KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/kmitl/1

Browse

Search Results

Now showing 1 - 2 of 2
  • Some of the metrics are blocked by your 
    Item type:Publication,
    Rainfall variability, land use and land cover dynamics, and implications for climate risk through a theoretical-conceptual model for the Eastern Northeast of Brazil
    (2025-06-01)
    da Silva Costa, Micejane
    ;
    Francisco de Oliveira Júnior, José
    ;
    Godoy de Barros, Bárbara
    ;
    Francisco Ferreira da Silva, Luís Felipe
    ;
    Alencar Cardoso, Kelvy Rosalvo
    The study assessed the variability of rainfall, land use and land cover (LULC), the influence of meteorological systems and their interactions, followed by the increase in climate risks in Maceió – Alagoas – ENEB. Monthly rainfall data were obtained from the 64-year historical series (1958–2022) of the TerraClimate platform. The TerraClimate precipitation data were validated ANA and SEMARH data, resulting in R<sup>2</sup> = 0.72 and r = 0.85. Spatially, the highest rainfall accumulations occurred from April to July (rainy season), with May and June being the wettest months (>800 mm). In contrast, the months from October to February (dry season) and March, August, and September (transition season) recorded lower rainfall, with November and December being the driest months (150–201 mm). The seasonal occurrence of rainfall and the relief in Maceió were associated with areas of climate risk. Maceió is a region vulnerable to extreme rainfall events due to its geographical location, the influence of the Atlantic Ocean and the Mundaú/Manguaba Lagoons, and the interaction with multi-scale meteorological systems. With LULC, there was an expansion of forest areas in rural areas and a reduction of agricultural areas, followed by increased urbanization in neighborhoods in the western part of the city (coastal plateau). The theoretical-conceptual model of climate risk provides essential information for planning and supports decision-making for public policies. The ability to assess the impacts of extreme rainfall underscores the importance of adaptive responses by local communities through the management of public policies and strategies for adapting to climate risk.
  • Some of the metrics are blocked by your 
    Item type:Publication,
    Wet and dry periods in the state of Alagoas (Northeast Brazil) via Standardized Precipitation Index
    (2021-11-01)
    Oliveira-Júnior, José Francisco de
    ;
    de Gois, Givanildo
    ;
    Silva, Iago José de Lima
    ;
    de Oliveira Souza, Edson
    ;
    Jardim, Alexandre Maniçoba da Rosa Ferraz
    The state of Alagoas has its economic base directed to agriculture, and the knowledge of wet and dry periods is fundamental for the success of agricultural enterprises. The objective was to define the wet and dry periods in the state of Alagoas via multivariate analysis applied to the Standardized Precipitation Index (SPI). Monthly rainfall data for 20 weather stations from 1960 to 2016 were used, with fault filling via imputation. Moreover, Cluster analysis (CA) was applied to the time series (definition of homogeneous regions) and SPI-12 (annual) - (determination of dry and wet spell periods). The annual accumulated rainfall showed two distinct periods: 1960 to 1990 (P1) and 1990 to 2016 (P2), according to Pettitt test. Both periods alternate according to the phases of La Niña and Neutral (rainy years) and El Niño (dry years). In the monthly rainfall, only the Eastern part of Alagoas increased the rainy months during long time series. In tested connection methods, the average connection, also known as cophenetic correlation coefficient (CCC = 0.8760), represented homogeneous groups of rainfall, featuring two regions: one on the coast (G<inf>1</inf>) and another hinterland and arid (G<inf>2</inf>); and two non-homogeneous groups (NA) in the state. The annual SPI helped identify dry and wet periods, regardless of El Niño-Southern Oscillation (ENSO) categorization. The SPI-12 categories show high annual and decadal variability in the groups, except for the extremely dry and wet categories. There is greater variability in dry and wet periods near the coast than hinterland of the state.