[Addison, P.S., Murray, K.B., Watson, J.N., 2001. Wavelet transform analysis of open channel wake flows. Journal of Engineering Mechanics, 127, 58–70.10.1061/(ASCE)0733-9399(2001)127:1(58)]Search in Google Scholar
[Bašta, M., 2014. Additive decomposition and boundary conditions in wavelet-based forecasting approaches. Acta Oeconomica Pragensia, 2014, 48–70.10.18267/j.aop.431]Search in Google Scholar
[Bonal, D., Burban, B., Stahl, C., Wagner, F., Hérault, B., 2016. The response of tropical rainforests to drought - lessons from recent research and future prospects. Annals of Forest Science, 73, 27–44. DOI: 10.1007/s13595-015-0522-510.1007/s13595-015-0522-5481088827069374]Search in Google Scholar
[Chai, T., Draxler, R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7, 1247–1250. DOI: 10.5194/gmd-7-1247-201410.5194/gmd-7-1247-2014]Search in Google Scholar
[Chau, K.W., Wu, C.L., 2010. A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. Journal of Hydroinformatics, 12, 458–473. DOI: 10.2166/hydro.2010.03210.2166/hydro.2010.032]Search in Google Scholar
[Cuo, L., Pagano, T.C., Wang, Q.J., 2011. A review of quantitative precipitation forecasts and their use in short-to-medium streamflow forecasting. Journal of Hydrometeorology, 12, 713–728.10.1175/2011JHM1347.1]Search in Google Scholar
[Daubechies, I., 1992. Ten Lectures on Wavelet. Society for Industrial and Applied Mathematics, Philadelphia. https://doi.org/10.1137/1.978161197010410.1137/1.9781611970104]Search in Google Scholar
[dos Santos, T.S., Mendes, D., Torres, R.R., 2016. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America. Nonlinear Processes in Geophysics, 23, 13–20. DOI: 10.5194/npg-23-13-201610.5194/npg-23-13-2016]Search in Google Scholar
[Du, K., Zhao, Y., Lei, J., 2017. The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series. J. Hydrol., 552, 44–51. DOI: 10.1016/j.jhydrol.2017.06.01910.1016/j.jhydrol.2017.06.019]Search in Google Scholar
[Espinoza, J.C., Segura, H., Ronchail, J., Drapeau, G., Gutierrez-Cori, O., 2016. Evolution of wet-day and dry-day frequency in the western Amazon basin: Relationship with atmospheric circulation and impacts on vegetation. Water Resources Research, 52, 8546–8560. https://doi.org/10.1002/2016WR01930510.1002/2016WR019305]Search in Google Scholar
[Fahimi, F., Yaseen, Z.M., El-shafie, A., 2017. Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theoretical and Applied Climatology, 128, 875–903. https://doi.org/10.1007/s00704-016-1735-810.1007/s00704-016-1735-8]Search in Google Scholar
[Falck, A.S., Maggioni, V., Tomasella, J., Vila, D.A., Diniz, F.L., 2015. Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins–Araguaia basin in Brazil. Journal of Hydrology, 527, 943–957. http://dx.doi.org/10.1016/j.jhydrol.2015.05.04210.1016/j.jhydrol.2015.05.042]Search in Google Scholar
[Frumau, K.A., Bruijnzeel, L.A., Tobón, C., 2011. Precipitation measurement and derivation of precipitation inclination in a windy mountainous area in northern Costa Rica. Hydrological Processes, 25, 499–509. https://doi.org/10.1002/hyp.786010.1002/hyp.7860]Search in Google Scholar
[Germano, M.F., Vitorino, M.I., Cohen, J.C.P., Costa, G.B., Souto, J.I.D.O., Rebelo, M.T.C., de Sousa, A.M.L., 2017. Analysis of the breeze circulations in Eastern Amazon: an observational study. Atmospheric Science Letters, 18, 67–75. https://doi.org/10.1002/asl.72610.1002/asl.726]Search in Google Scholar
[Gloor, M.R.J.W., Brienen, R.J., Galbraith, D., Feldpausch, T.R., Schöngart, J., Guyot, J.L., Phillips, O.L., 2013. Intensification of the Amazon hydrological cycle over the last two decades. Geophysical Research Letters, 40, 1729–1733. https://doi.org/10.1002/grl.5037710.1002/grl.50377]Search in Google Scholar
