Maximising Information Gain from Catchment Monitoring and Modelling: An Integrated, Iterative Solution
Supervised by Dr Sim Reaney & Dr Jeff Warburton
When trying to understand how complex linkages in natural environment work, the problem is often approached from either an environmental monitoring or simulation modelling perspective. Each of these methods takes a different approach and has different strengths and weaknesses. In environmental monitoring, the critical issue is to determine what, when and where to monitor for the maximum information gain about a system? With simulation modelling, there are important problems centred on predictive uncertainty and the need for data with higher information content than is currently available.
The aim of this PhD project will be to address these two problems through applying an integrated iterative method of monitoring and modelling. The initial model predictions will be used to design the monitoring scheme and the measurements will be used to assess the model performance. The spatial predictive uncertainty of the model will then be used to find the spatial locations with the greatest information content and the monitoring network adjusted to measure in these locations. The value of multiple low cost but low precision sensors as opposed to a limited number of high quality sensors will be assessed. For example, is fine fluvial sediment flux best estimated from a single high-resolution monitoring station, a network of low cost sensors or using a network of low cost mechanical time integrated massflux sediment traps?
The research work will focus on both diffuse (non-point source) pollution within the SCIMAP Framework and catchment hydrological processes within the fully distributed CRUM3 model. Field work will be based within field sites in northern England.