Empirically Derived Suitability Maps to Downscale Aggregated Land Use Data

Understanding mechanisms that drive present land use patterns is essential in order to derive appropriate models of land use change. When static analyses of land use drivers are performed, they rarely explicitly deal with spatial autocorrelation. Most studies are undertaken on autocorrelation-free data samples. By doing this, a great deal of information that is present in the dataset is lost. This paper presents a spatially explicit, cross-sectional, logistic analysis of land use drivers in Belgium. It is shown that purely regressive logistic models can only identify trends or global relationships between socio-economic or physico-climatic drivers and the precise location of each land use type. However, when the goal of a study is to obtain the best model of land use distribution, a purely autoregressive (or neighbourhood-based) model is appropriate. Moreover, it is also concluded that a neighbourhood based only on the 8 surrounding cells leads to the best logistic regression models at this scale of observation. This statement is valid for each land use type studied – i.e. built-up, forests, cropland and grassland.

Socio-economic Scenarios of Agricultural Land Use Change in Central and Eastern European Countries

The study presented in this paper is part of the ACCELERATES (Assessing Climate Change Effects on Land Use and Ecosystems from Regional Analysis to The European Scale) project whose main goal is the construction of integrated predictions of future land use in Europe. The scenarios constructed in the project include estimates not only due to changes in the climate baseline, but also estimates due to possible future changes in socio-economics. The overall aim of the ACCELERATES was to assess the vulnerability of European agroecosystems based on economic and environmental considerations in term of both their sensitivity and capacity to adapt changes. The historical background, the type of economy, the policy aim and governance and importance of agriculture in the overall national economy have created large differences between Western and Central and Eastern European countries (CEECs). This paper focuses on vulnerability of the farm sector and rural economy of CEECs.ACCELERATES, climate change, agricultural land use, scenario, Land Economics/Use, Q24,

How can social–ecological system models simulate the emergence of social–ecological crises?

The idea that human impacts on natural systems might trigger large-scale, social–ecological ‘crises’ or ‘breakdowns’ is attracting increasing scientific, societal and political attention, but the risks of such crises remain hard to assess or ameliorate. Social–ecological systems have complex dynamics, with bifurcations, nonlinearities and tipping points all emerging from the interaction of multiple human and natural processes. Computational modelling is a key tool in understanding these processes and their effects on system resilience. However, models that operate over large geographical extents often rely on assumptions such as economic equilibrium and optimisation in social–economic systems, and mean-field or trend-based behaviour in ecological systems, which limit the simulation of crisis dynamics. Alternative forms of modelling focus on simulating local-scale processes that underpin the dynamics of social–ecological systems. Recent improvements in data resources and computational tools mean that such modelling is now technically feasible across large geographical extents. We consider the contributions that the different types of model can make to simulating social–ecological crises. While no models are able to predict exact outcomes in complex social–ecological systems, we suggest that one new approach with substantial promise is hybrid modelling that uses existing model architectures to isolate and understand key processes, revealing risks and associated uncertainties of crises emerging. We outline convergent and efficient functional descriptions of social and ecological systems that can be used to develop such models, data resources that can support them, and possible ‘high-level’ processes that they can represent. A free Plain Language Summary can be found within the Supporting Information of this article.</p

How can social–ecological system models simulate the emergence of social–ecological crises?

The idea that human impacts on natural systems might trigger large‐scale, social–ecological ‘crises’ or ‘breakdowns’ is attracting increasing scientific, societal and political attention, but the risks of such crises remain hard to assess or ameliorate. Social–ecological systems have complex dynamics, with bifurcations, nonlinearities and tipping points all emerging from the interaction of multiple human and natural processes. Computational modelling is a key tool in understanding these processes and their effects on system resilience. However, models that operate over large geographical extents often rely on assumptions such as economic equilibrium and optimisation in social–economic systems, and mean‐field or trend‐based behaviour in ecological systems, which limit the simulation of crisis dynamics. Alternative forms of modelling focus on simulating local‐scale processes that underpin the dynamics of social–ecological systems. Recent improvements in data resources and computational tools mean that such modelling is now technically feasible across large geographical extents. We consider the contributions that the different types of model can make to simulating social–ecological crises. While no models are able to predict exact outcomes in complex social–ecological systems, we suggest that one new approach with substantial promise is hybrid modelling that uses existing model architectures to isolate and understand key processes, revealing risks and associated uncertainties of crises emerging. We outline convergent and efficient functional descriptions of social and ecological systems that can be used to develop such models, data resources that can support them, and possible ‘high‐level’ processes that they can represent

