HRAST ESSENFELDER Arthur
Arthur H. ESSENFELDER is a postdoctoral researcher at the Euro-Mediterranean Centre on Climate Change – CMCC and a course teacher on Methods and Tools for the Analysis of Climate Change Impacts and Policies (PHD028 | SECS-P/01) at the Ca’Foscari University of Venice. He holds a Ph.D. in Science and Management of Climate Change obtained at the Ca'Foscari University of Venice, Italy, a specialisation in Environmental Science and Sustainable Development from the Fundação Getúlio Vargas – FGV/RJ, in Brazil, and a BSc in Environmental Engineering from the Federal University of Parana, in Brazil. During the past few years, he has been working on topics related to eco-hydrologic, socio-hydrology and hydro-economics simulation and modelling, integrated water resources management, the utilisation of machine learning techniques as decision-making support tools in hydrologic systems, and flood hazard and risk mapping. Arthur has previously developed research and technical reports as part of the research teams of the Centre of Hydraulics and Hydrology Professor Parigot de Souza – CEHPAR (LACTEC Institutes), in Brazil, the Institute for Soil Science and Site Ecology (part of the Technische Universität of Dresden), in Germany, and the Fondazione Eni Enrico Mattei – FEEM, in Italy. During the past two years, he has being involved in a number of projects, with highlights to the AGRO AGAPT Project, which focuses on the modelling of complex human-water systems, the SAVEMEDCOASTS Project, which focuses on the Prevention Priority program of the European Commission A4 – Civil Protection Policy and aims to respond to the need for people and assets prevention from natural disasters in Mediterranean coastal areas, and the CLARA Project, which provides a set of leading edge climate services building upon the Copernicus Climate Change Services (C3S). His main research interest is on the modelling the dynamics of complex human-water systems under a perspective of socio-hydrology and climate change, while incorporating notions of machine learning and socio-economic agents’ behavioural preferences.
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