English

Accueil > Recherche > Thèses et Habilitation à Diriger des Recherches > Soutenances récentes et à venir > Developments in statistics applied to hydrometeorology : imputation of streamflow data and semiparametric precipitation modeling









Rechercher


Developments in statistics applied to hydrometeorology : imputation of streamflow data and semiparametric precipitation modeling

Patricia Tencaliec, le 1er février 2017

par Brice Boudevillain - 27 janvier 2017

Short abstract :
In the first part of this PhD thesis we propose an approach for streamflow imputation based on dynamic regression models, more specifically, a multiple linear regression with ARIMA residual modeling. We apply this method for reconstructing the data of eight stations situated in the Durance watershed in the south-east of France. The results showed that, without making use of additional variables, we manage to accurately reconstruct missing blocks of various lengths, ranging up to 20 years. The second part of this work addresses the statistical modeling of precipitation amounts. We develop two semiparametric models based on a new class of distributions, the extended generalized Pareto (EGPD). We compare the performance of these methods with the one obtained by applying EGPD, on both simulated samples and two precipitation data sets from south-east of France. The results show a reduced estimation error compared to EGPD, this effect being even more obvious as the sample size increases.

Jury members :
Stephane GIRARD (Directeur de recherche, INRIA Rhone-Alpes), Président
Véronique MAUME-DESCHAMPS (Professeur, Université Claude Bernard Lyon 1), Rapporteur
Valérie MONBET (Professeur, Université de Rennes 1), Rapporteur
Philippe NAVEAU (Directeur de recherche, LSCE CNRS), Examinateur
Benjamin RENARD (Chargé de recherche, IRSTEA), Examinateur
Thibault Mathevet (Ingénieur, EDF-DTG), Invité

Supervisors :
Clémentine PRIEUR (professor, Université Grenoble Alpes)
Anne Catherine FAVRE (professor, Grenoble INP)