Issues of Uncertainty and Scale in Derived Products

Lanscape decision makers and modellers require data to understand how natural and anthropogenic properties vary in space and then to decide which areas are suitable for specific infrastructure and activities or where hazards might occur. Organisations including the British Geological Survey provide such data as gridded products which map factors such as topography, land use, soil properties and weather variables. These products are normally provided at a single or small number of scales or resolutions and do not include information about uncertainty.

Different users of the products require information at different spatial scales. For example, decision makers concerned about the risk of a landslide occurring on a particular hillside might only be interested in rainfall data for the immediate locality. In contrast, water managers interested in the quantity of groundwater within an aquifer might utilise rainfall data from across a wider catchment. The uncertainty in data products propgate through data processing and modelling proceedures leading to unreliable decisions. 


Figure 1: Interpolated rainfall maps derived from Met Office (2019) data (provided under licence).
Figure 2: Spatially varying uncertainty of each rainfall map.

The products are often derived from a sample of measurements of the property of interest which are interpolated to cover the entire country or region of interest. The rainfall maps in Figure 1 are derived from UK Met Office (2019) rain gauges.  If this interpolation is performed using geostatistical methods then it is possible to determine the resultant uncertainty of the derived maps (Figure 2). However, this uncertainty computation can be time-consuming (particularly for products that vary in both time and space) and the uncertainty varies according to the scale of the required output (Figure 3).


Figure 3: The relationship between spatial scale of the output map and the uncertainty (width of the confidence level) for the site of a groundwater measurement borehole.

This project is concerned with providing information relevant to decision makers across all scales and in quantifying the uncertainty in this information. Statisticians will explore computationally efficient methodologies to producing multi-scale products and data scientists and software developers will suggest approaches by which these can be distributed. The propogation of uncertainty in data products through process models of groundwater levels will be explored and quantified. The number and arrangement of measurements required to produce multi-scale products will also be determined.     

Principle Investigator Ben Marchant introduces the project in this short video
BGS data product viewers:  https://www.bgs.ac.uk/data/mapViewers/home.html
Project outputs:
1. Review of the peat depth data from Gleann a Chlachainn published in Soil Use and Management https://onlinelibrary.wiley.com/doi/abs/10.1111/sum.12596
Link to the project page on the UKRI Gateway to Research https://gtr.ukri.org/projects?ref=NE%2FT004169%2F1