wSummary
@Models developed to identify the rates and origins of nutrient
export from land to stream require an accurate assessment of the
nutrient load present in the water body in order to calibrate
model parameters and structure. These data are rarely available
at a representative scale and in an appropriate chemical form
except in research catchments. Observational errors associated
with nutrient load estimates based on these data lead to a high
degree of uncertainty in modelling and nutrient budgeting studies.
Here, daily paired instantaneous P and flow data for 17 UK research
catchments covering a total of 39 water years (WY) have been used
to explore the nature and extent of the observational error associated
with nutrient flux estimates based on partial fractions and infrequent
sampling. The daily records were artificially decimated to create
7 stratified sampling records, 7 weekly records, and 30 monthly
records from each WY and catchment. These were used to evaluate
the impact of sampling frequency on load estimate uncertainty.
The analysis underlines the high uncertainty of load estimates
based on monthly data and individual P fractions rather than total
P. catchments with a high baseflow index and/or low population
density were found to return a lower RMSE on load estimates when
sampled infrequently than those with a low baseflow index and
high population density. Catchment size was not shown to be important,
though a limitation of this study is that daily records may fail
to capture the full range of P export behaviour in smaller catchments
with flashy hydrographs, leading to an underestimate of uncertainty
in load estimates for such catchments. Further analysis of sub-daily
records is needed to investigate this fully. Here, recommendations
are given on load estimation methodologies for different catchment
types sampled at different frequencies, and the ways in which
this analysis can be used to identify observational error and
uncertainty for model calibration and nutrient budgeting studies.
Keywords: Uncertainty; Load estimation; Phosphorus; Model calibration;
Nutrient budgetsx
Introduction
Sources of uncertainty in load estimates
@P fractionation trends in rivers
@Frequency distribution analysis
Determining load estimate uncertainty
@Uncertainty associated with selection of load estimation
methodology
@Uncertainty associated with sampling frequency
@@Stratified sampling
@@Weekly sampling
@@Monthly sampling
Recommendations for least uncertain P load estimation methodology
Conclusions
Acknowledgements
References