Groundwater system response at sites with different agricultural land uses: case of the Guarani Aquifer outcrop area, Brotas/SP-Brazil

References

• Box, G.E.P. and Jenkins, G.M., 1970. Time series analysis: forecasting and control. San Francisco: Holden-Day.
   Google Scholar
• Caetano-Chang, M.R., 1997. A Formação Pirambóia no centro-leste do estado de São Paulo. Thesis (PhD). Instituto de Geociências e Ciências Exatas – Rio Claro, Universidade Estadual Paulista (UNESP), Rio Claro. (In Portuguese)
   Google Scholar
• De Gruijter, J.J., et al., 2006. Sampling for natural resources monitoring. Berlin: Springer.
   Google Scholar
• FAO (Food and Agriculture Organization of the United Nations), 1998. Crop evapotranspiration - guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. Rome: FAO.
   Google Scholar
• Gastmans, D., et al., 2012. Modelo hidrogeológico conceptual del Sistema Acuífero Guaraní (SAG): una herramienta para la gestión. Boletín Geológico y Minero, 123 (3), 249–265. (In Spanish).
   Google Scholar
• Gastmans, D., Chang, H.K., and Hutcheon, I., 2010, Groundwater geochemical evolution in the northern portion of the Guarani Aquifer System (Brazil) and its relationship to diagenetic features. Applied Geochemistry, 25 (1), 16–33. doi:10.1016/j.apgeochem.2009.09.024
   Web of Science ®Google Scholar
• Hipel, K.W. and McLeod, A.I., 1994. Time series modeling of water resources and environmental systems. Amsterdam: Elsevier.
   Google Scholar
• Knotters, M., 2001. Regionalised time series models for water table depths. Thesis (PhD). Wageningen University.
   Google Scholar
• Knotters, M. and Van Walsum, P.E.V., 1997. Estimating fluctuation quantities from time series of water-table depths using models with a stochastic component. Journal of Hydrology, 197, 25–46. doi:10.1016/S0022-1694(96)03278-7
   Web of Science ®Google Scholar
• Le Maitre, D.C., et al., 1999. A review of information on interactions between vegetation and groundwater. Water SA, 25, 137–152.
   Web of Science ®Google Scholar
• Manzione, R.L., et al., 2010. Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados. Hydrogeology Journal, 18 (8), 1927–1937. doi:10.1007/s10040-010-0654-5
   Web of Science ®Google Scholar
• Nash, J.E., 1958. Determining runoff from rainfall. Proceedings of the Institution of Civil Engineers, 10, 163–184. doi:10.1680/iicep.1958.2025
   Google Scholar
• Obergfell, C., et al., 2013. Deriving hydrogeological parameters through time series analysis of groundwater head fluctuations around well fields. Hydrogeology Journal. doi:10.1007/s10040-013-0973-4
   PubMed Web of Science ®Google Scholar
• Sracek, O. and Hirata, R., 2002. Geochemical and stable isotopic evolution of the Guarani Aquifer System in the state of São Paulo, Brazil. Hydrogeology Journal, 10, 643–655. doi:10.1007/s10040-002-0222-8
   Web of Science ®Google Scholar
• Souza, L.D., Souza, L. da S., Ledo, C.A. de S., 2007. Sistema radicular dos citros em Neossolo Quartzarênico dos Tabuleiros Costeiros sob irrigação e sequeiro. Pesquisa Agropecuária Brasileira, 42, 1373–1381. doi:10.1590/S0100-204X2007001000002. (In Portuguese.)
   Google Scholar
• Tankersley, C.D. and Graham, W.D., 1994. Development of an optimal control system for maintaining minimum groundwater levels. Water Resources Research, 30, 3171–3181. doi:10.1029/94WR01790
   Web of Science ®Google Scholar
• Van Geer, F.C. and Zuur, A.F., 1997. An extension of Box-Jenkins transfer/noise models for spatial interpolation of groundwater head series. Journal of Hydrology, 192, 65–80. doi:10.1016/S0022-1694(96)03113-7
   Web of Science ®Google Scholar
• Von Asmuth, J.R., et al., 2002. Transfer function noise modeling in continuous time using predefined impulse response functions. Water Resources Research, 38 (12), 23-1–23-12. doi:10.1029/2001WR001136
   Web of Science ®Google Scholar
• Von Asmuth, J.R., et al., 2008. Modeling time series of ground water head fluctuation subjected to multiple stresses. Ground Water, 46, 30–40.
   PubMed Web of Science ®Google Scholar
• Von Asmuth, J.R., 2012. Groundwater system identification through time series analysis. Thesis (PhD). TU Delft.
   Google Scholar
• Von Asmuth, J.R. and Bierkens, M.F.P., 2005. Modeling irregularly spaced residual series as a continuous stochastic process. Water Resources Research, 41, W12404. doi:10.1029/2004WR003726
   Google Scholar
• Von Asmuth, J.R. and Knotters, M., 2004. Characterising groundwater dynamics based on a system identification approach. Journal of Hydrology, 296, 118–134. doi:10.1016/j.jhydrol.2004.03.015
   Web of Science ®Google Scholar
• Von Asmuth, J.R., et al., 2012. Software for hydrogeologic time series analysis, interfacing data with physical insight. Environmental Modelling & Software, 38, 178–190. doi:10.1016/j.envsoft.2012.06.003
   Web of Science ®Google Scholar
• Wendland, E., Barreto, C., and Gomes, L.H., 2007. Water balance in the Guarani Aquifer outcrop zone based on hydrogeologic monitoring. Journal of Hydrology, 342, 261–269. doi:10.1016/j.jhydrol.2007.05.033
   Web of Science ®Google Scholar
• Yi, M. and Lee, K., 2003. Transfer function-noise modeling or irregular observed groundwater heads using precipitation data. Journal of Hydrology, 288, 272–287. doi:10.1016/j.jhydrol.2003.10.020
   Web of Science ®Google Scholar
• Zadeh, L.A., 1956. On the identification problem. IRE Transactions on Circuit Theory, 3, 277–281. doi:10.1109/TCT.1956.1086328
   Google Scholar