Tendencias actuales en el modelado de la precipitación diaria

José Roldán Cañas

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Ingeniería del agua; ingeniería civil; ingeniería hidráulica

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Referencias

Akaike, H. 1.974. A new look at the statistical model identification. IEEE Trans. Autom. Control, 19(6):716-723. https://doi.org/10.1109/TAC.1974.1100705

Alcaide, M. 1.986. Análisis estacional v regional de la precipitación en el valle del Guadalquivir.Tesis Doctoral, Universidad de Córdoba, 249 pp.

Alcaide, M.; J. Roldan y A. Losada. 1.986. Análisis estacional y regional de la precipitación en el valle del Guadalquivir. Comunicaciones a las V Jornadas Técnicas sobre Riegos, Málaga, pp. 3-19.

Artacho, C. 1.991. Caracterización estocástica de las principales variables meteorológicas que influven en la evapotranspiración. TesisDoctoral, Universidad de Córdoba.

Artacho, C.; J. Roldán; A. García Guzmán y M. Alcaide. 1.989. Modelado de la temperatura y humedad relativa en Andalucía Occidental. Riegos y Drenajes XXI, 28:32-34.

Buishand, T.A. 1.977. Stochastic modeling of daily rainfa11 sequencs. MededeIingen Landbouwhogeschoul, Rep. 77-3, Wageningen, 211 pp.

Carey, D.A. y C.T. Haan. 1.978. Markov process for simulating daily point rainfall. J. of Irrig. and Drain. Div., 104(1): 111-125.

Carrera, J. y J. Samper. 1.985. Métodos geoestadísticos aplicados a la hidrología subterránea. Universidad Politécnica de Valencia, Valencia.

Chin, E.H. 1.977. Modeling daily precipitación occurrence process with Markov chain. Water Resour. Res., 13(6):949-956. https://doi.org/10.1029/WR013i006p00949

Coe, R. y R.D. Stern. 1.982. Fitting models to daily rainfall data. J. Appl. Meteorol., 21:1024-1031. https://doi.org/10.1175/1520-0450(1982)021<1024:FMTDRD>2.0.CO;2

Creutin, J.D. y C. Obled. 1.982. Objective analyses and mapping techniques for rainfall fields: an objective comparison. Water Resour. Res., 18(2):413-431. https://doi.org/10.1029/WR018i002p00413

Dean, J.D. y W.M. Snyder. 1.977. Temporally and areally distributed rainfall. J. of the Irrig. and Drain. Div., 103(2):221-229.

Eidsvik, K.J. 1.980. Identification of models for some time series of atmospheric origin with Akaike's information criterion. J of Appl. Meteorol., 19(4):357-369. https://doi.org/10.1175/1520-0450(1980)019<0357:IOMFST>2.0.CO;2

Enfield, D.B. 1.989. El Niño. Past and present. Rev.Geophys., 21:159-187. https://doi.org/10.1029/RG027i001p00159

Feyerherm, A.M. y L.D. Bark. 1.965. Statistical methods for persisten! precipitation patterns. J. Appl. Meteor., 4:320-328. https://doi.org/10.1175/1520-0450(1965)004<0320:SMFPPP>2.0.CO;2

Feyerherm, A.M.; L.D. Bark y W.C. Burrows. 1.965. Probabilities of sequences of wet and dry days in South Dakota. North Central Reg. Res. Publ. 161, Kansas Satate Univ., Manhattan.

Fletcher, R. 1.972. Fortran subroutines for minimization by quasi-Newton methods. Rep. AERE-R7125, Atomic Energy Research Establishment, Harwell, Gran Bretaña.

