Rüdiger Schaldach

Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison

Abstract Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

Evaluation of an integrated land use change model including a scenario analysis of land use change for continental Africa

The global integrated land use model, LandSHIFT, is evaluated by testing its performance against available data sets, by analyzing the sensitivity of model parameters and structure, and by conducting a scenario analysis of future land use change in Africa. Despite the paucity of suitable data sets, a range of different tests were designed to make best use of available data and to examine the models ability to compute cropland suitability, extent of cropland area, and location of deforestation. The model showed more ability to calculate the spatial distribution of cropland suitability and continental average deforestation rates than to compute the spatial distribution of deforestation. LandSHIFT was found to be particularly sensitive to assumptions about future climate change for simulations extending over several decades through the influence of climate on cropland and grassland productivity. With regards to the scenario analysis, the model was applied to two scenarios for Africa that cover a wide range of assumptions about future driving forces. Results showed that cropland land may expand greatly up to 2050 (34-40%, depending on the scenario) because of increasing food demand and despite expected increases in crop yield. This expansion comes largely at the expense of forested land, although the average continental deforestation rate computed from 2000 to 2050 is lower than the computed rate for the 1990s. The testing and scenario analysis showed the ability of the model to develop consistent scenarios of land use change on the continental scale by combining the effects of driving forces and competition between land uses in a single spatially-explicit framework.

Assessing uncertainties in land cover projections

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.

Conceptual design and implementation of a model for the integrated simulation of large-scale land-use systems

In the scientific literature, the relation between human land-use activities and the environment is often described as a coupled human-environment system with socio-economic as well as ecological and biophysical components as sub-systems (Mather, 2006). These “land-use systems” form the terrestrial part of the Earth System and are therefore critical components of global biogeochemical cycles and energy fluxes (GLP, 2005). One approach to simulate the spatial and temporal dynamics of land-use systems on the regional to global scale level is the LandSHIFT modelling framework, which is currently under development at CESR. First applications in the context of mid- to long-term scenario analysis focus on Africa (Schaldach et al, 2006), India (Schaldach et al., in review) and the Middle East (Koch et al., 2008). LandSHIFT has a modular structure, which supports the integration of various sub-models representing the key components of land systems. In this paper, we describe and discuss the design of the prototype of LandSHIFT, both on the conceptual and the implementation level. Additionally an exemplary model application is presented.

Modelling the effects of cross-sectoral water allocation schemes in Europe

Future renewable water resources are likely to be insufficient to meet water demand for human use and minimum environmental flow requirements in many European regions. Hence, fair and equitable water allocation to different water use sectors and environmental needs is important for climate change adaptation in order to reduce negative effects on human well-being and aquatic ecosystems. We applied a system of coupled sectoral metamodels of water availability and water use in the domestic, manufacturing industry, electricity generation, and agricultural sectors to simulate the effects of generic water allocation schemes (WAS) at the European level. The relative performance of WAS in balancing adverse impacts on the water use sectors and aquatic ecosystems was analysed for an ensemble of 16 scenarios for the 2050s, which were built from the combination of four socio-economic scenarios, developed in the CLIMSAVE project, and four climate projections based on IPCC A1. The results indicate that significant physical water shortages may result from climate and socio-economic change in many regions of Europe, particularly in the Mediterranean. In the energy sector, average annual water demand can largely be met even in water allocation schemes that deprioritise the sector. However, prioritisation of agricultural water demand has significant adverse impacts on the domestic and manufacturing industry sectors. Cross-sectoral impacts were found to be lowest if at least one of the domestic and manufacturing sectors is assigned higher priority than agriculture. We conclude that adapting spatial patterns of water-intensive activities to renewable water availability across Europe, such as shifting irrigated agriculture to less water-stressed basins, could be an effective demand-side adaptation measure, and thus a candidate for support through EU policy.

Future land use and land cover in Southern Amazonia and resulting greenhouse gas emissions from agricultural soils

The calculation of robust estimates of future greenhouse gas emissions due to agriculture is essential to support the framing of the Brazilian climate change mitigation policy. Information on the future development of land use and land cover change (LULCC) under the combination of various driving factors operating at different spatial scale levels, e.g., local land use policy and global demands for agricultural commodities, is required. The spatially explicit land use model, LandSHIFT, was applied to calculate a set of high-resolution land use scenarios for Southern Amazonia. The time frame of the analysis was 2010–2030. Based on the generated maps, emission coefficients were applied to calculate annual N2O, CH4, and CO2 emissions from agricultural soils (croplands and pastures). The results indicate that future land use pattern and the resultant greenhouse gas emissions in Southern Amazonia will be strongly determined by global and regional demands for agricultural commodities, as well as by the level of intensification of agriculture and the implementation of conservation policies.