[Gnanadesikan, R., 2011. Methods for Statistical Data Analysis of Multivariate Observations. John Wiley & Sons. DOI:10.1002/978111803267110.1002/9781118032671]Search in Google Scholar
[Golding, B.W., 2014. Regional prediction models. In: North, G., Pyle, J., Zhang, F. (Eds.): Encyclopedia of Atmospheric Sciences. 2nd Edition. Academic Press, p. 2008.]Search in Google Scholar
[Gomes, E.P., Blanco, C.J.C., Pessoa, F.C.L., 2018. Regionalization of precipitation with determination of homogeneous regions via fuzzy c-means. Revista Brasileira de Recursos Hídricos, 23. https://doi.org/10.1590/2318-0331.23182018007910.1590/2318-0331.231820180079]Search in Google Scholar
[Guimarães Santos, C.A., Silva, G.B.L.D., 2014. Daily stream-flow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59, 312–324. http://dx.doi.org/10.1080/02626667.2013.80094410.1080/02626667.2013.800944]Search in Google Scholar
[Gupta, A., Kamble, T., Machiwal, D., 2017. Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India. Environmental Earth Sciences, 76, 512. https://doi.org/10.1007/s12665-017-6814-310.1007/s12665-017-6814-3]Search in Google Scholar
[He, X., Guan, H., Qin, J., 2015. A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. Journal of Hydrology, 527, 88–100. http://dx.doi.org/10.1016/j.jhydrol.2015.04.0470022.]Search in Google Scholar
[Hellassa, S., Souag-Gamane, D., 2019. Improving a stochastic multi-site generation model of daily rainfall using discrete wavelet de-noising: a case study to a semi-arid region. Arabian Journal of Geosciences, 12, 53. https://doi.org/10.1007/s12517-018-4168-010.1007/s12517-018-4168-0]Search in Google Scholar
[Holdefer, A.E., Severo, D.L., 2015. Análise por ondaletas sobre níveis de rios submetidos à influência de maré. Revista Brasileira de Recursos Hídricos, 20, 192–201. DOI: 10.21168/rbrh.v20n1.p192-20110.21168/rbrh.v20n1.p192-201]Search in Google Scholar
[IBGE – INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA. Cobertura do uso da terra do Brasil (Land use coverage in Brazil). Rio de Janeiro: IBGE, 2014. Available from: https://www.ibge.gov.br/geocienciasnovoportal/informacoes-ambientais/cobertura-e-uso-da-terra (accessed in 13 Sept. 2017)]Search in Google Scholar
[Kisi, O., Cimen, M., 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399, 132–140.10.1016/j.jhydrol.2010.12.041]Search in Google Scholar
[Kisi, O., Shiri, J., 2011. Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resources Management, 25, 3135–3152. https://doi.org/10.1007/s11269-011-9849-310.1007/s11269-011-9849-3]Search in Google Scholar
[Kuo, C.C., Gan, T.Y., Yu, P.-S., 2010. Wavelet analysis on the variability, teleconnectivity, and predictability of the seasonal rainfall of Taiwan. Monthly Weather Review, 138, 162–175.10.1175/2009MWR2718.1]Search in Google Scholar
[Lang, K.J., Hinton, G.E., 1988. The development of the time-delay neural network architecture for speech recognition. Technical Report CMU-CS-88-152.]Search in Google Scholar
[Latrubesse, E.M., Arima, E.Y., Dunne, T., Park, E., Baker, V.R., d’Horta, F.M., Ribas, C.C., 2017. Damming the rivers of the Amazon basin. Nature, 546, 363–369. https://doi.org/10.1038/nature2233310.1038/nature22333]Search in Google Scholar
[Levy, M.C., Cohn, A., Lopes, A.V., Thompson, S.E., 2017. Addressing rainfall data selection uncertainty using connections between rainfall and streamflow. Scientific Reports, 7, 219. DOI: 10.1038/s41598-017-00128-510.1038/s41598-017-00128-5]Search in Google Scholar
[Maheswaran, R., Khosa, R., 2012. Comparative study of different wavelets for hydrologic forecasting. Computers & Geosciences, 46, 284–295. https://doi.org/10.1016/j.cageo.2011.12.01510.1016/j.cageo.2011.12.015]Search in Google Scholar
[Mallat, S., 2009. A Wavelet Tour of Signal Processing. Academic Press, 832 p. https://doi.org/10.1016/B978-0-12-374370-1.X0001-8.10.1016/B978-0-12-374370-1.X0001-8]Search in Google Scholar
