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.

The StockEx LSL 2021 proposed layer identifies proposed areas of "low slope land" where the Resource Management (Stock Exclusion) Regulations 2020 would apply if the low slope land map was updated as proposed in the "Stock exclusion regulations: Proposed changes to the low slope map" (MfE, 2021) discussion document. The layer shows the area of land defined as low slope land. These areas have a local mean slope is less than or equal to 5 degrees and are below 500m in altitude. Areas of lakes and ponds, estuarine open water, built-up areas, transport infrastructure, depleted grass, tall tussock grassland and urban parkland, as defined in Land Cover Database 5, are also excluded.

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Note: The current regulation layer is 'Stock Exclusion Low Slope Land 2020'

data.mfe.govt.nz/layer/104827

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 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.

Full report: Memorandum on implementing a national index for susceptibility to streambank erosion: environment.govt.nz/publications/national-index-st...

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This dataset contains predictions of the susceptibility to streambank erosion for every stream link in the RECv2.5 digital river network. Streambank erosion susceptibility is computed as a dimensionless index (range 0-100) for each stream link based on factors that may influence reach-scale bank erosion. These factors comprise stream power, the extent of riparian woody vegetation, valley confinement, channel sinuosity, and the erodibility of stream banks based on soil texture. The approach for determining streambank erosion susceptibility is described by Smith & Betts (2021) and based on the bank erosion model developed by Smith et al. (2019).

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***Smith HG, Spiekermann R, Dymond J, Basher L 2019. Predicting spatial patterns in riverbank erosion for catchment sediment budgets. New Zealand Journal of Marine and Freshwater Management 53: 338–362.

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***Smith HG, Betts H 2021. Memorandum on implementing a national index for susceptibility to streambank erosion. Technical memorandum prepared for the Ministry for the Environment by Manaaki Whenua – Landcare Research.

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 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 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 (NWT) was used to estimate the probability of groundwater discharging to the surface and separated into 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 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.