Two sessions have been organized by World Water Quality Alliance and GEO AquaWatch members at the next European Geosciences Union (EGU) virtual General Assembly for your abstract submission. The [extended] deadline for abstract submission is 15 January 2021, 13:00 CET.
SESSION 1: HS6.9: “From short-term detection to long-term projections: complementing water quality assessments by combining modelling and remote sensing”
This session focuses on regional and global water quality research where remote sensing and modelling are combined in order to complement a water quality assessment compared to one based on monitoring data only. This would cover studies such as on Covid-19 arising on short-term as well as long-time developments such as eutrophication. The following topics are of particular interest for this session:
- Processing water quality data from remote sensing products across scales
- Comparing near-real time remote sensing information with baseline
- conditions obtained from modelling
- Complementing results from modelling and remote sensing
- Remote sensing facilitating water quality model development and modelling
For more information see: https://meetingorganizer.copernicus.org/EGU21/session/39516
Convener team: Martina Flörke (Ruhr-University Bochum), Ilona Bärlund (UFZ), Stefan Simis (Plymouth Marine Laboratory) and Ting Tang (IIASA)
SESION 2: HS2.2.4: “Drivers and impacts of freshwater salinisation: from data to modelling approaches across spatio-temporal scales”
If you’re working on topics relating to freshwater salinisation – we are especially interested in research quantifying and predicting historic to future salinisation patterns, drivers and impacts at catchment to global scales, using both data and/or model-driven approaches – then please consider submitting to our session! Contributions including – but not limited to – any of the following topics are of particular interest for this session:
- Surface water and groundwater interactions and its effects on salinity dynamics
- Impacts of hydrological extremes and seasonality on salinity levels of freshwater resources
- Human and hydro-climatic drivers of freshwater salinisation across different spatial and temporal scales
- Implications of inland salinity for ecosystem health and sectoral water use
- Applications of surface and/or groundwater in-situ and remote sensing data, and/or data-driven models to determine salinity concentrations across multiple scales
- Global change (e.g. climate change, land use change) impacts on future freshwater salinisation
- Assessment of management and adaptation measures to salinity changes
Our invited speaker is Miguel Cañedo-Argüelles Iglesias of Barcelona University. For more information see: https://meetingorganizer.copernicus.org/EGU21/session/39649
Convener team: Josefin Thorslund (Stockholm University), Martina Flörke (Ruhr-University Bochum), Sujay Kaushal and Michelle van Vliet (Utrecht University)
Registration is required!
The LSI-VC series on CEOS Analysis Ready Data for Land (CARD4L) isscheduled for February 1-2, 2021. The webinar series is being organised as a means to strengthen the dialogue between CEOS and the broader community on the topic of ARD (ceos.org/ard), and to explore what interfaces and cooperative activities are needed to increase data use, choice and flexibility for users.
This second webinar will cover an introduction to the CARD4L specifications for Synthetic Aperture Radar (SAR) sensors, and provide some examples of actual uptake of CARD4L SAR products by the EO community, both public and private. The agenda will include significant time for discussion to promote dialogue between participants. Duration 75 minutes.
To accommodate participation at decent hours across most time zones the webinar will be repeated during two time slots 12 hours apart:
Time slot 1:
February 1, 2021 (Mon): 15:00-AKST/ 16:00-PST / 19:00-EST / 21:00-ART
February 2, 2021 (Tue): 08:00-CST / 09:00-JST / 11:00-AEDT
Time slot 2:
February 2, 2021 (Tue): 07:00-EST / 09:00-ART / 12:00-UTC / 13:00-CET / 15:30-IST / 20:00-CST / 21:00-JST / 23:00-AEDT
All are welcome to join. More information and a registration form can be found here. Connection details will be shared with all registered participants in due course. The CEOS ARD website has further background information and links to the CEOS ARD Strategy and CARD4L Product Family Specifications:
This PhD will bring together Earth observation, GEE processing and data analytics. To find out more and apply click here.
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.