[Mehr, A.D., Kahya, E., Bagheri, F., Deliktas, E., 2014. Successive-station monthly streamflow prediction using neurowavelet technique. Earth Science Informatics, 7, 217–229. DOI: 10.1007/s12145-013-0141-310.1007/s12145-013-0141-3]Search in Google Scholar
[Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I – A discussion of principles. Journal of Hydrology, 10, 282–290. http://doi.org/10.1016/0022-1694(70)90255-610.1016/0022-1694(70)90255-6]Search in Google Scholar
[Nerantzaki, S.D., Papalexiou, S.M., 2019. Tails of extremes: Advancing a graphical method and harnessing big data to assess precipitation extremes. Advances in Water Resources, 134, Article Number: 103448.10.1016/j.advwatres.2019.103448]Search in Google Scholar
[Nourani, V., Baghanam, A.H., Adamowski, J., Kisi, O., 2014. Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358–377. https://doi.org/10.1016/j.jhydrol.2014.03.05710.1016/j.jhydrol.2014.03.057]Search in Google Scholar
[Nourani, V., Andalib, G., Sadikoglu, F., 2017. Multi-station streamflow forecasting using wavelet denoising and artificial intelligence models. Procedia Computer Science, 120, 617–624. DOI: 10.1016/j.procs.2017.11.28710.1016/j.procs.2017.11.287]Search in Google Scholar
[Oliveira-Junior, J.F.D., Xavier, F.M.G., Teodoro, P.E., Gois, G.D., Delgado, R.C., 2017. Cluster analysis identified rainfall homogeneous regions in Tocantins State, Brazil. Bioscience Journal, 33, 333–340. https://doi.org/10.14393/BJ-v33n2-3273910.14393/BJ-v33n2-32739]Search in Google Scholar
[Osborn, T.J., Wallace, C.J., Harris, I.C., Melvin, T.M., 2016. Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation. Climatic Change, 134, 353–369. https://doi.org/10.1007/s10584-015-1509-910.1007/s10584-015-1509-9]Search in Google Scholar
[Partal, T., Kişi, Ö., 2007. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology, 342, 199–212. https://doi.org/10.1016/j.jhydrol.2007.05.02610.1016/j.jhydrol.2007.05.026]Search in Google Scholar
[Partal, T., Cigizoglu, H.K., 2009. Prediction of daily precipitation using wavelet—neural networks. Hydrological Sciences Journal, 54:2, 234–246, DOI: 10.1623/hysj.54.2.23410.1623/hysj.54.2.234]Search in Google Scholar
[Partal, T., Cigizoglu, H.K., Kahya, E., 2015. Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data. Stochastic Environmental Research and Risk Assessment, 29, 1317–1329. https://doi.org/10.1007/s00477-015-1061-110.1007/s00477-015-1061-1]Search in Google Scholar
[Percival, D.B., Walden, A.T., 2000. Wavelet methods for time series analysis. Cambridge Series in Statistical and Probabilistic Mathematics. 1st ed. Cambridge University Press, Cambridge.]Search in Google Scholar
[Ramana, R.V., Krishna, B., Kumar, S.R., Pandey, N.G., 2013. Monthly rainfall prediction using wavelet neural network analysis. Water Resources Management, 27, 3697–3711. https://doi.org/10.1007/s11269-013-0374-410.1007/s11269-013-0374-4]Search in Google Scholar
[Ramírez-Hernández, J., Infante-Prieto, S.O., Villa-Angulo, R., Hallack-Alegría, M., 2016. La influencia del efecto de borde en el pronóstico de precipitaciones utilizando DWT diádica, MODWT, ANN y ANFIS. Tecnología y ciencias del agua, 73, 93–113.]Search in Google Scholar
[Reichle, R.H., Liu, Q., Koster, R.D., Draper, C.S., Mahanama, S.P., Partyka, G.S., 2017. Land surface precipitation in MERRA-2. Journal of Climate, 30, 1643–1664. https://doi.org/10.1175/JCLI-D-16-0570.110.1175/JCLI-D-16-0570.1]Search in Google Scholar
[Rivera, D., Lillo, M., Uvo, C.B., Billib, M., Arumí, J.L., 2012. Forecasting monthly precipitation in Central Chile: a self-organizing map approach using filtered sea surface temperature. Theoretical and Applied Climatology, 107, 1–13. https://doi.org/10.1007/s00704-011-0453-510.1007/s00704-011-0453-5]Search in Google Scholar
