Centre de documentation
scientifique

Past and future evolution of French Alpine glaciers in a changing climate: a deep learning glacio-hydrological modelling approach / Jordi Bolibar Navarro (2020)  

Public Titre : Past and future evolution of French Alpine glaciers in a changing climate: a deep learning glacio-hydrological modelling approach Type de document : Thèse/HDR Auteurs : Jordi Bolibar Navarro, Auteur ; Antoine Rabatel, Auteur ; Isabelle Gouttevin, Auteur ; Eric Sauquet, Auteur Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2020 Importance : 143 p. Format : 21 x 30 cm Note générale : bibliographie Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, Spécialité : Sciences de la Terre et de l’Univers et de l’Environnement Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Alpes (France) [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] bilan de masse [Termes IGN] changement climatique [Termes IGN] glacier [Termes IGN] modèle de simulation [Termes IGN] modèle hydrographique [Termes IGN] modèle numérique de surface [Termes IGN] réseau neuronal artificiel Index. décimale : THESE Thèses et HDR Résumé : (auteur) The European Alps are among the most affected regions in the world by climate change, displaying some of the strongest glacier retreat rates. Long-term interactions between society, mountain ecosystems and glaciers in the region raise important questions on the future evolution of glaciers and their derived environmental and socioeconomical impacts. In order to correctly assess the regional response of glaciers in the French Alps to climate change, there is a need for adequate modelling tools. In this work, we explore new ways to tackle both glacier evolution and glacio-hydrological modelling at a regional scale. Glacier evolution modelling has traditionally been performed using empirical or physical approaches, which are becoming increasingly challenging to optimize with the ever growing amount of available data. Here, we present, to our knowledge, the first effort ever to apply deep learning (i.e. deep artificial neural networks) to simulate the evolution of glaciers. Since both the climate and glacier systems are highly nonlinear, traditional linear mass balance models offer a limited representation of climate-glacier interactions. We show how important nonlinearities in glacier mass balance are captured by deep learning, substantially improving model performance over linear methods.This novel method was first applied in a study to reconstruct annual mass balance changes for all glaciers in the French Alps for the 1967-2015 period. Using climate reanalyses, topographical data and glacier inventories, we demonstrate how such an approach can be successfully used to reconstruct large-scale mass balance changes from observations. This study also offered new insights on how glaciers evolved in the French Alps during the last half century, confirming the rather neutral observed mass balance rates in the 1980s and displaying a well-marked acceleration in mass loss from the 2000s onwards. Important differences between regions are found, with the Mont-Blanc massif presenting the lowest mass loss and the Chablais being the most affected one. Secondly, we applied this modelling framework to simulate the future evolution of all glaciers in the region under multiple (N=29) climate change scenarios. Our estimates indicate that most ice volume in the region will be lost by the end of the 21st century independently from future climate scenarios. We predict average glacier volume losses of 74%, 80% and 88% under RCP 2.6 (n=3), RCP 4.5 (n=13) and RCP 8.5 (n=13), respectively. By the end of the 21st century the French Alps will be largely ice-free, with glaciers only remaining in the Mont-Blanc and Pelvoux massifs. Note de contenu : Introduction 1- Glaciers 2- Glacierized mountain catchments 3- Outlook Numéro de notice : 28311 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences de la Terre et de l’Univers et de l’Environnement : Grenoble : 2020 Organisme de stage : Institut des Géosciences de l’Environnement (Grenoble) DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03052063v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98202