Growing degree days trend assessment, by site, 1972/3–2015/6

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Creative Commons Attribution 4.0 International

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5831
26
Added
18 Oct 2017

This dataset was first added to MfE Data Service on 18 Oct 2017.

Growing degree days (GDD) measures the amount of warmth available for plant and insect growth and can be used to predict when flowers will bloom and crops and insects will mature. GDD counts the total number of degrees Celsius each day is above a threshold temperature. In this report we used 10 degrees Celsius. Increased GDD means that plants and insects reach maturity faster, provided that other conditions necessary for growth are favourable, such as sufficient moisture and nutrients. As a measure of temperature, GDD experiences short-term changes in response to climate variations, such as El Niño, and in the longer-term is affected by our warming climate.
Growing degree days (GDD) counts the number of days that are warmer than a threshold temperature (Tbase) in a year. GDD is calculated by subtracting the Tbase from the average daily temperature (maximum plus minimum temperature divided by two). If the average daily temperature is less than Tbase the GDD for that day is assigned a value of zero.
This dataset gives the trend in GDD over growing seasons (July 1 – June 30 of the following year) for 30 sites.
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 89481
Data type Table
Row count 30
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Extreme wind, 1972–2016

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Creative Commons Attribution 4.0 International

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6116
78
Added
12 Oct 2017

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

Extreme wind annual statistics for 30 regionally representative sites. The number of days with a maximum gust in the 99th percentile provides information on the frequency of extreme wind events. Percentiles are obtained from all available daily maximum wind gust data. On average, the 99th percentile daily maximum wind gust will be exceeded on approximately 3.6 days per year. Therefore, annual counts higher than this indicate more days than usual with very strong wind gusts recorded; annual counts lower than 3.6 indicate fewer strong wind gust days than usual. By using a percentile threshold we can identify events that are extreme for a particular location. Some places are naturally subject to stronger winds than others, so vegetation can become ‘wind-hardened’ and may have a higher tolerance to high wind gusts (eg a 100 km/hr wind gust may be damaging at one location, but not at another). Using a relative threshold accounts for these differences and better captures extreme wind gust occurrences. The highest maximum gust per year and the average annual highest maximum wind gust both provide information on the magnitude of extreme wind events.
Steady wind can be an important resource, but strong gusts can damage property, topple trees, and disrupt transportation, communications, and electricity. Extreme wind events can occur with frontal weather systems, around strong convective storms such as thunderstorms, and with ex-tropical cyclones. Projections indicate climate change may alter the occurrence of extreme wind events, with the strength of extreme winds expected to increase over the southern half of the North Island and the South Island, especially east of the Southern Alps, and decrease from Northland to Bay of Plenty. Monitoring can help us gauge the potential of, and prepare for, such events.
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 89425
Data type Table
Row count 1327
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Particulate matter 10 concentrations, 2004-2021

70
3
Added
12 Oct 2021

This dataset was first added to MfE Data Service on 12 Oct 2021.

Particulate matter (PM) comprises solid and liquid particles in the air. PM10 particles have a diameter less than 10 micrometres. Coarse particles (2.5–10 micrometres) can be inhaled – they generally deposit in the upper airways; fine particles (smaller than 2.5 micrometres) can deposit deep in the lungs where air-gas exchange occurs.

Since PM10 is small enough to be inhaled, exposure can cause cardiovascular and respiratory health problems, such as heart attack, stroke, lung cancer, and premature death. It can also aggravate asthma and has been linked with diabetes. Children, the elderly, and people with existing heart or lung issues have a higher risk of health problems from exposure to PM10. These problems include decreased lung function, heart attack, and mortality.

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 106269
Data type Table
Row count 255775
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Sunshine hours: annual average 1972-2013

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Creative Commons Attribution 3.0 New Zealand

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7174
67
Added
18 Feb 2016

This dataset was first added to MfE Data Service on 18 Feb 2016.

"Sunshine is important for our health and recreation, and for the environment. It is also important for our agriculture-based economy, for example, for plant growth.

This dataset shows average annual sunshine hours across New Zealand for years 1972 to 2013.

The National Institute of Water and Atmospheric Research (NIWA) mapped mean annual sunshine hours from the virtual climate station network data (NIWA) generated from data in its National Climate Database, for the period 1981–2013. It generated the Units: percentage of normal by comparing the annual average to the long-term mean for 1981–2010. Maps were produced using the Virtual Climate Station network data. Data for each year are measured over the calendar year (January–December).

The accuracy of the data source is of high quality.

This dataset relates to the ""Sunshine hours in New Zealand"" measure on the Environmental Indicators, Te taiao Aotearoa website.

