Randomized trial assesses rainfall modeling in mountainous areas, suggesting improved accuracy for predictions.
In mountainous regions orographic effects create strong horizontal gradients of various rainfall statistics such as the frequency of occurrence, the distribution of intensity and the structure of spatial correlation. However, most statistical models of daily rainfall assume spatial stationarity (i.e., the spatial homogeneity of rainfall statistics) and are therefore not well suited for studying the highly non-homogeneous characteristics of orographic rainfall. To overcome this limitation, we design a non-stationary trans-Gaussian geostatistical model for the analysis of daily rainfall fields over complex topography. This framework infers rainfall statistics from sparse rain gauge observations, simulates realistic rainfall fields after calibration and stochastically interpolates rain gauge observations to create rainfall maps. The performance of the model is assessed with data from the Island of Hawai`i where extreme spatial gradients in rainfall are observed. Results demonstrate that the non-stationary trans-Gaussian model can skillfully reproduce orographic rainfall statistics as well as their variations in space.
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Benoît et al. (2026) studied this question.
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