Abstract
The agricultural zoning of climate risk (AZCR) is a fundamental tool for agricultural activities, as it identifies regions and times of lower climatic risk for planting and sowing crops. An AZCR representative of large areas requires a network of meteorological stations with dense spatial distribution, routine, and accurate observations. In Brazil, there is a good spatial distribution of rain gauges, but there is a scarcity in the spatial distribution of meteorological stations necessary to conduct an AZCR. One way to overcome the lack of data observed in situ is to use reanalysis data. However, it is necessary to show that it is possible to perform a reliable AZCR using it. Therefore, the objective of this research is to elaborate an AZCR for corn in the state of Bahia using reanalysis data and quantifying its viability. The ERA5-Land reanalysis database was chosen. When performing the validation of the ERA5-Land reanalysis data, it was verified that rainfall was the only meteorological variable needed for an AZCR that did not present seasonal and interannual values and variability similar to those already observed. Therefore, it was decided to validate the rainfall data from the CPC/NOAA Precipitation Project. With data from the ERA5-Land and CPC/NOAA Precipitation Project reanalysis validated, the AZCR for corn was performed in the state of Bahia, leading to the conclusion that it is possible to develop a reliable and robust AZCR with reanalysis data.
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Data availability
The datasets analyzed in the present study are available in the ECWMF repository [https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land], NASA repository [https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html], and INMET repository [https://bdmep.inmet.gov.br/].
References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration - guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56, Rome
Agência Nacional de Águas e Saneamento Básico - ANA (2021) Capacidade de água disponível - CAD ou AWC dos solos no Brasil. https://metadados.snirh.gov.br/geonetwork/srv/api/records/28fe4baa-66f3-4f6b-b0d2-890abf5910c4. Accessed 1 Feb 2022
Aparecido LZO, Lorençone PA, Lorençone JA, de Meneses KC, de Moraes JRSC, de Farias MF (2022) Soil water seasonal and spatial variability in Northeast Brazil. Environ Dev Sustain 24(5):6136–6152. https://doi.org/10.1007/s10668-021-01695-4
Araújo CSPD, Silva IACE, Ippolito M, Almeida CDGCD (2022) Evaluation of air temperature estimated by ERA5-Land reanalysis using surface data in Pernambuco. Brazil Environ Monit Assess 194(5):381. https://doi.org/10.1007/s10661-022-10047-2
Assad ED, Sano EE, Bezerra HS, Silva SC, Lobato EJE (1998) Uso de modelos numéricos de terreno na espacialização de épocas de plantio. In: Assad ED, Sano EE (eds) Sistemas de informações geográficas: Aplicações na agricultura. Embrapa Cerrados, Brasília, pp 231–248
Avala R, Cunha AP, Brito SS, Seluchi ME, Marengo JA, Moraes OL, Carvalho MA (2017) Drought monitoring in the Brazilian Semiarid region. An Acad Bras Ciênc 91:e20170209. https://doi.org/10.1590/0001-3765201720170209
Baba RK, Vaz MS, Costa JD (2014) Correção de dados agrometeorológicos utilizando métodos estatísticos. Rev Bras de Meteorol 29(4):515–526. https://doi.org/10.1590/0102-778620130611
Berthouex PM, Brown LC (2002) Statistics for environmental Engineers. Lewis Publishers, New York, USA
Bosilovich MG, Chen J, Robertson FR, Adler RF (2008) Evaluation of global precipitation in reanalyses. J Appl Meteorol Climatol 47(9):2279–2299. https://doi.org/10.1175/2008JAMC1921.1
Brasil (2019) Decreto nº 9.841, de 18 de junho de 2019. http://www.planalto.gov.br/ccivil_03/_ato2019-2022/2019/decreto/D9841.htm. Accessed 16 Sept 2022
Callegari-Jacques SM (2009) Bioestatística: Princípios E Aplicações. Artmed Editora, Porto Alegre, Brazil
