Data

The Stock Exclusion Low Slope Land 2020 layer identifies areas of "low slope land" as defined in the Resource Management (Stock Exclusion) Regulations 2020. The layer shows the land parcels, or part parcels, defined as low slope land. These areas have a mean slope is less than or equal to 10 degrees. Parcels with a parcel intent of "ROAD" are excluded. Areas of lakes, ponds, settlements and urban parkland, as defined in Land Cover Database 5, are also excluded. Areas of low-slope grassland and annual cropland within high-slope parcels are also included in the Stock Exclusion Low Slope Land extent.

This dataset shows land parcels within grassland and annual cropping areas which have an average slope of less than 10 degrees. Polygons are attributed into 3 slope classes: less than 5 degrees mean slope; 5 - 7 degrees mean slope; 7 - 10 degrees mean slope.

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 dataset provides boundaries for catchments that drain to the sea (i.e. sea draining catchments).

It is extracted from the Freshwater Ecosystems of New Zealand (FENZ) v1.0 geodatabase. Ministry for the Environment hosts this copy of this layer for convenience and visibility. For all inquiries please contact Department of Conservation directly.

FENZ requires specialist GIS knowledge for its technical operation and biodiversity knowledge for understanding the content. Because of FENZ’s complexity, DOC is providing advice, briefings and training (where possible) to ensure users understand its strengths, limitations and appropriate applications.

If you would like more information about FENZ or access to any FENZ data sets, email fenz@doc.govt.nz.

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www.doc.govt.nz/our-work/freshwater-ecosystems-of-...

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Variables:

Catch_id - This is a unique identifier that can be used to link to other datasets in the FENZ database, or datasets from other sources that also use a FENZ id.

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Catchment names:

Currently a definitive catchment names dataset does not exist. However for your convenience, an unofficial list has been provided in the attachments ("fenz_catnames.csv") which can be joined to the catchment boundaries to provide names. Feedback on the accuracy or completeness of these names is welcomed.

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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A national groundwater model was used to estimate near-surface groundwater flow amplitudes and separated into four classes to encompass the uncertainty of the dataset.

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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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 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.