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Maier H.R.,University of Adelaide | Guillaume J.H.A.,Aalto University | van Delden H.,University of Adelaide | van Delden H.,Research Institute for Knowledge Systems | And 4 more authors.
Environmental Modelling and Software | Year: 2016

A highly uncertain future due to changes in climate, technology and socio-economics has led to the realisation that identification of "best-guess" future conditions might no longer be appropriate. Instead, multiple plausible futures need to be considered, which requires (i) uncertainties to be described with the aid of scenarios that represent coherent future pathways based on different sets of assumptions, (ii) system performance to be represented by metrics that measure insensitivity (i.e. robustness) to changes in future conditions, and (iii) adaptive strategies to be considered alongside their more commonly used static counterparts. However, while these factors have been considered in isolation previously, there has been a lack of discussion of the way they are connected. In order to address this shortcoming, this paper presents a multidisciplinary perspective on how the above factors fit together to facilitate the development of strategies that are best suited to dealing with a deeply uncertain future. © 2016 Elsevier Ltd. Source


Volk M.,Helmholtz Center for Environmental Research | Lautenbach S.,Helmholtz Center for Environmental Research | Van Delden H.,Research Institute for Knowledge Systems | Newham L.T.H.,Australian National University | Seppelt R.,Helmholtz Center for Environmental Research
Environmental Management | Year: 2010

This article analyses the benefits and shortcomings of the recently developed decision support systems (DSS) FLUMAGIS, Elbe-DSS, CatchMODS, and MedAction. The analysis elaborates on the following aspects: (i) application area/decision problem, (ii) stakeholder interaction/users involved, (iii) structure of DSS/model structure, (iv) usage of the DSS, and finally (v) most important shortcomings. On the basis of this analysis, we formulate four criteria that we consider essential for the successful use of DSS in landscape and river basin management. The criteria relate to (i) system quality, (ii) user support and user training, (iii) perceived usefulness and (iv) user satisfaction. We can show that the availability of tools and technologies for DSS in landscape and river basin management is good to excellent. However, our investigations indicate that several problems have to be tackled. First of all, data availability and homogenisation, uncertainty analysis and uncertainty propagation and problems with model integration require further attention. Furthermore, the appropriate and methodological stakeholder interaction and the definition of 'what end-users really need and want' have been documented as general shortcomings of all four examples of DSS. Thus, we propose an iterative development process that enables social learning of the different groups involved in the development process, because it is easier to design a DSS for a group of stakeholders who actively participate in an iterative process. We also identify two important lines of further development in DSS: the use of interactive visualization tools and the methodology of optimization to inform scenario elaboration and evaluate trade-offs among environmental measures and management alternatives. © 2009 Springer Science+Business Media, LLC. Source


van Vliet J.,Research Institute for Knowledge Systems | Bregt A.K.,Wageningen University | Hagen-Zanker A.,University of Cambridge
Ecological Modelling | Year: 2011

Land-use change models are typically calibrated to reproduce known historic changes. Calibration results can then be assessed by comparing two datasets: the simulated land-use map and the actual land-use map at the same time. A common method for this is the Kappa statistic, which expresses the agreement between two categorical datasets corrected for the expected agreement. This expected agreement is based on a stochastic model of random allocation given the distribution of class sizes. However, when a model starts from an initial land-use map and makes changes to it, that stochastic model does not pose a meaningful reference level. This paper introduces K Simulation, a statistic that is identical in form to the Kappa statistic but instead applies a more appropriate stochastic model of random allocation of class transitions relative to the initial map. The new method is illustrated on a simple example and then the results of the Kappa statistic and K Simulation are compared using the results of a land-use model. It is found that only K Simulation truly tests models in their capacity to explain land-use changes over time, and unlike Kappa it does not inflate results for simulations where little change takes place over time. © 2011 Elsevier B.V. Source


Van Delden H.,Research Institute for Knowledge Systems | McDonald G.,Market Economics Ltd
Modelling for Environment's Sake: Proceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010 | Year: 2010

Although in the past decades several attempts have been made to integrate economic models with land use change (LUC) models, none of them have been fully satisfactory. In this paper we analyse four different integrated models for policy support that include economic and LUC models. We describe several functional forms used to integrate LUC and economic models, highlighting the various strengths and weaknesses of each form, and in turn, suggesting possible pathways for improved integration. Analysing the concepts and underlying assumptions of both types of models show their vast difference. When integrating these models, underlying assumptions and limitations of the existing individual models are passed on to the integrated model. A proper integration therefore requires a thorough understanding of the underlying theories of both types of models and a solution at this theoretical level. We argue that concepts of evolutionary economics and -spatially explicit- agent based modelling, where creation of ideas and learning are embodied into fully integrated LUC and economic models, provide some key mechanisms for bridging the described gap. These approaches are however very data demanding and have -at least at present- major limitations in performing a proper calibration and validation. Source


Hagen-Zanker A.,Research Institute for Knowledge Systems | Hagen-Zanker A.,TU Eindhoven | Martens P.,Maastricht University
Studies in Computational Intelligence | Year: 2011

Geosimulation is a form of microsimulation that seeks to understand geographical patterns and dynamics as the outcome of micro-level geographical processes. Geosimulation has been applied to understand such diverse systems as lake ecology, traffic congestion and urban growth. A crucial task common to these applications is to express the agreement between model and reality and hence the confidence one can have in model results. Such evaluation requires a geospatial perspective; it is not sufficient if micro-level interactions are realistic. Importantly, interactions should be such that meso- and macro- level patterns emerging from the model are realistic. In recent years, a host of map comparison methods have been developed, which address different aspects of the agreement between model and reality. This paper places such methods in a framework to systematically assess breadth and width of model performance. The framework expresses agreement at the continuum of spatial scales ranging from local to whole landscape and separately addresses agreement in structure and presence. A common reference level makes different performance metrics mutually comparable and guides the interpretation of results. The framework is applied for the evaluation of a constrained cellular automata model of the Netherlands. The case demonstrates that a performance assessment lacking either a multi-criteria and multi-scale perspective or a reference level would result in an unbalanced account and ultimately false conclusions. © 2011 Springer-Verlag Berlin Heidelberg. Source

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