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Determining the likelihood and cost of detecting reductions of nitrate‑nitrogen concentrations in groundwater across New Zealand

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posted on 2024-06-21, 03:53 authored by Matt Dumont, Zeb Etheridge, Rich McDowellRich McDowell
Nitrate‑nitrogen (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 packsage to enable easy detection power calculations with lag and temporal dispersion impacts, thereby supporting the development of robust change-detection monitoring networks.


Funded by the New Zealand Ministry for Business, Innovation and Employment's Our Land and Water National Science Challenge (Toitū te Whenua, Toiora te Wai) as part of project Monitoring Freshwater Improvement Actions


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  • English

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Journal title

Science of The Total Environment

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