Active sand seas are present in many arid environments across the globe. Normally associated with hot, dry conditions their location, extent and dune forms, are key indicators of past and current environmental processes. Dune patterns are known to develop over timescales of thousands of years, and as such they document past geomorphic and climatic situations. A better understanding of these relationships can help increase our knowledge of dune form controls and interpretation of stratigraphic evidence to reconstruct past climatic conditions.

In any one sand sea the morphological classification of dunes often varies spatially, a firm indication that at more localised scales there is a deeply complex mix between climatic and geomorphic processes, with bedform patterns developing due to self-organisation.

Transitions between different dune types are not well understood, particularly where there is frequent but intermittent fluvial activity in a predominantly aeolian environment. As a result, spatial distribution of dune-form, is a function of these disturbances both temporally and spatially. The boundary controls need to be assessed as they ultimately affect the evolution of dune field form in the sand seas.

In central Saudi Arabia, extensive sand seas that are on the margins of current weather systems such as the Indian monsoon, are likely to be highly sensitive to localised controls and longer term climatic change. The project aims to connect the present relationships between different dune forms and environmental processes to better understand the local controls embedded within the regional climate and test how far these processes can be simulated with a dune development model. The project will involve satellite-derived data mapping the variation in dune forms and local topographic characteristics. Using this evidence a dune model will then be run to simulate dune development under single and multiple, local and regional conditions from source to sink. 

This research will provide key information for palaeo-environmental reconstruction in a region where little is known about climatic changes, ever more relevant because central Saudi Arabia has been elevated in importance for the facilitation of historical human migration.

Sand Sea, Central Saudi Arabia


Digital elevation data GDEM and MODIS images will be used to map the extent and mineral characteristics of the sand seas in central Saudi Arabia. Collected samples will be analysed and the findings will drive an agent-based dune formation simulation model.

MODIS imagery will be used to spectrally identify the sand sea boundaries using image classification techniques and to identify spectral variations caused by subtle mineralogical changes. The images will also be used to assist with the DEM work to identify dune types and extent.

The DEM data, GIS and statistical techniques will be applied to: (i) identify and map geomorphic patterns; (ii) look for underlying structural differences in and under the sand sea using the technique ‘back stripping’; (iii) interpolation to model the underlying geological structures; (iv) map the hydrological patterns and morphology around the sand sea to identify local controls in the region; (v) identify and model complex dune patterns.

Training and Skills

CENTA students are required to complete 45 days training throughout their PhD including a 10 day placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to CENTA research themes. 

In the first year students will undertake training in research methods, desert geomorphology and Quaternary sciences. Training in years 2 and 3 will develop towards masterclasses that are specific to the project.

The student will gain training in analysis of remote sensing data, specific GIS techniques, and understanding and recognising desert sand dune features.  In addition, training will be given on the development and use of a cellular automata dune model.

Students will also receive training in the communication of science to academics as well as the general public, students, media etc. using a range of formats such as presentations, publications, interviews, social media and so forth.


Year 1: Literature review. Download MODIS images, and DEM. Pre-process images and image processing to define study area and build data set of study site. GIS analysis of DEM for (i-iv method). Submit manuscript to international conference.

Year 2: Complete GIS, Image processing of MODIS for mineralogical mapping, geochemical analysis of lab results. Set up and run dune model simulations. Prepare manuscript for publication. Submit abstract for international conference.

Year 3: Critically appraise and analyse results. Present at conference. Finalise papers. Write up thesis.


Partners and collaboration (including CASE)

Sue McLaren has supervised over ten PhD and MRes students (5 of whom were working in Saudi Arabia), examined PhD candidates both at Leicester and externally in the UK and abroad. She has worked extensively with Dr Bradley and Prof. Al Dughairi in KSA. Sue’s specialism is palaeoenvironmental reconstruction and geochemical sediments in drylands.

Andrew Bradley has supervised numerous masters by research and PhD students working in KSA. Andrew’s specialism is in remote sensing GIS and modelling.

Joerg Kaduk has also supervised a significant number of PhD and masters students. The skills he will lend to this project are in modelling.

Ahmed Al Dughairi works extensively in KSA covering Quaternary reconstructions, GIS and remote sensing. He has supervised numerous PhD and Master’s students at Qassim University.

Further Details

Contact Dr Sue McLaren: sjm11@le.ac.uk