Uncertainty in nutrient loads resulting from discharge data uncertainty

• Reliable estimates of nutrient loads discharged to the sea are important for managing marine and upstream water resources. Most major river mouths in Sweden are monitored for discharge and nutrient concentrations. These data are used together with direct point source loads as the basis for annual load estimates. The estimates are used as a basis for national environmental policy and they are reported to international bodies such as the EEA (SoE TCM report), OSPAR (OSPAR RID report) and HELCOM (PLC-Annual report). The Pollution Load Compilation-Annual (PLC-Annual) report to HELCOM is used as evidence for managing eutrophication in the Baltic Sea. The reliability of load estimates based on monitoring data in gauged catchments is affected by uncertainty in the discharge and nutrient concentration data. Knowledge about the uncertainty associated with the load estimate values is of fundamental importance when using these data as a basis for management decisions, e.g. for assessing fulfillment of national environmental objectives, or analysing trends in the loads over time (i.e. is it a real change that is observed or is it a result of data uncertainty?). In the PLC-Annual reports for 2015 and onwards both the loads and the estimated uncertainty in the loads should be reported. The aim of this study was to quantify uncertainty in discharge data resulting from rating curve uncertainty and its impact on uncertainty in the nitrogen and phosphorous PLC-Annual load estimates at selected river mouths. In total there are 34 river mouth stations for which the PLC-Annual load calculations are based entirely on measured discharge (i.e. no modelled data are used). We used data from the 12 river mouth stations that are run by the Swedish Meteorological and Hydrological Institute, SMHI, for which data were available to estimate discharge uncertainty. These stations are representative of all the 34 stations in terms of catchment characteristics except for a lower degree of river regulation. Discharge was calculated indirectly from water level (stage) at all these stations using a rating curve, which is fitted to stage–discharge gaugings. For each discharge station we used 15-minute water-level data for 2005–2014 and the stage–discharge gaugings that had been used to fit the current rating curve equation. Rating curve uncertainty was estimated with the Voting Point likelihood method in a Monte Carlo analysis, essentially estimating multiple feasible rating-curve parameter sets that are compatible with the uncertain gauging data. We estimated 40,000 rating curves and the method succeeded in 5 capturing the uncertainty in the gauging data at all stations. Only a few of the stations had extrapolated rating curves during the time period and most of the stations had a well constrained rating curve uncertainty for the mid and high flow range. For each rating curve a time series of discharge was calculated from the stage time series and aggregated to daily values. The 90% uncertainty interval for the relative uncertainty in the daily flow percentiles was around ±15–50% for low flows (Q95, i.e. the flow exceeded 95% of the time), ±10% for mean flow, and around ±10–25% for high flows (Q0.1 and Q0.01). These estimates include both model and data error, which means that they are likely conservative estimates. Rating curves are normally updated continuously as new data become available. Effects of historic rating curve updates were visible in the official discharge time series for some of the stations (as differences in comparison to the PLC-Annual discharge data). These differences were of a similar magnitude as the 90% uncertainty interval from the rating-curve uncertainty estimated here. The uncertainties we estimated from the gauging data were therefore representative of those stemming from real rating-curve changes in the last 10 years. The nutrient load uncertainty was estimated by repeating the PLC-Annual load calculation method (interpolation of monthly nutrient concentrations to daily values that are then multiplied by discharge) for each estimated discharge time series. As a result of the rating curve uncertainty, the estimated relative uncertainty in the PLC-Annual nitrogen and phosphorous loads was ±7–14% (the half-widths of the 90% interval). However, there were biased uncertainty distributions up to ±30–40% for individual years as a result of errors in the water-level time series (that had been updated after the data was delivered for the load calculations). We therefore recommend that the historic load estimates are updated regularly in the future to take advantage of successive data quality improvements. The potential impact of load uncertainty should be considered when load estimates are used for other analyses or as a basis for policy decisions. We expect a greater impact on load comparisons between catchments compared to between sea basins, and for individual years compared to long-term averages. Trend analyses are expected to be obscured where the magnitude of the uncertainty is large in relation to the strength of the measured trend. This study has quantified the uncertainty arising from discharge data and has established an uncertainty estimation method that can be extended to include other components of the total load uncertainty. We recommend that future studies build on the current method to include uncertainty

Ämnesord

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)

NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Miljövetenskap (hsv//swe)

NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Environmental Sciences (hsv//eng)

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Geokemi (hsv//swe)

NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Geochemistry (hsv//eng)

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