Space-time patterns of urban sprawl, a 1D cellular automata and microeconomic approach

We present a theoretical model of residential growth that emphasizes the path-dependent nature of urban sprawl patterns. The model is founded on the monocentric urban economic model and uses a cellular automata (CA) approach to introduce endogenous neighbourhood effects. Households are assumed to both like and dislike the density of their neighbourhood, and trade-off this density with housing space consumption and commuting costs. Discontinuous spatial patterns emerge from that trade-off, with the size of suburban clusters varying with time and distance to the centre. We use space-time diagrams inspired from 1D elementary CA to visualize changes in spatial patterns through time and space, and undertake sensitivity analyses to show how the pattern and timing of sprawl are affected by neighbourhood preferences, income level, commuting costs or by imposing a green belt.urban sprawl, open space, neighbourhood externalities, cellular automata, residential dynamics.

How modelling paradigms affect simulated future land use change

Land use models operating at regional to global scales are almost exclusively based on the single paradigm of economic optimisation. Models based on different paradigms are known to produce very different results, but these are not always equivalent or attributable to particular assumptions. In this study, we compare two pan-European integrated land use models that utilise the same climatic and socio-economic scenarios but which adopt fundamentally different modelling paradigms. One of these is a constrained optimising economic-equilibrium model, and the other is a stochastic agent-based model. We run both models for a range of scenario combinations and compare their projections of spatially aggregate and disaggregate land use changes and ecosystem service supply levels in food, forest and associated environmental systems. We find that the models produce very different results in some scenarios, with simulated food production varying by up to half of total demand and the extent of intensive agriculture varying by up to 25 % of the EU land area. The agent-based model projects more multifunctional and heterogeneous landscapes in most scenarios, providing a wider range of ecosystem services at landscape scales, as agents make individual, time-dependent decisions that reflect economic and non-economic motivations. This tendency also results in food shortages under certain scenario conditions. The optimisation model, in contrast, maintains food supply through intensification of agricultural production in the most profitable areas, sometimes at the expense of land abandonment in large parts of Europe. We relate the principal differences observed to underlying model assumptions and hypothesise that optimisation may be appropriate in scenarios that allow for coherent political and economic control of land systems, but not in scenarios in which economic and other scenario conditions prevent the changes in prices and responses required to approach economic equilibrium. In these circumstances, agent-based modelling allows explicit consideration of behavioural processes, but in doing so it provides a highly flexible account of land system development that is harder to link to underlying assumptions. We suggest that structured comparisons of parallel and transparent but paradigmatically distinct models are an important method for better understanding the potential scope and uncertainties of future land use change, particularly given the substantive differences that currently exist in the outcomes of such models

Assessing the quality of land system models: moving from valibration to evaludation

Reviews suggest that evaluation of land system models is largely inadequate, with undue reliance on a vague concept of validation. Efforts to improve and standardise evaluation practices have so far had limited effect. In this article we examine the issues surrounding land system model evaluation and consider the relevance of the TRACE framework for environmental model documentation. In doing so, we discuss the application of a comprehensive range of evaluation procedures to existing models, and the value of each specific procedure. We develop a tiered checklist for going beyond what seems to be a common practice of ‘valibration’ (the repeated variation of model parameter values to achieve agreement with data) to achieving ‘evaludation’ (the rigorous, broad-based assessment of model quality and validity). We propose the Land Use Change – TRACE (LUC-TRACE) model evaludation protocol and argue that engagement with a comprehensive protocol of this kind (even if not this particular one) is valuable in ensuring that land system model results are interpreted appropriately. We also suggest that the main benefit of such formalised structures is to assist the process of critical thinking about model utility, and that the variety of legitimate modelling approaches precludes universal tests of whether a model is ‘valid’. Evaludation is therefore a detailed and subjective process requiring the sustained intellectual engagement of model developers and users

Evaluation and Calibration of an Agent Based Land use Model Using Remotely Sensed Land Cover and Primary Productivity Data

Identifying and reducing uncertainties in future land use projections are becoming critical in integrated assessments of the climate and social change scenarios. Here, we quantified correspondence between remotely sensed land cover and a model-derived projection of European land use to build a calibration and evaluation framework for land use projection models. For an eight-year period (2006–2013), we compared simulated land uses from a model (CRAFTY-EU), defined as agent functional types, against remotely sensed MODIS land cover. Information between two datasets and spatial complexity are calculated, which allowed the evaluation of the CRAFTY model and calibration of the model parameters. The computational cost was high. Thus more efficient searching algorithms are called for. The evaluation framework holds promise for better calibration of land use decision models to increase model usability and improve the value of future land cover projections