Nele Reyniers

Nele Reyniers
University of East Anglia | UEA · Climatic Research Unit

Master of Bioscience Engineering - Land and Water Management

About

Introduction
My research interests genearlly cover climate change, climate dynamics, droughts & other extreme events, and interactions of the previous with humans. In my PhD, I focus on characterisation and quantification of drought risk and how climate change is affecting drought risk in the UK, with special attention to exploring uncertainty in modeling chains.
Skills and Expertise
Climate Change
Python
Hydrology
Git
Data Visualization
Data Analysis
Hydrological Modeling
Data Science
Climate Variability
Machine Learning
Additional affiliations
September 2018 - August 2019
European Space Agency
European Space Agency
  • Department of Science, Applications and Climate
  • Didcot, United Kingdom
Position
  • Young Graduate Trainee
September 2017 - July 2018
Ghent University
Ghent University
  • Department of Environment
  • Gent, Belgium
Position
  • Master's Student
Description
  • Master's thesis on the influence of the resolution of regional climate models on simulated latent heat flux.
August 2017 - September 2017
Federal University of Petroleum Resources
Federal University of Petroleum Resources
  • Department of Environmental Science
  • Warri, Nigeria
Position
  • Intern
Description
  • 1. Research on water quality in surrounding area 2. Morphological analysis of river basin using GIS
Education
September 2016 - July 2018
Ghent University
Ghent University
Field of study
  • Bio-engineering: Land and Water Management
September 2013 - July 2016
Ghent University
Ghent University
Field of study
  • Bio-engineering: Land and Forest Management (option: Land and Water Management)

Projects

Project (1)
Drought projections and uncertainties under climate change in the UK
Project
This PhD project aims to investigate how climate change is affecting (extreme) droughts in the UK and East Anglia, from meteorological anomalies to water resources impacts. This includes assessing the importance of different sources of uncertainty to the resulting uncertainty of projected drought changes.