@Article{SilvaSSPCGCFH:2022:PeAsDi,
               author = "Silva, Ewerton Hallan de Lima and Silva, Fabr{\'{\i}}cio Daniel 
                         dos and Silva J{\'u}nior, Rosiberto Salustiano da and Pinto, 
                         David Duarte Cavalcante and Costa, Rafaela Lisboa and Gomes, 
                         Heliof{\'a}bio Barros and Cabral J{\'u}nior, J{\'o}rio Bezerra 
                         and Freitas, Ismael Guidson Farias de and Herdies, Dirceu 
                         Lu{\'{\i}}s",
          affiliation = "{Universidade Federal de Alagoas (UFAL)} and {Universidade Federal 
                         de Alagoas (UFAL)} and {Universidade Federal de Alagoas (UFAL)} 
                         and {Universidade Federal de Alagoas (UFAL)} and {Universidade 
                         Federal de Alagoas (UFAL)} and {Universidade Federal de Alagoas 
                         (UFAL)} and {Universidade Federal de Alagoas (UFAL)} and 
                         {Universidade Federal de Campina Grande (UFCG)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Performance Assessment of Different Precipitation Databases 
                         (Gridded Analyses and Reanalyses) for the New Brazilian 
                         Agricultural Frontier: SEALBA",
              journal = "Water (Switzerland)",
                 year = "2022",
               volume = "14",
               number = "9",
                pages = "e1473",
                month = "May",
             keywords = "agribusiness, climate, grain, precipitation, reanalysis, SEALBA.",
             abstract = "Since the early 2000s, Brazil has been one of the worlds leading 
                         grain producers, with agribusiness accounting for around 28% of 
                         the Brazilian GDP in 2021. Substantial investments in research, 
                         coupled with the expansion of arable areas, owed to the advent of 
                         new agriculture frontiers, led the country to become the worlds 
                         greatest producer of soybean. One of the newest agricultural 
                         frontiers to be emerging in Brazil is the one known as SEALBA, an 
                         acronym that refers to the three Brazilian states whose areas it 
                         is comprised ofSergipe, Alagoas, and Bahiaall located in the 
                         Northeast region of the country. It is an extensive area with a 
                         favorable climate for the production of grains, including 
                         soybeans, with a rainy season that takes place in autumn/winter, 
                         unlike the Brazilian regions that are currently the main producers 
                         of these kinds of crops, in which the rainfall regime has the wet 
                         period concentrated in spring/summer. Considering that 
                         precipitation is the main determinant climatic factor for crops, 
                         the scarcity of weather stations in the SEALBA region poses an 
                         obstacle to an accurate evaluation of the actual feasibility of 
                         the region to a given crop. Therefore, the aim of this work was to 
                         carry out an assessment of the performance of four different 
                         precipitation databases of alternative sources to observations: 
                         two from gridded analyses, MERGE and CHIRPS, and the other two 
                         from ECMWF reanalyses, ERA5, and ERA5Land, and by comparing them 
                         to observational records from stations along the region. The 
                         analysis was based on a comparison with data from seven weather 
                         stations located in SEALBA, in the period 20012020, through three 
                         dexterity indices: the mean absolute error (MAE), the root mean 
                         squared errors (RMSE), and the coefficient of Pearsons correlation 
                         (r), showing that the gridded analyzes performed better than the 
                         reanalyses, with MERGE showing the highest correlations and the 
                         lowest errors (global average r between stations of 0.96, followed 
                         by CHIRPS with 0.85, ERA5Land with 0.83, and ERA5 with 0.70; 
                         average MAE 14.3 mm, followed by CHIRPS with 21.3 mm, ERA5Land 
                         with 42.1 mm and ERA5 with 50.1 mm; average RMSE between stations 
                         of 24.6 mm, followed by CHIRPS with 50.8 mm, ERA5Land with 62.3 mm 
                         and ERA5 with 71.4 mm). Since all databases provide up-to-date 
                         data, our findings indicate that, for any research that needs a 
                         complete daily precipitation dataset for the SEALBA region, 
                         preference should be given to use the data in the following order 
                         of priority: MERGE, CHIRPS, ERA5Land, and ERA5.",
                  doi = "10.3390/w14091473",
                  url = "http://dx.doi.org/10.3390/w14091473",
                 issn = "2073-4441",
             language = "en",
           targetfile = "water-14-01473-v2.pdf",
        urlaccessdate = "25 jun. 2024"
}