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<p>This study introduces a stochastic continuous hourly areal rainfall model designed to simulate areal rainfall time series for hydrological risk management. The rainfall model, named SCHYPRE (Simulation of Continuous HYetographs for Predictive Risk Estimation), extends an established at-site event-based rainfall model to a basin-scale and continuous rainfall model, integrating both extreme event modeling and continuous simulation of seasonal and long-duration rainfall patterns. The rainfall model parameters were calibrated using a 28.5&#xa0;years dataset of hourly rainfall observations at a 1&#xa0;km resolution. This dataset enabled the computation of areal rainfall time series across 2108 catchments in France, encompassing a wide range of climatic regimes from continental and Mediterranean to mountainous environments. The evaluation framework demonstrates the rainfall model’s ability to reproduce observed areal rainfall statistics, including mean and extreme values, seasonality, autocorrelation, and intermittency of rainfall. Frequency analysis conducted over durations from one hour to one year shows good agreement between the simulations and the adapted law. An advantage of rainfall modeling is its robustness in estimating extreme return levels. Unlike traditional probabilistic methods, which are more sensitive to sampling variability, the stochastic rainfall model whose parameters are calibrated on large observational datasets of internal variables, ensures a robust estimation of return levels across all return periods, including extremes. Additionally, rainfall modeling inherently avoids quantile-crossing inconsistencies, a common issue in independent duration-based probabilistic modeling.</p>

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

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Patrick Arnaud,
  • Philippe Cantet

摘要

This study introduces a stochastic continuous hourly areal rainfall model designed to simulate areal rainfall time series for hydrological risk management. The rainfall model, named SCHYPRE (Simulation of Continuous HYetographs for Predictive Risk Estimation), extends an established at-site event-based rainfall model to a basin-scale and continuous rainfall model, integrating both extreme event modeling and continuous simulation of seasonal and long-duration rainfall patterns. The rainfall model parameters were calibrated using a 28.5 years dataset of hourly rainfall observations at a 1 km resolution. This dataset enabled the computation of areal rainfall time series across 2108 catchments in France, encompassing a wide range of climatic regimes from continental and Mediterranean to mountainous environments. The evaluation framework demonstrates the rainfall model’s ability to reproduce observed areal rainfall statistics, including mean and extreme values, seasonality, autocorrelation, and intermittency of rainfall. Frequency analysis conducted over durations from one hour to one year shows good agreement between the simulations and the adapted law. An advantage of rainfall modeling is its robustness in estimating extreme return levels. Unlike traditional probabilistic methods, which are more sensitive to sampling variability, the stochastic rainfall model whose parameters are calibrated on large observational datasets of internal variables, ensures a robust estimation of return levels across all return periods, including extremes. Additionally, rainfall modeling inherently avoids quantile-crossing inconsistencies, a common issue in independent duration-based probabilistic modeling.