Fresh Water Data

This dataset is a result of Random Forest modelling to predict cyanobacterial biovolume in lakes nationally, which included spatial modelling of chlorophyll a, TN, TP, Secchi depth, Trophic Level Index, and Cyanobacterial biovolume.

More information about the methods used to create this dataset can be found here: www.mfe.govt.nz/publications/fresh-water/strategic...

LID - FENZ Lake ID

Name - Name of lake (where available)

RegionalCo - Regional council name

CHLA - Chlorophyll a (mg/L)

TN - Total nitrogen (mg/m3)

TP - Total phosphorus (mg/m3)

SECCHI - Secchi disc depth (m)

TLI3 - Trophic Level Index (unitless)

CyanoBioVo - Cyanobacterial biovolume (mm3/L)

Summary

Water quality for swimming categories for rivers 4th order and above. This dataset was used to compose the current state for water quality for swimming.

Note this data is under review and will be updated in due course. This information is based on modelled and measured data using the approach outlined at www.mfe.govt.nz/fresh-water/freshwater-management-... .

The modelling methods used are outlined in Snelder et al. (2016) Strategic assessment of New Zealand’s freshwaters for recreational use: a human health perspective. LWP Client Report 2016-011 www.mfe.govt.nz/publications/fresh-water/strategic...

Versions

This dataset is the geometric version of this: data.mfe.govt.nz/table/53620-river-water-quality-f...

This dataset has now been superseded due to consultation with local authorities, and this is the latest version: data.mfe.govt.nz/layer/95555-river-water-quality-f...

Column headings:

NZREACH: NZREACH from the River Environment Classification Version 1

ORDER: Strahler stream order

Category: Water quality for swimming category see www.mfe.govt.nz/fresh-water/national-targets-swimm...

PrGT540: percentage of samples that exceeded 540 E.coli per 100mL

PrGT260: percentage of samples that exceeded 260 E.coli per 100mL

Median: median E.coli per 100mL

Q95: 95th percentile E.coli per 100mL

Note: blank cells are reaches where a prediction was not possible because of missing predictor variables.

This dataset relates to the March 2018 report National E. coli modelling - Supplementary material to support setting draft regional targets for swimmable rivers.

Scenario 1 represents the level of stock exclusion and riparian planting for the nominal year 2030
when the CWP rules have been implemented assuming that the effects of the measures have been realised and water quality has attained a new attribute state. Scenario 1 also includes the impact of regional committed work (that is, work already committed to by councils in their policy plans, or planned infrastructure investment) in regions that have committed to mitigation beyond the CWP.

The geometries are based off REC1, and the field 'Swimability_band' defines the modelled E. coli attribute state NPS-FM human health for recreation value. The rest of the fields come from the River Environment Classification.

This dataset relates to the March 2018 report National E. coli modelling - Supplementary material to support setting draft regional targets for swimmable rivers.

It represents Scenario 0, baseline (i.e. 2017) including the current level of on-farm fencing and land use - further details available in the above report.

The geometries are based off REC1, and the field 'Swimability_band' defines the modelled E. coli attribute state NPS-FM human health for recreation value. The rest of the fields come from the River Environment Classification.

This dataset includes three “packages” of information, each with multiple variables. The packages include:

1. Load – results on mean annual suspended sediment load reported by REC 2 river reach scale for various model scenario variations.
2. Lakes – results on sediment inflow, outflow, and entrapment in lakes and reservoirs
3. Coastal – results on sediment inflows, trap efficiency, and sedimentation rates in coastal hydrosystems (estuaries)

For further detail on the modelling methods and discussing results, see Hicks, M., Semademi-Davies, A., Haddadchi, A., Shankar, U., Plew, D. (2019) Updated sediment load estimator for New Zealand. NIWA Client Report No. 2018341CH, prepared for Ministry for the Environment. January 2019. Available online: www.mfe.govt.nz/publications/fresh-water/updated-s...

Note that some portions of this dataset refine and update 2011 modelling on suspended sediment loads across New Zealand, whereas other components, especially the coastal package, report new modelling results.

This dataset reports erosion modelling results regarding annual mean sediment load reductions required to meet proposed suspended sediment attribute bottom lines. For more detail on the modelling process and methods, see Neverman et al. (2019). Impact testing of a proposed suspended sediment attribute: identifying erosion and sediment control mitigations to meet proposed sediment attribute bottom lines and the costs and benefits of those mitigations. Maanaki Whenua Landcare Research Client Report. Prepared for the Ministry for the Environment.

Results of modelled mitigations are reported at catchment scale and at the REC2 river reach scale. The modelled on-farm mitigations (per economic optimisation as reported in Neverman et al. (2019) are also mapped.