Foufoula-Georgiou, E. y D.P. Lettenmaier. 1.987. A Markov renewal model for rainfall occurrences. Water Resour. Res., 23(5):875-884. https://doi.org/10.1029/WR023i005p00875

Foufoula-Georgiou, E. y K.P. Georgakakos. 1.991. Hydrologic advances in space-time precipitation modelling and forecasting. En: Recent advances in the modeling of hydrologic systems, D.S. Bowles y P.E. O’Connell (eds.), Kluwer Academic Publishers, Dordrecht, Holanda, pp. 47-65. https://doi.org/10.1007/978-94-011-3480-4_3

Frasier, G.W.; J.R. Cox y D.A. Woolhiser. 1.987. Wet-dry cycle effects on warm-season grass seedling establishment. J. of Range Management, 40(l):2-6. https://doi.org/10.2307/3899350

Gabriel, K.R. y J. Neuman. 1.962. A Markov chain model for daily rainfall in Tel-Aviv. Quart. J. Roy. Meteor. Soc., 88:90-95. https://doi.org/10.1002/qj.49708837511

García Guzmán, A. 1.988. La lluvia: formación v distribución (2a parte). Apuntes del Curso de Doctorado Hidrología, E. T.S. I. Agrónomos, Universidad de Córdoba.

García Guzmán, A. y W.C. Torrez. 1.985. Daily rainfall probabilities: conditional upon prior occurrence and amount of rain. J. of Clim. and Appl. Meteor., 24(10): 10009-1014. https://doi.org/10.1175/1520-0450(1985)024<1009:DRPCUP>2.0.CO;2

García Guzmán, A.; J. Roldán y A. Losada. 1.981. Caracterización del régimen de lluvias en Córdoba. I Simposio sobre el Agua en Andalucía, Granada, Vol. 1:31-45.

Haan, C.T. 1.972. A water yield model for small watersheds. Water Resour. Res., 8(l):58-68. https://doi.org/10.1029/WR008i001p00058

Haan, C.T.; D.M. Alien y J.O. Street. 1.976. A Markov chain model for daily rainfall. Water Resour. Res., 12(3):443-449. https://doi.org/10.1029/WR012i003p00443

Hanson, C.L.; H.B. Osborn y D.A. Woolhiser. 1.989. Daily precipitation simulation model for mountainous areas. Trans. ASAE, 32(3):865-873.

Hocl, P.G. 1.971. Introduction to mathematical statistics. J. Wiley, New York.

Hubbard, K.G. y D.A. Willhitc. 1.987. A demonstraron and evaluation of the use ofclimate information to support irrigation scheduling and other agricultural operations. Rcport 87-4, Center for Agricultural Meteorology and Climatology, Univ. of Nebraska, Lincoln.

Ibáñez, V. 1.982. Elaboración de una metodología para estimar la persistencia de las precipitaciones. Su aplicación al cultivo del arroz en Levante.Tesis Doctoral. Universidad Politécnica de Madrid, 269 pp.

Insúa, F.; A. García Guzmán y J. Roldán. 1.981. Influencia del régimen pluviométrico sobre las labores agrícolas. J Simposio sobre el Agua en Andalucía, Vol. 1:63-76, Granada.

Ison, N.T.; A.M. Feyerherm y L.D. Bark. 1.971. Wet period precipitation and the gamma distribution. J. of Appl Meteor., 10(4):658-665. https://doi.org/10.1175/1520-0450(1971)010<0658:WPPATG>2.0.CO;2

Jensen, M.E. 1.972. Programming irrigation forgreater effíciency. En: Optimizing the soil physical environment toward greater crop yields, D. Hillel (ed.), Academic Press, New York, pp. 113-162.

Katz, R.W. 1.977a. Precipitation as a chain-dependent process. J. Appl. Meteor., 16(7):671-676. https://doi.org/10.1175/1520-0450(1977)016<0671:PAACDP>2.0.CO;2

Katz, R.W. 1.977b. An application of chain-dependent processes to meteorology. J. Appl. Prob., 14:598-603. https://doi.org/10.1017/S0021900200025845

Katz, R.W. 1.979. Parsimony in modeling daily precipitation. Water Resour. Res., 15(6):1628-1630. https://doi.org/10.1029/WR015i006p01628

Katz, R.W. 1.981. On some criteria for estimating the order of a Markov chain. Technometrics, 27(3):243-249. https://doi.org/10.2307/1267787

Katz, R.W. 1.984. Discusión del artículo de Stern y Coe. J. R. Statist. Soc., A, 147, parte 1:29.

Kavvas, M.L. y K.R. Herd. 1.985. A radar based stochastic model for short time increment rainfall. Water Resour. Res., 21(9): 1437-1455. https://doi.org/10.1029/WR021i009p01437

Lancaster, P. y K. Salkaustras. 1.986. Curve and surface fitting. An introduction. Academic Press, Londres.

