Water, Climate and Development: Google Earth Engine for water resources

Overview

Inland waters represent 3.7% of the world’s non-glaciated land surface but provide 84% of the freshwater resources that support ecosystem services essential for human survival, health, and wellbeing. Lakes and reservoirs integrate the impacts of human activity occurring within their catchments including nutrient inputs (fertiliser runoff, animal wastes and human sewage), pollution (heavy metals, plastics and organics), hydrological and hydromorphic modifications associated with energy production and navigation. In many regions, these pressures are further compounded by rapid climate change. The decline in the availability of good quality water is now considered to be the pre-eminent risk to society and the global economy. These effects are disproportionally felt in low and lower-middle income countries with lower resilience to climate change, poorer public health provision and weaker governance structures. Improvements in water governance are often impeded by a lack of data to support evidence-based decision-making to protect public health and the environment while sustaining economic growth.
Earth observation (EO) capabilities for global surface water observation over the last decades have radically transformed the approaches to monitor and sustainably manage inland and nearshore waters. However, EO of inland waters still faces some unique challenges. In some cases, new algorithms need to be considered for higher spatial resolution sensors (e.g. Sentinel-2) to serve user requirements for monitoring of near-shore waters and smaller inland water bodies. Ground data to validate EO product accuracy have started now finding their way to central databases (e.g. LIMNADES) data from low- and lower-middle income countries are very sparse. Google Earth Engine (GEE) provides a unique platform for analysis of EO and other geospatial data at regional to global scales. GEE now includes the Landsat and Sentinel-2 collections enabling EO research of not only aquatic but also terrestrial ecosystems.
The main aim of this PhD is twofold: 1) to understand water resources and their quality in low- and lower-middle income countries; and 2) to enable evidence-based management of degraded aquatic ecosystems in the context of climate change that look beyond water quality.
With data-driven knowledge, this PhD will ultimately suggest contextually and culturally acceptable, inclusive solutions for water quality management in specific regions.
This project builds on the world leading EO capability that was developed during the NERC GloboLakes project (2012-2018; www.globolakes.ac.uk) and the expertise that has been brought together through a number of projects funded by H2020, Global Challenges Research Fund, European Space Agency and Google. This will provide important areas of further research collaboration.

Methodology

This PhD will bring together Earth observation, GEE processing and data analytics.

Earth observation: The applicant will develop or optimise algorithms for the retrieval of water quality (focusing initially on chlorophyll-a) and water quantity parameters in water bodies identified by GEO Aquawatch and World Bank Group. Algorithm development will be based on optical water type frameworks (Spyrakos et al. 2018) or/and data driven approaches (Spyrakos et al. 2011). Ground data, for the development and validation of the models in these water systems, will be provided by initiatives led by USTIR such as Limnades (https://limnades.stir.ac.uk/Limnades_login/index.php) and Aquawatch (https://www.geoaquawatch.org/). Both Sentinel 2 and LandSat satellites will be exploited to retrieve water constituents. Simulated (Hydrolight) spectra will also be generated to fill gaps in the in situ data record, to contribute to algorithm development and uncertainty characterisation.
GEE: GEE will be used to process large volume of remotely sense data. These will include water quality and quantity parameters but also available data of land cover, catchment and climatic variables. GEE will make it easier to build inventories with high spatial and temporal resolutions, since processing of the often large remote sensing data can be performed in the cloud. It also allows for reanalysis of the data to build climatology.
Data analytics: Innovative tools in environmental data analytics including functional data analysis will be investigated and applied for temporal trend (O’Donnel et al., 2015) and climatology. These data analytics approaches will be applied (and developed) in the R software environment. Non-parametric time series analysis will be used to identify the presence and strength of key underlying long-term patterns in the EO data. Where relevant this analysis will be developed to account for autocorrelation, identify change points and explore patterns beyond the mean, modelling quantiles to assess if changes over time are constant across all levels of the variables of interest.

Project Timeline

Year 1

Development of the research proposal review and science plan, sites selection and initial training in research design, data analytics, GEE and EO. Initial collection of ground data and identification of data gaps

Year 2

Algorithm development and processing of EO data. Initial time-series analysis of water quality parameters. Collection of climate data and implementation of tools and algorithms in GEE

Year 3

Final analysis: Multi-sensor (Sentinel-2 MSI and LandSat) analysis and correlation between water quality, climatic data and land cover.

Year 3.5

Water management suggestions, Write up and submission.

Training
& Skills

The candidate will be trained in processing satellite image data and the application and tuning of algorithms for the accurate retrieval of in-water constituents, including atmospheric correction, and the development of processing chains. The candidate will be equipped with the know-how to perform accurate measurements of bio-optical properties and carry validation activities of remote sensing data and products for several available sensors. Though GEO Aquawatch the candidate will have access to basic and advanced training in Google Earth Engine, including machine learning. The candidate will present her/his/their findings annually within a postgraduate research symposium specific to Stirling, and international conferences. The student’s progress will be subject to annual progress reviews. All research students are members of Stirling’s Institute of Advanced Studies and are encouraged to attend seminars (that are particularly relevant to them in addition to the generic training skills provided by the IAPETUS DTP. Students also take advantage of the opportunities for networking with external visitors and students from other academic areas to promote interdisciplinarity.

References & further reading

IOCCG, 2018. Earth Observations in Support of Global Water Quality Monitoring. Greb, S., Dekker, A. and Binding, C. (eds.), IOCCG Report Series, No. 17, International Ocean Colour Coordinating Group, Dartmouth, Canada.
O’Donnell, Miller, and Scott, 2015. Spatially weighted functional clustering of river network data. Journal of the Roy. Stat. Soc.: Series C, 64(3):491-506.
Spyrakos, Gonzalez Vilas, Torres Palenzuela, and Barton, 2011. Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle, Remote Sensing of Environment, 2471-2485.
Spyrakos, O’Donnell, Hunter, Miller, Scott, Simis, Tyler, et al., 2018. Optical types of inland and coastal water, Limnology and Oceanography. 63, pp. 846-870.
Tyler, Hunter, Spyrakos, Groom, Constantinescu and Kitchen, 2016. Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters, Science of the Total Environment, 572, pp. 1307-1321.

Further Information

Dr Evangelos Spyrakos, evangelos.spyrakos@stir.ac.uk, +44 (0) 1786 467759

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