ALBERT

Beschreibung: This Comment addresses a key conclusion in the paper entitled "Using groundwater age and hydrochemistry to understand sources and dynamics of nutrient contamination through the catchment into Lake Rotorua, New Zealand" by Morgenstern et al. (2015). The authors analyse hydrochemistry data and conclude that "the only effective way to limit algae blooms and improve lake water quality in such environments is by limiting the nitrate load". We undertook the crucial task of examining this conclusion because it contradicts the current strategy of limiting both phosphorus and nitrogen loads to the lake, supported by a multi-million dollar programme of action. Following careful consideration, we believe that the conclusion is invalid and outline four reasons to support our assessment. Our comments do not relate to the methodology or results that are presented by Morgenstern et al. (2015), and we recognise that their paper makes an otherwise highly valuable contribution to understanding hydro-chemical processes in the catchment.

Beschreibung: The Soil Water Assessment Tool (SWAT) was configured for the Puarenga Stream catchment (77 km2), Rotorua, New Zealand. The catchment land use is mostly plantation forest, some of which is spray-irrigated with treated wastewater. A Sequential Uncertainty Fitting (SUFI-2) procedure was used to auto-calibrate unknown parameter values in the SWAT model. Model validation was performed using two data sets: (1) monthly instantaneous measurements of suspended sediment (SS), total phosphorus (TP) and total nitrogen (TN) concentrations; and (2) high-frequency (1–2 h) data measured during rainfall events. Monthly instantaneous TP and TN concentrations were generally not reproduced well (24 % bias for TP, 27 % bias for TN, and R2 〈 0.1, NSE 〈 0 for both TP and TN), in contrast to SS concentrations (〈 1 % bias; R2 and NSE both 〉 0.75) during model validation. Comparison of simulated daily mean SS, TP and TN concentrations with daily mean discharge-weighted high-frequency measurements during storm events indicated that model predictions during the high rainfall period considerably underestimated concentrations of SS (44 % bias) and TP (70 % bias), while TN concentrations were comparable (〈 1 % bias; R2 and NSE both ~ 0.5). This comparison highlighted the potential for model error associated with quick flow fluxes in flashy lower-order streams to be underestimated compared with low-frequency (e.g. monthly) measurements derived predominantly from base flow measurements. To address this, we recommend that high-frequency, event-based monitoring data are used to support calibration and validation. Simulated discharge, SS, TP and TN loads were partitioned into two components (base flow and quick flow) based on hydrograph separation. A manual procedure (one-at-a-time sensitivity analysis) was used to quantify parameter sensitivity for the two hydrologically separated regimes. Several SWAT parameters were found to have different sensitivities between base flow and quick flow. Parameters relating to main channel processes were more sensitive for the base flow estimates, while those relating to overland processes were more sensitive for the quick flow estimates. This study has important implications for identifying uncertainties in parameter sensitivity and performance of hydrological models applied to catchments with large fluctuations in stream flow and in cases where models are used to examine scenarios that involve substantial changes to the existing flow regime.

Beschreibung: The Soil Water Assessment Tool (SWAT) was configured for the Puarenga Stream catchment (77 km2), Rotorua, New Zealand. The catchment land use is mostly plantation forest, some of which is spray-irrigated with treated wastewater. A Sequential Uncertainty Fitting (SUFI-2) procedure was used to auto-calibrate unknown parameter values in the SWAT model which was applied to the Puarenga catchment. Discharge, sediment, and nutrient variables were then partitioned into two components (base flow and quick flow) based on hydrograph separation. A manual procedure (one-at a-time sensitivity analysis) was then used to quantify parameter sensitivity for the two hydrologically-separated regimes. Comparison of simulated daily mean discharge, sediment and nutrient concentrations with high-frequency, event-based measurements allowed the error in model predictions to be quantified. This comparison highlighted the potential for model error associated with quick-flow fluxes in flashy lower-order streams to be underestimated compared with low-frequency (e.g. monthly) measurements derived predominantly from base flow measurements. To overcome this problem we advocate the use of high-frequency, event-based monitoring data during calibration and dynamic parameter values with some dependence on discharge regime. This study has important implications for quantifying uncertainty in hydrological models, particularly for studies where model simulations are used to simulate responses of stream discharge and composition to changes in irrigation and land management.