I am a climate scientist at Ember, a clean energy think tank, where I use remote sensing data to monitor methane emissions from coal mines. My background lies in developing climate and hydrological models. In the past, I have:

• Developed a snow and glacier melt component for a hydrology model to predict the impact of glacier melting on river flows in Central Asian catchments.
• Created a glacier model for the Met Office land surface model, using it to predict future sea level rise.
• Developed a desert dust model to understand the impacts of shifting vegetation cover on desert dust concentrations, for my PhD.
• Applied computer vision techniques, including edge detection and image segmentation, to extract measurements from features in digital images.

I am excited by the potential of data science and AI to extract deeper insights from earth observation, drive advancements in climate model development, and create innovative solutions to tackle climate change

If you would like to find out more about my projects, click here.

news

Jul 1, 2024	I attended the Machine Learning for Earth Observation ML4EO conference at Exeter University. Here is my poster, which provides an overview of deep learning models for methane plume detection and emissions rates quantification.
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 < NSE < 0.87) and validation period (0.7 < NSE < 0.9) where the range of NSE values represent the 5th–95th percentile prediction limits across the gauging stations. The exception is the Uch-Kurgan station which exhibits a reduction in model performance during the validation period attributed to commissioning of the Toktugal reservoir in 1975 which impacted the observations. The model reproduces the spatial extent in seasonal snow cover well, capturing 86 % of the snow extent on average (2001–2007) for the median ensemble member of the best 0.5 % evaluation simulations, when evaluated against MODIS snow extent.We establish the present-day contributions of glacier melt, snow melt and rainfall to the total annual discharge and the timing of when these components dominate river flow. The model predicts the observed increase in discharge during the spring (April–May) associated with the onset of snow melting and peak discharge during the summer (June, July and August) associated with glacier melting well. At all stations snow melting is the largest component, followed by the rainfall and the glacier melt component. In August, glacier melting can contribute up to 66 % of the total discharge at the highly glacierised Naryn headwater sub-catchment. The glaciated area predicted by the best 0.5 % evaluation simulations overlap the Landsat observations for the late 1990s and mid-2000s. Despite good predictions for discharge, the model produces a large range of estimates for the glaciated area (680 km2–1,196 km2) (5th–95th percentile limits) at the end of the simulation period. To constrain these estimates further, additional observations such as glacier mass balance, snow depth or snow extent should be used directly to constrain model simulations.