Daily peak UV index value, 1981–2017

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Creative Commons Attribution 4.0 International

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2559
19
Added
14 Oct 2017

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

Daily peak UV index values at Invercargill, Lauder (Otago region), Christchurch, Paraparaumu (Wellington region), and Leigh (Auckland region). The strength of UV light is expressed as a solar UV index, starting from 0 (no UV) to 11+ (extreme).
Exposure to the sun's ultraviolet (UV) light helps our bodies make vitamin D, which we need for healthy bones and muscles. However, too much exposure to UV light can cause skin cancer. New Zealand has naturally high UV levels, and monitoring UV levels helps us understand the occurrence of skin cancer.
Ozone in the upper atmosphere absorbs some of the sun’s UV light, protecting us from harmful levels. The amount of UV radiation reaching the ground varies in relation to changes in the atmospheric ozone concentrations. The Antarctic ozone hole lies well to the south of New Zealand and does not have a large effect on New Zealand’s ozone concentrations.
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 89468
Data type Table
Row count 38993
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Interdecadal Pacific Oscillation, 1871–2016

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Creative Commons Attribution 4.0 International

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2539
42
Added
12 Oct 2017

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

The Interdecadal Pacific Oscillation (IPO) is a long-term oscillation of sea-surface temperatures in the Pacific Ocean that can last from 20 to 30 years. Its positive and negative phases affect the strength and frequency of El Niño and La Niña. In New Zealand, the positive phase is linked to stronger west to southwest winds and more rain in the west. This trend is reversed during the negative phase.
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 89382
Data type Table
Row count 730
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Potential evapotranspiration deficit (PED), 1972–2016

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Creative Commons Attribution 4.0 International

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2579
21
Added
13 Oct 2017

This dataset was first added to MfE Data Service on 13 Oct 2017.

Interpolated PED values at 30 regionally representative sites.
Soil moisture is vital for plant growth. When plants cannot access the water they need, growth is reduced, affecting crops and food for livestock, and native biodiversity. Over a sustained period, a drought can have significant social and economic costs, particularly for rural communities.
Potential evapotranspiration deficit (PED) can be thought of as a drought index. It is the difference between how much water could potentially be lost from the soil through evapotranspiration and how much is actually available. When PED is high, plants do not have the full amount of water available they need for growth. PED is measured in growing seasons (the 12 months from 1 July to 30 June of the following year. Data covers each of the growing seasons from 1 July 1972, with the last growing season in the series ending on 30 June 2016. 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 89437
Data type Table
Row count 1320
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Water physical stocks by region (1995–2014)

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Creative Commons Attribution 3.0 New Zealand

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2512
35
Added
01 Oct 2015

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

New Zealand is a water-rich country. Water is found in a network of waterways and lakes, as ground water, in glaciers, and in the soil and plants. Changes in temperature and precipitation patterns affect our water stocks, for example leading to low flows or floods. Water physical stocks show how climate changes can impact on our environment, its ecosystems, and ultimately our lifestyles.
Further information can be found in:
Collins, D, Zammit, C, Willsman, A & Henderson, R (2015) Surface water components of New Zealand’s National WaterAccounts, 1995-2014. Prepared for Ministry for the Environment May 2015. Available at data.mfe.govt.nz/x/Tebsax on the Ministry for the Environment dataservice (data.mfe.govt.nz/).
This dataset relates to the "Water physical stocks: precipitation and evapotranspiration" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52578
Data type Table
Row count 3520
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

The annual SOI compared with New Zealand's detrended temperature series, 1908/9–2015/6

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Creative Commons Attribution 4.0 International

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2532
6
Added
12 Oct 2017

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

The El Niño Southern Oscillation (ENSO) is the movement of warm equatorial water across the Pacific Ocean and the atmospheric response. It occurs every 2–7 years, typically lasting 6–18 months. ENSO has three phases: neutral, El Niño and La Niña. In New Zealand an El Niño phase in summer can bring increased westerly winds, more rain in the west, and drought in the east; in winter it can lead to more cool southerly winds. During a La Niña phase we may experience more north-easterly winds, wetter conditions in the north and east, and higher sea levels.
This dataset relates to annual ENSO and detrended temperature data.
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 89380
Data type Table
Row count 216
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Influenza hospitalisations, 2000–16

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Creative Commons Attribution 4.0 International

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2515
5
Added
14 Oct 2017

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

Influenza is a potentially life-threatening virus that spreads quickly from person to person. It is a significant public health issue in this country, with 10–20 percent of New Zealanders infected every year. While influenza can occur all year round, incidence generally peaks in winter and spring in New Zealand. Some studies suggest this is because the virus can survive longer outside the body in periods of colder weather and low humidity (dry conditions).
Influenza infections may decline as our climate changes. Warmer projected temperatures and higher humidity during winter and spring may contribute to reduced annual influenza rates. However, influenza infection is also affected by factors besides temperature and humidity.
These data are reported in an annual surveillance report by the Institute of Environmental Science and Research. See the 2015 report for more information (Institute of Environmental Science and Research, 2016).
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 89457
Data type Table
Row count 17
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Global greenhouse gas emissions (1990–2011)

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Creative Commons Attribution 3.0 New Zealand

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2497
29
Added
01 Oct 2015

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

Greenhouse gas (GHG) emissions from human activities increase the concentrations of these gases in the atmosphere. GHGs absorb some of the heat radiating from the Earth’s surface and warm the atmosphere. In turn, this warming changes our climate. Some GHG emissions are removed, primarily by forests. For this reason, we use net emission rather than gross emission values to represent the total amount of gas contributed to the atmosphere.
This data is compiled from two sources. The UNFCCC (United Nations) GHG data and CAIT 2.0 (World Resources Institute, climate analysis indicators tool 2014).
This dataset relates to the "Global greenhouse gas emissions" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52564
Data type Table
Row count 5258
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Autumn rainfall trends, 1960–2016

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Creative Commons Attribution 4.0 International

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2494
11
Added
12 Oct 2017

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

Autumn rainfall trends for 30 representative sites from 1960–2016.
Rain is vital for life – it supplies the water we need to drink and to grow our food, keeps our ecosystems healthy, and supplies our electricity. New Zealand’s mountainous terrain and location in the roaring forties mean rainfall varies across the country. Changes in rainfall amount or timing can significantly affect agriculture, energy, recreation, and the environment. For example, an increase or decrease of rainfall in spring can have marked effects on crops or fish populations.
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 89402
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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2442
41
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

Trends in number of days with a maximum gust in the 99th percentile, 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.

2469
6
Added
12 Oct 2017

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

Trends in number of days with a maximum gust in the 99th percentile, 1972–2016. 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.
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 89423
Data type Table
Row count 30
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed
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