Previsão de secas na primavera em Portugal Continental com base em indicadores climáticos de larga escala

Bibliographic References

• Agnew, C.T. (2000). Using the SPI to identify drought. Drought Network News, 12, 6-12.
• ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). Artificial neural networks in hydrology. I. Preliminary concepts. Journal of Hydrologic Engineering, 5(2), 115-123. doi:10.1061/(ASCE)1084-0699(2000)5:2(115)
• ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). Artificial neural networks in hydrology. II. Hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124–137. doi:10.1061/(ASCE)1084-0699(2000)5:2(124)
• Bordi, I., Fraedrich, K., Petitta, M., Sutera, A. (2005). Methods for predicting drought occurrences. In Proceedings of the 6th International Conference of the European Water Resources Association, Menton, France.
• Bowden, G.J., Dandy, G.C., Maier, H.R. (2005). Input determination for neural network models in water resources applications. Part 1—background and methodology. Journal of Hydrology, 301(1-4), 75-92. doi:10.1016/j.jhydrol.2004.06.021
• Campolo, M., Andreusi, P., Soldati, A. (1999). River flood forecasting with a neural network model. Water Resources Research, 35(4), 1191-1197. doi:10.1029/1998WR900086
• Cancelliere, A., Di Mauro, G., Bonaccorso, B., Rossi, G. (2005). Stochastic forecasting of Standardized Precipitation Index. In Proceedings of XXXI IAHR Congress Water Engineering for the future: Choice and Challenges, Seoul, Korea, 3252-3260.
• Cancelliere, A., Di Mauro, G., Bonaccorso, B., Rossi, G. (2007). Drought forecasting using the Standardized Precipitation Index. Water Resources Management, 21(5), 801-819. doi:10.1007/s11269-006-9062-y
• Cordery, I., McCall, M. (2000). A model for forecasting drought from teleconnections. Water Resources Research, 36(3), 763-768. doi:10.1029/1999WR900318
• Dastorani, M.T., Afkhami, H. (2011). Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert, 16, 39-48.
• Dawson, D.W., Wilby, R. (1998). An artificial neural network approach to precipitation-runoff modeling. Hydrological Sciences Journal, 43(1), 47-66. doi:10.1080/02626669809492102
• Demyanov, V., Kanevsky, M., Chernov, S., Savelieva, E., Timonin, V. (1998). Neural network residual kriging application for climatic data. Journal of Geographic Information and Decision Analysis, 2(2), 215-232.
• Di Mauro, G., Bonaccorso, G.B., Cancelliere, A., Rossi, G. (2008). Use of NAO index to improve drought forecasting in the Mediterranean area: Application to Sicily region. Options Méditerranéennes. Série A: Séminaires Méditerranéens, No. 80.
• Fernando, M.K.G., Maier, H.R., Dandy, G.C. (2009). Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach. Journal of Hydrology, 367(3-4), 165-176. doi:10.1016/j.jhydrol.2008.10.019
• Gámiz-Fortis, S., Esteban-Parra, M.J., Trigo, R.M., Castro-Díez, Y. (2010). Potential predictability of Iberian river flow based on its relationship with previous winter global SST. Journal of Hydrology, 385, 143-149. doi:10.1016/j.jhydrol.2010.02.010
• Gámiz-Fortis, S., Pozo-Vázquez, D., Trigo, R.M., Castro-Díez, Y. (2008a). Quantifying the predictability of winter river flow in Iberia. Part I: Interannual predictability. Journal of Climate, 21, 2484-2502. doi:10.1175/2007JCLI1774.1
• Gámiz-Fortis, S., Pozo-Vázquez, D., Trigo, R.M., Castro-Díez, Y. (2008b). Quantifying the predictability of winter river flow in Iberia. Part II: Seasonal predictability. Journal of Climate, 21, 2503-2518. doi:10.1175/2007JCLI1775.1
• Hoerling, M., Kumar, A. (2003). The perfect ocean for drought. Science, 299(5607), 691-694. Geophysical Research Abstracts, 12, EGU2010-8454, EGU General Assembly 2010, Viena, Austria. doi:10.1126/science.1079053
• Hurrell, J.W. (1995). Decadal trends in North Atlantic Oscillation: regional temperatures and precipitation. Science, 269(5224), 676-679. doi:10.1126/science.269.5224.676
• Hurrell, J.W., Kushnir, Y., Visbeck, M. (2001). The North Atlantic Oscillation. Science, 291(5504), 603-605. doi:10.1126/science.1058761
