Dietrich, J. P., Popp, A., & Lotze-Campen, H. (2013). Reducing the loss of information and gaining accuracy with clustering methods in a global land-use model. Ecol. Model., 263, 233–243.
Abstract: Global land-use models have to deal with processes on several spatial scales, ranging from the global scale down to the farm level. The increasing complexity of modern land-use models combined with the problem of limited computational resources represents a challenge to modelers. One solution of this problem is to perform spatial aggregation based on a regular grid or administrative units such as countries. Unfortunately this type of aggregation flattens many regional differences and produces a homogenized map of the world. In this paper we present an alternative aggregation approach using clustering methods. Clustering reduces the loss of information due to aggregation by choosing an appropriate aggregation pattern. We investigate different clustering methods, examining their quality in terms of information conservation. Our results indicate that clustering is always a good choice and preferable compared to grid-based aggregation. Although all the clustering methods we tested delivered a higher degree of information conservation than grid-based aggregation, the choice of clustering method is not arbitrary. Comparing outputs of a model fed with original data and a model fed with aggregated data, bottom-up clustering delivered the best results for the whole range of numbers of clusters tested. (C) 2013 Elsevier B.V. All rights reserved.
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König, H. J., Uthes, S., Schuler, J., Zhen, L., Purushothaman, S., Suarma, U., et al. (2013). Regional impact assessment of land use scenarios in developing countries using the FoPIA approach: findings from five case studies. J. Environ. Manage., 127 Suppl, S56–S64.
Abstract: The impact of land use changes on sustainable development is of increasing interest in many regions of the world. This study aimed to test the transferability of the Framework for Participatory Impact Assessment (FoPIA), which was originally developed in the European context, to developing countries, in which lack of data often prevents the use of data-driven impact assessment methods. The core aspect of FoPIA is the stakeholder-based assessment of alternative land use scenarios. Scenario impacts on regional sustainability are assessed by using a set of nine regional land use functions (LUFs), which equally cover the economic, social and environmental dimensions of sustainability. The cases analysed in this study include (1) the alternative spatial planning policies around the Merapi volcano and surrounding areas of Yogyakarta City, Indonesia; (2) the large-scale afforestation of agricultural areas to reduce soil erosion in Guyuan, China; (3) the expansion of soil and water conservation measures in the Oum Zessar watershed, Tunisia; (4) the agricultural intensification and the potential for organic agriculture in Bijapur, India; and (5) the land degradation and land conflicts resulting from land division and privatisation in Narok, Kenya. All five regions are characterised by population growth, partially combined with considerable economic development, environmental degradation problems and social conflicts. Implications of the regional scenario impacts as well as methodological aspects are discussed. Overall, FoPIA proved to be a useful tool for diagnosing regional human-environment interactions and for supporting the communication and social learning process among different stakeholder groups.
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Kersebaum, C., & Nendel, C. (2013). Requirements for data from variety trials – justification and purpose (in German)..
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Müller, C., Stehfest, E., van Minnen, J., Strengers, B., von, B. W., Beusen, A., et al. (2013). Reversal of the land biosphere carbon balance under climate and land-use change..
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Jorgenson, J. (2013). Review of Cloud Computing Opportunities (Vol. 1).
Abstract: This paper will begin by defining some of the challenges that we face on the MACSUR project in terms of evaluating model uncertainty and carrying out model integration. I will briefly review what cloud technologies are available, followed with some suggestions about how those cloud technologies can be used in order to contribute to meeting the challenges set out in the first part of the paper.’Month 12’ deliverable for WP1 is a review of the opportunities for using cloud computing to develop the potential for model inter-comparison and interlinking in MACSUR. A challenging aspect of compiling this review is that before an ‘opportunity’ for any kind of model linking/comparison can be identified, a lot of information about the specifics of extant models and workflows must be gathered from each of the three themes (TradeM, CropM, and LiveM).This deliverable must, however, be more than just saying ‘these are the computing tools that we can use to.’. There are a number of different challenges at different levels; a hierarchy of challenges, if you like. For example, in order to get models ‘talking’ to one another, adequate protocols for the transference of data and scaleability will need to be established, and then things like uncertainty analysis for these integrated models will need to be addressed. Further issues exist relating to human behaviour and logistics (e.g. MACSUR is a large project with many members from all over Europe, with substantial distances between many of it’s members).The term “Cloud” is very ambiguous, and Cloud Computing covers a huge range of services, and a number of innovative tools exist which can make international collaborative research more effective. Two examples (already implemented on the MACSUR website) are: a discussion forum (where project members can create topics, make or reply to posts, and upload documents) and a complete surveying platform (to provide an un-restricted and fully featured survey platform for MACSUR members’ information gathering needs.) No Label
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