Abstract
The importance of daily data on reference evapotranspiration (ET0) has increased in recent years due to its relevance in planning and decision making regarding irrigated agriculture, water production, and forest restoration. Facing the scarcity of this information measured in loco, the study of interpolation methods capable of representing ET0 becomes important. Therefore, this study aimed to evaluate the adequacy of the Random Forest (RF) method in the spatialization of ET0 in the watersheds of the Mid-South region of the Espírito Santo State, located within the Atlantic Forest biome, Brazil. From this study, it was found that the RF method is the most suitable one for ET0 spatialization when compared to the Angular distance weighting (ADW) and the inverse distance weighting (IDW) techniques. Also, the spatializations carried out by this method were transformed into databases in a grid format and made available online. Furthermore, the RF database was also compared to other ET0 grid databases, and it was concluded that the RF database also carried out a better performance than the other ones.
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Data availability
The daily ET0 maps for the watersheds of the Mid-South region of the Espírito Santo State created in this study are freely available at http://dx.doi.org/10.17632/m2tjd73b99.2.
References
AGERH. (2019). Comitês de Bacias Hidrográficas. Agência Estadual de Recursos Hídricos. Retrieved May 20, 2020, from https://agerh.es.gov.br/documentos-dos-comites
Alencar, L. P., Sediyama, G. C., & Mantovani, E. C. (2015). Estimativa da evapotranspiração de referência (ETo padrão FAO), para Minas Gerais, na ausência de alguns dados climáticos. Engenharia Agrícola, 35, 39–50. https://doi.org/10.1590/1809-4430-Eng.Agric.v35n1p39-50/2015
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. D. M., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728. https://doi.org/10.1127/0941-2948/2013/0507
Althoff, D., Dias, S. H. B., Filgueiras, R., & Rodrigues, L. N. (2020). ETo-Brazil: A daily gridded reference evapotranspiration data set for Brazil (2000–2018). Water Resources Research, 56(7), e2020WR027562. https://doi.org/10.1029/2020WR027562
Amaral, L. R. D., & Justina, D. D. D. (2019). Spatial dependence degree and sampling neighborhood influence on interpolation process for fertilizer prescription maps. Engenharia Agrícola, 39, 85–95. https://doi.org/10.1590/1809-4430-eng.agric.v39nep85-95/2019
Aparecido, L. E. D. O., Meneses, K. C. D., Torsoni, G. B., Moraes, J. R. D. S. C. D., & Mesquita, D. Z. (2020). Accuracy of potential evapotranspiration models in different time scales. Revista Brasileira De Meteorologia, 35, 63–80. https://doi.org/10.1590/0102-7786351026
Araújo, L. M., Bezerra, F. T. C., Borges, P. F., Pereira, A. R., Moscôso, J. S. C., & Araújo, L. S. (2018). Estimativas da evapotranspiração de referência para o município de Apodi, RN. Gaia Scientia, 12(3). https://doi.org/10.22478/ufpb.1981-1268.2018v12n3.34947
Bardin, L., Pedro Júnior, M. J., & de Moraes, J. F. (2010). Estimativa das temperaturas máximas e mínimas do ar para a região do Circuito das Frutas, SP. Revista Brasileira De Engenharia Agrícola e Ambiental, 14, 618–624. https://doi.org/10.1590/S1415-43662010000600008
Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7
