Data

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 layer applies the sediment attributes’ classification, per Appendix 2C of the NPS-FM 2020, to the digital River Environment Classification (REC) v2.4 network.

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This layer is meant to support stakeholders in their understanding and implementation of the suspended and deposited sediment attributes, but it does not form part of the regulation; it is not incorporated by reference.

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 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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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.