Dr Katherine Arrell
My current research interests can be organised into the following three inter-related categories geomorphological modelling, digital terrain modelling and environmental modelling. Further details of my research interests in these areas can be seen below.
The use of geographical information systems (GIS) and multi-scale modelling in geomorphology is my primary research interest. I am particularly interested in the link between the study of landforms and surface processes.
My current work on the spatial and temporal distribution of landsliding in Nepal following the 2015 Gorkha earthquake is looking at using measures of landscape position to better understand the controls and processes governing their distributions.
My PhD research modelled glacier accumulation areas using topographic and regional climate data. The research developed a model to predict glacier accumulation area distributions, where glacier initiation is a function of energy exchanges at the earth’s surface and local climate. Upland glacial environments are characterised by their highly variable local topography that creates strong localised gradients in climatic variables. The research found that it is only through the analysis of the interactions between these spatially variable climate and topographic patterns that accurate and precise predictions of glacier distributions can be made.
Digital terrain modelling
Error and uncertainty are present in all sampled data, the magnitude and types of error and uncertainty vary between datasets and data collection methods. As elevation data are commonly sampled at points and interpolated to a surface, many of the surface elevation values within the DEM will be estimated. It is therefore important to assess the accuracy of these estimates and look for any interpolation artefacts, this is especially important now, with the recent abundance of digital elevation datasets. I have used Fourier transforms to effectively remove spatially organised error components, so that unaffected pixels remain unaltered, thus avoiding the global smoothing.
Despite constant technological advances the increasing size of digital datasets requires efficient processing algorithms. The efficiency of viewshed algorithms (common within most GIS) have received some research interest however, their efficiency as a function of surface complexity and spatial organization has received little attention. My research findings suggest that viewshed algorithm efficiency is landscape dependent, and therefore varies with terrain type; specifically terrain roughness. This suggests that it is possible to determine a ‘more optimal’ viewshed algorithm with respect to terrain type, leading to considerable computational savings.
The research I developed during my MSc. objectively identified and defined the natural landforms in Snowdonia, Wales. Before this work little research had been undertaken on how landforms are defined and how to handle or identify landform boundaries. Using a fuzzy k-means classification of elevation derivatives, landforms present in the landscape are identified by the classification. The fuzzy membership values provide information on landform boundaries. This research not only aimed to advance further the fuzzy identification of landforms from DEMs by examining landform boundaries, but it also examined the change in landforms identified in a DEM of the same area, but at different resolutions. The research identified that most landforms are scale specific ridges are particularly scale specific largely as a result of the initial DEM spatial resolution and their discreteness in spatial and attribute space.
More recent work has looked to characterise landscapes by the scaling of their surface roughness, to generate a multi-scale index to characterise landscape types derived from variations in surface roughness at multiple scales. Landscape characterisations that reflect surface geomorphology, differentiating between surface types e.g. fluvial vs. glacial, erosional vs. depositional, soft vs. hard geology were found to exhibit different surface roughness scaling trends. Suggesting that scaling roughness trends provide meaningful measures, where local variability in surface properties governs the convergence and divergence of mass and energy which form critical controls on surface processes.
My interests in environmental modelling are closely linked to those above but incorporate more climatic and contextual datasets. Spatially distributed climate datasets are integral to the spatial accuracy of many surface models and their modelling forms a major component of my current research. Information on regional pressure and weather systems combined with local topographic information allows improved estimates of spatial and temporal patterns of local weather to be made, either using local data or downscaled general circulation model (GCM) datasets. These will form an important component of many environmental modelling initiatives.
I am interested in the role that environmental context, defined by topography and vegetation plays in prehistoric habitation. Initial results from field seasons in Laos with colleagues from the University of Pennsylvania, suggests that they form an important control on the location and duration of habitation which could provide a framework from which patterns of habitation could be explained.
Uncertainty is important with reference to these spatially and temporally distributed data, but perhaps more importantly, and less well researched is the propagation of this uncertainty through an investigation or model. I am interested in the usefulness of adopting a fuzzy logic approach to incorporate uncertainty into modelling applications.
Other research interests
My other research interests include the use of remotely sensed data in geomorphological analysis.
- White, Joyce C, Lewis, Helen, Bouasisengpaseuth, Bounheuang, Marwick, Ben & Arrell, Katherine (2009). Archaeological investigations in northern Laos new contributions to Southeast Asian prehistory. Antiquity, 83 (319): Antiquity Publications.
- Arrell, Katherine, Wise, Steve, Wood, Jo & Donoghue, Danny (2008). Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data. Earth Surface Processes and Landforms 33(6): 943.
- Arrell, K.E., Fisher, P.F., Tate, N.J. & Bastin, L. (2007). A fuzzy c-means classification of elevation derivatives to extract the morphometric classification of landforms in Snowdonia, Wales. Computers & Geosciences 33(10): 1366.
- Durham, Helen & Arrell, Katherine (2007). Introducing new cultural and technological approaches into institutional practice: an experience from geography. British Journal of Educational Technology 38(5): 795.