Carbon dioxide concentrations at Baring Head (1972–2013)

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Creative Commons Attribution 3.0 New Zealand

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7472
22
Added
01 Oct 2015

This dataset was first added to MfE Data Service on 01 Oct 2015.

Greenhouse gases (GHGS) in the atmosphere absorb heat radiating from Earth, warming the atmosphere. Emissions from human activities increase the concentrations of these gases. Increases in these gases increase ocean acidity and are extremely likely to contribute to increased global temperatures, sea levels, and glacier melt. Monitoring GHG concentrations allows us to infer long-term impacts on ocean acidity, temperature, sea level, and glaciers.
Greenhouse gases are generally well mixed around the globe. We use ‘clean air’ observations from Baring Head, near Wellington, to estimate global concentrations of the greenhouse gases – carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and carbon monoxide (CO). These observations are made only when the air’s trajectory is from the south and away from any likely local sources of gas emissions. This gives an estimate representative of the concentrations over the Southern Ocean.
The observations tell us how the global atmosphere responds to increasing emissions of greenhouse gases, and are an internationally representative measure of global concentrations. However, the Southern Hemisphere has slightly less greenhouse gas concentrations than the Northern Hemisphere, as well as a smaller seasonal variation.
Further information can be found in:
Mikaloff Fletcher, SE, & Nichol, S (2014) Measurements of Trace Gases in Well-mixed Air at Baring Head: Trends in carbon dioxide, methane, nitrous oxide and carbon monoxide. Prepared for Ministry for the Environment. Available at data.mfe.govt.nz/x/cZzREp on the Ministry for the Environment dataservice (data.mfe.govt.nz/).
This dataset relates to the "Greenhouse gas concentrations" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52568
Data type Table
Row count 493
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Number of extreme wave events exceeding 8m in coastal regions, 2008–15

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Creative Commons Attribution 3.0 New Zealand

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10893
38
Added
19 Oct 2016

This dataset was first added to MfE Data Service on 19 Oct 2016.

Extreme wave indexes estimate the occurrence of extreme wave events in coastal and oceanic waters. Extreme wave indexes estimate the number of times a significant wave height exceeds one of three threshold values for at least 12 hours in 24 marine regions. The three wave-height thresholds are four metres, six metres, and eight metres.
This indicator estimates the exceedances of a wave-height threshold for each year from 2008 to 2015 in coastal regions.
Significant wave height is a measure of the ‘typical’ wave height in a place over a time period. It is four times the standard deviation of the water surface if, for example, you were to measure water moving up and down a jetty piling for an hour. The largest individual wave will typically have a height around twice the significant wave height.
We use three wave-height thresholds because of the regional variation in extreme wave events. In general, the north experiences less exposure to consistently strong winds, and the waves generated by them, than the south. Four-metre tall waves are considered extreme in the northern-most parts of New Zealand but are more common in the south. For the southern-most parts of New Zealand, eight-metre waves better represent extreme wave events.
This dataset relates to the number of extreme wave events exceeding the eight metre threshold in coastal regions.

Layer ID 53502
Data type Vector multipolygon
Feature count 144
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Predicted average Macroinvertebrate Community Index (MCI) score, 2007–2011

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Creative Commons Attribution 3.0 New Zealand

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10127
59
Added
11 Jan 2016

This dataset was first added to MfE Data Service on 11 Jan 2016.

"Macroinvertebrates are small animals without backbones that live on and under submerged logs, rocks, and aquatic plants in the stream bed during some period of their life cycle. They play a central role in stream ecosystems by feeding on periphyton (algae or slime), macrophytes (aquatic plants), dead leaves and wood, or on each other. High Macroinvertebrate Community Index (MCI) scores generally indicate better stream health. Macroinvertebrates are good continuous indicators of the health of their stream environment. This is because they are relatively sedentary and long–lived (a year or more) which means they live with any stresses or changes that occur in their location (eg, pollution, habitat removal, floods and droughts). They complement discrete measures like chemical monitoring, which only reflects the condition at the exact time and place of sampling. Such monitoring might miss effects of a short–lived pollutant or an unanticipated type of disturbance.
This dataset relates to the ""River water quality: benthic macroinvertebrates"" measure on the Environmental Indicators, Te taiao Aotearoa website. "

Layer ID 52713
Data type Vector point
Feature count 512
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

BOMEC_15_Class_region

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Creative Commons Attribution 3.0 New Zealand

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20258
71
Updated
03 Jul 2018

This dataset was last updated on MfE Data Service on 03 Jul 2018.

The 15 class Benthic-Optimised Marine Environment Classification (BOMEC). The BOMEC divides the benthic environment into ecosystem types. These are grouped into three inshore groups, three continental shelf groups, and nine deeper-water groups. Each group represents areas with similar environmental variables, such as depth, temperature, salinity, and suspended sediment. The classification system considers the distributions of eight benthic taxonomic groups: asteroids, bryozoans, benthic foraminiferans, octocorals, polychaetes, matrix-forming scleratinian corals, sponges, and benthic fish.

