Validation and reconstruction of rain gauge–based daily time series for the entire Amazon basin

  • Véronique MichotEmail author
  • Damien Arvor
  • Josyane Ronchail
  • Thomas Corpetti
  • Nicolas Jegou
  • Paulo Sérgio Lucio
  • Vincent Dubreuil
Original Paper


Monitoring the spatio-temporal variability of rainfall regimes in the Amazon basin is difficult because (1) time series of remote sensing–based rainfall estimates are still too short for long-time variability analysis and (2) rain gauge time series are not fully reliable and operational in their current state due to frequent gaps and zero values. The objective of this paper is to introduce a quality control and reconstruction procedure designed to produce a robust database of rain gauge–based daily rainfall in the Amazon basin. Despite the low density and heterogeneous spatial distribution of the rain gauges network, we eliminated unexpected values and produced accurate estimates using spatial and mathematical relationships with neighboring rain gauges. Three reconstruction methods were tested: the nearest neighbor approach (NN), the arithmetic mean with neighboring stations (AM), and the multiple imputation by chained equations used with the predictive mean matching procedure (MICE). The quality of the reconstruction has been assessed through the mean annual rainfall and the mean annual number of rainy days. We concluded that the AM approach performed better at the scale of the whole Amazon basin. This method has then been preferred to reconstruct the whole database of rainfall time series.



The authors would like to express their special thanks to Naurinete J. C. Barreto and George Ulguim Pedra for their help and discussions from the Meteorology Department of the Brazilian National Institute for the Space Research (INPE) and Cláudio Moisés Santos E. Silva from the Centro de Ciências Exatas e da Terra (CCET) in Natal University (Brazil).


