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

Long‐term trend analysis in annual, seasonal and monthly rainfall in East Northeast of Brazil and the influence of modes of climate variability

“…In the State of Alagoas, it was possible to detect patterns of changes in monthly rainfall, in which in the wettest months (from May to September) there were trends of reduction of monthly totals in the central region of the State, corresponding to clusters C2 and C3 (region with intermediate rainfall totals in the state-transition from the wettest region to the arid region). Rainfall in these regions is associated with distance from the coast and the deviation of trade winds and breeze circulations caused by the topography (Lyra et al, 2014(Lyra et al, , 2017 but rainfall patterns in recent years have been reported to be under the influence of land use and land use change (Correia Filho et al, 2019;da Silva et al, 2022;Santos et al, 2022).…”

“…The results of this study indicate a reduction in rainfall in drier months and an increase in rainfall in rainy months, which intensifies the negative effects of local conditions. The reduction of rainfall can cause an increase in the number of days without rain, reduction of the rainy season, and conditions of drought or desertification process in more severe cases (Gois et al, 2005; Santos et al, 2022). In addition, the state has a semi‐arid region (Alvares et al, 2013) with great social vulnerability (IBGE, 2023), which can intensify the climate impacts on the populations of the region.…”

Monthly potential evapotranspiration estimated using the Thornthwaite method with gridded climate datasets for Rio de Janeiro state, Brazil

“…The Southeastern region of Brazil (SEB) includes Espírito Santo, Minas Gerais, São Paulo, and Rio de Janeiro states, and is one of the most developed regions in Brazil. The topography of the SEB region is complex, with extensive valleys (Paraíba do Sul, Jequitinhonha, and São Francisco do Sul), and mountains (e.g., Serra do Mar, and Mantiqueira), and altitudes ranging from 0 to 2800 m (Santos et al 2022). The land use and land cover are mainly for agriculture (63%), forests (29%), non-forest natural formations (4%), non-vegetated areas (2%), and water (1%) (Souza et al 2020).…”

“…Thornthwaite method is a strong candidate since it has lower data requirements compared to other methods, especially the PM-FAO56, and presents reasonable and good performances, depending on climate type and datasets used (Santos et al 2018a; Martins et al 2022). Since the Thornthwaite method is solely based on air temperature, it tends to underestimate the PET in arid climates and slightly overestimate in humid climates (Trajkovic and Kolakovic 2009; Almorox et al 2015; Valipour et al 2017) and the Southeast region of Brazil presents these climatic conditions(Santos et al 2022). Despite the several efforts have been performed to reduce errors and to adapt the Thornthwaite method for arid, semi-arid and humid climates(Trajkovic and Kolakovic 2009;Aschonitis et al 2022), we opted to use the original Thornthwaite method for estimating PET, similar to others studies(Santos et al 2018a).…”

Validação da SPEIbase para a Região Sudeste Brasileira

“…Aliada à sensibilidade dos índices a variações e tendências de longo-tempo, a escassez de dados de EMC pode restringir a avaliação de fenômenos climáticos, incluindo a seca, principalmente quando utilizados índices que necessitam de séries de precipitação e temperatura do ar (de Oliveira Roza et al, 2024;Morsy et al, 2022;Santos et al, 2022;Tostes et al, 2017). Para suprir séries climáticas de qualidade, contínuas e homogêneas no espaço e tempo, pode-se utilizar bases de dados em grade obtidas por interpolação espacial de séries climáticas de estações meteorológicas de superfície, simulados por modelos numéricos da atmosfera, obtidos por sensores remotos orbitais ou produtos híbridos, que consideram mais de uma destas abordagens (Santos et al, 2018;Nouri, 2023). O SPEIbase é uma base de dados em grade que fornece estimativas da SPEI em escala global e de longo prazo, que pode auxiliar na obtenção de informações climáticas e de índices de seca.…”