River water quality: Data to 2024

We describe river water quality in Aotearoa New Zealand using six indicators: River water quality – nitrogen River water quality – phosphorus River water quality – Escherichia coli River water quality – clarity and turbidity River water quality – macroinvertebrate community index River water quality – heavy metals Each indicator uses one or more variables to describe river water quality in further detail. Here, we provide information on the methodology, metadata, and references supporting the statistics in five of the six river water quality indicators. River water quality – heavy metals: Data to 2022 – DataInfo+ provides more information about River water quality – heavy metals, which was published in 2024. River water quality variables River water quality – nitrogen River water quality – nitrogen reports on three river water quality variables: total nitrogen (TN) – is the sum of all forms of nitrogen in water, including nitrate-nitrogen (NO3-N), nitrite-nitrogen (NO2-N), ammoniacal-nitrogen (NH4-N) and organic-nitrogen, measured as nitrogen (N). At high concentrations, it can become toxic to fish and macroinvertebrates. It can also cause excessive growth of algae. nitrate-nitrite-nitrogen (NNN) – is the sum of two nitrogen forms: nitrate nitrogen (NO3-N) and nitrite nitrogen (NO2-N), measured as nitrogen. Nitrate is typically the dominant form and is highly soluble and mobile in water. At high concentrations, NNN can become toxic to fish and macroinvertebrates. It can also cause excessive algal growth. ammoniacal nitrogen (NH4-N) – a form of nitrogen in water that exists as either ammonia (NH3) or ammonium (NH4) and is usually derived from human or animal waste and at high concentrations can be toxic. NH4-N is the concentration of ammoniacal nitrogen measured as nitrogen. River water quality – phosphorus River water quality – phosphorus reports on two river water quality variables: total phosphorus (TP) – is the sum of all forms of phosphorus in water, including both dissolved reactive phosphorus (DRP), which is easily taken up by plants, and phosphorus bound to sediments or particles. dissolved reactive phosphorus (DRP) – is the phosphorus that is dissolved and available for aquatic plants and algae growth. High levels of DRP can cause rapid weed growth or algal blooms, which can choke aquatic life. River water quality – Escherichia coli River water quality – Escherichia coli reports on Escherichia coli (E. coli) concentrations in rivers. E. coli are bacteria commonly found in the intestines of warm-blooded animals including humans. When found in fresh water, E. coli can indicate the presence of pathogens (disease-causing organisms) from animal or human faeces, which can cause illness when ingested. One of the most common such pathogens is Campylobacter, but it is difficult to measure. E. coli concentrations are used to infer Campylobacter infection risk in waterways, based on correlations between levels of E.coli and Campylobacter. River water quality – macroinvertebrate community index River water quality – macroinvertebrate community index reports on macroinvertebrate community index (MCI) scores. The MCI is a method for assessing the health of rivers by sampling the small invertebrates (insects, worms, shrimps, and snails) that live in them. A high MCI generally indicates a high level of river health, while lower scores are often associated with rivers affected by organic pollution or nutrient enrichment. River water quality – clarity and turbidity River water quality – clarity and turbidity reports on two river water quality variables: clarity – a measure of underwater visibility, generally assessed by how deep a black and white disk can be seen from the surface of the water. Poor clarity can affect the habitat and food supply of aquatic life and the growth of aquatic plants. turbidity – the cloudiness or haziness in a fluid caused by individual small particles (suspended solids) and water colour. Data Monitored data River water quality monitoring sites are operated by regional councils and unitary authorities (hereafter referred to as ‘councils’), and Earth Sciences New Zealand (ESNZ, formerly NIWA). To ensure national consistency, we only report on data collected over consistent time periods. As a result, our analyses may differ from those produced by councils and Land Air Water Aotearoa (LAWA). We recommend reading the relevant council’s environmental reports or the LAWA website if more detailed regional-level information is needed. Each river water quality variable we report on (except MCI) has the possibility of censored observations. Censored observations are measurements reported below a detection limit or above a measurement limit of the monitoring device. Censoring is common when monitoring variables are present in very low concentrations