Rainfall Disaggregation in Data-Scarce Regions Using the Random Bartlett-Lewis Rectangular Pulse Model

Rainfall disaggregation is a key challenge in hydrology, especially in regions with limited high-resolution records. This study applies the Random Bartlett–Lewis Rectangular Pulse Model to four regions of Hellas to generate hourly rainfall from daily totals. The work is novel in evaluating the model under data-scarce Mediterranean conditions, incorporating a two-tiered uncertainty analysis, testing alternative pulse intensity distributions (Gamma and Exponential), and comparing its performance with a deterministic machine learning (ML) approach. Results show that the RBLRPM reproduces essential rainfall properties such as variance, autocorrelation, skewness, and dry spell probabilities, even when calibrated with as little as three years of data. The ML approach ensures perfect conservation of daily totals and computational efficiency, but it smooths temporal variability and underestimates extremes. By contrast, the stochastic RBLRPM captures clustering, intermittency, and heavy tails more realistically, which is crucial for hydrological design and flood risk analysis. The Gamma distribution consistently outperforms the Exponential form, though both remain applicable. Overall, the Gamma-based RBLRPM offers a robust and transferable method for rainfall disaggregation in data-limited contexts, highlighting the importance of stochastic approaches for water resource management, infrastructure resilience, and climate adaptation.