One Dataset, Multiple Cascades: Insights from a Joint Evaluation of Stochastic Rainfall Downscaling Methods in France and the Netherlands

Downscaling of rainfall time series is the process of transforming rainfall data from a coarse temporal resolution (e.g., daily or hourly totals) into finer time scales (e.g., minutes) while preserving key statistical and physical characteristics of the original data. Downscaling techniques are widely used in hydrology, urban drainage design, flood modeling, and climate impact studies where fine-resolution rainfall data are essential for simulating hydrological response and studying the impact of extreme rainfall events.

Numerous stochastic downscaling approaches have been proposed in the literature, including point process models, random cascades, Markov chains, and weather generators, each designed to reproduce specific rainfall characteristics such as intermittency, intensity distributions, and temporal dependence. However, these methods are typically developed and evaluated independently, often using different datasets and climates, which makes it hard to assess their relative strengths and limitations.

This study presents the first joint and systematic comparison of two independently developed, state-of-the-art stochastic rainfall downscaling methods based on random cascades. Specifically, the Standard and Blunt extension cascades derived from the Universal Multifractal (UM) theory are compared with the Equal-Depth Area (EDA) approach. The methods are applied to 300 high-resolution (1-minute) rainfall events in the Netherlands and France, using increasingly challenging downscaling ratios of 4, 16, and 64. The raw data was collected with the help of optical disdrometers (OTT Parsivel2) located at three different sites.

We analyze (i) the estimation and selection of cascade generator models and their impact on performance going from event based to climatic average key parameters, (ii) the statistical properties of the downscaled rainfall time series across scales, events and cascade types, using both standard scores, quantile comparison and Universal Multifractal analysis and (iii) the relative strengths and limitations of each method in terms of ensemble spread, temporal dependence structure and extreme rainfall reproduction. By jointly evaluating multiple methods on identical datasets, we aim to advance the science behind stochastic rainfall disaggregation and lay the foundation for further model refinements and application-driven method selection.