A stochastic model of continuous hourly areal rainfall series applied to a wide range of French catchments

This study introduces a stochastic continuous hourly areal rainfall model designed to simulate rainfall time series for hydrological risk management. The rainfall model, named SCHYPRE (Simulation of Continuous HYetographs for Predictive Risk Estimation), extends an established event-based rainfall model to basin-scale applications, integrating both extreme event modeling and continuous simulation of seasonal and long-duration rainfall patterns. The rainfall model was calibrated using 28.5 years of rainfall data set with hourly and kilometric resolution across 2,108 catchments in France, covering diverse climatic regimes from continental to Mediterranean and mountainous. The evaluation framework demonstrates rainfall model’s ability to faithfully reproduce observed rainfall statistics, including mean and extreme values, seasonality, autocorrelation, and intermittency. Frequency analyses conducted over durations from one hour to one year show strong agreement between the simulations and the adapted law, with only limited bias in the estimation of extreme values. A major advantage of rainfall modelling is its robustness in estimating extreme quantiles. Unlike traditional probabilistic methods, which are more sensitive to sampling variability, the rainfall model’s Monte Carlo approach, calibrated on large observational datasets of interne variables, ensures stable quantile estimation across all return periods, including extremes. Additionally, rainfall modelling inherently avoids quantile crossing inconsistencies, a common issue in independent duration-based probabilistic modeling.