Search

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. [ABSTRACT FROM AUTHOR]

The objective of this paper was to establish disaggregation coefficients for the state of Minas Gerais, Brazil, under the premises of i) stationarity of the disaggregation coefficients according to the return period and ii) spatial validation of the disaggregation coefficients. The regression analysis was used as a statistical procedure to verify the significance of the coefficients of disaggregation slope coefficient as a function of return period in daily and sub-daily rainfall time series in rain gauges distributed throughout the state of Minas Gerais. Most of the disaggregation coefficients (94% of the situations) did not show a tendency to change due to the return period. The detected trends modified coefficients of disaggregation by up to 21% to the average obtained for different return periods. It was also verified that there is a significant difference between rainfall ratios of different durations, for different locations, indicating that the coefficients of disaggregation are not regional, but rather local. The use of disaggregation coefficients to obtain sub-daily rainfall is possible, provided that the representative coefficients of the locality are used. In this context, the spatialization of disaggregation coefficients is an important approach. • Relations between rainfall of different durations are disaggregation coefficients. • The use of disaggregation coefficients to obtain sub-daily rainfall is possible. • Disaggregation coefficients did not show a trend to change due to return period. • The disaggregation coefficients have local validity. • The spatialization technique can favor suitable disaggregation coefficients. [ABSTRACT FROM AUTHOR]