Braum, E. S. (2020). Espacialização da Temperatura do Ar diária considerando a Altitude. Jerônimo Monteiro, 2020. 67 pp. Dissertation (M.Sc.). Forest Sciences. Federal University of Espírito Santo.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Brokamp, C., Jandarov, R., Rao, M. B., LeMasters, G., & Ryan, P. (2017). Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. Atmospheric Environment, 151, 1–11. https://doi.org/10.1016/j.atmosenv.2016.11.066
Camera, C., Bruggeman, A., Hadjinicolaou, P., Pashiardis, S., & Lange, M. A. (2014). Evaluation of interpolation techniques for the creation of gridded daily precipitation (1× 1 km2); Cyprus, 1980–2010. Journal of Geophysical Research: Atmospheres, 119(2), 693–712. https://doi.org/10.1002/2013JD020611
Caporusso, N. B., & Rolim, G. D. S. (2015). Reference evapotranspiration models using different time scales in the Jaboticabal region of São Paulo, Brazil. Acta Scientiarum. Agronomy, 37, 1–9. https://doi.org/10.4025/actasciagron.v37i1.18277
Carvalho, D. F. D., Rocha, H. S. D., Bonomo, R., & Souza, A. P. D. (2015). Estimativa da evapotranspiração de referência a partir de dados meteorológicos limitados. Pesquisa Agropecuária Brasileira, 50(1), 1–11. https://doi.org/10.1590/S0100-204X2015000100001
Ceccherini, G., Ameztoy, I., Hernández, C. P. R., & Moreno, C. C. (2015). High-resolution precipitation datasets in South America and West Africa based on satellite-derived rainfall, enhanced vegetation index and digital elevation model. Remote Sensing, 7(5), 6454–6488. https://doi.org/10.3390/rs70506454
Chen, Z., Sun, S., Wang, Y., Wang, Q., & Zhang, X. (2020). Temporal convolution-network-based models for modeling maize evapotranspiration under mulched drip irrigation. Computers and Electronics in Agriculture, 169, 105206. https://doi.org/10.1016/j.compag.2019.105206
Condon, L. E., Atchley, A. L., & Maxwell, R. M. (2020). Evapotranspiration depletes groundwater under warming over the contiguous United States. Nature Communications, 11(1), 1–8. https://doi.org/10.1038/s41467-020-14688-0
Cunha, P. C. R. D., Nascimento, J. L. D., Silveira, P. M. D., & Alves Júnior, J. (2013). Eficiência de métodos para o cálculo de coeficientes do tanque classe A na estimativa da evapotranspiração de referência. Pesquisa Agropecuária Tropical, 43, 114–122. https://doi.org/10.1590/S1983-40632013000200005
Dias, S. H. B., Filgueiras, R., Fernandes Filho, E. I., Arcanjo, G. S., Silva, G. H. D., Mantovani, E. C., & Cunha, F. F. D. (2021). Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing. PLoS ONE, 16(2), e0245834. https://doi.org/10.1371/journal.pone.0245834
Douna, V., Barraza, V., Grings, F., Huete, A., Restrepo-Coupe, N., & Beringer, J. (2021). Towards a remote sensing data based evapotranspiration estimation in Northern Australia using a simple random forest approach. Journal of Arid Environments, 191, 104513. https://doi.org/10.1016/j.jaridenv.2021.104513.10.1007/s10661-021-08934-1
Essou, G. R. C., Arsenault, R., & Brissette, F. P. (2016). Comparison of climate datasets for lumped hydrological modeling over the continental United States. Journal of Hydrology, 537, 334–345. https://doi.org/10.1016/j.jhydrol.2016.03.063
Faraco, M. A., Uribe-Opazo, M. A., Silva, E. A. A. D., Johann, J. A., & Borssoi, J. A. (2008). Seleção de modelos de variabilidade espacial para elaboração de mapas temáticos de atributos físicos do solo e produtividade da soja. Revista Brasileira De Ciência Do Solo, 32, 463–476.
