Frost and warm days, 1972–2016

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Creative Commons Attribution 4.0 International

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2546
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 1.2 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

Water physical stocks for selected measures (1995–2014)

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Creative Commons Attribution 3.0 New Zealand

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2817
24
Added
15 Oct 2015

This dataset was first added to MfE Data Service on 15 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.
Variables: Abstraction for Hydrogeneration, Change in Ice, Change in Lakes, Change in Snow, Change in Soil Moisture, Discharge by Hydrogeneration, Evapotranspiration, Inflow from other regions, Outflow to other regions, Outflow to sea, Precipitation, Total.

Table ID 52596
Data type Table
Row count 240
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Growing degree days trend assessment, for New Zealand, the North Island, and the South Island, 1972/3–2015/6

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Creative Commons Attribution 4.0 International

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2593
11
Added
17 Oct 2017

This dataset was first added to MfE Data Service on 17 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 New Zealand and the North and South Islands.
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 89476
Data type Table
Row count 3
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Melanoma registration rates, by age group, 1996–2015

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Creative Commons Attribution 4.0 International

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2874
5
Added
14 Oct 2017

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

This csv reports melanoma registration rates, per 100,000 population, by age groups (eg 0–24 years old, 25–44 years old).
New Zealand and Australia have the world’s highest rates of melanoma, the most serious type of skin cancer. Melanoma is mainly caused by exposure to ultraviolet (UV) light, usually from the sun. New Zealand has naturally high UV levels, especially during summer.
The risk of developing melanoma is affected by factors such as skin colour and type, family history, and the amount of sun exposure. Melanoma can affect people at any age, but the chance of developing a melanoma increases with age. We report on age-standardised rates of melanoma to account for the increasing proportion of older people in our population.
Our data on melanoma registrations come from the New Zealand Cancer Registry and the Ministry of Health's Mortality Collection. The passing of the Cancer Registry Act 1993 and Cancer Registry Regulations 1994 led to significant improvements in data quality and coverage (Ministry of Health, 2013). A sharp increase in registrations after 1993 is likely to have been related to these legislative and regulatory changes; for this reason we have only analysed data from 1996.
2014–15 data are provisional and subject to change.
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 89459
Data type Table
Row count 360
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Trends in percent of annual rainfall in the 95th percentile (r95ptot), 1960–2016

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Creative Commons Attribution 4.0 International

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2899
7
Added
13 Oct 2017

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

Trends in percent of annual rainfall in the 95th percentile (r95ptot), 1960–2016.
Intense rainfall can result in flash floods or land slips that damage homes and property, disrupt transportation, and endanger lives. It can also interfere with recreation and increase erosion. Changes to the frequency of intense rainfall events can alter biodiversity.
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 89434
Data type Table
Row count 30
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Oceanic sea surface temperature trends, 1993–2016

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Creative Commons Attribution 4.0 International

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2920
5
Added
12 Oct 2017

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

We used NIWA’s sea-surface temperature archive, which is derived from the Advanced Very High Resolution Radiometer (AVHRR) satellite data it receives from the US National Oceanic and Atmospheric Administration. The archive provides high spatial (approximately 1km) and high temporal (approximately six-hourly in cloud-free locations) resolution estimates of sea-surface temperatures over the New Zealand region, dating from January 1993. Uddstrom & Oien (1999) and Uddstrom (2003) describe the methods used to derive and validate the data.
Our data extends from about 30°S to 55°S, and from 160°E to 170°W and is grouped into five areas: the exclusive economic zone (EEZ), the Chatham Rise, northern subtropical waters, subantarctic waters, and the Tasman Sea.
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 89407
Data type Table
Row count 4
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Trends in maximum highest annual wind gust, 1972–2016

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Creative Commons Attribution 4.0 International

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2919
7
Added
12 Oct 2017

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

Trends in maximum highest annual wind gust, 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 89424
Data type Table
Row count 30
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Global production of ozone depleting substances, 1986–2015

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Creative Commons Attribution 4.0 International

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2961
5
Added
17 Oct 2017

This dataset was first added to MfE Data Service on 17 Oct 2017.

Ozone in the stratosphere is destroyed in a catalytic reaction with a range of chemical species (mainly CFCs) that are emitted through human activities. The emission of these chemicals is closely related to the amount of the chemicals that are produced. The Montreal protocol helps the UNEP collect information on the production of ozone depleting substances.
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 89474
Data type Table
Row count 9
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Trends in annual maximum one–day rainfall (rx1day), 1960–2016

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Creative Commons Attribution 4.0 International

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You must attribute the creator in your own works.

2872
8
Added
13 Oct 2017

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

Trends in annual maximum one-day rainfall (rx1day), 1960–2016.
Intense rainfall can result in flash floods or land slips that damage homes and property, disrupt transportation, and endanger lives. It can also interfere with recreation and increase erosion. Changes to the frequency of intense rainfall events can alter biodiversity.
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 89433
Data type Table
Row count 30
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Influenza like illness weekly consultation rates, 2000–16

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Creative Commons Attribution 4.0 International

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2945
7
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 89456
Data type Table
Row count 374
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed
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