4.4 Article

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

Journal

NEW ZEALAND JOURNAL OF AGRICULTURAL RESEARCH
Volume 64, Issue 3, Pages 320-343

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/00288233.2019.1697708

Keywords

Overseer; SPARROW; SPASMO; total nitrogen; total phosphorus; E; coli; annual loads; uncertainty; bias; error

Funding

  1. Sources and Flows Programme of the Our Land and Water National Science Challenge [Ministry of Business, Innovation and Employment] [C10X1507]

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This paper presents the latest calibrations of the CLUES model for total nitrogen, total phosphorus and Escherichia coli in New Zealand rivers. The model provides reasonable load estimates at the catchment scale, but there is significant uncertainty in the parameterisation and no systematic bias was found in the calibration.
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.

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