Stochastic daily rainfall generation on tropical islands with complex topography

Stochastic rainfall generators are probabilistic models of rainfall space-time behavior. During parameterization and calibration, they allow the identification and quantification of the main modes of rainfall variability. Hence, stochastic rainfall models can be regarded as probabilistic conceptual models of rainfall dynamics. As with most conceptual models in earth sciences, the performance of stochastic rainfall models strongly relies on their adequacy in representing the rain process at hand. On tropical islands with high elevation topography, orographic rain enhancement challenges most existing stochastic models because it creates localized precipitations with strong spatial gradients, which break down the stationarity of rain statistics. To allow for stochastic rainfall modeling on tropical islands, despite non-stationarity of rain statistics, we propose a new stochastic daily multi-site rainfall generator specifically for areas with significant orographic effects. Our model relies on a preliminary classification of daily rain patterns into rain types based on rainfall space and intensity statistics, and sheds new light on rainfall variability at the island scale. Within each rain type, the distribution of rainfall through the island is modeled by combining a non-parametric resampling of past analogs of a latent field describing the spatial distribution of rainfall, and a parametric gamma transform function describing rain intensity. When applied to the stochastic simulation of rainfall on the islands of O'ahu (Hawai'i, United States of America) and Tahiti (French Polynesia) in the tropical Pacific, the proposed model demonstrates good skills in jointly simulating sitespecific and island-scale rain statistics. Hence, it provides a new tool for stochastic impact studies in tropical islands, in particular for watershed water resource management.

Automated summary

Stochastic rainfall generators are probabilistic models that simulate the space-time behavior of rainfall. They help identify and quantify the main modes of rainfall variability during parameterization and calibration. However, existing stochastic models face challenges in representing rainfall on tropical islands with high elevation topography due to localized orographic rain enhancement. To address this, a new stochastic daily multi-site rainfall generator specifically for areas with significant orographic effects is proposed. It classifies daily rain patterns into rain types based on rainfall space and intensity statistics and provides insights into rainfall variability at the island scale. The model combines non-parametric resampling and a parametric gamma transform function to simulate rainfall distribution and intensity. When applied to O'ahu (Hawai'i, United States of America) and Tahiti (French Polynesia), the model demonstrates good skills in simulating site-specific and island-scale rain statistics, making it a valuable tool for stochastic impact studies and water resource management in tropical islands.