Skew-normal distribution model for rainfall uncertainty estimation in a distributed hydrological model

References

• Allen, R.G., et al., 1998. Crop evapotranspiration: guidelines for computing crop requirements. Irrigation and Drainage Paper No. 56, FAO.
   Google Scholar
• Allen, R.G., 2000. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. Journal of Hydrology, 229 (1–2), 27–41. doi:10.1016/S0022-1694(99)00194-8
   Web of Science ®Google Scholar
• Andreassian, V., et al., 2001. Impact of imperfect rainfall knowledge on the efficiency and the parameters of watershed models. Journal of Hydrology, 250 (1–4), 206–223. doi:10.1016/S0022-1694(01)00437-1
   Web of Science ®Google Scholar
• Arellano-Valle, R.B., Bolfarine, H., and Lachos, V.H., 2005. Skew-normal Linear Mixed Models. Journal of Data Science, 3, 415–438.
   Google Scholar
• Azzalini, A., 1985. A class of distributions, which includes the normal ones. Scandinavian Journal of Statistics, 12 (2), 171–178.
   Web of Science ®Google Scholar
• Azzalini, A. and Capitanio, A., 1999. Statistical applications of the multivariate skew normal distribution. Journal of the Royal Statistical Society: Series B, 61 (3), 579–602. doi:10.1111/1467-9868.00194
   Google Scholar
• Azzalini, A. and Dalla Valle, A., 1996. The multivariate skew-normal distribution. Biometrika, 83 (4), 715–726. doi:10.1093/biomet/83.4.715
   Web of Science ®Google Scholar
• Bárdossy, A. and Das, T., 2008. Influence of rainfall observation network on model calibration and application. Hydrology and Earth System Sciences, 12 (1), 77–89. doi:10.5194/hess-12-77-2008
   Web of Science ®Google Scholar
• Biemans, H., et al., 2009. Effects of precipitation uncertainty on discharge calculations for main river basins. Journal of Hydrometeorology, 10 (4), 1011–1025. doi:10.1175/2008JHM1067.1
   Web of Science ®Google Scholar
• Castro, L.M., 2008. Distribución Skewnormal: identificabilidad, Reducción y Enfoques Bayesianos de Mezclas. PhD Thesis. Universidad Católica de Chile.
   Google Scholar
• Chilton, J., 1996. Chapter 9 - groundwater, and D. Chapman, ed. Water quality assessments. A guide to use of biota, sediments and water in environmental monitoring. Vol. 5. London: E&FN Spon, 651.
   Google Scholar
• Clark, M.P., et al., 2008. Framework for understanding structural errors (FUSE): a modular framework to diagnose differences between hydrological models. Water Resources Research, 44 (12). doi:10.1029/2007WR006735
   Web of Science ®Google Scholar
• CORTOLIMA, 2007. Land use and land cover study for department of Tolima. Colombia: Corporación Autónoma Regional del Tolima, Ibagué, Colombia.
   Google Scholar
• Cuo, L., et al., 2013. The impacts of climate change and land cover/use transition on the hydrology in the upper Yellow River Basin, China. Journal of Hydrology, 502, 37–52. doi:10.1016/j.jhydrol.2013.08.003
   Web of Science ®Google Scholar
• Daza, M.C., Hernandez, F., and Triana, F.A., 2009. Efecto de actividades agropecuarias en la capacidad de infiltración de los suelos del páramo del sumapaz. Ingeniería de Recursos Naturales y del Ambiente, 8 (8), 29–39.
   Google Scholar
• FAO. (1998). Irrigation and drainage paper No. 56. Crop evapotranspiration (guidelines for computing crop water requirements). FAO.
   Google Scholar
• Fraga, I., Cea, L., and Puertas, J., 2019. Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models. Hydrological Processes, 33 (1), 160–173. doi:10.1002/hyp.13319
   Web of Science ®Google Scholar
• Francés, F., Vélez, J.I., and Vélez, J.J., 2007. Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology, 332 (1–2), 226–240. doi:10.1016/j.jhydrol.2006.06.032
   Web of Science ®Google Scholar
• Geotec, 2012. Estudio de amenazas naturales, vulnerabilidad y escenarios de riesgo en los centros poblados de Villarestrepo, Llanitos, Juntas, Pastales, Pico de Oro, Bocatoma Combeima y Cay, por flujos torrenciales en las microcuencas del Río Combeima. Alcaldía de Ibagué, Cortolima. Ibagué, Colombia: Consorcio Geotec Group.
   Google Scholar
• Goetzinger, J. and Bárdossy, A., 2008. Generic error model for calibration and uncertainty estimation of hydrological models. Water Resources Research, 44, W00B07.
