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Simulating uncertainty in climate-pest models with fuzzy numbers.

H Scherm1

  • 1Department of Plant Pathology, University of Georgia, Athens, GA 30602, USA. scherm@uga.edu

Environmental Pollution (Barking, Essex : 1987)
|April 20, 2004
PubMed
Summary

Climate change impact assessments are improved by using fuzzy numbers to represent uncertain climate projections. This approach reveals significant shifts in pest environmental favorability, especially in Southern Europe, highlighting the need for reduced climate uncertainty.

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Area of Science:

  • Ecology and Environmental Science
  • Climate Change Modeling
  • Pest Management

Background:

  • Climate-pest models traditionally use point estimates, implying certainty not present in real-world data.
  • Climate projections, crucial for impact assessments, inherently contain significant uncertainty.
  • Uncertainty in climate data can lead to unreliable predictions of pest environmental favorability.

Purpose of the Study:

  • To investigate the impact of climate change uncertainty on pest risk models.
  • To implement a pest risk model using fuzzy numbers to represent climate projection uncertainty.
  • To compare the outcomes of crisp versus fuzzy climate change scenarios on pest environmental favorability.

Main Methods:

  • A generic pest risk model was developed incorporating temperature, soil moisture, and cold stress.

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  • The model was implemented in a fuzzy spreadsheet environment.
  • Three climate scenarios were used: present climate, crisp climate change, and fuzzy climate change, with fuzzy numbers representing a range of possible climate values.

    • Main Results:

    • Crisp climate change scenarios showed minor changes in environmental favorability.
    • Fuzzy climate change scenarios revealed substantial shifts in environmental favorability, particularly in Southern Europe.
    • Increased winter precipitation in Southern Europe, under fuzzy scenarios, led to higher favorability but with a broad range of potential outcomes.

    Conclusions:

    • Representing climate projection uncertainty with fuzzy numbers is crucial for accurate pest impact assessments.
    • Uncertainty in climate data significantly influences the predicted environmental favorability for pests.
    • Reducing uncertainty in climate change projections is essential for developing reliable pest management strategies.