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1. Pathways and pitfalls in extreme event attribution

Author

Roop Singh, Julie Arrighi, Karin van der Wiel, Andrew D. King, Sarah F. Kew, Robert Vautard, Fraser C. Lott, Friederike E. L. Otto, Sjoukje Philip, Geert Jan van Oldenborgh, Maarten van Aalst, Royal Netherlands Meteorological Institute (KNMI), University of Oxford, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), School of Earth Sciences [Melbourne], Faculty of Science [Melbourne], University of Melbourne-University of Melbourne, Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], Red Cross Red Crescent Climate Centre, University of Twente, Australian Research Council, ARC: DE180100638 European Commission, EC, This paper builds on results from the Copernicus COP 039 and C3S 62 contracts lead by KNMI, which focused on the development of a prototype for extreme events and attribution service within the context of the Copernicus Climate Change Service (C3S) implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) and funded by the European Union. The Red Cross Red Crescent Climate Centre authors were partly supported by the Partners for Resilience program. AK was funded by the Australian Research Council (DE180100638)., University of Oxford [Oxford], Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), University of Twente [Netherlands], Department of Earth Systems Analysis, Faculty of Geo-Information Science and Earth Observation, and UT-I-ITC-4DEarth

Subjects
Atmospheric Science, Global and Planetary Change, Extreme weather, 010504 meteorology & atmospheric sciences, Computer science, Vulnerability, Climate change, 010501 environmental sciences, Extreme event attribution, 01 natural sciences, Hazard, Data science, Public interest, ITC-HYBRID, Detection and attribution, 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, ITC-ISI-JOURNAL-ARTICLE, Meteorology & Atmospheric Sciences, Climate model, Attribution, 0105 earth and related environmental sciences, Event (probability theory)
Abstract

The last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science.

Published
2021

2. Flood forecasting based on an artificial neural network scheme

Author

Chafiq Titouna, Abdelhak Mourad Gueroui, Ousmane Thiare, Ado Adamou Abba Ari, Francis Yongwa Dtissibe, University of Maroua (UMa), Laboratoire d'Informatique Parallélisme Réseaux Algorithmes Distribués (LI-PaRAD), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Laboratoire d'Informatique Paris Descartes (LIPADE (URP_2517)), Université de Paris (UP), Laboratoire d'Analyse Numérique et Informatique [Sénégal] (LANI), Université Gaston Berger de Saint-Louis Sénégal (UGB), and We like to thank the editor and the anonymous reviewers for their valuable remarks that helped us in better improving the content and presentation of the paper.

Subjects
021110 strategic, defence & security studies, Atmospheric Science, Flood forecasting, 010504 meteorology & atmospheric sciences, Artificial neural network, Flood myth, Artificial neural networks, Computer science, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Industrial engineering, Hazard, 13. Climate action, Order (exchange), Multilayer perceptron, Natural hazard, Machine learning, [SDE]Environmental Sciences, Earth and Planetary Sciences (miscellaneous), 0105 earth and related environmental sciences, Water Science and Technology, Communication channel
Abstract

International audience; Nowadays, floods have become the widest global environmental and economic hazard in many countries, causing huge loss of lives and materials damages. It is, therefore, necessary to build an efficient flood forecasting system. The physical-based flood forecasting methods have indeed proven to be limited and ineffective. In most cases, they are only applicable under certain conditions. Indeed, some methods do not take into account all the parameters involved in the flood modeling, and these parameters can vary along a channel, which results in obtaining forecasted discharges very different from observed discharges. While using machine learning tools, especially artificial neural networks schemes appears to be an alternative. However, the performance of forecasting models, as well as a minimum error of prediction, is very interesting and challenging issues. In this paper, we used the multilayer perceptron in order to design a flood forecasting model and used discharge as input–output variables. The designed model has been tested upon intensive experiments and the results showed the effectiveness of our proposal with a good forecasting capacity.

Published
2020