Utilidad de la precipitación obtenida por satélite en la modelación hidrológica aplicada a la cuenca del río Júcar

Autores/as

  • Lía Ramos Fernández Universidad Nacional Agraria La Molina (Perú).
  • Félix Francés García

DOI:

https://doi.org/10.21704/ac.v75i1.958

Palabras clave:

lluvia estimada de satélite, PERSIANN, modelo hidrológico distribuido, río Júcar.

Resumen

Los actuales modelos hidrológicos distribuidos permiten simular caudales no únicamente en la salida de una cuenca, sino en cualquier parte de la misma, pero la eficacia de estos modelos depende de la disponibilidad de los datos de entrada. Es así que la lluvia estimada de satélite a escala global, se adapta a estos modelos distribuidos ya que se tienen datos de lluvia para toda la cuenca. Sin embargo, debido a la multidimensionalidad del error de la lluvia estimada de satélite, es difícil establecer a priori un producto que permita una óptima aplicación hidrológica en diferentes condiciones climáticas; es por eso que se hace necesario evaluar su desempeño a través de la modelación hidrológica. En este estudio, se evalúa la utilidad de la lluvia estimada por satélite a través de un modelo hidrológico lluvia-escorrentía y se emplea la lluvia estimada por el algoritmo PERSIANN a una resolución temporal diaria y resolución espacial de 0,25º para el periodo comprendido entre el 1° de marzo del 2000 al 31 de octubre del 2009 en la cuenca del río Júcar (España), obteniéndose resultados prometedores. Resulta el mejor rendimiento del modelo en calibración con valores de 0,384 y 0,499 del índice de Nash-Sutcliffe a la salida de las subcuencas Pajaroncillo y Sueca.

Descargas

Los datos de descarga aún no están disponibles.

Referencias

Allen, R.; Pereira, L.; Raes, D. y Smith, M. 2006. Evapotranspiración del cultivo: Guías para la determinación de los requerimientos de agua de los cultivos. Roma, Italia: FAO.

Andersen, J.; Dybkjaer, G.; Jensen, K.H. 2002. Use of remotely sensed precipitation and leaf area index in a distributed hydrological model. Journal of Hydrology, 264: 34-50.

Aonashi, K.; Awaka, J. e Hirose, M. 2009. GSMaP passive microwave precipitation retrieval algorithm: Algorithm description and validation. Journal of the Meteorological Society of Japan, 87A:119-136.

Asante, K.O.; Macuacua, R.D. y Artan, G.A. 2007. Developing a flood monitoring system from remotely sensed data for the Limpopo Basin. IEEE Transactions on Geoscience and Remote Sensing, 45 (6): 1709-1714.

Bardossy, A.; Gafurov, A. y Gotzinger, J. 2006. Hydrological modelling for meso-scale catchments using globally available data. Hydrology and Earth System Sciences, 4: 2209-2242.

Biftu, G.F. y Gan, T.Y. 2001. Semi-distributed, physically based, hydrologic modelling of the Paddle River Basin, Alberta, using remotely sensed data. Journal of Hydrology, 244(3-4): 137-156.

Boegh, E.; Thorsen, M. y Butts, M.B. 2004. Incorporating remote sensing data in physically based distributed agro-hydrological modelling. Journal of Hydrology, 287 (1-4): 279-299.

Chen, J.M.; Chen, X.Y. y Ju, W.M. 2005. Distributed hydrological model for mapping evapotranspiration using remote sensing inputs. Journal of Hydrology, 305 (1-4): 15-39.

Dinku, T.; Funk, C. y Grimes, D. 2009. The potential of satellite rainfall estimates for index insurance. International Research Institute for Climate and Society (IRI). The Earth Institute at Columbia University.

Engman, E.T. 1995. Recent advances in remote sensing in hydrology. Reviews of Geophysics. American Geophysical Union.

Francés, F.; Vélez, J.I. y Vélez, J.J. 2007. Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology, 332: 226-240.

Gochis, D. J.; Shuttleworth, W.J. y Yang, Z.L. 2002. Sensitivity of the modelled North American Monsoon regional climate to convective parameterization. Monthly Weather Review, 130: 1282-1298.

Goncalves, G.L.; Shuttleworth, W.J. y Nijssen, B. 2006. Evaluation of model-derived and remotely sensed precipitation products for continental South America. Journal of Geophysical Research. 111 D161113.

Gottschalck, J.; Meng, J. y Rodell, M. 2005. Analysis of multiple precipitation products and preliminary assessment of their impact on Global Land Data Assimilation System (GLDAS) land surface states. Journal of Hydrometeorology, 6 (5): 573-598.

Grimes, D.I.F. y Diop, M. 2003. Satellite-based rainfall estimation for river flow forecasting in Africa. I: Rainfall estimates and hydrological forecasts. Hydrological Sciences Journal des Sciences Hydrologiques, 48 (4): 567-584.

Guevara, J.M. 2002. Precipitation estimation over Mexico applying PERSIANN system and gauge data. (Master Thesis). University of Arizona.

