Abstract
Desertification is a land degradation phenomenon with dire and irreversible consequences, affecting different regions of the world. Assessment of spatial climate susceptibility to desertification requires long-term averages of precipitation (P) and potential evapotranspiration (PET). An alternative to desertification susceptibility analysis is the use of spatially gridded climate data. The aim of this study was to assess an approach based on gridded climate data and cartographic modeling to characterize climate susceptibility to desertification over Southeast Brazil. Two indices were used to identify climate desertification susceptibility: the aridity index Ia (P/PET) and D (PET/P). Precipitation gridded data from the Global Precipitation Climatology Centre (GPCC), and air temperature from the Global Historical Climatology Network (GHCN) were used. The PET was estimated by the Thornthwaite’s method using air temperature data. The assessment of these gridded climate series, PET and indices was performed using independent observed climate series (1961–2010) from the National Institute of Meteorology (INMET) of Brazil—(68 weather stations). Determination coefficient (r2) and the Willmott’s coefficient (d) between gridded and observed data revealed satisfactory precision and agreement for grids of precipitation (r2 > 0.93, d > 0.90), air temperature (r2 > 0.94, d > 0.53) and PET (r2 > 0.93, d > 0.63). Overall, the aridity indices based on climate gridded presented good performance when used to identify areas susceptible to desertification. Susceptible areas to desertification were identified by the index Ia over the Northern regions of Minas Gerais and Rio de Janeiro states. No susceptible areas to desertification were identified using the index D. However, both indices indicated large areas of sub-humid climate, which can be strongly affected by desertification in the future.
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The observed data used in the present study are made available by the National Meteorological Institute (INMET) of Brazil and the gridded data by the Climate Data Center – NOAA. National Meteorological Institute. https://portal.inmet.gov.br/servicos/bdmep-dados-hist%C3%B3ricos. Global Historical Climatology Network (GHCN). https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-ghcn. Global Precipitation Climatology Centre (GPCC). https://data.noaa.gov/dataset/dataset/global-precipitation-climatology-centre-gpcc.
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Acknowledgements
The authors are grateful to the Brazilian National Institute of Meteorology (INMET) for the climatic data and Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro - FAPERJ, Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES for the financial support.
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Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (No. 141.663)—D.Sc. Gustavo Bastos Lyra. Conselho Nacional de Desenvolvimento Científico e Tecnológico (No. 483643/2011–4 and No. 435238/2018–3)—D.Sc. Gustavo Bastos Lyra. Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (No. E-26/201.501/2014)—D.Sc. Gustavo Bastos Lyra. Conselho Nacional de Desenvolvimento Científico e Tecnológico (No. 309681/2019–7)—Dr. José Francisco de Oliveira-Júnior. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (No. 88887.308408/2018–00) M.Sc. Gisleine Cunha-Zeri. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (No. 0001)—Ms. Janaína Cassiano dos Santos.
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11069_2021_5147_MOESM3_ESM.tif
Root Mean Squared Error (RMSE, %) of gridded precipitation from the Global Precipitation Climatology Center (GPCC) in relation to observed data (TIF 3568 kb)
11069_2021_5147_MOESM6_ESM.tif
Root Mean Squared Error (RMSE, %) of gridded air temperature from the Global Historical Climatology (GHCN) in relation to observations (TIF 3541 kb)
11069_2021_5147_MOESM9_ESM.tif
Root Mean Squared Error (RMSE, %) of evapotranspiration from gridded data (GHCN) in relation to observations (TIF 3569 kb)
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dos Santos, J.C., Lyra, G.B., Abreu, M.C. et al. Aridity indices to assess desertification susceptibility: a methodological approach using gridded climate data and cartographic modeling. Nat Hazards 111, 2531–2558 (2022). https://doi.org/10.1007/s11069-021-05147-0
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DOI: https://doi.org/10.1007/s11069-021-05147-0