HR: 1330h
AN: H12B-0968    [PDF]
TI: Bivariate Drought Characterization Using Nonparametric Approaches
AU: * Kim, T
EM: taek@email.arizona.edu
AF: The University of Arizona and SAHRA (Sustainability of semi-Arid Hydrology and Riparian Areas), Dept. of Civil Engineering, Tucson, AZ 85721-0072 United States
AU: Valdes, J B
EM: jvaldes@u.arizona.edu
AF: The University of Arizona and SAHRA (Sustainability of semi-Arid Hydrology and Riparian Areas), Dept. of Civil Engineering, Tucson, AZ 85721-0072 United States
AU: Yoo, C
EM: envchul@korea.ac.kr
AF: Korea University, Dept. of Civil and Environmental Engineering, Seoul, 136-701 Korea, Republic of
AU: Aparicio, F J
EM: japaricio@tlaloc.imta.mx
AF: Mexican Institute of Water Technology, Paseo Cuauhnahuac 8532, Jiuttepec, Mor 62550 Mexico
AB: Droughts cause significant damages both in natural environment and human society, especially, in a transboundary region, where sustainable water use and water right are one of main issues among countries and communities during droughts. Nonparametric approaches allow more flexibility in practice by better approximating the characteristics of the probability distribution of the records. This paper presents new development in nonparametric methods in which, using a kernel density estimator, a nonparametric random generation is proposed for synthetic generation of hydrologic time series. Based on the nonparametric probability density function estimator, comprehensive approaches for evaluation of drought characteristics at a site and over a region are presented. The nonparametric method using a kernel density estimator easily extends to the estimation of a drought probability density function in two dimensions. Based on the synthetically generated data from the nonparametric distribution, a methodology is introduced for estimating the bivariate characteristics of droughts. The proposed approach was applied to a catchment in the Lower Rio Bravo/Grande and the results compared satisfactory with several parametric approaches.
DE: 1812 Drought
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2003 Fall Meeting