Faculté des sciences

Stochastic simulation of rainfall and climate variables using the direct sampling technique

Oriani, Fabio ; Renard, Philippe (Dir.)

Thèse de doctorat : Université de Neuchâtel, 2015.

An accurate statistical representation of hydrological processes is of paramount importance to evaluate the uncertainty of the present scenario and make reliable predictions in a changing climate. A wealth of historic data has been made available in the last decades, including a consistent amount of remote sensing imagery describing the spatio-temporal nature of climatic and hydrological... More

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    Summary
    An accurate statistical representation of hydrological processes is of paramount importance to evaluate the uncertainty of the present scenario and make reliable predictions in a changing climate. A wealth of historic data has been made available in the last decades, including a consistent amount of remote sensing imagery describing the spatio-temporal nature of climatic and hydrological processes. The statistics based on such data are quite robust and reliable. However, to explore their variability, most stochastic simulation methods are based on low-order statistics that can only represent the heterogeneity up to a certain degree of complexity.
    In the recent years, the stochastic hydrogeology group of the University of Neuchâtel has developed a multiple-point simulation method called Direct Sampling (DS). DS is a resampling technique that allows the preservation of the complex data structure by simply generating data patterns similar to the ones found in the historical data set. Contrarily to the other multiple-point methods, DS can simulate either categorical or continuous variables, or a combination of both in a multivariate framework.
    In this thesis, the DS algorithm is adapted to the simulation of rainfall and climate variables in both time and space. The developed stochastic weather or climate generators include the simulation of the target variable with a series of auxiliary variables describing some aspects of the complex statistical structure characterizing the simulated process. These methods are tested on real application cases including the simulation of rainfall time-series from different climates, the variability exploration of future climate change scenarios, the missing data simulation within flow rate time-series and the simulation of spatial rainfall fields at different scales. If a representative training data set is used, the proposed methodologies can generate realistic simulations, preserving fairly well the statistical properties of the heterogeneity. Moreover, these techniques result to be practical simulation tools, since they are adaptive to different data sets with minimal effort from the user perspective. Although leaving large room for improvement, the proposed simulation approaches show a good potential to explore the variability of complex hydrological processes without the need of a complex statistical model.