Aridity indices to assess desertification susceptibility: a methodological approach using gridded climate data and cartographic modeling

Janaína Cassiano Santos, Gustavo Bastos Lyra (), Marcel Carvalho Abreu, José Francisco Oliveira-Júnior, Leonardo Bohn, Gisleine Cunha-Zeri and Marcelo Zeri
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Janaína Cassiano Santos: Federal Fluminense University (UFF)
Gustavo Bastos Lyra: Federal Rural University of Rio de Janeiro (UFRRJ)
Marcel Carvalho Abreu: Federal Rural University of Rio de Janeiro (UFRRJ)
José Francisco Oliveira-Júnior: Federal University of Alagoas (UFAL)
Leonardo Bohn: Federal University of Rio Grande do Sul (UFRGS)
Gisleine Cunha-Zeri: National Institute for Space Research (INPE)
Marcelo Zeri: National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN)

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 3, No 16, 2558 pages

Abstract: 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.

Keywords: Land degradation; Spatial modeling; Geoprocessing; Climate (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05147-0

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