Forecasting coastal water quality using earth observations for future resilience

Project Goal: The project will aim to develop a novel water quality forecast model to enable better coastal ecosystem management. This new modelling approach will rapidly integrate large volumes of satellite and in-situ earth observations of climate, terrestrial and ocean parameters in a cloud computing platform to provide coastal water quality forecasts across Australia.

Research challenge

Water quality degradation in coastal aquatic ecosystems is a global problem[1-2]. In Australia, reduced water quality impacts coastal ecosystems (total annual asset value >$900 billion [3]) and related marine industries (which contribute >$50 billion/year to the Australian economy[4])[5-6]. Climate-driven changes such as droughts, forest fires, cyclones and extreme floods influence inland water, land-to-ocean outflows and coastal ecosystems[7-8]. Estuarine and coastal aquatic ecosystems are also impacted by human activity[9] in catchments, such as mining, deforestation, infrastructure development and poor agricultural practices[10]. Degradation of water quality changes the structure, composition and extent of aquatic habitats and has flow-on effects such as fish populations and species diversity, sometimes driving species to extinction[11]. Degrading marine environments eventually disrupts marine ecosystem functioning, resulting in socio-economic loss to human populations that depend on aquatic resources[4,3,5]. Thus, in the context of changing climate and anthropogenic influences on ecosystems, there is an urgent need to develop approaches that can rapidly generate knowledge of water quality degradation (as scenario modelling projections and forecasts) that can support better decision-making for coastal aquatic ecosystem management and restoration.

Current limitation

Current in-situ sampling programs and ecosystem models have many limitations in offering management-relevant observations and forecasts. Monitoring programs involving seasonal in-situ sampling and fixed location measurements offer limited spatial and temporal knowledge of water quality[12]. Ocean colour remote sensing provides long historical records[13] but does not offer insights into climate-land-ocean connections[14] that influence coastal water quality. Coastal ecosystem model-based forecasts are very expensive to establish (for example, the eReefs project for GBR has cost about $3 million), time-consuming to develop and available only in limited locations[15]. Thus, due to the limitations in existing monitoring programs and the lack of region-specific land-to-ocean ecosystem models, it is challenging to offer coastal water quality forecasts (across Australia) with appropriate lead times (i.e., 1-24 months) that are best aligned with management and industry requirements[16].

Pathway to solution

Lack of knowledge about regional drivers and pressures that impact coastal oceans is a challenge for ecosystem managers (such as the New South Wales Department of Planning, Industry and Environment and Marine Estate Management Authority; NSW DPIE and MEMA) in addressing the sources of water quality degradation and their impact on estuarine and coastal ecosystems[17]. The solution to overcome this environmental challenge and thereby support more effective management of estuarine and coastal waters in the project study area (NSW coastal regions) involves the following objectives:

  1. Quantify relationships between climate, terrestrial ecosystems, catchment hydrology and land-to-ocean outflows.
  2. Model the variability in land-to-ocean outflows and its impact on coastal water quality
  3. Forecast coastal water quality using earth observation data.
  4. Communicating scientific advances and end-user engagement

The study focuses on developing an entirely new earth observation data-driven approach for forecasting coastal water quality. Earth observation data-driven coastal water quality forecasting approaches are in their infancy worldwide; hence this project represents a significant scientific advancement that would enable a broad range of subsequent innovations in coastal ecosystem monitoring, management, and restoration.

Currently used coastal ecosystem model-based forecasts are very expensive to establish and are available only at limited locations. To overcome this challenge, the work emphasises novel approaches to use synergistically, satellite observations of multiple biogeophysical parameters (describing variability in climate, land, and ocean) and in-situ sensor network data in a fully integrated modelling approach with uncertainty quantification(e.g., machine learning, Bayesian or hybrid), to significantly advance our capability to forecast coastal water quality. This project innovatively uses recent advancements in open DataCube technology, including the CSIRO-EASI platform, to rapidly process and analyse multidecadal multi-sensor data to provide coastal water quality forecasts. Project outputs include new methods of identifying pollution hotspots, recognising region-specific drivers and forecasting water quality. Such results will provide a step change towards supporting coastal managers to achieve sustainable development(SDG14) and support marine resource-dependent blue economy industries.

Research Team

Supervisory Committee

References

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  2. Das, N.; Mahanta, C.; Kumar, M. 2020. Water quality under the changing climatic condition: A review of the Indian scenario. In Emerging Issues in the Water Environment during Anthropocene; Springer: Berlin/Heidelberg, Germany, pp. 31–61.
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  7. Lewis, Stephen E. et al., 2021. “Land use change in the river basins of the Great Barrier Reef, 1860 to 2019: A foundation for understanding environmental history across the catchment to reef continuum.” Marine Pollution Bulletin 166: 112193.
  8. Joehnk, K., Biswas, T.K., Karim, F., Kumar, A., Guerschman, J., Wilkinson, S., Rees, G., McInerney, P., Zampatti, B., Sullivan, A. and Nyman, P., 2020. Water quality responses for post 2019-20 bushfires floods in south eastern Australia: a catchment scale analysis.
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  13. Cherukuru, Nagur, et al., 2021. “A semi-analytical optical remote sensing model to estimate suspended sediment and dissolved organic carbon in tropical coastal waters influenced by peatland-draining river discharges off Sarawak, Borneo.” Remote Sensing,13.1, 99.
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  15. Steven, Andrew DL, et al., 2019. “eReefs: an operational information system for managing the Great Barrier Reef.” Journal of Operational Oceanography, 12.sup2: S12-S28.
  16. Jacox, Michael G., et al.,2020. “Seasonal-to-interannual prediction of North American coastal marine ecosystems: Forecast methods, mechanisms of predictability, and priority developments.” Progress in Oceanography 183, 102307.
  17. Bugnot, A. B., et al., 2018. “A novel framework for the use of remote sensing for monitoring catchments at continental scales.” Journal of environmental management, 217, pp. 939-950.