Extreme Runoff Estimation for Ungauged Watersheds Using a New Multisite Multivariate Stochastic Model MASVC

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.

Automated summary

This paper proposes a multisite multivariate model to predict daily-scale precipitation, using stochastic models to generate maximum precipitation with different return periods. The modeling process consists of three phases, including estimating precipitation occurrence with a two-state multivariate Markov model and estimating precipitation amount through normalization and generation of synthetic series. Compared to other methods, the use of probability density functions in this study requires less data and provides greater reliability. The consistency of maximum surface runoff for different observed return periods allows for more accurate estimation. Our approach enhances the use of stochastic models in generating synthetic series with spatial and temporal variability, while reducing the number of parameters required.