Lomas, J. 1.972. Economic significance of dry-land farming in the arid northern Negev of Israel.Agrie. Meteor., 10:383-392. https://doi.org/10.1016/0002-1571(72)90039-8

Lund, LA. y D.A. Granthan. 1.977. Persistence, runs and reeurrence of precipitation. J. Appl. Meteor., 16:346-358. https://doi.org/10.1175/1520-0450(1977)016<0346:PRAROP>2.0.CO;2

Mielke, P.W. 1.973. Another family of distributions for describing and analyzing precipitation data. J. of Appl. Meteor., 10(2):275-280. https://doi.org/10.1175/1520-0450(1973)012<0275:AFODFD>2.0.CO;2

Mielke, P.W. y E.S. Johnson. 1.973. Three-parameter kappa distribution maximum likelihood estimates and likelihood ratio tests. Mon.Weather Rev., 101(9):701-707. https://doi.org/10.1175/1520-0493(1973)101<0701:TKDMLE>2.3.CO;2

Moreno, F.; D.A. Woolhiser y J. Roldán. Regionalización de parámetros en modelos estocásticos de precipitación diaria. En preparación.

Nelder, J.A. y R. Mead. 1.965. A simplex method for function minimization. Compu. J., 7(4):308-313. https://doi.org/10.1093/comjnl/7.4.308

Newnhan, E.V. 1.916. The persistence of wet and dry weather. Quart. J. Roy. Met. Soc., 42:153-162. https://doi.org/10.1002/qj.49704217903

Palmer, W.L.; B.J. Barfield y C.T. Haan. 1.982. Sizing farm reservoirs for’supplemental irrigation of corn. Part I: Modeling reservoir size yield relationships. Trans. ASAE, 25:372-376.

Richardson, C.W. y D.A. Wright. 1.984. WGEN: A model for generating daily weather variables. U.S.D.A., Agrie. Res. Serv., ARS-8: l-83.

Pérez Lucena, J. 1.994. Modelo estocástico de la precipitación diaria. Aplicación a las estaciones de Córdoba v Sevilla.Trabajo Fin de Carrera, E.T.S.I.-Agrónomos y de Montes, Universidad de Córdoba, 151 pp.

Pérez Lucena, J.; J. Roldán; M. Alcaide y D.A. Woolhiser. 1.994. Techniques to improve the fit on stochastic rainfall models in southwestern Spain. Presentado a la XIX General Assembly of the European Geophysical Society, 25-29 abril, Grenoble, Francia.

Roldán, J. 1.979. Caracterización del régimen hidrológico de una región: sistemas de vientos y lluvias. Tesis Doctoral, Universidad de Córdoba, 216 pp.

Roldán, J. y D.A. Woolhiser. 1.982. Stochastic daily precipitation models. 1. A comparison of occurrence processes. Water Resour. Res., 18(5): 1451-1459. https://doi.org/10.1029/WR018i005p01451

Roldán, J.; A. García Guzmán y A. Losada. 1.982. A stochastic model for wind occurrence. J. of Appl. Meteor., 21(5):740-744. https://doi.org/10.1175/1520-0450(1982)021<0740:ASMFWO>2.0.CO;2

Roldán, J.; M. Alcaide; A. Laguna y J.V. Giráldez. 1.989. Mapping techniques for rainfall fields. En: Application of computerizad EC soil map and climate data. EUR 12039, Commission of the European Communitics, Luxemburgo, pp. 167-176.

Schwarz, G. 1.978. Estimating the dimensión of a model.Annals of Statixt., 6:461-464. https://doi.org/10.1214/aos/1176344136

Shaw, R.H.; G.L. Barger y R.F. Dale. 1.960. Precipitation probabilities in the North Central States. Bull. 753, Missouri Agrie. Exp. Sta., Columbia, Mo.

