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The application of high temporal resolution data in river catchment modelling and management strategies

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Abstract

Modelling changes in river water quality, and by extension developing river management strategies, has historically been reliant on empirical data collected at relatively low temporal resolutions. With access to data collected at higher temporal resolutions, this study investigated how these new dataset types could be employed to assess the precision and accuracy of two phosphorus (P) load apportionment models (LAMs) developed on lower resolution empirical data. Predictions were made of point and diffuse sources of P across ten different sampling scenarios. Sampling resolution ranged from hourly to monthly through the use of 2000 newly created datasets from high frequency P and discharge data collected from a eutrophic river draining a 9.48 km2 catchment. Outputs from the two LAMs were found to differ significantly in the P load apportionment (51.4% versus 4.6% from point sources) with reducing precision and increasing bias as sampling frequency decreased. Residual analysis identified a large deviation from observed data at high flows. This deviation affected the apportionment of P from diffuse sources in particular. The study demonstrated the potential problems in developing empirical models such as LAMs based on temporally relatively poorly-resolved data (the level of resolution that is available for the majority of catchments). When these models are applied ad hoc and outside an expert modelling framework using extant datasets of lower resolution, interpretations of their outputs could potentially reduce the effectiveness of management decisions aimed at improving water quality.

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Acknowledgements

The authors would like to thank the Teagasc Agricultural Catchments Programme (ACP—funded by the Department of Agriculture, Food and Marine, Ireland) scientists, technologists, technicians and advisors for support received throughout this project. The authors also acknowledge John Haslett of the School of Computer Science and Statistics, Trinity College Dublin, for model conceptualization and statistical support, and two anonymous referees for their very helpful comments. The lead author was supported by a Walsh Fellowship through Teagasc and the second author was funded, in part, by Science Foundation Ireland, grant 10/CE/1855 to Lero—the Irish Software Engineering Research Centre (www.lero.ie). Concepts of LAM testing with high-resolution P data were developed with help from Dr. Rachel Cassidy, Agri-Food Biosciences Institute, Belfast. The research is based on a confidential dataset collected by a government funded research programme and, as a result, is unavailable for public access. However, summary statistics of all model outputs are provided in the online resource for transparency.

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Authors and Affiliations

  1. The Agricultural Catchments Programme, Teagasc, Johnstown Castle, Wexford, Ireland

    L. Crockford, G. Shortle & P. Jordan

  2. Geography, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland

    L. Crockford

  3. Crop and Environment Sciences, Harper Adams University, Edgmond, Shropshire, TF10 8NB, UK

    L. Crockford

  4. Statistics, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

    S. O’Riordain

  5. Department of Geography, National University of Singapore, Singapore, Singapore

    D. Taylor

  6. National Centre for Engineering in Agriculture, University of Southern Queensland, Queensland, Australia

    A. R. Melland

  7. School of Environmental Sciences, University of Ulster, Coleraine, Northern Ireland, UK

    P. Jordan

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  1. L. Crockford
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Crockford, L., O’Riordain, S., Taylor, D. et al. The application of high temporal resolution data in river catchment modelling and management strategies. Environ Monit Assess 189, 461 (2017). https://doi.org/10.1007/s10661-017-6174-1

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