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Université de Neuchâtel

Simulating rainfall time-series : how to account for statistical variability at multiple scales?

Oriani, Fabio ; Mehrotra, R ; Mariéthoz, Grégoire ; Straubhaar, Julien ; Sharma, A ; Renard, Philippe

In: Stochastic Environmental Research and Risk Assessment, 2018, vol. 32, p. 321-340

Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is...

Université de Neuchâtel

Simulation of rainfall time series from different climatic regions using the direct sampling technique

Oriani, Fabio ; Straubhaar, Julien ; Renard, Philippe ; Mariethoz, Grégoire

In: Hydrology and Earth System Sciences, 2014, vol. 18, p. 3015-3031

The direct sampling technique, belonging to the family of multiple-point statistics, is proposed as a nonparametric alternative to the classical autoregressive and Markovchain-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...

Université de Neuchâtel

Simulation of braided river elevation model time series with multiple-point statistics

Pirot, Guillaume ; Straubhaar, Julien ; Renard, Philippe

In: Geomorphology, 2014, vol. 214, p. 148-156

A new method is proposed to generate successive topographies in a braided river system. Indeed, braided river morphologymodels are a key factor influencing river–aquifer interactions and have repercussions in ecosystems, flood risk or water management. It is essentially based on multivariate multiple-point statistics simulations and digital elevation models as training data sets. On the one...

Université de Neuchâtel

A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm

Meerschman, Eef ; Pirot, Guillaume ; Mariethoz, Gregoire ; Straubhaar, Julien ; Van Meirvenne, Marc ; Renard, Philippe

In: Computers & Geosciences, 2013, vol. 52, p. 307-324

The Direct Sampling (DS) algorithm is a recently developed multiple-point statistical simulation technique. It directly scans the training image (TI) for a given data event instead of storing the training probability values in a catalogue prior to simulation. By using distances between the given data events and the TI patterns, DS allows to simulate categorical, continuous and multivariate...