• Hurrell, J.W., Kushnir, Y., Ottersen, G., Visbeck, M. (2003). The North Atlantic Oscillation: climatic significance and environmental impact. Geophysical Monograph Series, 134, American Geophysical Union, Washington, DC, USA. https://doi.org/10.1029/GM134
• Ionita, M., Lhomann, G., Rimbu, N. (2008). Prediction of spring Elbe discharge based on stable teleconnections with winter global temperature and precipitation. Journal of Climate, 21(23), 6215-6226. doi:10.1175/2008JCLI2248.1
• Ionita, M., Lohmann, G., Rimbu, N., Chelcea, S., Dima, M. (2012). Interannual to decadal summer drought variability over Europe and its relationship to global sea surface temperature. Climate Dynamics, 38(1), 363-377. doi:10.1007/s00382-011-1028-y
• Iyer, M.S., Rhinehart, R.R. (1999). A method to determine the required number of neural-network training repetitions. IEEE Transactions on Neural Networks, 10(2), 427-432. doi:10.1109/72.750573
• Jain, A., Kumar, A.M. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7(2), 585-592. doi:10.1016/j.asoc.2006.03.002
• Jones, P.D., Jonsson, T., Wheeler, D. (1997). Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and South-West Iceland. International Journal of Climatology, 17(13), 1433-1450. doi:10.1002/(SICI)1097-0088(19971115)17:13<1433::AID-JOC203>3.0.CO;2-P
• Jones, P.D., Osborn, T.J., Briffa, K.R. (2003). Pressure-based measures of the North Atlantic oscillation (NAO): a comparison and an assessment of changes in the strength of the NAO and in its influence on surface climate parameters in The North Atlantic Oscillation: climate significance and environmental impact. Geophysics Monogram 134, 51-62, American Geophysical Union. https://doi.org/10.1029/134GM03
• Karunanithi, N., Grenney, W.J., Whitely, D., Bovee, K. (1994). Neural networks for river flow prediction. Journal of Computing Civil Engineering, 8(2), 201-219. doi:10.1061/(ASCE)0887-3801(1994)8:2(201)
• Kim T. e Juan B. Valdés, (2003). Nonlinear Model for Drought Forecasting Based on a Conjunction of Wavelet Transforms and Neural Networks. Journal of Hydrologic Engineering, 8(6), 319-328. doi:10.1061/(ASCE)1084-0699(2003)8:6(319)
• Kitanidis, P.K., Bras, R.L. (1980). Real time forecasting with a conceptual hydrological model. 2. Applications and results. Water Resources Research, 16(6), 1034-1044. doi:10.1029/WR016i006p01034
• Kurnik, B. (2009). DESERT Action JRC, Drought forecasting methods. Ljubljana on 24 September 2009 – 1st DMCSEE – JRC Workshop on Drought Monitoring.
• Legates, D.R., McCabe Jr., G.J. (1999). Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241. doi:10.1029/1998WR900018
• Lloyd-Hughes, B. (2002). The long range predictability of European drought. PhD Thesis, Department of Space and Climate Physics, University of London, University College London, UK.
• López-Moreno, J.I., Vicente-Serrano, S.M. (2008). Extreme phases of the wintertime North Atlantic Oscillation and drought occurrence over Europe: a multi-temporal-scale approach. Journal of Climate, 21(6), 1220-1243. doi:10.1175/2007JCLI1739.1
• López-Moreno, J.I., Beguería, S., Vicente-Serrano, S.M., García-Ruiz, J.M. (2007). The influence of the NAO on water resources in central Iberia: precipitation, streamflow anomalies and reservoir management strategies. Water Resources Research, 43,W09411, doi:10.1029/2007WR005864
• Martín, M.L., Luna, M.Y., Morata, A., Valero, F. (2004). North Atlantic teleconnection patterns of low-frequency variability and their links with springtime precipitation in the western Mediterranean. International Journal of Climatology, 24(2), 213-230. doi:10.1002/joc.993
• Martín-Vide, J., Fernández, D. (2001). El índice NAO y la precipitación mensual en la España peninsular. Investigaciones Geográficas, 26, 41-58. doi:10.14198/INGEO2001.26.07
• May, R.J., Maier, H.R., Dandy, G.C., Fernando, T.M.K.G. (2008). Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling and Software, 23(10-11), 1312-1326. doi:10.1016/j.envsoft.2008.03.007
• McKee, T.B., Doesken, N.J., Kleist, J. (1993).The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society, Boston, USA, 179-184.