  1. Aguilar E, Peterson TC, Obando PR, Frutos R, Retana JA, Solera M, Soley J, García IG, Araujo RM, Santos AR et al (2005) Changes in precipitation and temperature extremes in Central America and northern South America, 1961–2003. J Geophys Res 110(D23):107CrossRefGoogle Scholar
  2. Barbosa Santos E, Sérgio Lucio P, Silva CM (2015) Precipitation regionalization of the Brazilian Amazon. Atmos Sci Lett 16:185–192CrossRefGoogle Scholar
  3. Boyard-Micheau J (2013) Prévisibilité potentielle des variables climatiques à impact agricole en Afrique de l’est et application au sorgho dans la région du mt kenya. Thèse de doctorat. Université de Bourgogne, FranceGoogle Scholar
  4. Brito, A.L., Paix, J.A., Yoshida, M.C.,et al (2014). Extreme rainfall events over the Amazon basin produce significant quantities of rain relative to the rainfall climatology. Atmos Climate Sci, 4: 179–191Google Scholar
  5. Brunetti, M., Maugeri, M., and Nanni, T. (2006). Trends of the daily intensity of precipitation in Italy and teleconnections. Il Nuovo Cimento C 105Google Scholar
  6. Camberlin P, Boyard-Micheau J, Philippon N, Baron C, Leclerc C, Mwongera C (2012) Climatic gradients along the windward slopes of Mount Kenya and their implication for crop risks. Part 1: climate variability. Int J Climatol 34:2136–2152CrossRefGoogle Scholar
  7. Campozano L, Sánchez E, Aviles A, Samaniego E (2015) Evaluation of infilling methods for time series of daily precipitation and temperature: the case of the Ecuadorian Andes. Maskana 5:99–115Google Scholar
  8. Camps-Valls G, Bruzzone L (2009) Kernel methods for remote sensing data analysis. John Wiley & Sons, United KingdomCrossRefGoogle Scholar
  9. Cardenas, R., and Krainski, E.T. (2011). Preenchimentos de falhas em bancos de dados meteorologicos diarios: comparação de abordagens. XVII Congresso Brasileiro de Agrometeorologia, Guarapari-BrasilGoogle Scholar
  10. Carvalho LM, Jones C, Posadas AN, Quiroz R, Bookhagen B, Liebmann B (2012) Precipitation characteristics of the South American monsoon system derived from multiple datasets. J Clim 25:4600–4620CrossRefGoogle Scholar
  11. Caussinus H, Mestre O (2004) Detection and correction of artificial shifts in climate series. J R Stat Soc: Ser C: Appl Stat 53:405–425CrossRefGoogle Scholar
  12. Chen J, Del Genio AD, Carlson BE, Bosilovich MG (2008) The spatiotemporal structure of twentieth-century climate variations in observations and reanalyses. Part II: Pacific pan-decadal variability. J Clim 21:2634–2650CrossRefGoogle Scholar
  13. Cressie N, Chan NH (1989) Spatial modeling of regional variables. J Am Stat Assoc 84:393–401CrossRefGoogle Scholar
  14. Delahaye F, Kirstetter P-E, Dubreuil V, Machado LA, Vila DA, Clark R (2015) A consistent gauge database for daily rainfall analysis over the Legal Brazilian Amazon. J Hydrol 527:292–304CrossRefGoogle Scholar
  15. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39:1–38Google Scholar
  16. Eischeid JK, Pasteris PA, Diaz HF, Plantico MS, Lott NJ (2000) Creating a serially complete, national daily time series of temperature and precipitation for the western United States. J Appl Meteorol 39:1580–1591CrossRefGoogle Scholar
  17. Espinoza Villar JC, Ronchail J, Guyot JL, Cochonneau G, Naziano F, Lavado W, De Oliveira E, Pombosa R, Vauchel P (2009) Spatio-temporal rainfall variability in the Amazon basin countries (Brazil, Peru, Bolivia, Colombia, and Ecuador). Int J Climatol 29:1574–1594CrossRefGoogle Scholar
  18. Espinoza JC, Chavez S, Ronchail J, Junquas C, Takahashi K, Lavado W (2015) Rainfall hotspots over the southern tropical Andes: spatial distribution, rainfall intensity, and relations with large-scale atmospheric circulation. Water Resour Res 51:3459–3475CrossRefGoogle Scholar
  19. Figueroa SN, Nobre CA (1990) Precipitation distribution over central and western tropical South America. Climanalise 5:36–45Google Scholar
  20. Getirana AC, Espinoza JCV, Ronchail J, Rotunno Filho OC (2011) Assessment of different precipitation datasets and their impacts on the water balance of the Negro River basin. J Hydrol 404:304–322CrossRefGoogle Scholar
  21. Glasson-Cicognani, M., and Berchtold, A. (2010). Imputation des données manquantes: Comparaison de différentes approches. In 42èmes Journées de Statistique, Marseille-FranceGoogle Scholar
  22. Hansen JW, Challinor A, Ines A, Wheeler T, Moron V (2006) Translating climate forecasts into agricultural terms: advances and challenges. Clim Res 33:27–41CrossRefGoogle Scholar
  23. Juárez RIN, Hodnett MG, Fu R, Goulden ML, von Randow C (2007) Control of dry season evapotranspiration over the Amazonian Forest as inferred from observations at a southern Amazon Forest site. J Clim 20:2827–2839CrossRefGoogle Scholar
  24. Liebmann B, Allured D (2005) Daily precipitation grids for South America. Bull Am Meteorol Soc 86:1567–1570CrossRefGoogle Scholar
  25. Liebmann B, Marengo J (2001) Interannual variability of the rainy season and rainfall in the Brazilian Amazon basin. J Clim 14:4308–4318CrossRefGoogle Scholar
  26. Little RJA, Rubin DB (2002) Statistical analysis with missing data. John Wiley & Sons, Inc, USACrossRefGoogle Scholar
  27. Makhuvha T, Pegram G, Sparks R, Zucchini W (1997a) Patching rainfall data using regression methods: 1. Best subset selection, EM and pseudo-EM methods: theory. J Hydrol 198:289–307CrossRefGoogle Scholar
  28. Makhuvha T, Pegram G, Sparks R, Zucchini W (1997b) Patching rainfall data using regression methods. 2. Comparisons of accuracy, bias and efficiency. J Hydrol 198:308–318CrossRefGoogle Scholar
  29. Mestre O, Gruber C, Prieur C, Caussinus H, Jourdain S (2011) SPLIDHOM: a method for homogenization of daily temperature observations. J Appl Meteorol Climatol 50:2343–2358CrossRefGoogle Scholar
  30. Moron V, Robertson AW, Ward MN, Camberlin P (2007) Spatial coherence of tropical rainfall at the regional scale. J Clim 20:5244–5263CrossRefGoogle Scholar
  31. Ronchail J, Cochonneau G, Molinier M, Guyot J-L, De Miranda Chaves AG, Guimarães V, de Oliveira E (2002) Interannual rainfall variability in the Amazon basin and sea-surface temperatures in the equatorial Pacific and the tropical Atlantic Oceans. Int J Climatol 22:1663–1686CrossRefGoogle Scholar
  32. Silva V, Kousky V, Shi W, Higgins RW (2007) An improved gridded historical daily precipitation analysis for Brazil. J Hydrometeorol 8:847–861CrossRefGoogle Scholar
  33. Simões Reibota M, Gan MA, Porfirio da Rocha R, Ambrizzi T (2010) Regimes de precipitacao na America do sul. Rev Bras Meteorol 25:185–204CrossRefGoogle Scholar
  34. van Buuren S, Groothuis-Oudshoorn K (2011) MICE: multivariate imputation by chained equations in R. J Stat Softw 45:1–68CrossRefGoogle Scholar
  35. Vicente-Serrano SM, Beguería S, López-Moreno JI, García-Vera MA, Stepanek P (2010) A complete daily precipitation database for Northeast Spain: reconstruction, quality control, and homogeneity. Int J Climatol 30:1146–1163CrossRefGoogle Scholar
  36. Williams E, Dall’ Antonia A, Dall’ Antonia V, de Almeida JM, Suarez F, Liebmann B, Malhado ACM (2005) The drought of the century in the Amazon basin: an analysis of the regional variation of rainfall in South America in 1926. Acta Amazon 35:231–238CrossRefGoogle Scholar
  37. WMO (1989). Calculation of monthly and annual 30 year standard normal (World Meteorological Organization)Google Scholar
  38. WMO (2007). Guide to the global observing system (World Meteorological Organization)Google Scholar
  39. WMO (2011). Guide des pratiques climatologiques (World Meteorological Organization)Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Univ-Rennes, LETG, French National Center for Scientific Research (CNRS), UMR 6554RennesFrance
  2. 2.UMR 7159 Laboratoire d’Océanographie et du Climat (LOCEAN, CNRS-IRD-MNHN-SU), Institut Pierre Simon Laplace (IPSL)Université Pierre et Marie CurieParisFrance
  3. 3.Université Paris Diderot, Sorbonne Paris CitéParis Cedex 13France
  4. 4.Univ Rennes, CNRS, IRMAR - UMR 6625RennesFrance
  5. 5.Programa de Pós-Graduação em Ciências ClimáticasUniversidade Federal do Rio Grande do NorteNatalBrazil

Personalised recommendations