A New Climatology of Vegetation and Land Cover Information for South America

Laurizio Emanuel Ribeiro Alves (), Luis Gustavo Gonçalves de Gonçalves, Álvaro Vasconcellos Araújo de Ávila, Giovana Deponte Galetti, Bianca Buss Maske, Giuliano Carlos do Nascimento and Washington Luiz Félix Correia Filho
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Laurizio Emanuel Ribeiro Alves: Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil
Luis Gustavo Gonçalves de Gonçalves: Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil
Álvaro Vasconcellos Araújo de Ávila: Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil
Giovana Deponte Galetti: Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil
Bianca Buss Maske: Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil
Giuliano Carlos do Nascimento: Centro de Monitoramento de Alerta e Alarme da Defesa Civil (CEMADEC), Defesa Civil de Salvador (CODESAL), Salvador 40301-110, Brazil
Washington Luiz Félix Correia Filho: Programa de Pós-Graduação em Ambientometria, Universidade Federal do Rio Grande (FURG), Rio Grande 96203-900, Brazil

Sustainability, 2024, vol. 16, issue 7, 1-20

Abstract: Accurate information on vegetation and land cover is crucial for numerical forecasting models in South America. This data aids in generating more realistic forecasts, serving as a tool for decision-making to reduce environmental impacts. Regular updates are necessary to ensure the data remains representative of local conditions. In this study, we assessed the suitability of ‘Catchment Land Surface Models-Fortuna 2.5’ (CLSM), Noah, and Weather Research and Forecasting (WRF) for the region. The evaluation revealed significant changes in the distribution of land cover classes. Consequently, it is crucial to adjust this parameter during model initialization. The new land cover classifications demonstrated an overall accuracy greater than 80%, providing an improved alternative. Concerning vegetation information, outdated climatic series for Leaf Area Index (LAI) and Greenness Vegetation Fraction (GVF) were observed, with notable differences between series, especially for LAI. While some land covers exhibited good performance for GVF, the Forest class showed limitations. In conclusion, updating this information in models across South America is essential to minimize errors and enhance forecast accuracy.

Keywords: land parameters; land input; climatology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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