• Mishra, A.K., Desai, V.R. (2006). Drought forecasting using feed-forward recursive neural network. Ecological Modelling, 198(1-2), 127-138. doi:10.1016/j.ecolmodel.2006.04.017
• Mo, K.C., Jae-Kyung, E., Schemm, E., Yoo, S.-H. (2009). Influence of ENSO and the Atlantic multi-decadal Oscillation on drought over the United States. Journal of Climate, 22, 5962-5982. doi:10.1175/2009JCLI2966.1
• Mutlu, E., Chaubey, I., Hexmoor, H., Bajwa, S.G. (2008). Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrological Processes, 22(26), 5097-5106. doi:10.1002/hyp.7136
• Michie, D., Spiegelhalter, D.J., Taylor, C.C. (1994). Machine learning, neural and statistical classification. Project StatLog, Department of Statistics, University of Leeds, UK.
• Ochoa-Rivera, J.C., García-Bartual, R., Andreu, J. (2002). Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks. Journal of Hydrology and Earth System Sciences, 6(4), 641-654. doi:10.5194/hess-6-641-2002
• Ochoa-Rivera, J.C., García-Bartual, R., Andreu, J. (2007). Influence of Inflows Modeling on Management Simulation of Water Resources System. Journal of Water Resources Planning and Management, ASCE, 133(2), 106-116. doi:10.1061/(ASCE)0733-9496(2007)133:2(106)
• Portela, M.M., Quintela, A.C. (2006). Estimação em Portugal Continental de escoamento e de capacidades úteis de albufeiras de regularização na ausência de informação. Recursos Hídricos, 27(2), 7-18.
• Pulido-Calvo, I., Portela, M.M. (2007). Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. Journal of Hydrology, 332(1-2), 1-15. doi:10.1016/j.jhydrol.2006.06.015
• Pulido-Calvo, I., Gutiérrez-Estrada, J.C., Savic, D. (2012). Heuristic modelling of the water resources management in the Guadalquivir River Basin, Southern Spain. Water Resources Management, 26(1), 185-209. doi:10.1007/s11269-011-9912-0
• Qian, B., Corte-Real, J.M., Xu, H. (2000a). Is the North Atlantic Oscillation the most important atmospheric pattern for precipitation in Europe? Journal of Geophysical Research, 105(D9), 901-910. doi:10.1029/2000JD900102
• Qian, B., Xu, H., Corte-Real, J.M. (2000b). Spatial-temporal structures of the quasi-periodic oscillations in precipitation over Europe. International Journal of Climatology, 20(13), 1583-1598. doi:10.1002/1097-0088(20001115)20:13<1583::AIDJOC560>3.0.CO;2-Y
• Rodwell, M.J. (2003). On the predictability of the North Atlantic climate. The North Atlantic Oscillation: climate significance and environmental impact, Geophysical Monograph, 134, 173-192, Amer. Geophys. Union. doi:10.1029/134GM08
• Rossi, G. (2003). Requisites for a drought watch system. In: G. Rossi et al. (eds), Tools for Drought Mitigation in Mediterranean Regions, pp. 147-157. Kluwer Academic Publishing: Dordrecht. doi:10.1007/978-94-010-0129-8_9
• Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536. doi:10.1038/323533a0
• Santos, J.A., Corte-Real, J., Leite, S.M. (2005). Weather regimes and their connection to the winter precipitation in Portugal. International Journal of Climatology, 25(1), 33-50. doi:10.1002/joc.1101
• Santos, J.F., Portela, M.M., Pulido-Calvo, I. (2011). Regional frequency analysis of droughts in Portugal. Water Resources Management, 25(14), 3537-3558. doi:10.1007/s11269-011-9869-z
• Santos, J.F., Portela, M.M., Pulido-Calvo, I. (2013). Dimensionality reduction in drought modelling. Hydrological Processes, 27(10), 1399-1410. doi:10.1002/hyp.9300
