Determining the likelihood and cost of detecting reductions of nitrate­nitrogen concentrations in groundwater across New Zealand.

Nitrate­nitrogen (NO3-N) is a contaminant of concern in groundwater worldwide. Stakeholders need information on the ability to detect changes in NO3-N concentrations to prove that land management practices are meeting water quality aims. We created a database of quarterly to monthly NO3-N measurements in 948 sites across New Zealand; 186 of those sites had mean residence time (MRT) data. New Zealand has set a target of sufficient land use mitigations in the next 30 years to ensure steady state surface water concentrations do not exceed 2.4 mg L-1. Here we assess whether the current monitoring network could identify the impacts of these mitigations, assuming that the mitigations are successfully implemented at the source. Only 41 % of the network could detect statistically significant reductions with the current standard quarterly sampling after 30 years of monitoring. The percentage of sites increased to 60 % with increased monitoring frequency (often weekly) but this required a 100-300 % increase in monitoring costs. However, policy makers and stakeholders typically require information on policy and mitigation effectiveness within 5-10 years. Detection within 5-10 years was very unlikely (0-20 % of sites) regardless of the sampling frequency. Importantly, these analyses include the impacts of groundwater lag and temporal dispersion on the likelihood of detecting change, ignoring these impacts, incorrectly, yields a much higher likelihood of detecting reductions. We conclude that the current monitoring network is unlikely to be fit for the purpose of detecting NO3-N reductions within practical timeframes or budgets. Furthermore, we conclude that lag and temporal dispersion effects must be included in detection power calculations; we therefore recommend that MRT data is regularly collected. We also provide a python package to enable easy detection power calculations with lag and temporal dispersion impacts, thereby supporting the development of robust change-detection monitoring networks.

The implications of lag times between nitrate leaching losses and riverine loads for water quality policy.

Understanding the lag time between land management and impacts on riverine nitrate-nitrogen (N) loads is critical to understand when action to mitigate nitrate-N leaching losses from the soil profile may start improving water quality. These lags occur due to leaching of nitrate-N through the subsurface (soil and groundwater). Actions to mitigate nitrate-N losses have been mandated in New Zealand policy to start showing improvements in water quality within five years. We estimated annual rates of nitrate-N leaching and annual nitrate-N loads for 77 river catchments from 1990 to 2018. Lag times between these losses and riverine loads were determined for 34 catchments but could not be determined in other catchments because they exhibited little change in nitrate-N leaching losses or loads. Lag times varied from 1 to 12 years according to factors like catchment size (Strahler stream order and altitude) and slope. For eight catchments where additional isotope and modelling data were available, the mean transit time for surface water at baseflow to pass through the catchment was on average 2.1 years less than, and never greater than, the mean lag time for nitrate-N, inferring our lag time estimates were robust. The median lag time for nitrate-N across the 34 catchments was 4.5 years, meaning that nearly half of these catchments wouldn't exhibit decreases in nitrate-N because of practice change within the five years outlined in policy.