Scenarios of land-use change in a deforestation corridor in the Brazilian Amazon: combining two scales of analysis

Local, regional, and global processes affect deforestation and land-use changes in the Brazilian Amazon. Characteristics are: direct conversions from forest to pasture; regional processes of indirect land-use change, described by the conversion of pastures to cropland, which increases the demand for pastures elsewhere; and teleconnections, fueled by the global demands for soybeans as animal fodder. We modeled land-use changes for two scenarios Trend and Sustainable Development for a hot spot of land-use change along the BR-163 highway in Mato Grosso and Pará, Brazil. We investigated the differences between a coupled modeling approach, which incorporates indirect land-use change processes, and a noncoupled land-use model. We coupled the regional-scale LandSHIFT model, defined for Mato Grosso and Pará, with a subregional model, alucR, covering a selected corridor along the BR-163. The results indicated distinct land-use scenario outcomes from the coupled modeling approach and the subregional model quantification. We found the highest deforestation estimates returned from the subregional quantification of the Trend scenario. This originated from the strong local dynamics of past deforestation and land-use changes. Land-use changes exceeded the demands estimated at regional scale. We observed the lowest deforestation estimates at the subregional quantification of the Sustainable Development story line. We highlight that model coupling increased the representation of scenario outcomes at fine resolution while providing consistency across scales. However, distinct local dynamics were explicitly captured at subregional scale. The scenario result pinpoints the importance of policies to aim at the cattle ranching sector, to increase land tenure registration and enforcement of environmental laws.

Exploring future agricultural development and biodiversity in Uganda, Rwanda and Burundi: a spatially explicit scenario-based assessment

Abstract Competition for land is increasing as a consequence of the growing demands for food and other commodities and the need to conserve biodiversity and ecosystem services. Land conversion and the intensification of current agricultural systems continues to lead to a loss of biodiversity and trade-offs among ecosystem functions. Decision-makers need to understand these trade-offs in order to better balance different demands on land and resources. There is an urgent need for spatially explicit information and analyses on the effects of different trajectories of human-induced landscape change in biodiversity and ecosystem services. We assess the potential implications of a set of plausible socio-economic and climate scenarios for agricultural production and demand and model-associated land use and land cover changes between 2005 and 2050 to assess potential impacts on biodiversity in Uganda, Rwanda and Burundi. We show that different future socio-economic scenarios are consistent in their projections of areas of high agricultural development leading to similar spatial patterns of habitat and biodiversity loss. Yet, we also show that without protected areas, biodiversity losses are higher and that expanding protected areas to include other important biodiversity areas can help reduce biodiversity losses in all three countries. These results highlight the need for effective protection and the potential benefits of expanding the protected area network while meeting agricultural production needs.

Inter- and transdisciplinary scenario construction to explore future land-use options in southern Amazonia

Our aim with this paper is to present a novel approach for developing story lines and scenarios by combining qualitative knowledge and quantitative data from different disciplines and discussing the results with relevant decision makers. This research strategy offers a solid foundation for perspectives into the future. The “laboratory” is the Brazilian Amazon, one of the hotspots of land-use change where local and global interests both collide and converge: local livelihoods are affected by regional and global climate change and by the loss of biodiversity caused by local and global economic interests in agro-industrial land use; such use contributes, in turn, to climate change. After decades of diverse policy interventions the question arises: What can we learn from past trajectories for a more sustainable development in the future? To answer this question, we combined qualitative story lines for the region, reviewed by local experts, with quantitative land-use scenarios, to study their regional and local manifestations in space. These results were then discussed again with local and national experts. Our findings suggest that in-depth knowledge of the diverging perspectives at a very local level is a fundamental prerequisite for downscaling global scenarios and upscaling local approaches to sustainable land-use management and thus, to producing communicable and applicable results.

A model-based assessment of the environmental impact of land-use change across scales in Southern Amazonia

This article describes the design of a new model-based assessment framework to identify and analyse possible future trajectories of agricultural development and their environmental consequences within the states of Mato Grosso and Pará in Southern Amazonia, Brazil. The objective is to provide a tool for improving the information basis for scientists and policy makers regarding the effects of global change and national environmental policies on land-use change and the resulting impacts on the loss of natural vegetation, greenhouse gas emissions, hydrological processes, and soil erosion within the region. For this purpose, the framework combines the regional land-use models, LandSHIFT and alucR, the farm-level model, MPMAS, and the MONICA crop model, with a set of environmental impact models that are operating at the regional and watershed levels. As a first application of the framework, four scenarios with the time horizon 2030 were specified and analysed. Future land-use change will strongly depend on the interplay between the production of agricultural commodities, the agricultural intensification in terms of increasing crop yields and pasture biomass productivity, and the enforcement of environmental laws and policies. On the regional level, the scenarios with the highest increase in agricultural production in combination with weak law enforcement (Trend and Illegal Intensification) generated the highest losses in natural vegetation due to the expansion of agricultural area as well as the highest greenhouse gas emissions. Also, at the watershed level, these scenarios are characterised by the highest changes in river discharge and soil erosion that might lead to a further decline in soil fertility in the long term. Moreover, the analysis of the Sustainable Development scenario indicates that a shift in agricultural production patterns from livestock to crop cultivation, together with effective law enforcement, can effectively reduce land-use change and its negative effects on the environment. With the scenario analysis, we could illustrate that our assessment framework is capable to provide a large variety of valuable information to support the development of future land-use strategies in the study region.