Environmental Modelling
Research Team
Prof. Neil Crout
Dr Scott Young
Dr Sacha Mooney
Research Overview
The environmental science section within Bioscience undertakes a wide range of environmental modelling research. This includes applied work such as predicting the transport and fate of environmental contaminants (especially radionuclides and metals); more fundamental biogeochemical modelling such as the cycling of nitrogen in arctic ecosystems; and methodological work such as approaches for controlling the complexity of environmental models. General Research Themes include:
• Soil Pore Network Modelling
• Phytoremediation
• Transfer of radionuclides and other trace contaminants
• Biogeochemical cycling
• Model evaluation and parameterisation
Lots more information at www.nottingham.ac.uk/environmental-modelling/
Current Projects
Environmental Modelling of Radionuclides
We have undertaken a variety of work modelling the fate of radionuclides in the environment. Much of this arose from the Chernobyl accident and therefore has focussed on radiocaesium but other nuclides have also been investigated. Most of our work has focussed on the human food chain with soil-plant transfer being a particular interest. In turn this has led to spatial models of radiocaesium in the entire european foodchain (!).
We have also developed models for the 'metabolism' of specific nuclides in various important food producing animals (sheep, cows, and goats). Mostly these are research tools rather for direct use in predicting food chain transfer.
For further information please visit:
http://www.nottingham.ac.uk/environmental-modelling/Radionuclides%20Home.htm
Methods for the Development of Parsimonious Models
Models of complex environmental processes and systems are widely used as tools to assist the development of research, and to support decision making at a number of levels (e.g. international, national government, corporate). Many models become unwieldy, over-parameterised and difficult to test as they seek to capture the temporal and spatial dynamics of relevant processes. The performance of most models is usually assessed through some kind of 'test' against observed data. However this testing is commonly a simple comparison between a given model and a given set of observed data. Invariably there are many plausible model representations of particular processes and the influence of these alternatives on model performance is rarely investigated. We believe that models should be parsimonious, i.e. as simple as possible, but no simpler. Although this view is often expressed, the tendency, has been for the development of complex models, rarely with any investigation of simpler, potentially equally reliable, models. Our aim in this work is to develop an approach for systematic model reduction to achieve improved model parsimony.
For further information please visit:
www.nottingham.ac.uk/environmental-modelling/ParsimonyHome.htm