• Santos, J.F., Portela, M.M., Pulido-Calvo, I., (2014). Spring drought prediction based on winter NAO and global SST in Portugal, Hydrological Processes, 28(3), 1009-1024. doi:10.1002/hyp.9641
• Santos, J.F., Pulido-Calvo, I., Portela, M.M. (2010). Spatial and temporal variability of droughts in Portugal. Water Resources Research, 46(3). DOI: 10.1029/2009WR008071. doi:10.1029/2009WR008071
• Senthil-Kumar, A.R., Sudheer, K.P., Jain, S.K., Agarwal, P.K. (2005). Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrological Processes, 19(6), 1277-1291. doi:10.1002/hyp.5581
• Silva, A.T., Portela, M.M., Naghettini, M. (2012), Nonstationarities in the occurrence rates of flood events in Portuguese watersheds. Journal of Hydrology and Earth System Sciences, 16, 241-254. doi:10.5194/hess-16-241-2012
• Smith, T.M., Reynolds, R.W., Peterson, T.C. Lawrimore, J. (2008). Improvements to NOAA’s Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006). Journal of Climate, 21, 2283-2296. doi:10.1175/2007JCLI2100.1
• Snedecor, G.W., Cochran, W.G. (1989). Statistical methods, Ames, Iowa State University Press (8th edition), Iowa, USA.
• Trigo, R.M., Osborn, T.J., Corte-Real, J.M. (2002). The North Atlantic Oscillation influence on Europe. Climate impacts and associated physical mechanisms. Climate Research, 20, 9-17. doi:10.3354/cr020009
• Trigo, R.M., Pozo-Vázquez, D., Osborn, T.J., Castro-Díez, Y., Gámiz-Fortis, S., Esteban-Parra, M.J. (2004). North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula. International Journal of Climatology, 24(8), 925-944. doi:10.1002/joc.1048
• Trigo, R., Xoplaki, E., Zorita, E., Luterbacher, J., Krichak, S.O., Alpert, P., Jacobeit, J., Sáenz, J., Fernández, J., González-Rouco, F., García-Herrera, R., Rodo, X., Brunetti, M., Nanni, T., Maugeri, M., Trkes, M., Gimeno, L., Ribera, P., Brunet, M., Trigo, I.F., Crepon, M., Mariotti, A. (2006). Relations between Variability in the Mediterranean region and mid-latitude variability. In: Mediterranean Climate Variability, edited by: Lionello P., Malanotte-Rizzoli P., e R. Boscolo. Amsterdam, Elsevier, 179-226. doi:10.1016/s1571-9197(06)80006-6
• Vicente-Serrano, S.M., López-Moreno, J.I., Lorenzo-Lacruz, J., El Kenawy, A., Azorin-Molina, C., Morán-Tejeda, E., Pasho, E., Zabalza, J., Beguería, S., Angulo-Martínez, M. (2011). The NAO impact on droughts in the Mediterranean region. In: VicenteSerrano S.M. e Trigo R. (Eds.), Hydrological, socioeconomic and ecological impacts of the North Atlantic Oscillation in the Mediterranean region. Advances in Global Research (AGLO) series, Springer-Verlag. doi:10.1007/978-94-007-1372-7_3
• Vinther, B.M., Andersen, K.K., Hansen, A.W., Schmith, T., Jones, P.D. (2003). Improving the Gibraltar/Reykjavik NAO Index. Geophysical Research Letters, 30(23), 2222. doi:10.1029/2003GL018220
• Xoplaki E., González-Rouco J.F., Luterbacher J. e H. Wanner, (2004). Wet season Mediterranean precipitation variability: influence of large-scale dynamics and predictability. Climate Dynamiques 23, 63–78. https://doi.org/10.1007/s00382-004-0422-0
• Xue, Y., Smith, T.M., Reynolds, R.W. (2003). Interdecadal changes of 30-yr SST normals during 1871-2000. Journal of Climate, 16, 1601-1612. doi:10.1175/1520-0442-16.10.1601
• Yevjevich, V. (1972). Stochastic Processes in Hydrology. Water Resources Publications, Fort Collins, Co.