Abstract
Abstract. The direct sampling technique, belonging to the family of multiple-point statistics, is proposed as a nonparametric alternative to the classical autoregressive and Markov-chain-based models for daily rainfall time-series simulation. The algorithm makes use of the patterns contained inside the training image (the past rainfall record) to reproduce the complexity of the signal without inferring its prior statistical model: the time series is simulated by sampling the training data set where a sufficiently similar neighborhood exists. The advantage of this approach is the capability of simulating complex statistical relations by respecting the similarity of the patterns at different scales. The technique is applied to daily rainfall records from different climate settings, using a standard setup and without performing any optimization of the parameters. The results show that the overall statistics as well as the dry/wet spells patterns are simulated accurately. Also the extremes at the higher temporal scale are reproduced adequately, reducing the well known problem of overdispersion.
Highlights
The stochastic generation of rainfall time series is a key topic for hydrological and climate science applications: the challenge is to simulate a synthetic signal honoring the high-order statistics observed in the historical record, respecting the seasonality and persistence from the daily to the higher temporal scales
We propose the use of some lower-frequency covariates of daily rainfall in a completely unusual framework: the direct sampling (DS) technique (Mariethoz et al, 2010), which belongs to multiple-point statistics (MPS)
The generated rainfall looks similar to the reference: the extreme events inside the 10-year samples are reproduced with an analogous frequency and magnitude
Summary
The stochastic generation of rainfall time series is a key topic for hydrological and climate science applications: the challenge is to simulate a synthetic signal honoring the high-order statistics observed in the historical record, respecting the seasonality and persistence from the daily to the higher temporal scales. An alternative method proposed is model nesting (Wang and Nathan, 2002; Srikanthan, 2004, 2005; Srikanthan and Pegram, 2009), which implies the correction of the generated daily rainfall using a multiplicative factor to compensate the bias in the higher-scale statistics. These techniques generally allow a better reproduction of the statistics up to the annual scale, but they imply the estimation of a more complex prior model and cannot completely capture a complex dependence structure
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