Hansen, J.E.; Ruedy, R. y Sato, M. 2009. NASA GISS Surface Temperature (GISTEMP) Analysis. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Ten n., U.S.A.: Center for Climate Systems Research, NASA Goddard Institute for Space Studies.

Hong, Y.; Hsu, K. y Gao, X. 2004. Precipitation estimation from remotely sensed imagery using artificial neural network-cloud classification system. Journal of Applied Meteorology, 43(12): 1834-1853.

Hong, Y.; Hsu, K.y Sorooshian, S. 2005. Enhanced signal of diurnal variability of rainfall retrieval from TRMM-adjusted PERSIANN algorithm. Journal of Geophysical Research, 110 - D06102.

Hsu, K.; Soroosh, S. y Gupta, H.V. 2002. Hydrologic Modelling and Analysis Using a Self-Organizing Linear Output Network. Switzerland: The International Environmental Modelling & Software Society i. iEMSs.

Hsu, K. y Sorooshian, S. 2008. Satellite-Based Precipitation Measurement Using PERSIANN System. In: Sorooshian, S., Hsu, K., Coppola, E. (Editors), Hydrological Modelling and the Water Cycle. Water Science and Technology Library.

Huffman, G.J.; Adler, R.F. y Morrissey, M.M. 2001. Global precipitation at one-degree daily resolution from multisatellite observations. Journal of Hydrometeorology, 2: 36-50.

Huffman, G.J.; Adler, R.F. y Stocker, E.F. 2002. A TRMM-based system for real-time quasi-global merged precipitation estimates.

Hughes, D.A. 2006. Comparison of satellite rainfall data with observations from gauging station networks. Journal of Hydrology, 327: 399-410.

Joyce, R.J., Janowiak, J.E. y Arkin, P.A. 2004. CMORPH: A Method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5: 487-503.

Khakbaz, B.; Imam, B. y Hsu, K. 2009. From lumped to distribute via semi-distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models. Journal of Hydrology.

Kidd, C.; Kniveton, D.R. y Todd, M.C. 2003. Satellite rainfall estimation using combined passive microwave and infrared algorithms. Journal of Hydrometeorology, 4(1088): 1104.

Li, J.; Gao, X. y Maddox, R.A. 2003. Summer weather simulation for the semi-arid lower Colorado River basin. Monthly Weather Review, 131(3): 521-541.

Moradkhani, H.; Hsu, K. y Hong, Y. 2006. Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting. Geophysical Research Letters, 33, L12401.

Sorooshian, S.; Gao, X. y Hsu, K. 2002. Diurnal variability of tropical rainfall retrieved from combined GOES and TRM satellite information. Journal of Climate, 15: 983-1001.

Sorooshian, S.; Hsu, K. y Bisher, I. 2005. Global Precipitation Estimation from Satellite Image using Artificial Neural Networks. International G-WADI Modelling Workshop. India: National Institute of Hydrology.

Sorooshian, S.; Hsu, K. y Gao, X. 2000. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bulletin of American Meteorology Society, 81: 2035-2046.

Stisen, S.; Jensen, K.H. y Sandholt, I. 2008. A remote sensing driven distributed hydrological model of the Senegal River basin. Journal of Hydrology, 354: 131-148.

Tapiador, F.J. 2002. A new algorithm to generate global rainfall rates from satellite infrared imagery. Revista de Teledetección de España. AET. 18: 57-61.

Turk, J.; Ebert, E. y Oh, H.J. 2002. Verification of an operational global precipitation analysis at short time scales. Madrid, Spain: 1st Intl. Precipitation Working Group (IPWG) Workshop.

Vélez, J.J.; Puricelli, M.; López, F. y Francés, F. 2009. Parameter extrapolation to ungauged basins with a hydrological distributed model in a regional framework. Hydrol. Earth Syst. Sci, 13: 229-246.

Wagner, S.; Kunstmann, H. y Bárdossy, A. 2009. Water balance estimation of a poorly gauged catchment in West Africa using dynamically downscaled meteorological fields and remote sensing information. Physics and Chemistry of the Earth, 34: 225-235.

Wardah, T.; Bakar, S.H. y Bardossy, A. 2008. Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting. Journal of Hydrology, 356: 283-298.

Xu, J.; Gao, X. y Sorooshian, S. 2004. Impact of radar derived and satellite derived rainfall assimilation on the rainfall predict in the southwest United States. Tollus.

Yi, H. 2002. Assimilation of satellite-derived precipitation into the regional atmospheric modelling system (RAMS) and its impact on the weather and hydrology in the Southwest United States. (Ph.D. Dissertation). Department of Hydrologic and Water Resources, University of Arizona.

Yucel, I.; Shuttleworth, W.J. y Pinker, R.T. 2002. Impact of ingesting satellite-derived cloud into the regional atmospheric modelling system. Monthly Weather Review, 13: 610-628.

Descargas

Publicado

2014-06-30

Número

Sección

Artículos originales / Ciencias medioambientales

Cómo citar

Ramos Fernández, L., & Francés García, F. (2014). Utilidad de la precipitación obtenida por satélite en la modelación hidrológica aplicada a la cuenca del río Júcar. Anales Científicos, 75(1), 80-89. https://doi.org/10.21704/ac.v75i1.958