[Sang, Y.F., 2012. A practical guide to discrete wavelet decomposition of hydrologic time series. Water Resources Management, 26, 3345–3365. https://doi.org/10.1007/s11269-012-0075-410.1007/s11269-012-0075-4]Search in Google Scholar
[Santos, C.A., Freire, P.K., Silva, R.M.D., Akrami, S.A., 2019. Hybrid wavelet neural network approach for daily inflow forecasting using Tropical Rainfall Measuring Mission data. Journal of Hydrologic Engineering, 24, Article Number: 04018062. https://doi.org/10.1061/(ASCE)HE.1943-5584.000172510.1061/(ASCE)HE.1943-5584.0001725]Search in Google Scholar
[Shoaib, M., Shamseldin, A.Y., Melville, B.W., Khan, M.M., 2016. A comparison between wavelet based static and dynamic neural network approaches for runoff prediction. Journal of Hydrology, 535, 211–225. http://dx.doi.org/10.1016/j.jhydrol.2016.01.07610.1016/j.jhydrol.2016.01.076]Search in Google Scholar
[Siad, S.M., Iacobellisb, V., Zdrulie, P., Gioiab, A., Stavid, I., Hoogenboom, G., 2019. A review of coupled hydrologic and crop growth models. Agricultural Water Management, 224, Article Number: 105746.10.1016/j.agwat.2019.105746]Search in Google Scholar
[Silva, I.D., Spatti, D.H., Flauzino, R.A., 2010. Redes neurais artificiais para engenharia e ciências aplicadas. Artliber, São Paulo, Brasil, 646 p.]Search in Google Scholar
[Sulaiman, S.O., Shiri, J., Shiralizadeh, H., Kisi, O., Yaseen, Z.M., 2018. Precipitation pattern modeling using cross-station perception: regional investigation. Environmental Earth Sciences, 77, 709. https://doi.org/10.1007/s12665-018-7898-010.1007/s12665-018-7898-0]Search in Google Scholar
[Tealab, A., Hefny, H., Badr, A., 2017. Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal, 2, 39–47. https://doi.org/10.1016/j.fcij.2017.05.00110.1016/j.fcij.2017.05.001]Search in Google Scholar
[Teodoro, P.E., de Oliveira-Júnior, J.F., Da Cunha, E.R., Correa, C.C.G., Torres, F.E., Bacani, V.M., Ribeiro, L.P., 2016. Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil. Meteorology and Atmospheric Physics, 128, 197–209. DOI: 10.1007/s00703-015-0408-y10.1007/s00703-015-0408-y]Search in Google Scholar
[Wang, X.Y., Li, X., Zhu, J., Tanajura, C.A., 2018. The strengthening of Amazonian precipitation during the wet season driven by tropical sea surface temperature forcing. Environmental Research Letters, 13, Article Number: 094015. https://doi.org/10.1088/1748-9326/aadbb910.1088/1748-9326/aadbb9]Search in Google Scholar
[Wilks, D.S., 1989. Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resources Research, 25, 1429–1439. https://doi.org/10.1029/WR025i006p0142910.1029/WR025i006p01429]Search in Google Scholar
[Wilks, D.S., 1999. Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agricultural and Forest Meteorology, 93, 153–169. https://doi.org/10.1016/S0168-1923(98)00125-710.1016/S0168-1923(98)00125-7]Search in Google Scholar
[Yaseen, Z.M., Jaafar, O., Deo, R.C., Kisi, O., Adamowski, J., Quilty, J., El-Shafie, A., 2016. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. Journal of Hydrology, 542, 603–614. http://dx.doi.org/10.1016/j.jhydrol.2016.09.03510.1016/j.jhydrol.2016.09.035]Search in Google Scholar
[Zhang, X., Peng, Y., Zhang, C., Wang, B., 2015. Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences. J. Hydrol., 530, 137–152. http://dx.doi.org/10.1016/j.jhydrol.2015.09.04710.1016/j.jhydrol.2015.09.047]Search in Google Scholar
[Zeri, M., Cunha-Zeri, G., Gois, G., Lyra, G.B., Oliveira-Júnior, J.F., 2019. Exposure assessment of rainfall to inter-annual variability using the wavelet transform. International Journal of Climatology, 39, 568–578. https://doi.org/10.1002/joc.581210.1002/joc.5812]Search in Google Scholar