Geometry: grid
Unit: hrs/yr"

Layer ID 53313
Data type Grid
Resolution 5110.000m
Services Raster Query API, Catalog Service (CS-W), data.govt.nz Atom Feed

River water quality, state, 2013–2017

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Creative Commons Attribution 4.0 International

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5694
89
Updated
15 Apr 2020

This dataset was last updated on MfE Data Service on 15 Apr 2020.

16 April 2020: Subsequent to publication in April 2019 we discovered two small errors with this dataset. These included:

  • Errors in the coordinates of some sites and their associated metadata (such as landcover and elevation).
  • Errors in our calculation of dominant landcover.

In addition, flow data from TopNet has also been updated.

These changes have a minor impact on overall results. These changes have have been corrected, and are republished here, as part of the Our freshwater 2020 release.

This dataset contains ten parameters of water quality based on measurements made at monitored river sites:

  • Nitrate-nitrogen
  • Ammoniacal nitrogen
  • Ammoniacal nitrogen (adjusted)
  • Total nitrogen
  • Total phosphorus
  • Dissolved reactive phosphorus
  • Water clarity
  • Turbidity
  • Escherichia coli
  • Macroinvertebrate community index

This dataset includes:

  • Median values for the period 2013 to 2017
  • for selected indicators, how these values compare to the National Objectives Framework (NOF) (MfE, 2017) bands related to ecosystem health and human health for recreation, and to expected concentrations in natural conditions, as shown by the default guideline values in the Australian and New Zealand guidelines for fresh and marine water quality (ANZG, 2018)

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.

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

Table ID 99867
Data type Table
Row count 7988
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Acidity (pH) of subantarctic waters east of New Zealand (1998–2014)

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Creative Commons Attribution 3.0 New Zealand

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5684
32
Added
28 Sep 2015

This dataset was first added to MfE Data Service on 28 Sep 2015.

Ocean acidification, measured by the reduction in sea water pH, is mainly caused by oceans absorbing and storing carbon dioxide from the atmosphere. Ocean acidification affects marine species in various ways. The growth and survival rates of some shell-building species are affected because they struggle to build their shells. The behaviour and physiology of some fish is also affected. This could influence marine ecosystems and commercial, customary, and recreational fishing or harvesting.
This dataset relates to the "Ocean acidification" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52522
Data type Table
Row count 588
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Water clarity trends, 2009–2013

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Creative Commons Attribution 3.0 New Zealand

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You must attribute the creator in your own works.

9118
52
Added
11 Jan 2016

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

Water clarity is a measure of underwater visibility in rivers and stream. Water clarity can be reduced by the presence of fine particles like silt, mud or organic material in the water. This affects the habitat and feeding of aquatic life like fish and aquatic birds. Water clarity is an important indicator of the health of a waterway, and is also a consideration for recreational activities like swimming and wading.
This dataset relates to the "River water quality trends: clarity" measure on the Environmental Indicators, Te taiao Aotearoa website.

Layer ID 52685
Data type Vector point
Feature count 722
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

PM10 concentrations in towns and cities 2006–13

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Creative Commons Attribution 3.0 New Zealand

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6111
68
Added
02 Dec 2015

This dataset was first added to MfE Data Service on 02 Dec 2015.

Particulate matter 10 micrometres or less in diameter (PM10) in the air comprises solid particles and liquid droplets from both natural and human-made sources. PM10 can be emitted from the combustion of fuels, such as wood and coal (eg from home heating and industry), and petrol and diesel (from vehicles). Natural sources of PM10 include sea salt, dust, pollen, smoke (from bush fires), and volcanic ash. Nationally, burning wood or coal for home heating is the main human-made source of PM10.

PM10 is of particular concern because it is found in high concentrations in some areas and can damage health. It is associated with effects ranging from respiratory irritation to some forms of cancer.

Column headings:
- Con_mcg_m3 = Concentration in micrograms per cubic metre (μg/m3)

This dataset relates to the "Annual average PM10 concentrations in towns and cities" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52625
Data type Table
Row count 648
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Livestock numbers, 1994–2017

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Creative Commons Attribution 4.0 International

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9020
164
Added
16 Apr 2018

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

Livestock farming is a widespread land use in New Zealand and contributes to our economy. High livestock numbers and the distribution of livestock across land environments can affect indigenous biodiversity and soil health (eg compaction). High livestock numbers and density in some land environments can also affect water quality, as nitrogen and bacteria from urine and faeces can leach into groundwater or run off the land into rivers and lakes.

We measure changes in the numbers of farmed livestock (eg beef and dairy cattle, deer, and sheep) across regions in New Zealand.

Table ID 95344
Data type Table
Row count 2075
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Frost and warm days trend assessment, 1972–2016

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Creative Commons Attribution 4.0 International

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You must attribute the creator in your own works.

5469
17
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 1.2m 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 trend in frost and warm days for New Zealand, the North and South Islands, and for 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).
Trend direction was assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level.
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 89388
Data type Table
Row count 60
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed
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