Carvalho AAD, Montenegro AADA, Assis F, Tabosa JN, Cavalcanti RQ, Almeida TA (2019) Spatial dependence of attributes of rainfed maize under distinct soil cover conditions. Rev Bras De Engenharia Agrícola e Ambiental 23:33–39. https://doi.org/10.1590/1807-1929/agriambi.v23n1p33-39
Casanovas EM, Barassi CA, Andrade FH, Sueldo RJ (2003) Azospillum-lnoculated maize plant responses to irrigation restraints imposed during flowering. Cereal Res Commun 31(3):395–402. https://doi.org/10.1007/BF03543370
Chaves RR, Nobre P (2004) Interactions between the sea surface temperature over the South Atlantic Ocean and the South Atlantic Convergence Zone. Geophys Res Lett 31:L03204. https://doi.org/10.1029/2003GL018647
Chen M, Shi W, Xie P, Silva VB, Kousky VE, Higgins RW, Janowiak JE (2008) Assessing objective techniques for gauge-based analyses of global daily precipitation. J Geophys Res: Atmos 113:D04110. https://doi.org/10.1029/2007JD009132
Companhia Nacional de Abastecimento – CONAB (2022) Boletim Da Safra de Grãos: 9º Levantamento - Safra 2022/23. https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos. Accessed 24 June 2023
Cui W, Dong X, Xi B, Kennedy A (2017) Evaluation of reanalyzed precipitation variability and trends using the gridded gauge-based analysis over the CONUS. J Hydrometeorol 18:2227–2248. https://doi.org/10.1175/JHM-D-17-0029.1
de Souza TS, Nascimento PS (2020) Análise da variabilidade espacial e temporal da precipitação pluviométrica na região hidrográfica do recôncavo sul (BA). Rev Bras De Climatol 27:1–18. https://doi.org/10.5380/abclima.v27i0.68353
Escobar GCJ, Reboita MS (2022) Relationship between daily atmospheric circulation patterns and South Atlantic Convergence Zone (SACZ) events. Atmósfera 35(1):1–25. https://doi.org/10.20937/atm.52936
Fernandes, IG, Pimenta, FM, Saavedra, OR, Silva, AR (2021) Offshore validation of ERA5 reanalysis with hub height wind observations of Brazil. IEEE PES innovative smart grid technologies conference-Latin America, 1–5. https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9542993
Fonseca HP, Pires GF, Brumatti LM (2022) Spatial and temporal evolution of sowing and the onset of the rainy season in a region of large agricultural expansion in Brazil. Agronomy 12(7):1679. https://doi.org/10.3390/agronomy12071679
Frère M, Popov G (1986) Early agrometeorological crop yield assessment. FAO Plant Production and Protection Paper 73. Food and Agricultural Organization of the United Nations, Rome, Italy
Gleixner S, Demissie T, Diro GT (2020) Did ERA5 improve temperature and precipitation reanalysis over East Africa? Atmosphere 11(9):996. https://doi.org/10.3390/atmos11090996
Gomes HB, Ambrizzi T, Silva BFP, Hodges K, Dias PLS, Herdies DL, Silva MCL, Gomes HB (2019) Climatology of easterly wave disturbances over the tropical South Atlantic. Clim Dyn 53(3):1393–1411. https://doi.org/10.1007/s00382-019-04667-7
Instituto Brasileiro de Geografia e Estatística - IBGE (2021) SIDRA: Produção Agrícola Municipal. Rio de Janeiro. https://sidra.ibge.gov.br/pesquisa/pam/tabelas. Accessed 29 June 2021
Jelinek AR, Chemale F, Van der Beek PA, Guadagnin F, Cupertinon JA, Viana A (2014) Denudation history and landscape evolution of the northern East-Brazilian continental margin from apatite fission-track thermochronology. J S Am Earth Sci 54:158–181. https://doi.org/10.1016/j.jsames.2014.06.001
Kousky VE (1979) Frontal influences on northeast Brazil. Mon Weather Rev 107(9):1140–1153. https://doi.org/10.1175/1520-0493(1979)107%3c1140:FIONB%3e2.0.CO;2
Kousky VE (1980) Diurnal rainfall variation in northeast Brazil. Mon Weather Rev 108(4):488–498. https://doi.org/10.1175/1520-0493(1980)108%3c0488:DRVINB%3e2.0.CO;2
Kousky VE, Chu PS (1978) Fluctuations in annual rainfall for Northeast Brazil. J Meteorol Soc Jpn. Ser. II 56(5):457–465. https://doi.org/10.2151/jmsj1965.56.5_457
Kousky VE, Gan MA (1981) Upper tropospheric cyclonic vortices in the tropical South Atlantic. Tellus 33(6):538–551. https://doi.org/10.3402/tellusa.v33i6.10775
