Efficient stochastic generation of multi-site synthetic precipitation data

Summary Although weather generators have been used for a long time, recent years have seen a surge in interest in them as potential downscaling tools for climate change impacts studies. Weather generators can only generate weather data at a single point, or independently at several points, whereas many climate change impact studies require information at the basin scale. Such studies would require the coherent generation of weather data at several locations over a basin. The literature on multi-site weather data generation is very thin, with most of the work based on the approach described by Wilks (Wilks, D.S., 1998. Multi-site generalization of a daily stochastic precipitation generation model. J. Hydrol. 210, 178–191). This approach demands significant work to set up, and may suffer from ill-defined correlation matrices due to noise in the observed data. In addition, multi-site generation must address the complex problem of spatial intermittence, in which precipitation amounts depend on neighbouring stations being wet or dry. This likely explains why multi-site generation, despite its obvious advantages, has not been widely used. This paper presents an algorithm for the efficient stochastic generation of multi-site precipitation data following the Wilks approach. The effect of noise on the performance of the algorithm is examined, and the results indicate that it performs very well, even with excessive noise added to the data. The algorithm is fast, simple, easy to implement, and significantly simplifies the generation of multi-site precipitation data for impact studies of climate scenarios. The spatial intermittence problem is dealt with by linking average precipitation to an index that describes the distribution of precipitation occurrence at the basin scale.

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