Spatial structure of precipitation in the Brazilian Amazonia: geostatistics with block kriging
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The Geostatistical analysis is an important tool for identifying distances over which rainfall shows spatial correlation. The objectives of the present work are: to model the spatial structure of rainfall patterns in the Brazilian rainforest using three timescales and to assess kriging interpolation for predicting rainfall data at ungauged sites. We used Geostatistical modeling combined with block kriging. The rainfall data were collected from 218 rain gauges corresponding to 64 municipalities located within 9 Amazonia states. Data corresponded to monthly mean rainfall and annual rainfall computed from historical records. The last date for data collection was December 2015. Three timescales were considered: months with minimum and maximum rainfall, January-June and July-December time periods and annual rainfall. The spherical model fitted reasonably well the semivariograms corresponding to March and January-June, the Gaussian model fitted quite well the semivariograms corresponding to September and July-December periods while the exponential model fitted the annual rainfall. The cross-validation analyses based on Mean Absolute Error and goodness-of-prediction statistics showed that kriged values (kriging maps) could be better predictors of rainfall for areas without rain gauges (unsampled zones) than mean rainfall values computed from adjacent sampled areas. It was found a significant negative statistical relationship between forest loss and rainfall occurrence which confirms the influence of deforestation on the hydrology of the Amazon basin.
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Millán-VegaH., Rabelo-LimaJ., & Valderá-FigueredoN. (2020). Spatial structure of precipitation in the Brazilian Amazonia: geostatistics with block kriging. Revista Cubana De Meteorología, 26(4). Recuperado a partir de http://rcm.insmet.cu/index.php/rcm/article/view/529
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Citas
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MCBRATNEY, A.B. & WEBSTER, R. (1986): Choosing functions for semivariograms of soil properties and fitting them to sampling estimates. Journal of Soil Science , 37: 617-639.
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SARANGI, A.; COX, C.A. & MADRAMOOTOO, C.A. (2005): Geostatistical methods for prediction of spatial variability of rainfall in a mountainous region. Transactions of the American Society of Agricultural Engineers, 48: 943-954.
SELLERS, P.J.; DICKINSON, R.E.; RANDALL, D.A. & BETTS, A.K et. al. (1997): Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science , 275: 502-509.
SHEIL, D. & MURDIYARSO, D. (2009): How forests attract rain: an examination of a new hypothesis. Bioscience, 4: 341-347.
STATSOFT. INC. (2011): STATISTICA (Data Analysis Software System), version 10, Tulsa, OK.
TAKEMI, T. (2010): Dependence of the precipitation intensity in mesoscale convective systems to temperature lapse rate. Atmospheric Research , 96: 273-285.
VAN DE BEEK, C.Z.; LEIJNSE, H.; TORFS, P.J.J.F. & UIJLENHOET, R. (2011): Climatology of daily rainfall semi-variance in The Netherlands. Hydrology and Earth System Sciences, 15: 171-183.
WANG, G.; DOLMAN, A.J.; BLENDER, R. & FRAEDRICH, K. (2010): Fluctuation regimes of soil moisture in ERA-40 re-analysis data. Theoretical and Applied Climatology, 99: 1-8.
WEBSTER, R. & OLIVER, M.A. (1992): Sample adequately to estimate variograms of soil properties. Journal of Soil Science , 43: 177-192.
YOON, J.H.& ZENG, N. (2010): An Atlantic influence on Amazon rainfall. Climate Dynamics, 34: 249-264.
BAGLEY, J.E.; DESAI, A.R.; HARDING, K.J. & SNYDER, P.K. et. al. (2014): Drought and deforestation: has land cover change influenced recent precipitation extremes in the Amazon? Journal of Climate, 27: 345-361.
BONINI, I.; RODRIGUES, C.; DALLACORT, R. & MARIMON, B.H.Jr. et. al. (2014): Rainfall and deforestation in the municipality of Colíder, Southern Amazon. Revista Brasileira de Meteorología, 29: 483-493.
BURGESS, T.M. & WEBSTER, R. (1980): Optimal interpolation and isarithmic mapping of soil properties. II: Block kriging. Journal of Soil Science, 31:333-343.
BUTLER, R. (2014): Rainforest of Brazil: An Environmental Status Report ( (2014): Rainforest of Brazil: An Environmental Status Report (http://www.mongabay.com ). Accessed 30/11/2015.
CAMBARDELLA, C.A.; MOORMAN, T.B.; NOVAK, J.M. & PARKIN, T.B. et. al. (1994): Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58: 1501-1511.
CHAVES, R.R. & CAVALCANTI, I.F.A. (2001): Atmospheric circulation features associated with rainfall variability over southern Northeast Brazil. Monthly Weather Review, 129: 2614-2626.
CHEN, J.; CARLSON, B.E. & DEL GENIO, A.D. (2002): Evidence for strengthening of the tropical circulation in the 1990s. Science, 295: 838-841.
COE, M.; MARTHEWS, T.R.; COSTA, M.H. & GALBRAITH, D. et. al. (2013): Deforestation and climate feedbacks threaten the ecological integrity of south-southeastern Amazonia. Philosophical Transactions of the Royal Society B, 368: 20120155.