or very high concentrations. Multiple censoring levels are common in long-term datasets, as measurement resolution changes with technology advancements. Booker et al., (2025) provides more information about monitoring sites. Modelled data ESNZ estimated patterns in various river water quality variables across New Zealand by applying statistical models that relate river water quality data measured between 1 January 2020 and 31 December 2024 to river catchment characteristics. This allowed water quality state to be estimated for river segments that do not have monitoring sites. Median values and other summary statistics describing aspects of the data distribution were predicted for all river segments using random forest models and predictors (explanatory variables). The digital river network version 2.4 (DN2.4) (Whitehead and Booker 2020) was used to provide the spatial framework for the random forest models of river attribute state. Spatial data layers describing the climate, topography, geology, vegetation, infrastructure, and hydrology of New Zealand were used to calculate predictor variables mapped onto DN2.4. Pre-determined criteria showed that model performance for each variable was satisfactory or better, indicating that the models represented landscape-scale patterns well. Wood et al., (2025) provides more information about ESNZ’s modelling methodology. State analyses We report on river water quality state in each indicator using summary statistics calculated by ESNZ from measured values for each variable for the five-year period between 1 January 2020 and 31 December 2024, alongside modelled statistics for the same period. Summary statistics were compared to different thresholds: default guideline values (DGVs) in the Australian and New Zealand guidelines for fresh and marine water quality (ANZG, 2018) bands of the National Objectives Framework (NOF) in the National policy statement for freshwater management 2020. Amended December 2025 (MfE, 2025) natural reference conditions for Escherichia coli (McDowell et al., 2013). We also reported on the relationship between river water quality, and the proportion of human modified land cover in upstream catchments. Water quality data can be affected by seasons, so it is important that each season is well-represented over the period of analysis. In New Zealand, monthly or quarterly sampling is common, and in these cases, seasons are defined by months or quarters. For each variable, summary statistics were calculated by ESNZ for each monitoring site that had at least one observation in at least 90 percent of the sampling intervals (months, bi-months, or quarters) for the 5-year period between 2020 and 2024. ESNZ replaced censored values by imputation for the purposes of calculating summary statistics. When an observation is left-censored (below the detection limit), values were replaced with imputed values generated using regression on order statistics (ROS). When an observation is right-censored (above the detection limit), values were replaced with estimated values from a procedure based on survival analysis. Booker et al., (2025) describes these methods in further detail, including when there is insufficient non-censored data to perform imputation. Censored values may not influence the summary statistics. For example, more than half of the observations at a site would have to be censored for the median to be influenced by censoring. Table 1 shows the number of sites used in the state analyses by variable for the 5-year period between 2020 and 2024 after inclusion rules were applied. Indicator Variable Number of sites used in state analyses River water quality – nitrogen Total nitrogen 990 Nitrate-nitrite-nitrogen 991 Ammoniacal nitrogen 974 River water quality – phosphorus Total phosphorus 990 Dissolved reactive phosphorus 992 River water quality – Escherichia coli Escherichia coli 965 River water quality – macroinvertebrate community index Macroinvertebrate community index 874 River water quality – clarity and turbidity Clarity 695 Turbidity 940 Default guideline values For the nitrogen, phosphorus, clarity, and turbidity variables, we compared summary statistics to the DGVs for monitored sites and river segments with ANZG classification. The DGVs for nitrogen, phosphorus, and turbidity variables are based on the 80th percentile of the reference conditions (where reference means rivers and streams with minimal or no anthropogenic influence). For the clarity variable, the DGVs are based on the 20th percentile of the reference conditions because low values of clarity can indicate some degree of human impact. The ANZG 2018 guidelines provide a default guideline value for nitrate, however, monitoring data are commonly reported as nitrate-nitrite-nitrogen, which combines nitrate and nitrite. Because nitrate is typically the dominant