Smith, R.E. y H.A. Schreiber. 1.973. Point process of seasonal thunderstorm rainfall. 1. Distribution of rainfall events. Water Resour. Res., 9(4):871-884. https://doi.org/10.1029/WR009i004p00871

Smith, R.E. y H.A. Schreiber. 1.974. Point process of seasonal thunderstorm rainfall. 2. Rainfall depth probabilities. Water Resour. Res., 10(3):418-423. https://doi.org/10.1029/WR010i003p00418

Stern, R.D. 1.980a. Analysis of daily rainfall at Samaru, Nigeria, using a simple two-part model. Arch. Meteorol. Geophys. Bioklimatol, Ser. B, 28:123-135. https://doi.org/10.1007/BF02243840

Stern, R.D. 1.980b. The calculation of probability distributions of models of daily precipitation. Arch. Meteorol. Geophys. Bioklimatol, Ser. B, 28:137-147. https://doi.org/10.1007/BF02243841

Stern, R.D. y R. Coe. 1.984. A model fitting analysis of daily rainfall data. J. Roy. Statist. Soc., A, 147, parte 1:1-34. https://doi.org/10.2307/2981736

Todorovic, P. y D.A. Woolhiser. 1.974. Stochastic model of daily rainfall. U.S.D.A. Mise. Publ 1215:232-246.

Todorovic, P. y D.A. Woolhiser. 1.975. Stochastic model of n-day precipitation. J. Appl. Meteor., 14(1): 17-24. https://doi.org/10.1175/1520-0450(1975)014<0017:ASMODP>2.0.CO;2

Villalobos, F.J. y E. Fereres. 1.989. A simulation model for irrigation scheduling under variable rainfall. Trans. ASAE, 32(1): 181-188.

Welch, S.M. 1.984. Developments in computer-based IPM extensión delivery systems. Ann. Rev. of Entomology, 29:359-381. https://doi.org/10.1146/annurev.en.29.010184.002043

Williams, J.R. y K.G. Renard. 1.985. Assesments of soil erosion and crop productivity with process models (EPIC). Cap. 5. En: Soil erosion and crop productivity, R.F. Follett y B.A. Stewart (eds.), American Society of Agronomy y Crop Science Society of America, Madison, WI, pp. 67-103.

Woolhiser, D.A. 1.992. Modeling daily precipitation. Progress and problems. En: Statistics in the environmental and earth sciences, A. Walden y P. Guttorp (eds.), Edward Arnold, Londres, pp. 71-89.

Woolhiser, D.A. y G.G.S. Pegram. 1.979. Maximum likelihood estimation of Fourier coefficients to describe seasonal variations of parameters in sto-chastic daily precipitation models. J. Appl. Meteo-rol., 18:34-42. https://doi.org/10.1175/1520-0450(1979)018<0034:MLEOFC>2.0.CO;2

Woolhiser, D.A. y J. Roldán. 1.982. Stochastic daily precipitation models. 2. A comparison of distribution of amounts. ater Resour. Res., 18(5):1461-1468. https://doi.org/10.1029/WR018i005p01461

Woolhiser, D.A. y J. Roldán. 1.986. Seasonal and regional variability of parameters in Stochastic daily precipitation models: South Dakota,U.S.A. Water Resour. Res., 22(6):965-978. https://doi.org/10.1029/WR022i006p00965

Woolhiser, D.A.; E.W. Rovey y P. Todorovic. 1.973. Temporal and spatial variation of parameters for the distribution of n-day precipitation. En: Floods and Droughts, Proceedings of the Second International Symposium in Hydrology, E. F. Schulz, V.A. Koelzer y K. Mahmood (eds.), Water Resources Publications, Fort Collins, Colorado, pp. 605-614.

Woolhiser, D.A.; C.L. Hanson y C.W. Richardson. 1.988. Microcomputer program for daily weather simulation. U.S.D.A., Agrie. Rea. Serv., ARS-75, 49 pp.

Woolhiser, D.A.; T.O. Keefer y K.T. Redmond. 1.993. Southern oscillation effects on daily precipitation in the southwestern U.S. Water Resour. Res., 29(4): 1287-1295. https://doi.org/10.1029/92WR02536

Zucchini, W. y P.T. Adamson. 1.984. The occurrence and severity of droughts in South Africa. Dept. of Civil Engineering, Univ. of Stellenbosch y ept. of Water Affairs, WRC Repon No.91/1/84, 198 pp.

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Ingeniería del agua  vol: 7  num.: 2  primera página: 117  año: 2000  
doi: 10.4995/ia.2000.2839



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