   Web of Science ®Google Scholar
• Gupta, H.V., Beven, K., and Wagener, T., 2005. Model calibration and uncertainty estimation. In: M.G. Anderson and J.J. McDonnell, eds. Encyclopedia of hydrological sciences. Chichester, Reino Unido: John Wiley & Sons.
   Google Scholar
• IGAC, 2008. Estudio general de suelos y zonificación de tierras del departamento del Tolima. Instituto Geográfico Agustín Codazzi. Bogotá, Colombia: IGAC.
   Google Scholar
• Kavetski, D., Franks, S., and Kuczera, G., 2003. Confronting input uncertainty in environmental modelling. In: Q. Duan, et al., eds. Calibration of watershed models. Water Science and Applications Series. Washington D.C.: AGU, 49–68.
   Google Scholar
• Kavetski, D., Kuczera, G., and Franks, S.W., 2006a. Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42 (3), W03407.
   Web of Science ®Google Scholar
• Kavetski, D., Kuczera, G., and Franks, S.W., 2006b. Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water Resources Research, 42 (3), W03408.
   Web of Science ®Google Scholar
• Kheradmandi, A., and Rasekh, A., 2005. Estimation in skew-normal linear mixed measurement error models. Journal of Multivariate Analysis, 136, 1–11.
   Google Scholar
• Liu, Y. and Gupta, H.V., 2007. Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resources Research, 43 (7), 1–18. doi:10.1029/2006WR005756
   PubMed Web of Science ®Google Scholar
• Liu, Y. and Luo, Y., 2010. A consolidated evaluation of the FAO-56 dual crop coefficient approach using the lysimeter data in the North China Plain. Agricultural Water Management, 97 (1), 31–40. doi:10.1016/j.agwat.2009.07.003
   Web of Science ®Google Scholar
• Montanari, A. and Grossi, G., 2008. Estimating the uncertainty of hydrological forecasts: a statistical approach. Water Resources Research, 44 (12), W00B08. doi:10.1029/2008WR006897
   Web of Science ®Google Scholar
• Montoya, J.J., 2008. Desarrollo de un modelo conceptual de producción, transporte y depósito de sedimentos Tesis doctoral no publicada. Universitat Politècnica de València. doi:10.4995/Thesis/10251/8303
   Google Scholar
• Moradkhani, H., et al., 2006. Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting. Geophysical Research Letters, 33 (12). doi:10.1029/2006GL026855
   PubMed Web of Science ®Google Scholar
• Moradkhani, H., and Sorooshian, S., 2008. General Review of Rainfall-Runoff Modeling: Model Calibration, Data Assimilation, and Uncertainty Analysis. In: S. Sorooshian, K.-L. Hsu, E. Coppola, B. Tomassetti, M. Verdecchia, and G. Vischonti, eds. Hydrological Modelling and the Water Cycle: Coupling the Atmospheric and Hydrological Models. Vol. 63. Berlin Heidelberg: Springer.
   Google Scholar
• NASA, 2005. Shuttle radar topography mission: the mission to map the world, available from Jet Propulsion Laboratory, California Institute of Technology. Available from: http://www2.jpl.nasa.gov/srtm/spanish.htm
   Google Scholar
• Pappenberger, F., et al., 2005. Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS). Hydrology and Earth System Sciences, 9 (4), 381–393. doi:10.5194/hess-9-381-2005
   Web of Science ®Google Scholar
• Renard, B., et al., 2011. Toward a reliable decomposition of predictive uncertainty in hydrological modeling: characterizing rainfall errors using conditional simulation. Water Resources Research, 47 (11), W11516. doi:10.1029/2011WR010643
   Web of Science ®Google Scholar
• Rodrigues, J., 2006. A Bayesian inference for the extended skew-normal measurement error model. Brazilian Journal of Probability and Statistics, 20, 179–190.
   Google Scholar
• Saxton, K.E. and Rawls, W.J., 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal, 70 (5), 1569–1578. doi:10.2136/sssaj2005.0117
   Web of Science ®Google Scholar
• Steduto, P., et al, 2012. Crop yield response to water. Report 66. Rome.
   Google Scholar
• Vélez, J.J., 2003. Desarrollo de un modelo distribuido de predicción en tiempo real para eventos de crecidas. PhD Tesis. Universidad Politécnica de Valencia.
   Google Scholar
• Wong, L.S., Hashim, R., and Ali, F.H., 2009. A review on hydraulic conductivity and compressibility of peat. Journal of Applied Sciences, 9 (18), 3207–3218. doi:10.3923/jas.2009.3207.3218
   Google Scholar
• Younger, P.M., Freer, J.E., and Beven, K.J., 2009. Detecting the effects of spatial variability of rainfall on hydrological modelling within an uncertainty analysis framework. Hydrological Processes, 23 (14), 1988–2003. doi:10.1002/hyp.7341
   Web of Science ®Google Scholar