Author(s): J. Daniel E. Villarreal; C. Enrique A. Moreno; L. Juan C. Salinas; E. Eliud R. Fernandez; P. David C. Lopez; F. Jose L. Bruster; F. Adrian L. Ferrino

Linked Author(s):

Keywords: Return periods; Uncertainty; Precipitation; Discharge; ECMWF reforecast

Abstract: Estimation of return periods of precipitation and discharge involves huge uncertainties due to the quantity and quality of available records. A methodology to generate synthetic time series with longer length than the observed records and estimate return periods with less uncertainty is used. It is performed using different lead time periods from the ECMWF reforecasts data with the objective to determine the best lead times to be used in these type of analysis. For discharge, a continuous hydrological model with good representation of the rainfall-runoff process is used. This methodology is applied in a case study in the Rio La Silla, Monterrey, Mexico. Results showed that the most suitable lead times to generate synthetic time series are from 5 to 14 days. At smaller lead times, correlation between members is higher. At larger lead times, intensity of extreme events seems to be underestimated. Return periods estimated from synthetic time series are smaller, but show a similar behavior and are within the uncertainty range to those obtained with observed data. Uncertainty was reduced on average by 1.6 to 7.4 times, and for a return period of 1000 years, from 38% to 86%, depending on the variable, lead time and return period.

DOI: https://doi.org/10.3850/978-90-833476-1-5_iahr40wc-p0588-cd

Year: 2023