COSTA, A.N.; DURÃO, R.; SOARES, A. & PEREIRA, M.J. (2008): A geostatistical exploratory analysis of precipitation extremes in Southern Portugal. Statistical Journal, 6: 21-32.
DAVIDSON, E.A.; DE ARAUJO, A.C.; ARTAXO, P. & BALCH, J.K. et. al. (2012): The Amazon basin in transition. Nature, 481: 321-328.
DAVIS, B.M. (1987): Uses and abuses of cross-validation in geostatistics. Mathematical Geology, 19: 241-248.
DEBORTOLI, N.S.; DUBREUIL, V.; FUNATZU, B. & DELAHAYE, F. et. al. (2015): Rainfall Patterns in the Southern Amazon: a chronological perspective (1971-2010). Climate Change, https://doi.org/10.1007/s10584015-1415-1.
FERREIRA, R.N.; NISSENBAUM, M.R. & RICKENBACH, T.M. (2018): Climate changes effects on summertime precipitation organization in the Southeast United States. Atmospheric Research, 214: 348-363.
GAMMA DESIGN SOFTWARE (2001): GS+ Geostatistics for the Environmental Sciences, version 5.1.1. Professional Edition, Plainwell, MI.
GOOVAERTS, P. (1998): Geostatistical tools for characterizing the spatial variability of microbiological and physic-chemical soil properties. Biology and Fertility of Soils, 27: 315-334.
JONGMAN, R.H.G.; TER BRAAK, C.J.F. & VAN TONGEREN, O.F.R. (1995): Data analysis in community and landscape ecology. Cambridge University Press, Cambridge, London.
KUZYAKOVA, I.F.; ROMANENKOV, V.A. & KUZYAKOV, Ya.V. (2001): Geostatistics in soil agrochemical studies. Eurasian Soil Science , 34: 1011-1017.
LLOPART, M.; REBOITA, M.S.; COPPOLA, E. & GIORGI, F. et. al. (2018): Land use change over the Amazon Forest and its impact on the local climate. Water, 10: 149.
MAKARIEVA, A.M. & GORSHKOV, V.G. (2007): Biotic pump of atmospheric moisture as driver of the hydrological cycle on land. Hydrology and Earth System Sciences, 11: 1013-1033.
MARENGO, J.; TOMASELLA, J.; ALVES, L.M. & SOARES, W.R. et. al. (2011): The drought of 2010 in the context of historical droughts in the Amazon region. Geophysical Research Letters, 38: L12703.
MARTINS, G., VON RANDOW, C.; SAMPAIO, G. & DOLMAN, A.J. (2015): Precipitation in the Amazon and its relationship with moisture transport and tropical Pacific and Atlantic SST from the CMIP5 simulation. Hydrology and Earth System Sciences (Discussions), 12: 671-704.
MCBRATNEY, A.B. & WEBSTER, R. (1986): Choosing functions for semivariograms of soil properties and fitting them to sampling estimates. Journal of Soil Science , 37: 617-639.
PENDERGRASS, A.G.; REED, K.A. & MEDEIROS, B. (2016): The link between extreme precipitation and convective organization in a warming climate: global radiative-convective equilibrium simulations. Geophysical Research Letters , 43: 11445-11452.
RONCHAIL, J.; GERARD, C.; MOLINIER, M. & GUYOT, J.P. et. al. (2002): Interannual rainfall variability in the Amazon basin and sea-surface temperatures in the Equatorial Pacific and the Tropical Atlantic Oceans. International Journal of Climatology, 22: 1663-1686.
SALESKA, S.R.; DIDAN, K.; HUETE, A.R. & DA ROCHA, H.R. (2007): Amazon forests green-up during 2005 drought. Science , 318: 612.
SARANGI, A.; COX, C.A. & MADRAMOOTOO, C.A. (2005): Geostatistical methods for prediction of spatial variability of rainfall in a mountainous region. Transactions of the American Society of Agricultural Engineers, 48: 943-954.
SELLERS, P.J.; DICKINSON, R.E.; RANDALL, D.A. & BETTS, A.K et. al. (1997): Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science , 275: 502-509.
SHEIL, D. & MURDIYARSO, D. (2009): How forests attract rain: an examination of a new hypothesis. Bioscience, 4: 341-347.
STATSOFT. INC. (2011): STATISTICA (Data Analysis Software System), version 10, Tulsa, OK.
TAKEMI, T. (2010): Dependence of the precipitation intensity in mesoscale convective systems to temperature lapse rate. Atmospheric Research , 96: 273-285.
VAN DE BEEK, C.Z.; LEIJNSE, H.; TORFS, P.J.J.F. & UIJLENHOET, R. (2011): Climatology of daily rainfall semi-variance in The Netherlands. Hydrology and Earth System Sciences, 15: 171-183.
WANG, G.; DOLMAN, A.J.; BLENDER, R. & FRAEDRICH, K. (2010): Fluctuation regimes of soil moisture in ERA-40 re-analysis data. Theoretical and Applied Climatology, 99: 1-8.
WEBSTER, R. & OLIVER, M.A. (1992): Sample adequately to estimate variograms of soil properties. Journal of Soil Science , 43: 177-192.
YOON, J.H.& ZENG, N. (2010): An Atlantic influence on Amazon rainfall. Climate Dynamics, 34: 249-264.