A new world natural vegetation map for global change studies
David M Lapola1, Marcos D Oyama, Carlos A Nobre
1Centro de Previsão de Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, 12630-970, Brazil.
Anais Da Academia Brasileira De Ciencias
|May 29, 2008
Summary
A new global natural vegetation map was created for climate models, incorporating tropical seasonal forests. This map integrates existing data, improving accuracy for large-scale and regional vegetation distribution.
Area of Science:
- Earth System Science
- Biogeography
- Climate Modeling
Background:
- Global climate models require accurate vegetation maps for simulating Earth's climate.
- Existing global vegetation maps show discrepancies, particularly in tropical regions.
- The Dorman and Sellers classification lacked a distinct category for tropical seasonal forests.
Purpose of the Study:
- To develop an improved global natural vegetation map for climate modeling.
- To incorporate a new biome category: tropical seasonal forest.
- To reconcile differences between existing global vegetation maps.
Main Methods:
- Utilized the Dorman and Sellers vegetation classification, adding tropical seasonal forest.
- Created a consensus map by merging two widely used global vegetation maps.
- Filled non-consensus areas using regional consensus from over 100 internet-sourced regional maps.
- Ensured data quality by selecting reliable sources and independent verification.
Main Results:
- A new global natural vegetation map at 1-degree resolution was generated.
- Significant discrepancies (approx. 1/3 of continental grid points) were found between existing maps.
- The new map integrates large-scale vegetation patterns with regional details.
Conclusions:
- The developed map offers a more accurate representation of global vegetation for climate studies.
- Incorporating tropical seasonal forests enhances the classification's completeness.
- The methodology provides a robust approach for creating consensus-based global environmental datasets.
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