Feng, T., Su, T., Ji, F., Zhi, R., & Han, Z. (2018). Temporal characteristics of actual evapotranspiration over China under global warming. Journal of Geophysical Research: Atmospheres, 123(11), 5845–5858. https://doi.org/10.1029/2017JD028227
Feng, Y., Cui, N., Gong, D., Zhang, Q., & Zhao, L. (2017). Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management, 193, 163–173. https://doi.org/10.1016/j.agwat.2017.08.003
Fernandes, A. L. T., Fraga, E. F., Jr., & Takay, B. Y. (2011). Avaliação do método Penman-Piche para estimativa de evapotranspiração de referência em Uberaba, MG. Revista Brasileira De Engenharia Agrícola e Ambiental. https://doi.org/10.1590/S1415-43662011000300008
Fuentes, I., Padarian, J., van Ogtrop, F., & Vervoort, R. W. (2019). Comparison of surface water volume estimation methodologies that couple surface reflectance data and digital terrain models. Water, 11(4), 780. https://doi.org/10.3390/w11040780
Granata, F. (2019). Evapotranspiration evaluation models based on machine learning algorithms—A comparative study. Agricultural Water Management, 217, 303–315. https://doi.org/10.1016/j.agwat.2019.03.015
Granata, F., & Di Nunno, F. (2021). Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks. Agricultural Water Management, 255, 107040. https://doi.org/10.1016/j.agwat.2021.107040
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518. https://doi.org/10.7717/peerj.5518
Hodam, S., Sarkar, S., Marak, A. G., Bandyopadhyay, A., & Bhadra, A. (2017). Spatial interpolation of reference evapotranspiration in India: Comparison of IDW and Kriging methods. Journal of the Institution of Engineers (india): Series A, 98(4), 511–524. https://doi.org/10.1007/s40030-017-0241-z
Hofstra, N., & New, M. (2009). Spatial variability in correlation decay distance and influence on angular-distance weighting interpolation of daily precipitation over Europe. International Journal of Climatology: A Journal of the Royal Meteorological Society, 29(12), 1872–1880. https://doi.org/10.1002/joc.1819
Honda, E. A., & Durigan, G. (2017). A restauração de ecossistemas e a produção de água. Hoehnea, 44, 315–327. https://doi.org/10.1590/2236-8906-82/2016
Ibrahim, G. R. F., Rasul, A., Hamid, A. A., Ali, Z. F., & Dewana, A. A. (2019). Suitable site selection for rainwater harvesting and storage case study using Dohuk Governorate. Water, 11(4), 864. https://doi.org/10.3390/w11040864
INMET. (2021). Banco de Dados Meteorológicos do INMET. Instituto Nacional de Meteorologia. Retrieved September 13, 2021, from https://bdmep.inmet.gov.br/
Jesus, J. B. D., Rosa, C. N. D., Barreto, Í. D. D. C., & Fernandes, M. M. (2020). Análise da incidência temporal, espacial e de tendência de fogo nos biomas e unidades de conservação do Brasil. Ciência Florestal, 30, 176–191. https://doi.org/10.5902/1980509837696
Kisi, O. (2016). Modeling reference evapotranspiration using three different heuristic regression approaches. Agricultural Water Management, 169, 162–172. https://doi.org/10.1016/j.agwat.2016.02.026
Krajewski, W. F., Ceynar, D., Demir, I., Goska, R., Kruger, A., Langel, C., Mantilla, R., Niemeier, J., Quintero, F., Seo, B. C., Small, S. J., Webber, L. J., & Young, N. C. (2017). Real-time flood forecasting and information system for the state of Iowa. Bulletin of the American Meteorological Society, 98(3), 539–554. https://doi.org/10.1175/BAMS-D-15-00243.1
Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6(3–4), 228–241. https://doi.org/10.1029/2018JD028984
Lima, C. H., AghaKouchak, A., & Randerson, J. T. (2018). Unraveling the role of temperature and rainfall on active fires in the Brazilian Amazon using a nonlinear Poisson model. Journal of Geophysical Research: Biogeosciences, 123(1), 117–128. https://doi.org/10.1002/2017JG003836
Liu, W., Wang, L., Sun, F., Li, Z., Wang, H., Liu, J., Yang, T., Zhou, J., Qi, J. (2018) Snow hydrology in the upper yellow river basin under climate change: a land surface modeling perspective. Journal of Geophysical Research: Atmospheres, 123(22), 676–691. https://doi.org/10.1029/2018JD028984
Machado, N. G., Ventura, T. M., de Moraes Danelichen, V. H., Querino, C. A. S., & Biurdes, M. S. (2015). Estimation of rainfall by neural network over a neotropical region. Revista Brasileira De Climatologia. https://doi.org/10.5380/abclima.v17i0.40799
Mardikis, M. G., Kalivas, D. P., & Kollias, V. J. (2005). Comparison of interpolation methods for the prediction of reference evapotranspiration—An application in Greece. Water Resources Management, 19(3), 251–278. https://doi.org/10.1007/s11269-005-3179-2
Mello, C. D., & Silva, A. D. (2013). Hidrologia: Princípios e aplicações em sistemas agrícolas. UFLA, Lavras, pp. 455.
