Nitrate‑nitrogen (NO3-N) is a contaminant of concern in groundwater worldwide. Stakeholders need information on our ability to detect changes in NO3-N concentrations to prove that land management practices are meeting water quality aims.
New Zealand has set a target of implementing sufficient land use mitigations within the next 30 years to ensure steady state surface water concentrations do not exceed 2.4 mg L− 1. We created a database of quarterly to monthly NO3-N measurements in 948 sites across New Zealand, 186 of which had mean residence time (MRT) data, and evaluated whether the current monitoring network could identify the impacts of these mitigations, assuming that they are successfully implemented.
Our results showed that only 41 % of the network could detect statistically significant reductions with the current standard of 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. Policy makers and stakeholders typically require information on policy and mitigation effectiveness within 5–10 years. The analysis showed that change detection within 5–10 years was very unlikely (0–20 % of sites), regardless of the sampling frequency. Our analyses of the the likelihood of detecting chang accounted for groundwater lag and temporal dispersion and demonstrated that ignoring these processes yields an erroneous conclusion that the likelihood of detecting reduction is relatively high.
We conclude that New Zealand’s 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 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.
See full details of this first phase of the project output in Science of the Total Environment, via the webapp and in the design guide report.