I am a climate scientist based at the University of Bristol, specialising in the prediction of climate change impacts on water resources in glacier and snow-fed catchments. My research also involves developing models to accurately forecast future sea-level rise resulting from melting glaciers and ice sheets.

My work centres around three core themes: first, I write code to model physical processes; second, I calibrate and validate models against historical observations, including remote sensing datasets; and finally, I run multi-model projections to predict the impacts of climate change under various CO₂ mitigation scenarios.

My research journey began as an atmospheric scientist, where I developed a desert dust model to explain high concentrations of Saharan dust observed in Barbados during the 1980s. More recently, I have focused on assessing the links between climate change and glacier lake outburst floods (GLOFs) in Peru and Chile.

I am currently interested in working at the intersection between climate change and AI.

If you would like to find out more about my projects, click on the research link to see a summary of my current and past research projects.

Projects

Glacier melting and water security in Central Asia, 2018-2022

Glacial Hazards in Peru #projectGLOP, 2018-2022

Glacier Model Intercomparison Project II, 2015-2019

Glacial Hazards in Chile, 2016-2018

High-End Climate Impacts and Extremes, 2013-2017

Ice2sea, 2009-2013

news

Aug 29, 2023	I am soon to join the ember energy think tank, where I will be using satellite data to identify methane emissions from coal mines.
Jun 1, 2023	Completed the Andrew Ng machine learning specialisation course, containing an excellent explanation the maths behind AI algorithms.
Apr 16, 2023	Just completed a 5-week intensive Science to data science (S2DS) bootcamp . Alongside four data scientists, we developed an automatic computer vison tool to calculate the properties of welds from images for the Electrical Power Research institute.

selected publications

1. HESS

   A snow and glacier hydrological model for large catchments – case study for the Naryn River, Central Asia

   Shannon, S. R., Payne, A., Freer, J., Coxon, G., Kauzlaric, M., Kriegel, D., and Harrison, S.

   Hydrology and Earth System Sciences 2023

   Abs HTML PDF

   In this paper we implement a degree day snow and glacier melt model into the Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) model. The purpose is to develop a hydrological model that can be applied to large glaciated and snow-fed catchments, yet is computationally efficient enough to include model uncertainty in streamflow predictions. The model is evaluated by simulating monthly discharge at six gauging stations in the Naryn River catchment (57,833 km2) in Central Asia over the period 1951 to a variable end date between 1980 and 1995 depending on the availability of discharge observations. The spatial distribution of simulated snow cover is validated against MODIS weekly snow extent for the years 2001–2007. Discharge is calibrated by selecting parameter sets using Latin Hypercube sampling and assessing the model performance using six evaluation metrics.The model shows good performance at simulating monthly discharge for the evaluation period (NSE is 0.74