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Uncertainty in Landscape Models: Sources, Impacts and Decision Making

  • Kim E Lowell
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

This chapter focuses on impacts on decision making of uncertainty associated with outputs of complex systems-based models. Fundamental underlying sources of uncertainty that can impact quantitative model outputs are discussed — these sources include: model structure; natural variability of phenomena; spatial, temporal, and taxonomic scales of data; and the fundamental nature of data used for modelling. These are discussed relative to map complexity, data characteristics, how they impact model calibration and validation, and how model structure and usage is impacted by them.

The potential impact of uncertainty information on decision making is discussed. It is suggested that the importance of uncertainty information in the decision-making process depends on how `obvious' a decision is and the amount of influence that model outputs have on the decision being made. Knowing the uncertainty of model outputs is most useful in decision making when several different options appear to have comparable acceptability, and model outputs have a high influence on the decision being made. Conversely, uncertainty is much less important in decision making if one option is clearly superior to all others, or if factors such as policy considerations are given more weight than model outputs.

In situations where uncertainty information would be useful, the high level of complexity of the uncertainty in outputs of spatial systems-based models makes it difficult for such information to be used efficiently by decision makers. An example is presented to demonstrate that having uncertainty information will not necessarily change a decision made, nor will it provide more confidence that a correct decision has been made.

Keywords

Model Output Model Uncertainty Model User Natural Variability Digital Terrain Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kim E Lowell
    • 1
    • 2
  1. 1.Primary Industries Research VictoriaParkville CentreCarltonAustralia
  2. 2.Cooperative Research Centre for Spatial InformationCarltonAustralia

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