component of nitrate-nitrite-nitrogen in rivers, comparing concentrations with the ANZG nitrate guideline provides a precautionary indication of potential nitrate-related effects at a national scale. For the nitrogen, phosphorus, E. coli, and MCI indicators, sites that are ‘at or below’ ANZG default guideline values are assumed to have a low risk of environmental impairment (we use the terminology, ‘meets’ for the clarity and turbidity indicator), whereas sites that are ‘above’ the ANZG default guideline values are at potential risk of adverse effects, and site-specific investigations are needed to determine whether adverse effects may be occurring (ANZG, 2018) (we use the terminology, ‘does not meet’ for the clarity and turbidity indicator). Management action may be required, depending on what the site-specific investigations reveal. Default guideline values were derived from models detailed in McDowell et al. (2013). It is not mandatory for councils to report against these values. In the river water quality maps, we use the terminology ‘meets’ when median values are at or below the ANZG default guideline values and 'does not meet' when median values are above the default guideline values, for each indicator except clarity. For clarity, we use the terminology ‘meets’ when median values are at or above the ANZG default guideline values and 'does not meet' when median values are below the default guideline values. National Objectives Framework For ammoniacal nitrogen, nitrate-nitrite-nitrogen, dissolved reactive phosphorus, E. coli, macroinvertebrate community index, and clarity, summary statistics based on measured and modelled values were assigned to the bands of the NOF in the National policy statement for freshwater management 2020. Amended December 2025 (MfE, 2025) by ESNZ. The NOF bands are designed to help communities make decisions on how to manage water quality. Each NOF band is defined by a numeric range and a description that corresponds to a scientifically determined range of effects (MfE, 2025). NOF Bands A through D/E represent different water quality states for rivers within the framework, with A representing the highest rating band and D/E the lowest. For E. coli, the NOF bands describe the risk of Campylobacter infection (based on E. coli as an indicator). The predicted average infection risk is the overall average infection to swimmers based on a random exposure on a random day, ignoring any possibility of not swimming during high flows or when a surveillance advisory is in place (assuming that the E. coli concentration follows a lognormal distribution). Actual risk will generally be less if a person does not swim during high flows (MfE, 2025). For ammonia and nitrate the bands indicate toxicity effects of ammonia and nitrate but do not reflect the negative ecosystem health effects of nitrogen enrichment at lower concentrations such as promotion of excessive periphyton growth. As required for comparison with the NOF bands, ammoniacal nitrogen concentrations were adjusted to account for pH. For trend analysis, ammoniacal nitrogen concentrations were not pH-adjusted. Reference conditions for E. coli For E. coli, we compared summary statistics based on measured and modelled E. coli concentrations to the River Environment Classification (REC) class specific trigger values applied by McDowell et al. (2013). The trigger values are based on the 95th percentile of the reference conditions (where reference means rivers and streams with minimal or no anthropogenic influence). Values above the trigger indicate that there is a potential risk of adverse effects, and management action or site-specific investigations may be needed (McDowell et al., 2013). In the river water quality maps, we use the terminology ‘meets’ when median values are at or below the E. coli trigger value and 'does not meet' when median values are above the trigger value. Human modified land cover We report on how human modified land cover is related to river water quality by calculating what proportion of land in the upstream catchment (the area of land that drains into a river) is affected by human activity for each site’s river segment. To measure this, we used the New Zealand Land Cover Database version 6.0 (LCDB v6.0). We included the following detailed land cover classes as human modification: Built-up area (settlement) Urban parkland/open space Transport infrastructure Surface mine or dump High producing exotic grassland Short-rotation cropland Orchards, vineyards or other perennial crops Exotic forest Forest - harvested Trend analyses We calculate and report on 20-year river water quality trends, based on measured data between 1 January 2005 and 31 December 2024, using two methods: Mann-Kendall trend tests and Akritas-Theil-Sen slope estimates to assess overall trend direction, likelihood, and rate of