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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 12
Hydrol. Earth Syst. Sci., 21, 6425-6444, 2017
https://doi.org/10.5194/hess-21-6425-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 6425-6444, 2017
https://doi.org/10.5194/hess-21-6425-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 18 Dec 2017

Research article | 18 Dec 2017

Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data

Mary C. Ockenden1, Wlodek Tych1, Keith J. Beven1, Adrian L. Collins2, Robert Evans3, Peter D. Falloon4, Kirsty J. Forber1, Kevin M. Hiscock5, Michael J. Hollaway1, Ron Kahana4, Christopher J. A. Macleod6, Martha L. Villamizar7, Catherine Wearing1, Paul J. A. Withers8, Jian G. Zhou9, Clare McW. H. Benskin1, Sean Burke10, Richard J. Cooper5, Jim E. Freer11, and Philip M. Haygarth1 Mary C. Ockenden et al.
  • 1Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster, LA1 4YQ, England, UK
  • 2Rothamsted Research North Wyke, Okehampton, Devon, EX20 2SB, England, UK
  • 3Global Sustainability Institute, Anglia Ruskin University, Cambridge, CB1 1PT, England, UK
  • 4Met Office Hadley Centre, Exeter, Devon, EX1 3PB, England, UK
  • 5School of Environmental Sciences, Norwich Research Park, University of East Anglia, Norwich, NR4 7TJ, England, UK
  • 6James Hutton Institute, Aberdeen, AB15 8QH, Scotland, UK
  • 7School of Engineering, Liverpool University, Liverpool, L69 3GQ, England, UK
  • 8School of Environment, Natural Resources and Geography, Bangor University, Bangor, Gwynedd, LL57 2UW, Wales, UK
  • 9School of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, Manchester, M1 5GD, UK
  • 10British Geological Survey, Keyworth, Nottingham, NG12 5GG, England, UK
  • 11School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK

Abstract. Excess nutrients in surface waters, such as phosphorus (P) from agriculture, result in poor water quality, with adverse effects on ecological health and costs for remediation. However, understanding and prediction of P transfers in catchments have been limited by inadequate data and over-parameterised models with high uncertainty. We show that, with high temporal resolution data, we are able to identify simple dynamic models that capture the P load dynamics in three contrasting agricultural catchments in the UK. For a flashy catchment, a linear, second-order (two pathways) model for discharge gave high simulation efficiencies for short-term storm sequences and was useful in highlighting uncertainties in out-of-bank flows. A model with non-linear rainfall input was appropriate for predicting seasonal or annual cumulative P loads where antecedent conditions affected the catchment response. For second-order models, the time constant for the fast pathway varied between 2 and 15h for all three catchments and for both discharge and P, confirming that high temporal resolution data are necessary to capture the dynamic responses in small catchments (10–50km2). The models led to a better understanding of the dominant nutrient transfer modes, which will be helpful in determining phosphorus transfers following changes in precipitation patterns in the future.

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This paper describes simple models of phosphorus load which are identified for three catchments in the UK. The models use new hourly observations of phosphorus load, which capture the dynamics of phosphorus transfer in small catchments that are often missed by models with a longer time step. Unlike more complex, process-based models, very few parameters are required, leading to low parameter uncertainty. Interpretation of the dominant phosphorus transfer modes is made based solely on the data.
This paper describes simple models of phosphorus load which are identified for three catchments...
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