Méndez, M., & Calvo-Valverde, L. A. (2020). Comparison performance of machine learning and geostatistical methods for the interpolation of monthly air temperature over Costa Rica. In IOP Conference Series: Earth and Environmental Science, 432(1),012011. IOP Publishing. https://doi.org/10.1088/1755-1315/432/1/012011
Mendez, M., Calvo-Valverde, L. A., Maathuis, B., & Alvarado-Gamboa, L. F. (2019). Generation of monthly precipitation climatologies for costa rica using irregular rain-gauge observational networks. Water, 11(1), 70. https://doi.org/10.3390/w11010070
Mendonça, E. A., & Dantas, R. T. (2010). Estimativa da evapotranspiração de referência no município de Capim, PB. Revista Brasileira De Engenharia Agrícola e Ambiental, 14(2), 196–202. https://doi.org/10.1590/S1415-43662010000200011
Menezes, S. J. M. D. C. D., Sediyama, G. C., Soares, V. P., Gleriani, J. M., & Andrade, R. G. (2011). Estimativa dos componentes do balanço de energia e da evapotranspiração em plantios de eucalipto utilizando o algoritmo SEBAL e imagem Landsat 5-TM. Revista Árvore, 35, 649–657. https://doi.org/10.1590/S0100-67622011000400009
MMA. (2018). Mata Atlântica: Patrimônio nacional dos Brasileiros. Ministério do Meio Ambiente. Retrieved September 13, 2021, from https://livroaberto.ibict.br/handle/1/984
MMA. (2010). Biomas. Ministério do Meio Ambiente. Retrieved September 13, 2021, from http://www.mma.gov.br/biomas
New, M., Hulme, M., & Jones, P. (2000). Representing twentieth-century space–time climate variability. Part II: Development of 1901–96 monthly grids of terrestrial surface climate. Journal of Climate, 13(13), 2217–2238. https://doi.org/10.1175/1520-0442(2000)013%3c2217:RTCSTC%3e2.0.CO;2
Noia, C. P. Z., Pereira, S. B., Rosa, D. R. Q., & Almeida, R. A. (2014). Evapotranspiração de referência estimada pelos métodos Penman–Monteith-FAO (56) e Hargreaves & Samani para o município de Dourados, MS. Agrarian, 7(24), 300–308.
Oliveira, D., & Ferreira, C. (2017). Aspectos climáticos da bacia hidrográfica do rio Preto–MG/RJ, Brasil, influência dos fatores geográficos na formação desse clima regional. Revista de Geografia e Ordenamento do Território, 11, 283. https://doi.org/10.17127/got/2017.11.013
Oliveira, V. M. R., de Figueredo Dantas, G., Palaretti, L. F., Dalri, A. B., dos Santos, M. G., & Fischer Filho, J. A. (2015). Estimativa de evapotranspiração de referência na região de Rio Paranaíba-MG. Irriga, 20(4), 790–798. https://doi.org/10.15809/irriga.2015v20n4p790
Pereira, D. D. R., Yanagi, S. D. N. M., Mello, C. R. D., Silva, A. M. D., & Silva, L. A. D. (2009). Desempenho de métodos de estimativa da evapotranspiração de referência para a região da Serra da Mantiqueira, MG. Ciência Rural, 39, 2488–2493. https://doi.org/10.1590/S0103-84782009000900016
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 9(2), 181–199. https://doi.org/10.1007/s10021-005-0054-1
Ravazzani, G., Ceppi, A., & Davolio, S. (2020). Wind speed interpolation for evapotranspiration assessment in complex topography area. Bulletin of Atmospheric Science and Technology. https://doi.org/10.1007/s42865-019-00001-5
Regoto, P., Dereczynski, C., Silva, W. L., Santos, R., & Confalonieri, U. (2019). Tendências de extremos de precipitação para o estado do Espírito Santo. Anuário do Instituto de Geociências, 41(1), 365–381. https://doi.org/10.11137/2018_1_365_381
Santana, J. S., Silva, W. A., Lima, E. F., & Oliveira, G. C. (2018). Análise Espaço-Temporal Da Evapotranspiração De Referência Para O Estado Do Maranhão. Revista Brasileira De Agricultura Irrigada, 12(5), 2866–2876.
