A Nonparametric Wet/Dry Spell Model for Resampling Daily Precipitation

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Abstract
A nonparametric wet/dry spell model is developed for resampling daily precipitation at a site. The model considers alternating sequences of wet and dry days in a given season of the year. All marginal, joint, and conditional probability densities of interest (e.g., dry spell length, wet spell length, precipitation amount, and wet spell length given prior to dry spell length) are estimated nonparametrically using at‐site data and kernel probability density estimators. Procedures for the disaggregation of wet spell precipitation into daily precipitation and for the generation of synthetic sequences are proffered. An application of the model for generating synthetic precipitation traces at a site in Utah is presented.

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