Project institution: Lancaster University
Project supervisor(s): Prof Jess Davies (Lancaster University), Prof Lindsay Beevers (University of Edinburgh), Dr Simon Moulds (University of Edinburgh) and Prof Gordon Blair (Lancaster University)
Overview and Background
Soil-water interactions are fundamental in a number of environmental processes and play a pivotal role in flood management, plant growth, and nutrient cycling. However, these interactions are highly complex and we currently rely on computationally intensive process-based models to help understand these processes and predict their influence on ecosystem services. With recent advances in satellite imagery and sensing, soil moisture data and other relevant data products are now available at spatial and temporal scales suitable for enhancing these models. However, integrating large volumes of data with these complex models is challenging. This studentship focuses on taking advantage of new exascale computing approaches to facilitate data assimilation, exploring how the fusion of big-data with soil-water process models can help unlock new insights and understanding.
Teaser Project 1: Improve process-based model representation of the long-term effects of extreme weather on soil carbon and nutrient cycling through remote sensing data assimilation
The lack or over-abundance of water can have large effects on plants, especially on annual crops where water conditions can severely affect the plant’s growth and survival. With changing water patterns and increasing frequency of heat waves and extreme rainfall events, the effects on plant productivity are expected to be large, and there will be knock-on effects for soil carbon storage and nutrient cycling in the longer-term. Remote sensing offers many data products that can provide us with data-based insights into plant productivity and soil moisture conditions. However, remote sensing of soil carbon is much more difficult, and understanding of the long-term response to changes in plant productivity still requires process-based models. In this project we will experiment with combining remote sensing data and process-based models to better understand the long-term effects of extreme weather on soil carbon and nutrient cycling.
Methods and PhD Pathway:
- The process-based model N14CP, which simulates plant-soil carbon, nitrogen and phosphorus cycling and export of dissolved nutrients to waterways will be adapted to assimilate (Gross or Net) Primary Productivity (GPP and NPP) and soil moisture data remote sensing products.
- The student will explore a range of approaches: from direct insertion to machine learning methods. The first teaser will begin with NPP as this has a direct proxy in the model. Freely available datasets for example from MODIS and SMAP that match the spatial resolution of the model will provide a starting point.
- To develop this path into a full PhD: multiple methods for assimilation will be explored; two-way learning between data and models will be considered; and methods for making data-assimilation real-time will be explored, with the use of exascale/GPU computing, helping move towards a digital twin.
Teaser Project 2: Estimate the contribution of soils to mitigating or increasing flood risk in a case study catchment by combining remote sensed soil moisture data and hydrological models
Antecedent soil saturation conditions can play a significant role in mitigating or increasing flood risk. If the soil has significant water held in storage, then its ability to act as a storage during times of high rainfall reduces.
Soil moisture is an important component in semi-distributed or distributed hydrological models, however, it is not routinely updated dynamically throughout the process of a simulation. With newly available satellite observations soil moisture is now available at good resolution temporally and spatially, such that is could be used to improve flood routing and water balance within catchment hydrological models.
Combining remote sensing and soil water probes offers an opportunity to develop real-time estimation for soil water, across catchments. The student will explore different approaches to data assimilation and upscaling to the catchment scale.
Methods and PhD pathway:
- Combining soil moisture estimates into hydrological and eventually hydraulic modelling for flood inundation estimation will entail significant challenges; firstly in the assimilation of data, secondly through the computational burden, and thirdly through the coupling of models in an online dynamic manner.
- Each of these challenges requires different innovative study and a range of methods.
- The student will be able to pick one or more of these three challenges to explore and develop into a full PhD, should they choose this pathway.
- For example, the coupling between hydrology requires the exploration of different coupling approaches, and will require consideration of processes such as the Basic Model Interface (BMI).
References and Further Reading
