Re-considering the status quo: Improving calibration of land use change models through validation of transition potential predictions

The increasing complexity of the dynamics captured in Land Use and Land Cover (LULC) change modelling has made model behaviour less transparent and calibration more extensive. For cellular automata models in particular, this is compounded by the fact that validation is typically performed indirectly, using final simulated change maps; rather than directly considering the probabilistic predictions of transition potential. This study demonstrates that evaluating transition potential predictions provides detail into model behaviour and performance that cannot be obtained from simulated map comparison alone. This is illustrated by modelling LULC transitions in Switzerland using both Logistic Regression and Random Forests. The results emphasize the need for LULC modellers to explicitly consider the performance of individual transition models independently to ensure robust predictions. Additionally, this study highlights the potential for predictor variable selection as a means to improve transition model generalizability and parsimony, which is beneficial for simulating future LULC change

Pattern-oriented calibration and validation of urban growth models: Case studies of Dublin, Milan and Warsaw

Urban growth models are established to simulate complex dynamic processes of urban development, such as urban sprawl. According to the pattern-oriented modelling (POM) paradigm, recently gaining weight in ecology as a strategy for modelling complex systems, patterns at multiple scales should be considered to reflect the underlying processes of a complex system. Yet, calibration and validation of urban growth models is typically performed with a goal function of locational (cell-by-cell) agreement only, thus not in line with POM. We therefore examined POM as an approach to calibrate and validate (constrained) cellular automata for the European cities Warsaw, Milan, and Dublin. For Milan and Warsaw, the model structures identified with POM outperformed reference solutions calibrated on a single pattern with improvements up to 25% and 30%, respectively. For Dublin, no good model structure was found, but POM did help to recognize this problem, while locational agreement only failed to do so. Furthermore, the model structures identified with POM were more diverse, i.e. including more driving factors. In these diverse structures, the importance of the neighborhood effect relative to the infrastructure and land use effects reflected the polycentricity of the city as well as its type of sprawl: from monocentric edge expansion in Dublin to in-between ribbon sprawl in Warsaw to polycentric infill development in Milan. We conclude that POM improves the robustness of urban growth model calibration and validation, and obtains more dependable information about the processes driving urban sprawl that may serve the design of instruments to limit it

Integrated modeling in urban hydrology: reviewing the role of monitoring technology in overcoming the issue of ‘big data’ requirements

Increasingly, the application of models in urban hydrology has undergone a shift toward integrated structures that recognize the interconnected nature of the urban landscape and both the natural and engineered water cycles. Improvements in computational processing during the past few decades have enabled the application of multiple, connected model structures that link previously disparate systems together, incorporating feedbacks and connections. Many applications of integrated models look to assess the impacts of environmental change on physical dynamics and quality of landscapes. Whilst these integrated structures provide a more robust representation of natural dynamics, they often place considerable data requirements on the user, whereby data are required at contrasting spatial and temporal scales which can often transcend multiple disciplines. Concomitantly, our ability to observe complex, natural phenomena at contrasting scales has improved considerably with the advent of increasingly novel monitoring technologies. This has provided a pathway for reducing model uncertainty and improving our confidence in modeled outputs by implementing suitable monitoring regimes. This commentary assesses how component models of an exemplar integrated model have advanced over the past few decades, with a critical focus on the role of monitoring technologies that have enabled better identification of the key physical process. This reduces the uncertainty of processes at contrasting spatial and temporal scales, through a better characterization of feedbacks which then enhances the utility of integrated model applications

How to keep it adequate: A protocol for ensuring validity in agent-based simulation

There has so far been no shared understanding of validity in agent-based simulation. We here conceptualise validation as systematically substantiating the premises on which conclusions from simulation analysis for a particular modelling context are built. Given such a systematic perspective, validity of agent-based models cannot be ensured if validation is merely understood as an isolated step in the modelling process. Rather, valid conclusions from simulation analysis require context-adequate method choices at all steps of the simulation analysis including model construction, model and parameter inference, uncertainty analysis and simulation. We present a twelve-step protocol to highlight the (often hidden) premises for methodological choices and their link to the modelling context. It is designed to aid modelers in understanding their context and in choosing and documenting context-adequate and mutually consistent methods throughout the modelling process. Its purpose is to assist reviewers and the community as a whole in assessing and discussing context-adequacy

Study Report on Reporting Requirements on Biofuels and Bioliquids stemming from the Directive (EU) 2015/1513

This report was commissioned to gather comprehensive information on, and to provide systematic analysis of the latest available scientific research and the latest available scientific evidence on indirect land use change (ILUC) greenhouse gas emissions associated with production of biofuels and bioliquids. The EU mandatory sustainability criteria for biofuels and bioliquids do not allow the raw material for biofuel production to be obtained from land with high carbon stock or high biodiversity value. However, this does not guarantee that as a consequence of biofuels production such land is not used for production of raw materials for other purposes. If land for biofuels is taken from cropland formerly used for other purposes, or by conversion of grassland in arable land for biofuel production, the former agricultural production on this land has to be grown somewhere else. And if there is no regulation that this must happen sustainably, conversion of land may happen, which is not allowed to be used under the EU sustainability criteria for biofuels. This conversion may take place in other countries than where the biofuel is produced. This is called indirect land use change (ILUC). According to Article 3 of the European Union’s Directive (EU) 2015/1513 of 9 September 2015, the European Commission has to provide information on, and analysis of the available and the best available scientific research results, scientific evidence regarding ILUC emissions associated to the production of biofuels, and in relation to all production pathways. Besides, according to Article 23 of the revised European Union’s Directive 2009/28/EC (RES Directive), the Commission also has to provide the latest available information with regard to key assumptions influencing the results from modelling ILUC GHG emissions, as well as an assessment of whether the range of uncertainty identified in the analysis underlying the estimations of ILUC emissions can be narrowed down, and if the possible impact of the EU policies, such as environment, climate and agricultural policies, can be factored in. An assessment of a possibility of setting out criteria for the identification and certification of low ILUCrisk biofuels that are produced in accordance with the EU sustainability criteria is also required

Detecting systemic change in a land use system by Bayesian data assimilation

A spatially explicit land use change model is typically based on the assumption that the relationship between land use change and its explanatory processes is stationary. This means that model structure and parameterization are usually kept constant over the model runtime, ignoring potential systemic changes in this relationship resulting from societal changes. We have developed a methodology to test for systemic changes and demonstrate it by assessing whether or not a land use change model with a constant model structure is an adequate representation of the land use system given a time series of observations of past land use. This was done by assimilating observations of real land use into a land use change model, using a Bayesian data assimilation technique, the particle filter. The particle filter was used to update the prior knowledge about the model structure, i.e. the selection and relative importance of the explanatory processes for land use change allocation, and about the parameters. For each point in time for which observations were available the optimal model structure and parameterization were determined. In a case study of sugar cane expansion in Brazil, it was found that the assumption of a constant model structure was not fully adequate, indicating systemic change in the modelling period (2003-2012). The systemic change appeared to be indirect: a factor has an effect on the demand for sugar cane, an input variable, in such a way that the transition rules and parameters have to change as well. Although an inventory was made of societal changes in the study area during the studied period, none of them could be directly related to the onset of the observed systemic change in the land use system. Our method which allows for systemic changes in the model structure resulted in an average increase in the 95% confidence interval of the projected sugar cane fractions of a factor of two compared to the assumption of a stationary system. This shows the importance of taking into account systemic changes in projections of land use change in order not to underestimate the uncertainty of future projections