Lavers DA, Simmons A, Vamborg F, Rodwell MJ (2022) An evaluation of ERA5 precipitation for climate monitoring. Q J R Meteorol Soc 148(748):3152–3165. https://doi.org/10.1002/qj.4351
Lenters JD, Kratzb TK, Bowser CJ (2005) Effects of climate variability on lake evaporation: results from a long-term energy budget study of Sparkling Lake, northern Wisconsin (USA). J Hydrol 308:168–195. https://doi.org/10.1016/j.jhydrol.2004.10.028
Lopes JRF, Dantas MP, Ferreira FEP (2019) Variabilidade da precipitação pluvial e produtividade do milho no semiárido brasileiro através da análise multivariada. Nativa 7(1):77–83. https://doi.org/10.31413/nativa.v7i1.6243
Ministério da Agricultura Pecuária e Abastecimento - MAPA (2022) Portaria nº 175, de 25 de maio de 2022. https://www.gov.br/agricultura/pt-br/assuntos/riscos-seguro/programa-nacional-de-zoneamento-agricola-de-risco-climatico/portarias/safra-vigente/bahia. Accessed 31 May 2021
Ministério da Agricultura e Pecuária. Programa Nacional de Zoneamento Agrícola de Risco Climático – MAPA (2023). Zoneamento Agrícola. https://www.gov.br/agricultura/pt-br/assuntos/riscos-seguro/programa-nacional-de-zoneamento-agricola-de-risco-climatico/zoneamento-agricola. Accessed 22 June 2023
Marengo JA, Galdos MV, Challinor A, Cunha AP, Marin FR, Vianna MDS, Avala RCS, Alves LM, Moraes OL, Bender F (2022) Drought in Northeast Brazil: a review of agricultural and policy adaptation options for food security. Clim Res Sustain 1(1):e17. https://doi.org/10.1002/cli2.17
Martins MA, Tomasella J, Dias CG (2019) Maize yield under a changing climate in the Brazilian Northeast: impacts and adaptation. Agric Water Manag 216:339–350. https://doi.org/10.1016/j.agwat.2019.02.011
Mencia FPH, Zanchi FB, Lopes ERN (2021) Climatic characteristics and their implications among the pedological and topographical aspects of southern Bahia, Brazil. Rev Bras De Climatol 29:24–48. https://doi.org/10.5380/abclima.v29i0.71108
Monteiro AFM, Martins FB (2019) Global solar radiation models in Minas Gerais, southeastern Brazil. Adv Meteorol 2019:1–17. https://doi.org/10.1155/2019/9515430
Morais MDC, Gan MA, Yoshida MC (2021) Features of the upper tropospheric cyclonic vortices of Northeast Brazil in life cycle stages. Int J Climatol 41:E39–E58. https://doi.org/10.1002/joc.6839
Muñoz-Sabater J (2019) ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.e2161bac
Pezzi LP, Quadro MF, Lorenzzetti JA, Miller J, Rosa EB, Lima LN, Sutil UA (2022) The effect of Oceanic South Atlantic Convergence Zone episodes on regional SST anomalies: the roles of heat fluxes and upper-ocean dynamics. Clim Dyn 59:2041–2065. https://doi.org/10.1007/s00382-022-06195-3
Rao VB, Hada K (1990) Characteristics of rainfall over Brazil: annual variations and connections with the Southern Oscillation. Theoret Appl Climatol 42(2):81–91. https://doi.org/10.1007/BF00868215
Rao VB, Lima MC, Franchito SH (1993) Seasonal and interannual variations of rainfall over eastern northeast Brazil. J Clim 6(9):1754–1763. https://doi.org/10.1175/1520-0442(1993)006%3c1754:SAIVOR%3e2.0.CO;2
Reis JS, Gonçalves WA, Mendes D (2021) Climatology of the dynamic and thermodynamic features of upper tropospheric cyclonic vortices in Northeast Brazil. Clim Dyn 57(11):3413–3431. https://doi.org/10.1007/s00382-021-05873-y
Rolim GS, Sentelha PC, BarbieriV (1998) Planilhas no ambiente EXCEL TM para os cálculos de balanços hídricos: normal, sequencial, de cultura e de produtividade real e potencial. Revista Brasileira de Agrometeorologia 6:133–137. http://www.sbagro.org/files/biblioteca/171.pdf. Accessed 3 Oct 2020
Rosa EB, Pezzi LP, Quadro MFLD, Brunsell N (2020) Automated detection algorithm for SACZ, oceanic SACZ, and their climatological features. Front Environ Sci 8:18. https://doi.org/10.3389/fenvs.2020.00018
