PhD: The implications of climate and land cover change for river water quality: model development and scenario assessment
Overview: This studentship aims to quantify the response of multiple pollutants in river-systems to projected changes in climate and land cover, and to policy instruments. This understanding is needed to improve UK water quality within the context of projected population increase, agricultural intensification and climate change. Specifically, the project will develop a cutting-edge multi-pollutant water quality model based on existing approaches and using the most detailed observations of water chemistry available in the UK.
Background: UK government departments, devolved administrations and national environment agencies are charged with improving the chemical and ecological status of UK rivers, but it is unclear how the nine policy instruments spanning regulation, protected areas and pollution control will affect different water quality constituents in combination. Given this, new catchment-scale water quality models are needed to help assess which policy instruments and intervention measures (e.g. buffer strips, adapted land management) will improve multiple aspects of water pollution, where intervention measures should be placed, how long the response will be, and whether climate change will confound these actions. Most water quality models have been developed for individual chemical constituents and a step change is needed to simulate multiple pollutants at appropriate scales for management.
Aims/Objectives: The overall aim is to develop a process-based, catchment-scale water quality model that simulates the response of multiple pollutants (carbon, nitrogen and phosphorous) to multiple drivers. To achieve this aim, there are three objectives: (1) To develop and test a new catchment-scale water quality model that simulates the response of multiple pollutants to multiple drivers at spatial and temporal scales suitable for the assessment of a range of measures at field and catchment scale; (2) To investigate uncertainty introduced by model structure, model parameters as well as input data (measurement uncertainty, resolution); (3) To produce modelled outcomes, with uncertainty estimates, from environmental change scenarios, including intervention measure effectiveness.
Methods/Approach: A catchment-scale multi-pollutant model will be developed (obj.1) based on two existing single-pollutant models developed at The James Hutton Institute, STREAM-N (Dunn et al. 2013) and SimplyP (Jackson-Blake et al. 2017), and/or the addition of C, N and P cycles into the new fully distributed, open-source, hydrological model mHM based on what has been learnt from the development of STREAM-N and SimplyP (and the INCA suite of models developed at Reading). The use of the model will allow inclusion of the relevant hydrological and biochemical processes at the spatial and temporal scales suitable for assessing the effectiveness of a range of measures including field scale. The new model will be set up and parameterized for two contrasting agricultural catchments in the UK– the River Kennet catchment (A = 1200 km2), a lowland catchment in southern England and the Tarland Burn catchment (A = 51 km2), a catchment at the upland fringe in northeast Scotland– by calibration against observed concentrations. The model performance will be tested on different temporal (River Kennet: daily, weekly, monthly, annual; Tarland Burn: monthly and annual) and spatial (catchment outlet and tributaries) scales. The uncertainty introduced by input data, model structure and model parameters (obj. 2) will be investigated by comparing simulation results using input data of different spatial and temporal resolution, different model structures (e.g. coupled vs. independent C, N and P processes) as well as different model parameterisations. Potential impacts of environmental change scenarios, including intervention measure effectiveness will be simulated (obj. 3) using the method defined in Skuras et al. (2013). The analysis will focus on the question to what extent and at which spatial and temporal scales uncertainties in input data, model structures and model parameterisations would influence the effectiveness of mitigation measures in the River Kennet and Tarland Burn catchments.
Training opportunities: The training will be split between James Hutton Institute and the University of Reading. Training will be given in environmental data analysis, Geographical Information Systems and statistical and process - based environmental modelling including programming. There will be opportunities to visit the field study sites and the student will be part of a large community of environmental scientists at Reading and James Hutton Institute.
Student profile: This project is suitable for those curious about the natural world and interested in mathematical modelling with a background in environmental science, environmental engineering, hydrology, mathematics, physics, civil engineering or other numerate discipline. Flexibility to work between Aberdeen and Reading is expected and the details will be worked out together with
The studentship is funded under the James Hutton Institute/University Joint PhD programme, in this case with the University of Reading.. Applicants should have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent).Shortlisted candidates will be interviewed in Jan/Feb 2018. A more detailed plan of the studentship is available to candidates upon application. Funding is available for European applications, but Worldwide applicants who possess suitable self-funding are also invited to apply.