Literature DB >> 23954777

Clarity versus complexity: land-use modeling as a practical tool for decision-makers.

Terry L Sohl1, Peter R Claggett.   

Abstract

The last decade has seen a remarkable increase in the number of modeling tools available to examine future land-use and land-cover (LULC) change. Integrated modeling frameworks, agent-based models, cellular automata approaches, and other modeling techniques have substantially improved the representation of complex LULC systems, with each method using a different strategy to address complexity. However, despite the development of new and better modeling tools, the use of these tools is limited for actual planning, decision-making, or policy-making purposes. LULC modelers have become very adept at creating tools for modeling LULC change, but complicated models and lack of transparency limit their utility for decision-makers. The complicated nature of many LULC models also makes it impractical or even impossible to perform a rigorous analysis of modeling uncertainty. This paper provides a review of land-cover modeling approaches and the issues causes by the complicated nature of models, and provides suggestions to facilitate the increased use of LULC models by decision-makers and other stakeholders. The utility of LULC models themselves can be improved by 1) providing model code and documentation, 2) through the use of scenario frameworks to frame overall uncertainties, 3) improving methods for generalizing key LULC processes most important to stakeholders, and 4) adopting more rigorous standards for validating models and quantifying uncertainty. Communication with decision-makers and other stakeholders can be improved by increasing stakeholder participation in all stages of the modeling process, increasing the transparency of model structure and uncertainties, and developing user-friendly decision-support systems to bridge the link between LULC science and policy. By considering these options, LULC science will be better positioned to support decision-makers and increase real-world application of LULC modeling results. Published by Elsevier Ltd.

Keywords:  Clarity; Complexity; Decision support; Land use; Model; Policy

Mesh:

Year:  2013        PMID: 23954777     DOI: 10.1016/j.jenvman.2013.07.027

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  4 in total

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Review 2.  Meta-studies in land use science: Current coverage and prospects.

Authors:  Jasper van Vliet; Nicholas R Magliocca; Bianka Büchner; Elizabeth Cook; José M Rey Benayas; Erle C Ellis; Andreas Heinimann; Eric Keys; Tien Ming Lee; Jianguo Liu; Ole Mertz; Patrick Meyfroidt; Mark Moritz; Christopher Poeplau; Brian E Robinson; Ralf Seppelt; Karen C Seto; Peter H Verburg
Journal:  Ambio       Date:  2015-09-25       Impact factor: 5.129

3.  Farmers' Preferences for Future Agricultural Land Use Under the Consideration of Climate Change.

Authors:  Ulrike Pröbstl-Haider; Nina M Mostegl; Julia Kelemen-Finan; Wolfgang Haider; Herbert Formayer; Jochen Kantelhardt; Tobias Moser; Martin Kapfer; Ryan Trenholm
Journal:  Environ Manage       Date:  2016-07-02       Impact factor: 3.266

4.  Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques-A case study of a hilly area, Jiangle, China.

Authors:  Chen Liping; Sun Yujun; Sajjad Saeed
Journal:  PLoS One       Date:  2018-07-13       Impact factor: 3.240

  4 in total

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