@Article{SantosLyAbOlBoCuZe:2022:MeApUs,
               author = "Santos, Janaina Cassiano dos and Lyra, Gustavo Bastos and Abreu, 
                         Marcel Carvalho and Oliveira J{\'u}nior, Jos{\'e} Francisco and 
                         Bohn, Loonardo and Cunha Zeri, Gisleine da Silva and Zeri, 
                         Marcelo",
          affiliation = "{Universidade Federal Fluminense (UFF)  } and {Universidade 
                         Federal Rural do Rio de Janeiro (URRFJ)  } and {Universidade 
                         Federal Rural do Rio de Janeiro (URRFJ)  } and {Universidade 
                         Federal de Alagoas (UFAL)  } and {Universidade Federal do Rio 
                         Grande do Sul (UFRGS)  } and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)  } and {Centro Nacional de Monitoramento e 
                         Alertas de Desastres Naturais (CEMADEN)}",
                title = "Aridity indices to assess desertification susceptibility: a 
                         methodological approach using gridded climate data and 
                         cartographic modeling",
              journal = "Natural Hazards",
                 year = "2022",
               volume = "111",
                pages = "2531--2558",
                month = "Jan.",
             keywords = "Land degradation, Spatial modeling, Geoprocessing, Climate.",
             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 I-a (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 
                         (r(2)) and the Willmott's coefficient (d) between gridded and 
                         observed data revealed satisfactory precision and agreement for 
                         grids of precipitation (r(2) > 0.93, d > 0.90), air temperature 
                         (r(2) > 0.94, d > 0.53) and PET (r(2) > 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 I-a 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.",
                  doi = "10.1007/s11069-021-05147-0",
                  url = "http://dx.doi.org/10.1007/s11069-021-05147-0",
                 issn = "0921-030X",
             language = "en",
           targetfile = "Santos_2022_aridity.pdf",
        urlaccessdate = "02 maio 2024"
}