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CLUES model calibration: residual analysis to investigate potential sources of model error

journal contribution
posted on 2023-05-03, 21:51 authored by Annette Semadeni-Daviesa, Charlotte Jones-Todd, M.S. Srinivasan, Richard MuirheadRichard Muirhead, Alexander Elliott, Ude Shankar, Chris Tanner
This paper presents the results of the latest calibrations of the CLUES model for each of total nitrogen, total phosphorus and Escherichia coli. The model provides estimates of in-stream annual loads of these contaminants for every river reach in the New Zealand River Environments Classification and has been used in New Zealand for catchment planning and policy development. CLUES has been calibrated nationally against loads estimated from data collected from water quality monitoring stations located across the country which could lead to parameter bias. To ascertain whether there is any systematic bias, the calibration residuals were evaluated against regional (geographical) location and a range of upstream catchment characteristics, namely land use, soil drainage properties, slope, network order, proportion of baseflow in total stream flow, climate, geology and source of flow. We found that CLUES gives reasonable load estimates at the catchment scale (Nash-Sutcliff efficiencies >0.8 for all the contaminants). However, there was significant uncertainty in the parameterisation. While several significant relationships were found between upstream catchment characteristics and the model residuals, these relationships were weak and are unlikely to point to any systematic bias in the calibration.

History

Rights statement

© 2019 The Royal Society of New Zealand

Language

  • English

Does this contain Māori information or data?

  • No

Publisher

Taylor & Francis Group

Journal title

New Zealand Journal of Agricultural Research

ISSN

0028-8233

Citation

Semadeni-Daviesa, A., Jones-Todd, C., Srinivasan, M. S., Muirhead, R. W., Elliott, A. H., Shankar, U., & Tanner, C. (2020). CLUES model calibration: residual analysis to investigate potential sources of model error. New Zealand Journal of Agricultural Research. doi:10.1080/00288233.2019.1697708

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