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Dr Ben Evans

Dr Ben Evans

Research Assistant

Ben's research focuses on the coastal zone. I have particular interests in the interactions of biophysical processes and controls within saltmarsh ecosystems. I am currently working with the Cambridge Coastal Research Unit as part of the NERC RESIST-UK project ('Response of Ecologically-mediated Shallow Intertidal Shores and their Transitions to extreme hydrodynamic forcing in UK settings').


Ben's undergraduate research, undertaken within the Department of Geography, University of Cambridge (2006-2009) focused on the thermal effects of coastal management interventions in the context of managed realignment, and their implications for commercial oyster fisheries on the Essex coast (UK). His MPhil research developed methodologies for the detection of morphodynamic changes within complex coastal systems using colour and panchromatic aerial photographs and investigated the potential for inference of processes based on these data.


  • 2012-2018 PhD Coastal Geomorphology, University of Cambridge, Clare College
  • 2010-2011 MPhil Environmental Science, University of Cambridge, Clare College
  • 2006-2009 BA (Cantab. Hons.) Geography, University of Cambridge, Clare College


Until December 2013 Ben worked on the Cambridge Coastal Research Unit's contribution to the NERC Biodiversity and Ecosystem Services programme. The project involved an interdisciplinary team from a variety of institutions to establish relationships between biodiversity in a range of landscapes and the ecosystem services those landscapes provide. Ben's specific contribution, in conjunction with Dr Tom Spencer and Dr Iris Möller was an evaluation of the linkages between biodiversity within saltmarsh ecosystems and their capacity to attenuate incident wave energy, thereby providing coastal defence services. He continues to be involved with this project in addition to his current activities.

Ben subsequently worked with Dr Iris Möller and Dr Tom Spencer on the EU FP7 project FAST (Foreshore Assessment using Space Technology), in which Cambridge were leading the field data acquisition and had major contributions to the remote sensing component. This project aimed to assess the coastal protection and stabilisation functions of foreshores and floodplains using satellite remote sensing.

Ben studied for his PhD concurrently with FAST. His thesis, entitled "Data-driven Prediction of Salt Marsh Morphodynamics", investigated the longer-term stability of saltmarsh environments and the implications this may have for their future provision of critical ecosystem services. He used satellite and airborne imagery alongside numerical model predictions and secondary data sources to train spatially-explicit machine-learning algorithms that were capable of capturing and representing the complexity and dimensionality of coastal processes across a regional extent between The Humber and The Thames. The resulting models were then queried to investigate marsh morphological response under potential future environemental conditions.

Ben is currently working on the NERC-funded project RESIST-UK and the EU Hydralab+ project RESIST. Within these projects he will be investigating the geotechnical properties of marsh sediments, how these are modified by the vegetation growing on top them, and the implications thereof for marsh stability. Within RESIST-UK the work is largely based on field and remotely-sensed observations to address the question of whether it is possible to map controls on sediment stability across space using satellites and drones. Meanwhile, within RESIST the work is laboratory-based and involves characterising internal structures of sediments and roots using micro-CT scanning (in collaboration with Queen Mary University of London) and exposing the sediments to true-to-scale storm conditions in a large experimental facility in Hanover in order to assess interactions between structure and morphological response to known hydrodynamic conditions.

By bringing together the two projects, Ben's work will contribute to improved understanding of the physical oncrols on marsh erosion and an ability to map predictive parameters across space, thereby allowing for anticipation of vulnerable locations.