Search

Your search keyword '"Mariéthoz, Grégoire"' showing total 162 results
Start Over Author "Mariéthoz, Grégoire"
162 results on '"Mariéthoz, Grégoire"'

4. Multiple Point Statistics

Author

Caers, Jef, Mariethoz, Gregoire, Ortiz, Julian M., Finkl, Charles W., Series Editor, Fairbridge, Rhodes W., Series Editor, Daya Sagar, B. S., editor, Cheng, Qiuming, editor, McKinley, Jennifer, editor, and Agterberg, Frits, editor

Published
2023
Full Text
View/download PDF

6. Semi-parametric resampling with extremes

Author

Opitz, Thomas, Allard, Denis, and Mariéthoz, Grégoire

Subjects
Statistics - Methodology
Abstract

Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features such as spatial patterns from observed datasets while using only minimal assumptions. However, such methods cannot generate extreme events beyond the observed range of data values. We here propose using tools from extreme value theory for stochastic processes to extrapolate observed data towards yet unobserved high quantiles. Original data are first enriched with new values in the tail region, and then classical resampling algorithms are applied to enriched data. In a first approach to enrichment that we label "naive resampling", we generate an independent sample of the marginal distribution while keeping the rank order of the observed data. We point out inaccuracies of this approach around the most extreme values, and therefore develop a second approach that works for datasets with many replicates. It is based on the asymptotic representation of extreme events through two stochastically independent components: a magnitude variable, and a profile field describing spatial variation. To generate enriched data, we fix a target range of return levels of the magnitude variable, and we resample magnitudes constrained to this range. We then use the second approach to generate heatwave scenarios of yet unobserved magnitude over France, based on daily temperature reanalysis training data for the years 2010 to 2016.

Published
2020

20. Future shifting of annual extreme flows under climate change in the Volta River basin

Author

Dembélé, Moctar, Vrac, Mathieu, Ceperley, Natalie, Zwart, Sander J., Larsen, Josh, Dadson, Simon J., Mariéthoz, Grégoire, Schaefli, Bettina, Dembélé, Moctar, Vrac, Mathieu, Ceperley, Natalie, Zwart, Sander J., Larsen, Josh, Dadson, Simon J., Mariéthoz, Grégoire, and Schaefli, Bettina

Abstract

Global warming is projected to result in changes in streamflow in West Africa with implications for frequent droughts and floods. This study investigates projected shifting in the timing, seasonality and magnitude of mean annual minimum (MAM) and annual maximum flows (AMF) in the Volta River basin (VRB) under climate change, using the method of circular statistics. River flow is simulated with the mesoscale hydrologic model (mHM), forced with bias-corrected climate projection datasets consisting of 43 regional and global climate model combinations under three representative concentration pathways (RCPs). Projected changes indicate that AMF increases between +1 % and +80 % across sub-basins, particularly in the near future (2021–2050), whereas MAM decreases between −19 % and −7 %, mainly from the late century (2071–2100), depending on RCPs. The date of occurrence of AMF is projected to change between −4 and +3 d, while MAM could shift between −4 and +14 d depending on scenarios over the 21st century. Annual high flows denote a strong seasonality with negligible future changes, whereas the seasonality of low flows has a higher variation, with a slight drop in the future.

Published
2024

21. Feature-preserving interpolation and filtering of environmental time series

Author

Mariethoz, Gregoire, Linde, Niklas, Jougnot, Damien, and Rezaee, Hassan

Subjects
Physics - Geophysics, Physics - Data Analysis, Statistics and Probability
Abstract

We propose a method for filling gaps and removing interferences in time series for applications involving continuous monitoring of environmental variables. The approach is non-parametric and based on an iterative pattern-matching between the affected and the valid parts of the time series. It considers several variables jointly in the pattern matching process and allows preserving linear or non-linear dependences between variables. The uncertainty in the reconstructed time series is quantified through multiple realizations. The method is tested on self-potential data that are affected by strong interferences as well as data gaps, and the results show that our approach allows reproducing the spectral features of the original signal. Even in the presence of intense signal perturbations, it significantly improves the signal and corrects bias introduced by asymmetrical interferences. Potential applications are wide-ranging, including geophysics, meteorology and hydrology., Comment: 6 pages, 4 figures

