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Estimation of catchment nutrient loads in New Zealand using monthly water quality monitoring data

journal contribution
posted on 2023-05-03, 16:14 authored by Ton Snelder, Richard McDowell, C Fraser
Causes of variation between loads estimated using alternative calculation methods and their repeatability were investigated using 20 years of daily flow and monthly concentration samples for 77 rivers in New Zealand. Loads of dissolved and total nitrogen and phosphorus were calculated using the Ratio, L5, and L7 methods. Estimates of loads and their precision associated with short-term records of 5, 10, and 15 years were simulated by subsampling. The representativeness of the short-term loads was quantified as the standard deviation of the 20 realizations. The L7 method generally produced more realistic loads with the highest precision and representativeness. Differences between load estimates were shown to be associated with poor agreement between the data and the underlying model. The best method was shown to depend on the match between the model and functional and distributional characteristics of the data, rather than on the contaminant. Short-term load estimates poorly represented the long-term load estimate, and deviations frequently exceeded estimated imprecision. The results highlight there is no single preferred load calculation method, the inadvisability of “unsupervised” load estimation and the importance of inspecting concentration-flow, unit load-flow plots and regression residuals. Regulatory authorities should be aware that the precision of loads estimated from monthly data are likely to be “optimistic” with respect to the actual repeatability of load estimates.


Rights statement

© 2016 American Water Resources Association


  • English

Does this contain Māori information or data?

  • No



Journal title

Journal of the American Water Resources Association




Snelder, T. H., McDowell, R. W., & Fraser, C. E. (2017). Estimation of catchment nutrient loads in New Zealand using monthly water quality monitoring data. Journal of the American Water Resources Association, 53(1), 158–178. doi:10.1111/1752-1688.12492

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