Resumen
Precipitation is influential in determining runoff at different scales of analysis, whether in minutes, hours, or days. This paper proposes the use of a multisite multivariate model of precipitation at a daily scale. Stochastic models allow the generation of maximum precipitation and its association with different return periods. The modeling is carried out in three phases. The first is the estimation of precipitation occurrence by using a two-state multivariate Markov model to calculate the non-rainfall periods. Once the rainfall periods of various storms have been identified, the amount of precipitation is estimated through a process of normalization, standardization of the series, acquisition of multivariate parameters, and generation of synthetic series. In comparison, the analysis applies probability density functions that require fewer data and, consequently, represent greater certainty. The maximum values of surface runoff show consistency for different observed return periods, therefore, a more reliable estimation of maximum surface runoff. Our approach enhances the use of stochastic models for generating synthetic series that preserve spatial and temporal variability at daily, monthly, annual, and extreme values. Moreover, the number of parameters reduces in comparison to other stochastic weather generators.