Improved predictability of peak periphyton in rivers using site-specific accrual periods and long-term water quality datasets.

Prevention of excessive periphyton standing crop (quantified as chlorophyll a) is among primary objectives for river management. Defensible instream nutrient criteria to achieve periphyton chlorophyll a targets at the site scale require robust predictive models. Such models have proved elusive because peak chlorophyll a depends on multiple factors in addition to nutrients. A key predictor may be accrual period, which depends on river flow variability and the flow magnitudes (effective flows, EF) at which periphyton biomass removal is initiated. In this study we used a seven-year dataset from 44 gravel-bed river sites in the Manawatu-Whanganui region, New Zealand, to explore the relative importance of accrual period, nutrients, and other variables in explaining peak chlorophyll a, using a regression approach. We also assessed the effect of combining data from multiple years. Previous empirical studies have used a universal flow metric (3 × median flow) to define accrual period (Da3). We calculated site-specific EF, which varied from 2 × to 15 × median flow. Accrual period based on EF (DaEF) outperformed Da3 in models. However, in the study region, more variance in chlorophyll a was explained by conductivity (EC) and dissolved inorganic nitrogen (DIN) than by DaEF. The best models derived from multi-year datasets included EC, DIN and DaEF as predictors and accounted for up to 82% of the variance in peak chlorophyll a. Models from annual data were weaker and more variable in strength and predictors. The models indicated that EC and DaEF should be considered when setting DIN criteria for periphyton outcomes in the study region. The principles we used in developing the models may have broad relevance to the management of periphyton in other regions.

Influence of sampling frequency and load calculation methods on quantification of annual river nutrient and suspended solids loads.

Better management of water quality in streams, rivers and lakes requires precise and accurate estimates of different contaminant loads. We assessed four sampling frequencies (2 days, weekly, fortnightly and monthly) and five load calculation methods (global mean (GM), rating curve (RC), ratio estimator (RE), flow-stratified (FS) and flow-weighted (FW)) to quantify loads of nitrate-nitrogen (NO3--N), soluble inorganic nitrogen (SIN), total nitrogen (TN), dissolved reactive phosphorus (DRP), total phosphorus (TP) and total suspended solids (TSS), in the Manawatu River, New Zealand. The estimated annual river loads were compared to the reference 'true' loads, calculated using daily measurements of flow and water quality from May 2010 to April 2011, to quantify bias (i.e. accuracy) and root mean square error 'RMSE' (i.e. accuracy and precision). The GM method resulted into relatively higher RMSE values and a consistent negative bias (i.e. underestimation) in estimates of annual river loads across all sampling frequencies. The RC method resulted in the lowest RMSE for TN, TP and TSS at monthly sampling frequency. Yet, RC highly overestimated the loads for parameters that showed dilution effect such as NO3--N and SIN. The FW and RE methods gave similar results, and there was no essential improvement in using RE over FW. In general, FW and RE performed better than FS in terms of bias, but FS performed slightly better than FW and RE in terms of RMSE for most of the water quality parameters (DRP, TP, TN and TSS) using a monthly sampling frequency. We found no significant decrease in RMSE values for estimates of NO3-N, SIN, TN and DRP loads when the sampling frequency was increased from monthly to fortnightly. The bias and RMSE values in estimates of TP and TSS loads (estimated by FW, RE and FS), however, showed a significant decrease in the case of weekly or 2-day sampling. This suggests potential for a higher sampling frequency during flow peaks for more precise and accurate estimates of annual river loads for TP and TSS, in the study river and other similar conditions.