Santos, L. O. F. D., Querino, C. A. S., Querino, J. K. A. D. S., Pedreira, A. L., Moura, A. R. D. M., Machado, N. G., & Biudes, M. S. (2018). Validation of rainfall data estimated by GPM satellite on Southern Amazon region. Revista Ambiente & Água. https://doi.org/10.4136/ambi-agua.2249
Saraiva, G. S., Bonomo, R., & de Souza, J. M. (2017). Avaliação de interpoladores geoestatísticos e determinísticos da evapotranspiração de referência diária para o estado do Espírito Santo. Revista Agro@ mbiente On-line, 11(1), 21–30. https://doi.org/10.18227/1982-8470ragro.v11i1.3647
Schumacher, V., Justino, F., Fernández, A., Meseguer-Ruiz, O., Sarricolea, P., Comin, A., Venancio, L. P., & Althoff, D. (2020). Comparison between observations and gridded data sets over complex terrain in the Chilean Andes: Precipitation and temperature. International Journal of Climatology, 40(12), 5266–5288. https://doi.org/10.1002/joc.6518
Sekulić, A., Kilibarda, M., Heuvelink, G., Nikolić, M., & Bajat, B. (2020). Random forest spatial interpolation. Remote Sensing, 12(10), 1687. https://doi.org/10.3390/rs12101687
Silva, G. H. D., Dias, S. H., Ferreira, L. B., Santos, J. É., & Cunha, F. F. D. (2018). Performance of different methods for reference evapotranspiration estimation in Jaíba, Brazil. Revista Brasileira De Engenharia Agrícola e Ambiental, 22, 83–89. https://doi.org/10.1590/1807-1929/agriambi.v22n2p83-89
Silva Junior, J. C., Medeiros, V., Garrozi, C., Montenegro, A., & Gonçalves, G. E. (2019). Random forest techniques for spatial interpolation of evapotranspiration data from Brazilian’s Northeast. Computers and Electronics in Agriculture, 166, 105017. https://doi.org/10.1016/j.compag.2019.105017
Silva, M. G., Arraes, F. D. D., Ledo, E. R. F., Santos, N. T., & da Silva Filho, J. A. (2013). Avaliação da evapotranspiração de referência por Penman-Monteith usando dados climáticos mínimos no sertão do Ceará. Revista Agro@ mbiente On-line, 7(3), 284–293. https://doi.org/10.18227/1982-8470ragro.v7i3.1245
Souza, A F., Campelo Júnior, J H. (2017) Desempenho de métodos de estimativa da evapotranspiração de referência para região da Baixada Cuiabana, MT. Agrometeoros. http://dx.doi.org/10.31062/agrom.v25i2.26298
Souza, A. P. D., Carvalho, D. F. D., Silva, L. B. D. D., Almeida, F. T. D., & Rocha, H. S. D. (2011). Estimativas da evapotranspiração de referência em diferentes condições de nebulosidade. Pesquisa Agropecuária Brasileira, 46, 219–228. https://doi.org/10.1590/S0100-204X2011000300001
Strong, C., Khatri, K. B., Kochanski, A. K., Lewis, C. S., & Allen, L. N. (2017). Reference evapotranspiration from coarse-scale and dynamically downscaled data in complex terrain: Sensitivity to interpolation and resolution. Journal of Hydrology, 548, 406–418. https://doi.org/10.1016/j.jhydrol.2017.02.045
Tanaka, A. A., Souza, A. P. D., Klar, A. E., Silva, A. C. D., & Gomes, A. W. A. (2016). Evapotranspiração de referência estimada por modelos simplificados para o Estado do Mato Grosso. Pesquisa Agropecuária Brasileira, 51, 91–104. https://doi.org/10.1590/S0100-204X2016000200001
Tyralis, H., Papacharalampous, G., & Langousis, A. (2019). A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water, 11(5), 910. https://doi.org/10.3390/w11050910
