Oral Presentation Freshwater Sciences 2023

Are nitrate loss mitigations improving water quality? Nitrate loss mitigation effectiveness monitoring design for groundwater (#421)

Zeb Etheridge 1 2 , Matt Dumont 2 , Evelyn Charlesworth 2 , Olivier Ausseil 3 , Richard McDowell 4 , Alasdair Noble 4
  1. Waterways Department, University of Canterbury, Christchurch, Canterbury, New Zealand
  2. Komanawa Solutions Ltd, Christchurch, Canterbury, New Zealand
  3. Aquanet Ltd, Palmerston North, Horizons, New Zealand
  4. AgResearch, Christchurch, Canterbury, New Zealand

Aotearoa New Zealand is making significant investments in planning processes and on-the-ground actions to reduce diffuse nitrate discharges to freshwater. Investment in monitoring systems to evaluate the effectiveness of this investment has been limited to date.

Existing freshwater monitoring networks are designed to yield information on the state and trend of freshwater but not to robustly establish cause-effect relationships between improvement actions and their effect (e.g., reduced contaminant loads in spring-fed streams). Time lags, attenuation and other uncertainties and complexities make the measurement of freshwater improvement challenging. As a result, we often have limited evidence on which specific policies or land management actions result in improvements in water quality. The aim of this work is to provide tools and guidance on the design of monitoring systems to track the effectiveness of land mitigation and land management actions in improving freshwater health.

Three key questions for groundwater nitrate monitoring system design are: 1) How frequently and for what duration do nitrate samples need to be collected to provide a statistically robust dataset given temporal variance and the input change magnitude? 2) When should improvements be expected given natural lags in the hydrological system? and 3) What network density is required to provide robust results given spatial variance in nitrate loading, dilution and dispersion?

Question 1 and 2 were addressed via statistical analysis of time series data and age tracer data to determine the statistical power of change detection and lag times respectively. An online interactive map application has been developed for Aotearoa New Zealand to provide location and typological-based detection power and lag time estimates. A series of numerical experiments were undertaken using MODFLOW/MT3D coupled with APSIM-based nitrate leaching model time series data to evaluate groundwater network density requirements given spatial variance and monitoring well type, depth and pumping rate.