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Your search keyword '"Masson V."' showing total 6 results
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6 results on '"Masson V."'

1. Turn Down the Heat. Confronting the New Climate Normal

Author

Schellnhuber, H. J., Reyer, C., Hare, W., Waha, K., Otto, I. M., Serdeczny, O., Schaeffer, M., Schleußner, C.-F., Reckien, D., Marcus, R., Kit, O., Eden, A., Adams, S., Aich, V., Albrecht, T., Baarsch, F., Boit, A., Trujillo, N. C., Cartsburg, M., Coumou, D., Fader, M., Hoff, H., Jobbins, G., Jones, L., Krummenauer, L., Langerwisch, F., Le Masson, V., Ludi, E., Mengel, M., Möhring, J., Mosello, B., Norton, A., Perette, M., Pereznieto, P., Rammig, A., Reinhardt, J., Robinson, A., Rocha, M., Sakschewski, B., Schaphoff, S., Schewe, J., Stagl, J., and Thonicke, K.

Subjects
ddc:550
Published
2014

4. A new 27 ky high resolution East Antarctic climate record

Author

Jouzel, J., Masson, V., Cattani, O., Falourd, S., Stievenard, M., Stenni, B., Longinelli, A., Johnsen, S.J., Steffenssen, J.P., Petit, J.R., Schwander, J., Souchez, R., and Barkov, N.I.

Subjects
13. Climate action, 530 Physics
Abstract

The ice core recently drilled at the Dome Concordia site on the East Antarctic plateau provides a new high resolution isotope record covering part of the last glacial, the last transition and the Holocene. The two step shape of the deglaciation is remarkably similar for all the ice cores now available on the East Antarctic plateau. The first warming trend ends about 14000 years ago and is followed by the well marked Antarctic Cold Reversal (ACR) with a secondary peak common to all records. During the deglaciation, there are more similarities between the near coastal site of Taylor Dome and inland East Antarctica than between Taylor Dome and central Greenland. However, the results for EPICA do appear to confirm the Taylor Dome timescale after about 14 ka, showing cooling into the ACR roughly in phase between Greenland and Antarctica. While the overall deglacial pattern is asynchronous, this suggests that the now classical picture of a temperature seesaw between Antarctica and Greenland may be too simplistic.

5. A machine learning approach for evaluating the impact of land use and management practices on streamwater pollution by pesticides

Author

Cordier, M. O., Garcia, F., Gascuel-Odoux, C., Masson, V., Jordy Salmon-Monviola, Tortrat, F., Trepos, R., Diagnosing, Recommending Actions and Modelling (DREAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), Institut National de la Recherche Agronomique (INRA), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Centre Armoricain de Recherche en Environnement (CAREN), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-Ecole Nationale Supérieure Agronomique de Rennes-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS), Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ). Canberra, AUS., Modelling and Simulation Society of Australia and New Zealand, Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Ecole Nationale Supérieure Agronomique de Rennes-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Streamwater pollution by pesticides is a critical environmental issue in farmed catchment areas. Many important factors are involved in this pollution phenomenon, like weather, area topology, land use and crop management practices, which all influence streamwater quality. The purpose of the ongoing study presented in this paper is to evaluate the impact of land use and management practices on streamwater pollution. We use modelling, simulation and machine learning techniques for acquiring knowledge about this complex domain. Our main objective is to learn qualitative rules relating the pollution factors to the temporal distribution of the stream pesticide concentration. The study area is the farmed catchment of Fremeur (~17 km2), located in Brittany, France. Our approach relies on a simulation model, called SACADEAU, based on two main components: a transfer model and a management model. The biophysical transfer model is the core of the model. It aims at simulating on a daily basis the pesticide transfer through the catchment area from application locations on maize parcels to the river. The management model simulates farmers\\\' operations like tillage, sowing, weeding (pesticide treatments described by dates, molecules, quantities) on maize crop parcels. A climate model, which provides daily weather data such as temperature and rainfall amount and a spatial model, which distributes in space the agricultural activities according to the fields and catchment area topology, are the two other components of the SACADEAU model. Using the outputs of these three sub-models, a biophysical transfer model determines herbicide outflow, modelling transfer from application locations, through the catchment area, to the stream. This simulation model is used for generating a large number of trajectories of the catchment system, considering different weather series or spatial distributions of land use and agricultural activities. The complexity of the model inputs and outputs makes difficult interpretation of results. Qualitative trajectories, or scenarios, are thus obtained by using machine learning techniques on these simulation data. ICL, an inductive logic programming software, is used for learning a set of rules which summarizes the scenarios. The Sacadeau model is not already fully implemented and first results have been obtained with a simplified model. We were able to check the coherence and the feasibility of our approach, and to build a first view of the role of some attributes in stream-water quality. Thanks to the complete model, more specific rules declined in terms of temporal and spatial variations should be established in the future.

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