Tomas-Burguera, M., Vicente-Serrano, S. M., Grimalt, M., & Beguería, S. (2017). Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula. Agricultural Water Management, 182, 103–116. https://doi.org/10.1016/j.agwat.2016.12.013
Wang, S., Lian, J., Peng, Y., Hu, B., & Chen, H. (2019). Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China. Agricultural Water Management, 221, 220–230. https://doi.org/10.1016/j.agwat.2019.03.027
Wang, Z., Xie, P., Lai, C., Chen, X., Wu, X., Zeng, Z., & Li, J. (2017). Spatiotemporal variability of reference evapotranspiration and contributing climatic factors in China during 1961–2013. Journal of Hydrology, 544, 97–108. https://doi.org/10.1016/j.jhydrol.2016.11.021
Willmott, C. J. (1981). On the validation of models. Physical Geography, 2(2), 184–194. https://doi.org/10.1080/02723646.1981.10642213
Wu, L., Peng, Y., Fan, J., & Wang, Y. (2019). Machine learning models for the estimation of monthly mean daily reference evapotranspiration based on cross-station and synthetic data. Hydrology Research, 50(6), 1730–1750. https://doi.org/10.2166/nh.2019.060
Xavier, A. C., King, C. W., & Scanlon, B. R. (2015). Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology, 36(6), 2644–2659. https://doi.org/10.1002/joc.4518
Xing, W., Wang, W., Shao, Q., Yu, Z., Yang, T., & Fu, J. (2016). Periodic fluctuation of reference evapotranspiration during the past five decades: Does Evaporation Paradox really exist in China? Scientific Reports, 6(1), 1–12. https://doi.org/10.1038/srep39503
Yang, Y., Sun, H., Xue, J., Liu, Y., Liu, L., Yan, D., & Gui, D. (2021). Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms. Environmental Monitoring and Assessment, 193(3), 1–15.
Yanto, Livneh, B., Rajagopalan, B. (2017) Development of a gridded meteorological dataset over Java island, Indonesia 1985-2014. Scientific Data. https://doi.org/10.1038/sdata.2017.72
Yin, Y., Wu, S., & Zhao, D. (2013). Past and future spatiotemporal changes in evapotranspiration and effective moisture on the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 118(19), 10–850. https://doi.org/10.1002/jgrd.50858
Zanetti, S. S., Dohler, R. E., Cecílio, R. A., Pezzopane, J. E. M., & Xavier, A. C. (2019). Proposal for the use of daily thermal amplitude for the calibration of the Hargreaves-Samani equation. Journal of Hydrology, 571, 193–201. https://doi.org/10.1016/j.jhydrol.2019.01.049
Zhang, Y., Peña-Arancibia, J. L., McVicar, T. R., Chiew, F. H., Vaze, J., Liu, C., & Lu., X., Zheng, H., Wang, Y., Miralles, D. G., & Pan, M. (2016). Multi-decadal trends in global terrestrial evapotranspiration and its components. Scientific Reports, 6(1), 1–12. https://doi.org/10.1038/srep19124
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This study was supported by the National Council for Scientific and Technological, Brazil (Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq) and the Coordination for the Improvement of Higher Education Personnel, Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES, grant number 001).
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Baratto, P.F.B., Cecílio, R.A., de Sousa Teixeira, D.B. et al. Random forest for spatialization of daily evapotranspiration (ET0) in watersheds in the Atlantic Forest. Environ Monit Assess 194, 449 (2022). https://doi.org/10.1007/s10661-022-10110-y
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DOI: https://doi.org/10.1007/s10661-022-10110-y