This dataset shows land that would be covered by the option 1 of section 8.4 Immediate action to reduce nitrogen loss.

This web map has been developed by the Ministry for the Environment to support policy proposals in the Action for Healthy Waterways discussion document. The proposals are currently being consulted on.

It provides extra detail on Option 1 in section 8.4 of the discussion document (Immediate action to reduce nitrogen loss). The map indicates the pastoral catchments and sub-catchments specified as high-nitrate in Option 1, where regional rules are not already in place or proposed, and shows the land considered to be low-slope.

Low-slope is defined in this option as land parcels with an average slope of less than 5, 7 or 10 degrees. We are seeking feedback on the appropriate slope threshold to use.

The catchments are those with the highest 10% of nitrate levels in the MfE Environmental Reporting River Water Quality dataset found here. Catchments where the predominant sources of nitrate are non-pastoral in origin are excluded.

Under Option 1, a per-hectare cap, or threshold, for nitrogen losses will be set for each sub-catchment with similar soil type and rainfall. This threshold will be based on a ranking of nitrogen losses from farms within each sub-catchment, and could be set at the 90th percentile, or the 70th, or a point between. Feedback is sought on where this threshold should be set.

This is only one of the options being consulted on, The areas indicated are provisional and may not equate to areas included in regulation.

This set of data sets provides a classification of geological units in terms of their importance for groundwater flow and storage. For more detail on the process and methods, see White et al. (2019). New Zealand groundwater atlas: hydrogeological-unit map of New Zealand. Lower Hutt (NZ): GNS Science. 88 p. Consultancy Report 2019/144.

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New Zealand Hydrogeological unit map (HUM) separated into aquifers, aquitards, aquicludes and basement developed in a nationally-consistent manner. This dataset includes only outcropping hydrogeological units. This dataset was also joined to the hydrogeological system dataset (Moreau et al. 2019), to provide a single polygon for each unique combination of HUM and hydrogeological system. Summary statistics of surficially mapped products are provided for each polygon (groundwater use, flow, recharge, discharge to the surface; depth to hydrogeological basement; and number of drinking water wells serving >100 people).

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Attachment: New Zealand Hydrogeological unit map (HUM) separated into aquifers, aquitards, aquicludes and basement developed in a nationally-consistent manner. This dataset includes overlapping stacked polygons that represent different aged hydrogeological units.

This data set provides an update of New Zealand’s depth to hydrogeological basement map. Depth to hydrogeological basement can be loosely defined as the ‘base of aquifers’; or more strictly as ‘the depth to where primary porosity and permeability of geological material is low enough such that flued volumes and flow rates can be considered negligible’. For more detail on the process and methods, see Westerhoff et al. (2019). New Zealand groundwater atlas: depth to hydrogeological basement. Lower Hutt (NZ): GNS Science. 19 p. Consultancy Report 2019/140.

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A national model was used to estimate depth to hydrogeological basement. Hydrogeological basement refers to geological material with primary porosity and permeability that is low enough such that fluid volumes and flow rates can be considered negligible.

This set of data sets provides an estimation of groundwater flux. There are three components: Groundwater recharge: development of nationwide mean (daily and seasonal) groundwater recharge data sets through the combination of three pre-existing groundwater recharge models; Groundwater–surface water exchange: development of a national indicative groundwater discharge data set using an existing national groundwater flow model, as well as comparison with a pre-existing gaining/losing stream prediction data set; Groundwater flow: development of a national groundwater flow data set using an existing national groundwater flow model. For more detail on the process and methods, see Westerhoff et al. (2019). New Zealand groundwater atlas: Groundwater Fluxes. Lower Hutt (NZ): GNS Science. 60 p. Consultancy Report 2019/126.

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Three national models of groundwater recharge in New Zealand were used (NGRM, TopNet, IrriCalc) to create a mean model of groundwater recharge. This dataset summarises the gridded groundwater recharge from this model mean for the period 2000-2015 in mm/day.

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_Attachment 1: _A complementary dataset describing the standard deviation of the NZGroundwaterRecharge_mean_20002015 dataset.

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Attachment 2: This dataset summarises the gridded autumn groundwater recharge from this model mean for the period 2000-2015 in mm/day. Also complementary dataset of standard deviation.

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Attachment 3: This dataset summarises the gridded spring groundwater recharge from this model mean for the period 2000-2015 in mm/day. Also complementary dataset of standard deviation.

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Attachment 4: This dataset summarises the gridded summer groundwater recharge from this model mean for the period 2000-2015 in mm/day. Also complementary dataset of standard deviation.

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Attachment 5: This dataset summarises the gridded winter groundwater recharge from this model mean for the period 2000-2015 in mm/day. Also complementary dataset of standard deviation.

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