Surface and subsurface water contamination is the leading cause of many diseases, death, and human disaster. Tracking groundwater volubility in fine temporal scale resolution is one of the main challenges in conceptualization of the what-if scenarios modeling framework. The proposed project aims to advance the use of the integration of remote sensing, GIS and statistical methods to assess groundwater vulnerability, temporally (less than 1 day time step), by explicitly introducing the time dimension, remote sensed soil moisture data and land use data in the analysis. This project will be based on developing an innovative integrated framework to map groundwater vulnerability applying GIS and remote sensing in a fine temporal scale. This project will use integrated datasets of observed groundwater quality data and satellite images from NASA’s SMAP project for soil moisture and several statistical downscaling techniques. In addition land use trends will be modelled the through radar backscatter data, acquired by the SeaWinds scatterometer aboard the QuikSCAT satellite. The proposed framework will enable breakthrough advances to improve the mapping of hazardous areas with different levels of vulnerability and risks and to assess the efficacy of land use planning toward groundwater protection.
Project objectives:
· To develop an innovative integrated framework to map groundwater vulnerability in a fine temporal scale by applying an integrated AI, GIS, remote sensing and statistical downscaling methods.
· To analysis satellite images from NASA’s SMAP project for soil moisture over the North-West Region of Ireland.
· To modelling the land use trends through radar backscatter data acquired by the SeaWinds scatterometer aboard the QuikSCAT satellite.
· To use low-cost water quality sensors to collect fine temporal-scale groundwater quality datasets.
Relevant Qualification Disciplines: Environmental or civil engineering, environmental science, hydrology or a related discipline with a strong component of GIS and hydrological modelling.
Candidate Attributes:
The ideal candidate is a highly motivated individual with a first-class or high second-class honours degree/master’s in environmental or civil engineering, environmental science, hydrology or a related discipline with a strong component of GIS and hydrological modelling. The PhD study includes an essential component of fieldwork at the selected case study (field site), and therefore, practical experience in fieldwork will also be an advantage. Coding skills in R, Python or MATLAB are desirable. Previous research experience in environmental modelling will be advantageous.
APPLICATIONS
Please send to Veronica Cawley – [Email Address Removed] Only using the application form.
Application Form / Terms of Conditions can be obtained on the website:
https://www.itsligo.ie/mochas/
The closing date for receipt of applications is 5pm, (GMT) 9th of May 2022
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