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

Ockenden, Mary C., Tych, Wlodek, Beven, Keith J., Collins, Adrian L., Evans, Robert, Falloon, Peter D., Forber, Kirsty J., Hiscock, Kevin M., Hollaway, Michael J., Kahana, Ron, Macleod, Christopher J.A., Villamizar, Martha L., Wearing, Catherine, Withers, Paul J.A., Zhou, Jian G., Benskin, Clare McW. H., Burke, Sean, Cooper, Richard J., Freer, Jim E. and Haygarth, Philip M. (2017) Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data. Hydrology and Earth System Sciences (HESS), 21 (12). pp. 6425-6444. ISSN 1027-5606

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    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 nonlinear 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 15 h 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 (10–50 km2/. 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.

    Item Type: Article
    Faculty \ School: Faculty of Science > School of Environmental Sciences
    Depositing User: Pure Connector
    Date Deposited: 03 Jan 2018 15:14
    Last Modified: 09 Apr 2019 12:55
    URI: https://ueaeprints.uea.ac.uk/id/eprint/65846
    DOI: 10.5194/hess-21-6425-2017

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