes. We distinguish five regional stress domains with different prevailing horizontal stress directions (S_H): 1. Southern Coastal Cordillera and Longitudinal Valley (S_H = SSW–NNE), 2. Chiloé Island (S_H = SW–NE), 3. Northern Patagonian Andes (S_H = WSW–ENE), 4. Argentinean Pampa and Atlantic margin (S_H = WNW–ESE) and 5. Southern Patagonian Andes (S_H = WNW–ESE). These stress regimes can be related to the geometry of the subducting Nazca- and Antarctic plates, to the transform fault between the South America and Scotia plates and to passive margin processes along the Atlantic coast. Absolute plate motion and rapid relative plate convergence control the subduction geometry and therefore the stress directions along the convergent margin of the South America Plate and the structural style within and landward of the Magmatic Arc. The knowledge of current local stress directions permits the characterisation of potential fault kinematics. By in situ measuring of electromagnetic emissions from rocks we determined the maximum horizontal stress orientation in the uppermost crust using a new geophysical tool. Our investigations on the orientation of the stress regimes also allow conclusions about the causative forces of either tectonic or gravitational origin in this part of the South-America Plate.     

Strategies to improve the explanatory power of a dynamic slope stability model by enhancing land cover parameterisation and model complexity

Despite the importance of land cover on landscape hydrology and slope stability, the representation of land cover dynamics in physically based models and their associated ecohydrological effects on slope stability is rather scarce. In this study, we assess the impact of different levels of complexity in land cover parameterisation on the explanatory power of a dynamic and process-based spatial slope stability model. Firstly, we present available and collected data sets and account for the stepwise parameterisation of the model. Secondly, we present approaches to simulate land cover: 1) a grassland landscape without forest coverage; 2) spatially static forest conditions, in which we assume limited knowledge about forest composition; 3) more detailed information of forested areas based on the computation of leaf area development and the implementation of vegetation-related processes; 4) similar to the third approach but with the additional consideration of the spatial expansion and vertical growth of vegetation. Lastly, the model is calibrated based on meteorological data sets and groundwater measurements. The model results are quantitatively validated for two landslide-triggering events that occurred in Western Austria. Predictive performances are estimated using the Area Under the receiver operating characteristic Curve (AUC). Our findings indicate that the performance of the slope stability model was strongly determined by model complexity and land cover parameterisation. The implementation of leaf area development and land cover dynamics further yield an acceptable predictive performance (AUC ~0.71-0.75) and a better conservativeness of the predicted unstable areas (FoC ~0.71). The consideration of dynamic land cover expansion provided better performances than the solely consideration of leaf area development. The results of this study highlight that an increase of effort in the land cover parameterisation of a dynamic slope stability model can increase the explanatory power of the model. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Bioenergy in energy transformation and climate management

This study explores the importance of bioenergy to potential future energy transformation and climate change management. Using a large inter-model comparison of 15 models, we comprehensively characterize and analyze future dependence on, and the value of, bioenergy in achieving potential long-run climate objectives. Model scenarios project, by 2050, bioenergy growth of 1 to 10 % per annum reaching 1 to 35 % of global primary energy, and by 2100, bioenergy becoming 10 to 50 % of global primary energy. Non-OECD regions are projected to be the dominant suppliers of biomass, as well as consumers, with up to 35 % of regional electricity from biopower by 2050, and up to 70 % of regional liquid fuels from biofuels by 2050. Bioenergy is found to be valuable to many models with significant implications for mitigation and macroeconomic costs of climate policies. The availability of bioenergy, in particular biomass with carbon dioxide capture and storage (BECCS), notably affects the cost-effective global emissions trajectory for climate management by accommodating prolonged near-term use of fossil fuels, but with potential implications for climate outcomes. Finally, we find that models cost-effectively trade-off land carbon and nitrous oxide emissions for the long-run climate change management benefits of bioenergy. The results suggest opportunities, but also imply challenges. Overall, further evaluation of the viability of large-scale global bioenergy is merited.     

A new scenario framework for Climate Change Research: scenario matrix architecture

This paper describes the scenario matrix architecture that underlies a framework for developing new scenarios for climate change research. The matrix architecture facilitates addressing key questions related to current climate research and policy-making: identifying the effectiveness of different adaptation and mitigation strategies (in terms of their costs, risks and other consequences) and the possible trade-offs and synergies. The two main axes of the matrix are: 1) the level of radiative forcing of the climate system (as characterised by the representative concentration pathways) and 2) a set of alternative plausible trajectories of future global development (described as shared socio-economic pathways). The matrix can be used to guide scenario development at different scales. It can also be used as a heuristic tool for classifying new and existing scenarios for assessment. Key elements of the architecture, in particular the shared socio-economic pathways and shared policy assumptions (devices for incorporating explicit mitigation and adaptation policies), are elaborated in other papers in this special issue.