Sadeghi M, Asanjan AA, Faridzad M, Gorooh VA, Nguyen P, Hsu K, Sorooshian S, Braithwaite D (2019) Evaluation of PERSIANN-CDR constructed using GPCP V2.2 and V2.3 and a comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale. Remote Sens 11(23):2755. https://doi.org/10.3390/rs11232755
Santos PM, Pezzopane JRM, Mendonça FC, Bettiol GM, Evangelista BA, Silva FAMD (2012) Climatic risk zoning for corn and palisade grass (Brachiaria brizantha cv: Marandu) cultivated in integrated crop-livestock systems in São Paulo state, Brazil. Rev Bras De Zootecnia 41:36–40. https://doi.org/10.1590/S1516-35982012000100006
Santos GM, Dattilo W, Presley SJ (2014) The seasonal dynamic of ant-flower networks in a semi-arid tropical environment. Ecol Entomol 39(6):674–683. https://doi.org/10.1111/een.12138
Santos Neto JC, Gama DC, Silva LF, Jesus JB (2022) Caracterização da precipitação e da vazão no trecho sudoeste da Bacia Hidrográfica do rio Paraguaçu, Bahia, Brasil. Res, Soc Dev 11(9):e32911931659–e32911931659. https://doi.org/10.33448/rsd-v11i9.31659
Siefert CAC, Dombrowski Netto N, Marangon FHS, Schultz GB, Silva LMDR, Fontenelle TH, Santos ID (2021) Avaliação de Séries de Velocidade do Vento de Produtos de Reanálises Climáticas para o Brasil. Rev Bras De Meteorol 36:689–701. https://doi.org/10.1590/0102-7786360026
Silva BKN, Amorim ACB, Silva C, Lucio PS, Barbosa LM (2019) Rainfall-related natural disasters in the Northeast of Brazil as a response to ocean-atmosphere interaction. Theoret Appl Climatol 138(3):1821–1829. https://doi.org/10.1007/s00704-019-02930-9
Simões YS, Silva EHBC, Araújo HA (2018) Rainfall zoning of Bahia State, Brazil: an update proposal. Ambiente Água an Interdiscip J Appl Sci 13(1):e2171. https://doi.org/10.4136/ambi-agua.2171
Simões YDS, Araújo HAD, Cohim E (2020) Trend analysis of rain indicators for the state of Bahia Brazil. Int J Hydrol 4(2):67–74
Sousa FDASD, Macedo MJH, Guedes RVS, Silva VPR (2016) O Índice de Precipitação Padronizada (IPP) na identificação de extremos de chuvas e secas na bacia do rio Paraguaçu (BA). Ambiência Guarapuava (PR) 12(2):707–719. https://doi.org/10.5935/ambiencia.2016.02.14
Sun Q, Miao C, Duan Q, Ashouri H, Sorooshian S, Hsu KL (2018) A review of global precipitation data sets: Data sources, estimation, and inter-comparisons. Rev Geophys 56:79–107. https://doi.org/10.1002/2017RG000574
World Meteorological Organization – WMO (1981) Guide to Hydrological Practice. Switzerland, Geneva
World Meteorological Organization – WMO (2017) Guidelines on the Calculation of Climate Normals. Switzerland, Geneva
World Meteorological Organization - WMO (2008) Guide to Hydrological Practice: Hydrology. Switzerland, Geneva
Xavier AC, King CW, Scanlon BR (2016) Daily gridded meteorological variables in Brazil (1980–2013). Int J Climatol 36(6):2644–2659. https://doi.org/10.1002/joc.4518
Xavier AC, Scanlon BR, King CW, Alves AI (2022) New improved Brazilian daily weather gridded data (1961–2020). Int J Climatol 2(16):8390–8404. https://doi.org/10.1002/joc.7731
Yamazaki Y, Rao VB (1977) Tropical cloudiness over the south Atlantic Ocean. J Meteorol Soc Jpn. Ser. II 55(2):205–207. https://doi.org/10.2151/jmsj1965.55.2_205
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The preparation data was made by and analysis were performed by WKM, GCAJ and JIBB. The first draft of the manuscript was written by WKM and the authors ESGS, FFL, JIBB, CACS and EPL commented, rating and proofreading on previous versions of the manuscript to improve. All authors contributed to this study and approved the final manuscript.
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Matsunaga, W.K., Sales, E.S.G., Júnior, G.C.A. et al. Application of ERA5-Land reanalysis data in zoning of climate risk for corn in the state of Bahia—Brazil. Theor Appl Climatol 155, 945–963 (2024). https://doi.org/10.1007/s00704-023-04670-3
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DOI: https://doi.org/10.1007/s00704-023-04670-3