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Stochastic simulation of climatic data with the Direct Sampling method

Applicant Renard Philippe
Number 134614
Funding scheme Project funding (Div. I-III)
Research institution Centre d'hydrogéologie et de géothermie Université de Neuchâtel
Institution of higher education University of Neuchatel - NE
Main discipline Hydrology, Limnology, Glaciology
Start/End 01.08.2011 - 31.07.2015
Approved amount 237'903.00
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Keywords (2)

Spatial and temporal statistics; Climatic variables

Lay Summary (English)

Lead
Lay summary

records of more than a century exist for a number of locations.  has been made available in the last decades, and this is especially true for rainfall data for which historic climatic dataA wealth of

, DS) that allows exploiting historical data (or training data) in their whole complexity using non-parametric high-order statistics Direct SamplingIn the recent years, the stochastic hydrogeology group of the University of Neuchâtel has developed a multiple-point simulation method ((Mariethoz, et al., 2010). non-linear or even multi-modal correlations. We believe this ability is of tremendous potential for applications related to climatic time series. In particular, the method reproduces the statistical properties of the training data set up to a very high order, and regardless of the complexity of these statistics. If the example data inform several variables, all variables are jointly simulated and the multivariate statistics are reproduced as well, including possibly simulate either categorical or continuous variables, or a combination of both in a multivariate framework. Contrarily to existing training-image based methods, the Direct Sampling can

. simplifying the work of the modeler of the simulations accounting for the physical complexity of the phenomena while increasing the realism of the Direct Sampling method to climatic data sets and to evaluate its performances as compared to other well established or newly developed methods such as copulas based geostatistics. We expect that this work will lead to improve stochastic modeling of climatic data by extend the current range of applicationsThe objective of this project is therefore to

Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
Missing data simulation inside flow rate time-series using multiple-point statistics
Oriani Fabio Andrea Borghi Julien Straubhaar Grégoire Mariethoz and Philippe Renard (2016), Missing data simulation inside flow rate time-series using multiple-point statistics, in Environmental Modelling and Software, 86, 264-276.
Simulation of rainfall time-series from different climatic regions using the direct sampling technique
Oriani Fabio, Straubhaar Julien, Renard Philippe, Mariethoz Grégoire (2014), Simulation of rainfall time-series from different climatic regions using the direct sampling technique, in Hydrology and Earth System Sciences, 18, 3015-3031.

Collaboration

Group / person Country
Types of collaboration
University of New South-Wales Australia (Oceania)
- in-depth/constructive exchanges on approaches, methods or results

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
Petroleum Geostatistics Talk given at a conference Multiple-point statistics simulations accounting for block data 01.09.2015 Biarritz, France Renard Philippe; Straubhaar Julien;
AGU Fall Meeting 2014 Poster Spatial Rainfall Simulation: Trading Time for Space with Multiple Point Statistics. 15.12.2014 San Francisco, United States of America Renard Philippe; Oriani Fabio;
geoENV - 10th conference on Geostatistics for Environmental Applications Talk given at a conference Reproducing Chaos And Persistence In A Daily Rainfall Time-Series: A Comparison Between A Markov-Chain Approach And Multiple-Point Statistics 09.07.2014 Paris, France Renard Philippe; Oriani Fabio; Straubhaar Julien;
Research Seminars in Applied Statistics 2014 - University of Zurich Individual talk Stochastic rainfall simulation: reproducing high-order statistics with the Direct Sampling technique 10.04.2014 Zurich, Switzerland Oriani Fabio;
Research Seminar in Applied Statistics Talk given at a conference Stochastic rainfall simulation: reproducing high-order statistics with the Direct Sampling technique 10.04.2014 University of Zurich, Zurich, Switzerland Oriani Fabio;
15th Annual Conference of the International Association for Mathematical Geosciences Talk given at a conference Modeling daily rainfall time-series using multiple-point geostatistics. 02.09.2013 University of Madrid, Madrid, Spain Renard Philippe; Oriani Fabio; Straubhaar Julien;
The Water Cycle in a Changing Climate Symposium Poster Modeling future climatic time-series according to climate change scenarios 01.07.2013 ETHZ Zürich, Switzerland Oriani Fabio;


Awards

Title Year
Prix Adrien Guébhard-Séverine de la Faculté des Sciences de l’Université de Neuchâtel 2016
International Association for Mathematical Geosciences - Student travel grant 2013 2013

Associated projects

Number Title Start Funding scheme
132249 Integrated methods for stochastic ensemble aquifer modelling (ENSEMBLE) 01.03.2011 Sinergia
106557 Mathematical hydrogeology: from characterization to forecasts 01.10.2005 SNSF Professorships
153637 Efficient hydrogeological Monte Carlo inversion based on Multiple-Point Statistics 01.01.2015 Project funding (Div. I-III)

Abstract

A wealth of historic climatic data has been made available in the last decades, and this is especially true for rainfall data for which records of more than a century exist for a number of locations. The statistics based on such long records are quite robust and reliable. However, most methods proceed by first deriving parametric, low order statistics from these data, and in a second step use this model for prediction. The statistical model is a simplification of reality, including possibly restrictive assumptions. Therefore the statistical models traditionally employed can only represent models up to a certain degree of complexity, and are limited by their underlying hypotheses. We argue that this does not allow using the full richness of the data sets.Training-image based methods and especially multiple-point statistics simulation have been initially developed for subsurface applications (Boucher, 2009, De Vries, et al., 2009, Guardiano and Srivastava, 1993, Hu and Chugunova, 2008, Strebelle, 2002, Wu, et al., 2008). In this field, the major limitation of most numerical models resides in the lack of data on the subsurface. Multiple-point geostatistics introduced the concept of a training image that represents the typical structures that a geologist would expect. High-order statistics of the training image are then reproduced in the simulation domain. One of the major drawbacks of the multiple-point approach is the lack of availability of relevant training data. This problem does not exist with climatic applications where data are available in large amounts.In the recent years, the stochastic hydrogeology group of the University of Neuchâtel has developed a multiple-point simulation method (Direct Sampling, DS) that allows exploiting historical data (or training data) in their whole complexity using non-parametric high-order statistics (Mariethoz, et al., 2010). Contrarily to existing training-image based methods, the Direct Sampling can simulate either categorical or continuous variables, or a combination of both in a multivariate framework. We believe this ability is of tremendous potential for applications related to climatic time series. In particular, the method reproduces the statistical properties of the training data set up to a very high order, and regardless of the complexity of these statistics. If the example data inform several variables, all variables are jointly simulated and the multivariate statistics are reproduced as well, including possibly non-linear or even multi-modal correlations. The objective of this project is therefore to extend the current range of applications of the Direct Sampling method to climatic data sets and to evaluate its performances as compared to other well established or newly developed methods such as copulas based geostatistics. We expect that this work will lead to improve stochastic modeling of climatic data by increasing the realism of the simulations accounting for the physical complexity of the phenomena while simplifying the work of the modeler.
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