change. We report trend likelihoods using categories describing the certainty of trends adapted from the Intergovernmental Panel on Climate Change (Mastrandrea et al., 2010). Generalised additive models (GAMs) to estimate smooth trends over years to show how trends change over time. Time was the only explanatory variable used in the model. We use the Akritas-Theil-Sen (Helsel, 2012) slope estimator (an extension of the Theil-Sen slope estimate) to accommodate censored observations when calculating rate of change. The estimator may overestimate the rate of change in a small number of cases. We used the mgcv R package (Wood, 2011) to calculate the GAMs using the thin plate regression spline as the smoothing basis, the censored normal distribution family to account for censored observations, and restricted maximum likelihood (REML) as the method for estimating the smoothing parameters. Effective degrees of freedom are controlled by the degree of penalization selected during fitting, meaning the exact choice of basis dimension, k, is not generally critical. We found that for each site, setting k equal to the number of observations multiplied by 0.3 was sufficient. Values were log-transformed since the data are right-skewed. We classify trends as ‘likely’ when the probability of an increasing or decreasing trend is above 66 percent, and as ‘very likely’ when the probability is above 90 percent. We use the term ‘indeterminate’ when there is either no trend direction determined or not enough statistical certainty to determine trend direction (less than or equal 66 percent certainty). The categories used to describe the certainty of trends are adapted from Mastrandrea et al. (2010). For all river water quality variables except MCI and clarity, an increasing trend indicates worsening river water quality, while a decreasing trend indicates improving river water quality. For MCI and clarity, an increasing trend indicates improving river water quality, while a decreasing trend indicates worsening river water quality. We report trends for the 20-year period between 1 January 2005 and 31 December 2024 for each variable except MCI. For MCI, we report trends for the 20-year period between 1 July 2004 and 30 June 2024. A 20-year period was chosen to balance reducing uncertainty in trend determination (by including more data points) against increasing the chance of detecting climate-induced trends (by shortening the assessment period). The assessment period can have a noticeable effect on the estimated trend. Natural climate cycles may mask the effects of anthropogenic drivers of water quality trends at shorter time scales (Snelder et al., 2021). We applied inclusion criteria to manage censoring in addition to the inclusion criteria described in Booker et al., (2025). For each variable, trends were calculated for each monitoring site that had at least one observation in at least 90 percent of the sampling intervals (months, bi-months, quarters, or years) for the 20-year period between 2005 and 2024. Further, we excluded sites where 80 percent or more of the observations were left-censored. This aligns with our approach for Groundwater quality: Data to 2024. Sites were downsampled to standardise the time series when sampling frequency changed. Table 2 shows the number of sites used in our trend analyses by variable for the 20-year period between 2005 and 2024 after inclusion rules were applied. Indicator Variable Number of sites used in state analyses River water quality – nitrogen Total nitrogen 453 Nitrate-nitrite-nitrogen 506 Ammoniacal nitrogen 500 River water quality – phosphorus Total phosphorus 495 Dissolved reactive phosphorus 547 River water quality – Escherichia coli Escherichia coli 543 River water quality – macroinvertebrate community index Macroinvertebrate community index 422 River water quality – clarity and turbidity Clarity 362 Turbidity 574 Limitations Results from monitored data provide insight into river water quality only at the monitoring sites and only for the periods covered by the data. They do not represent conditions at unmonitored rivers or outside the monitoring period. The number of river monitoring sites and their geographic coverage limit the extent to which results from state and trend analyses represent conditions across all of New Zealand’s rivers. Further, the river water quality trends we report don't determine the cause of changing water quality. Monitored sites over-represent larger catchments and catchments with human modified landcover. Rivers in low-lying and hilly areas in the North and South islands are well represented, while mountainous areas in the South Island and parts of the central North Island are less well represented. River water quality measurements can be made with a variety of laboratory and field methods. Differences in methods used to measure the same