Layer ID 52748
Data type Vector multipolygon
Feature count 15
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Frost and warm days, 1972–2016

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Creative Commons Attribution 4.0 International

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6792
39
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

The number of frost and warm days changes from year to year in response to climate variation, such as the warming pattern induced by El Niño. Climate models project we may experience fewer cold and more warm extremes in the future. Changes in the number of frost and warm days can affect agriculture, recreation, and our behaviour, for example, what we do to keep safe on icy roads or whether to use air conditioning to keep cool.
A frost day is when the minimum temperature recorded is below 0 degrees Celsius. It refers to a temperature measured in an instrument screen 1.2 m above the ground rather than a ‘ground frost’. We define a warm day as having a maximum recorded temperature above 25 degrees Celsius. The threshold of 25 degrees Celsius is chosen to represent days where action might be taken to keep cool (eg turn air conditioning on).
This dataset gives the number of frost and warm days per month and calendar year for New Zealand, the North and South Islands, and all 30 sites.
For frost days we have used calendar years. For warm days we have used growing season (July 1 – June 30 of the following year).
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89387
Data type Table
Row count 32667
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Seamount closures

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Creative Commons Attribution 3.0 New Zealand

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11312
32
Added
11 Jan 2016

This dataset was first added to MfE Data Service on 11 Jan 2016.

The location and extent of seamount closures designated in the exclusive economic zone.

Layer ID 52761
Data type Vector multipolygon
Feature count 17
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Annual ozone concentrations, 1979–2016

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Creative Commons Attribution 4.0 International

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6618
20
Added
14 Oct 2017

This dataset was first added to MfE Data Service on 14 Oct 2017.

NIWA supplied ozone data in two forms, with different starting dates:
- measurements made using a Dobson spectrophotometer (number 72), from 1987
- data assimilated from satellite measurements recalibrated against the global Dobson network, from 1978.
NIWA takes measurements using the Dobson spectrophotometer 72 under clear-sky, direct sunlight conditions at Lauder in Otago. There are gaps in the time series due to days with cloud, rain, or too much wind. However, over the whole period, each individual calendar day of the year was measured. This allows us to calculate statistics based on the day of the year.
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89463
Data type Table
Row count 114
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Soil quality and land use, 1995–2017

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Creative Commons Attribution 4.0 International

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8923
110
Added
16 Apr 2018

This dataset was first added to MfE Data Service on 16 Apr 2018.

Soil supports the productivity of agriculture, horticulture, and forestry, and filters water to help prevent waterways from becoming contaminated. Different land uses put pressure on the land environment and can change soil quality. Soil quality is assessed under four different groups of land uses: forestry, cropping and horticulture, dairy, and dry stock by measuring the following soil properties: acidity (pH), fertility (Olsen P), organic reserves (total carbon, total nitrogen, mineralisable nitrogen), and physical status (macroporosity and bulk density). Soil scientists have identified the target range for each of these indicators, for maintaining production but with a prime focus for managing risk to the environment.

This measure reports on soil quality, by land use and soil order.

Table ID 95345
Data type Table
Row count 1179
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

River water quality, heavy metals, trend, 2011-2017

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Creative Commons Attribution 4.0 International

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3577
11
Added
06 Apr 2020

This dataset was first added to MfE Data Service on 06 Apr 2020.

Heavy metals in river waters are an indicator of river water quality. We monitor a subset of rivers and streams in predominantly urban areas in Auckland, Wellington, and Christchurch, and look at how these values are changing over time. This indicator shows:

  • trends in concentrations based on measurements made at monitoring sites during the seven-year period from 2011 to 2017

Summary report available at www.mfe.govt.nz/publications/fresh-water/urban-riv....

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 104562
Data type Table
Row count 93
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Coastal sea level rise, 1891–2015

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Creative Commons Attribution 4.0 International

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8945
144
Added
14 Oct 2017

This dataset was first added to MfE Data Service on 14 Oct 2017.

Sea-level rise is a consequence of climate change. Increased global temperatures lead to rising sea-levels because warmer waters take up more space and glaciers and polar ice sheets melt into the ocean. Sea-level varies naturally from place to place due to local ocean circulation and temperatures and the movement of the land relative to the sea. For example, earthquakes can lift or drop the land.
Linear trends were provided by NIWA and Emeritus Professor John Hannah (previously University of Otago). Ideally, linear trends in sea level would be reported if there are at least 50 years of data to account for climate variability from climate oscillations such as the 20–30 year Interdecadal Pacific Oscillation (IPO) and the shorter ENSO cycle. Such climate variability can be seen in the increase in annual mean sea level in 1999–2000, when the IPO across the entire Pacific Ocean changed to a negative phase. While the Moturiki data cover 43 years, it was considered appropriate to apply a linear trend to further extend the number of reported sites. Further detail on the data processing (including adjustments for historic datum changes) and methods used for the trend analysis can be found in Hannah (1990), Hannah (2004), and Hannah and Bell (2012).
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89454
Data type Table
Row count 533
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed
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