How Can We Make Progress with Decision Support Systems in Landscape and River Basin Management? Lessons Learned from a Comparative Analysis of Four Different Decision Support Systems

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.     

The dynamics of shifting cultivation captured in an extended Constrained Cellular Automata land use model

This paper presents an extension to the Constrained Cellular Automata (CCA) land use model of White et al. [White, R., Engelen, G., Uljee, I., 1997. The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design 24(3), 323–343] to make it better suited for modelling the dynamics of shifting cultivation. In the extended model the time passed since the last land use transition of a location is a factor of its land use potential. The model can now account for the gradual decrease in soil fertility after an area of forest has been cleared for cultivation and also capture the process of regeneration once the plot is fallowed. The model is applied for the Ruhunupura area of Sri Lanka where chena, a particular practice of shifting cultivation, is a common land use that dominates the landscape dynamics. The model is calibrated for the period 1985–2001 and the results are assessed in terms of location to location overlap as well as structural similarity at multiple scales. These results give confidence in the representation of land use dynamics for the main land use classes. On the basis of a long term scenario run for the period 2001–2030, it is verified that the model captures stylized facts related to chena dynamics, in particular shortening fallow periods and increasingly long cultivation periods of chena, as a result of increasing land use pressure.     

Integrated assessment of agricultural policies with dynamic land use change modelling

With about half of its territory being farmed, agriculture is the main land use in the European Union (EU). As over 10% of the total EU manufacturing output comes from the agri-food sector, it also is an economic factor of great importance. Moreover, EU policy in this sector has far-reaching consequences ranging from the EU's status as a global trade partner to landscape preservation and development. The LUMOCAP Policy Support System is targeted towards policy makers in the European Commission (EC) and its Member States (MS) and aims to provide support in the field of sustainable agricultural and rural development. To this end it incorporates an integrated model with socio-economic and bio-physical processes, operating at different spatial scales. For supporting integrated assessment, a large number of policy levers is included as inputs for these models and outputs are transformed into policy-relevant social, economic and environmental indicators. The whole system is framed in a flexible, modular and easy to use software package that is useable for process experts and policy-analysts alike.This paper describes the integrated model, the individual models and a first calibration of the system. It demonstrates the system's behaviour for typical scenario runs and concludes with a reflection on the current status of the system and some recommendations for further development.