variable at different sites may introduce systematic differences between sites or regions, affecting comparability of estimated state. Changes in methods over time can also create “step changes” in the data, which may complicate interpretation of trends. Trend analyses assume that observations are comparable through time and that changes in measured values reflect real environmental change. While efforts are made to ensure consistency, these assumptions may not always be met in practice, and apparent trends may arise from methodological changes rather than changes in water quality (Davies-Colley and McBride, 2016; Wood, 2024; Booker et al., 2025).Model performance differed between variables, likely due to differences in the number of monitored sites, uncertainty in predictor data, and the biophysical processes that control the water quality variables, as well as measurement errors (Wood et al., 2025). As a result, the level of unexplained variation in the modelled data differs between river water quality variable. References ANZG. (2018). Australian and New Zealand guidelines for fresh and marine water quality. Australian and New Zealand Governments and Australian state and territory governments. Canberra ACT, Australia. https://www.waterquality.gov.au/anz-guidelines Booker, D., Smith, R., Wood, D., Fraser, C., Snelder, T. (2025) Water quality state and trends in New Zealand rivers. Analysis of national data ending in 2024. Earth Sciences New Zealand client report prepared for Ministry for the Environment. ESNZ, Christchurch. https://environment.govt.nz/publications/water-quality-state-and-trends-in-new-zealand-rivers-analysis-of-national-data-ending-in-2024/ Clapcott, J.E., Goodwin, E. O., Snelder, T. H., Collier, K. J., Neale M. W., Greenfield, S. (2017). Finding reference: a comparison of modelling approaches for predicting macroinvertebrate community index benchmarks. New Zealand Journal of Marine and Freshwater Research, 51:1, 44-59. DOI: 10.1080/00288330.2016.1265994 Davies-Colley, R., McBride, G. (2016). Accounting for changes in method in long-term nutrient data: recommendations based on analysis of paired SoE data from Wellington rivers. NIWA Client Report, HAM2016-070: 34. https://webstatic.niwa.co.nz/library/HAM2016-070.pdf Helsel, D. R. (2012). Statistics for censored environmental data using MiniTab and R. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024411915 &sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Mastrandrea, M. D., Field, C. B., Stocker, T. F., Edenhofer, O., Ebi, K. L., Frame, D. J., Held, H., Kriegler, E., Mach, K. J., Matschoss, P. R., Plattner, G.-K., Yohe, G. W., & Zwiers, F. W. (2010). Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental Panel on Climate Change (IPCC). https://www.ipcc.ch/site/assets/uploads/2018/05/uncertainty-guidance-note.pdf McDowell, R., Snelder, T., & Cox, N. (2013). Establishment of reference conditions and trigger values for chemical, physical and micro-biological indicators in New Zealand streams and rivers. https://environment.govt.nz/publications/establishment-of-reference-conditions-and-trigger-values-for-chemical-physical-and-micro-biological-indicators-in-new-zealand-streams-and-rivers/ Ministry for the Environment (MfE). (2025) National policy statement for freshwater management 2020. Amended December 2025. https://environment.govt.nz/publications/national-policy-statement-for-freshwater-management/ Snelder, T.H., Larned, S.T., Fraser, C., De Malmanche, S. (2021). Effect of climate variability on water quality trends in New Zealand rivers. Marine and Freshwater Research, 73: 20-34. https://doi.org/10.1071/MF21087 Stark, J.D., Maxted, J.R. (2007) A user guide for the Macroinvertebrate Community Index. Cawthron Report 1166. Cawthron Institute, Nelson. Whitehead, A., Booker, D. (2020). NZ River Maps: An interactive online tool for mapping predicted freshwater variables across New Zealand. https://shiny.niwa.co.nz/nzrivermaps/ Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B), 73(1), 3-36. https://doi.org/10.1111/j.1467-9868.2010.00749.x Wood, D. (2024). Assessing and accounting for the influence of changes in laboratory measurement methods on the interpretation of long-term time-series data. NIWA Client Report, 2024055CH: 54. https://www.envirolink.govt.nz/assets/Envirolink/2328-HBRC269-Assessing-and-accounting-for-the-influence-of-changes-in-laboratory-measurement-methods-on-the-interpretation-of-long-term-time-series-data.pdf Wood, D., Booker, D., Smith, R. (2025). Spatial modelling of river water-quality state. Incorporating monitoring data from 2020 to 2024. Earth Sciences New Zealand client report prepared for Ministry for the Environment. ESNZ, Christchurch. https://environment.govt.nz/publications/spatial-modelling-of-river-water-quality-state-incorporating-monitoring-data-from-2020-to-2024/ en-NZ