Published
2015
Full Text
View/download PDF

31. Distribution-based pooling for combination and multi-model bias correction of climate simulations.

Author

Vrac, Mathieu, Allard, Denis, Mariéthoz, Grégoire, Thao, Soulivanh, and Schmutz, Lucas

Subjects
CLIMATE change models, ATMOSPHERIC models, CLIMATE change, SENSITIVITY analysis, QUANTILES
Abstract

For investigating, assessing, and anticipating climate change, tens of global climate models (GCMs) have been designed, each modelling the Earth system slightly differently. To extract a robust signal from the diverse simulations and outputs, models are typically gathered into multi-model ensembles (MMEs). Those are then summarized in various ways, including (possibly weighted) multi-model means, medians, or quantiles. In this work, we introduce a new probability aggregation method termed "alpha pooling" which builds an aggregated cumulative distribution function (CDF) designed to be closer to a reference CDF over the calibration (historical) period. The aggregated CDFs can then be used to perform bias adjustment of the raw climate simulations, hence performing a "multi-model bias correction". In practice, each CDF is first transformed according to a non-linear transformation that depends on a parameter α. Then, a weight is assigned to each transformed CDF. This weight is an increasing function of the CDF closeness to the reference transformed CDF. Key to the α pooling is a parameter α that describes the type of transformation and hence the type of aggregation, generalizing both linear and log-linear pooling methods. We first establish that α pooling is a proper aggregation method by verifying some optimal properties. Then, focusing on climate model simulations of temperature and precipitation over western Europe, several experiments are run in order to assess the performance of α pooling against methods currently available, including multi-model means and weighted variants. A reanalysis-based evaluation as well as a perfect model experiment and a sensitivity analysis to the set of climate models are run. Our findings demonstrate the superiority of the proposed pooling method, indicating that α pooling presents a potent way to combine GCM CDFs. The results of this study also show that our unique concept of CDF pooling strategy for multi-model bias correction is a credible alternative to usual GCM-by-GCM bias correction methods by allowing handling and considering several climate models at once. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

36. Using synthetic snow cover maps to determine the daily degree-day snowmelt factor of a distributed hydrological model

Author

Wiersma, Pau, Zakeri, Fatemeh, and Mariéthoz, Grégoire

Abstract

Snowmelt can vary largely across time and space, especially in complex terrain. However, hydrological models often represent snowmelt using a single static degree-day factor that relates the melt runoff with air temperature. Seasonally or spatially varying degree-day factors have been shown to better capture the snowmelt heterogeneity, but still rely on simplified parameterizations. One interesting solution proposed in the literature is to use MODIS satellite imagery to capture the true snowmelt heterogeneity, and use it to inform hydrological models on the temporal and spatial evolution of the degree-day factor on a near-daily basis. However, the limited spatial resolution of MODIS makes this process difficult to apply in complex mountainous terrain. Meanwhile, Landsat or Sentinel 2 satellite imagery could be an interesting alternative as they have a much higher spatial resolution but fall short in temporal resolution. In this study, we overcome both these obstacles with a synthetically generated daily snow cover time series based on Landsat resampling. We use the daily synthetic snow cover maps to derive the snow cover depletion within each coarse resolution hydrological model grid cell, which in turn defines the degree-day factor for each cell using a transfer function. To capture the inherent uncertainty of this methodology, we run an ensemble of models using different meteorological forcings and different stochastic realizations of the synthetic snow cover maps. The resulting degree-day factors are evaluated through the skill of the modeled streamflow and snow water equivalent, using different transfer functions in several snow-influenced catchments in Switzerland., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023

45. Handling Soft Probabilities in Multiple Point Statistics Simulation

Author

Biver, Pierre, Mariethoz, Gregoire, Straubhaar, Julien, Chugunova, Tatiana, Renard, Philippe, Blondel, Philippe, Series editor, Reitner, Joachim, Series editor, Stüwe, Kurt, Series editor, Trauth, Martin H., Series editor, Yuen, David A., Series editor, Pardo-Igúzquiza, Eulogio, editor, Guardiola-Albert, Carolina, editor, Heredia, Javier, editor, Moreno-Merino, Luis, editor, Durán, Juan José, editor, and Vargas-Guzmán, Jose Antonio, editor

Published
2014
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results