Macroinvertebrate Community Index trends, by monitoring site, 2004-13

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Creative Commons Attribution 3.0 New Zealand

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4320
65
Added
29 Sep 2015

This dataset was first added to MfE Data Service on 29 Sep 2015.

Benthic macroinvertebrates are small animals without backbones (eg insects and worms). They live on and under submerged logs, rocks, and aquatic plants on the beds of rivers and streams during some part of their life cycle. Macroinvertebrates play a central role in stream ecosystems by feeding on periphyton (algae), macrophytes (aquatic plants), dead leaves and wood, or on each other. A high macroinvertebrate community index (MCI) indicates a high level of river health.

File contains trend statistics and calculation results for the period 2004-2013.

For more information please see:
Larned, S, Snelder, T, Unwin, M, McBride, G, Verburg, P, McMillan, H (2015).Analysis of Water Quality in New Zealand lakes and Rivers: data sources, data sets, assumptions, limitations, methods and results. NIWA Client Report no. CHC2015-033. Available at data.mfe.govt.nz/x/DDui3u from the Ministry for the Environment dataservice.

This dataset relates to the "River water quality: Benthic macroinvertebrates" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52535
Data type Table
Row count 462
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Macroinvertebrate Community Index percentiles, by monitoring site, 2009-13

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Creative Commons Attribution 3.0 New Zealand

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4360
41
Added
29 Sep 2015

This dataset was first added to MfE Data Service on 29 Sep 2015.

Benthic macroinvertebrates are small animals without backbones (eg insects and worms). They live on and under submerged logs, rocks, and aquatic plants on the beds of rivers and streams during some part of their life cycle. Macroinvertebrates play a central role in stream ecosystems by feeding on periphyton (algae), macrophytes (aquatic plants), dead leaves and wood, or on each other. A high macroinvertebrate community index (MCI) indicates a high level of river health.

File contains state results for each monitored site, expressed as percentile calculations for the period 2009-2013. See Larned et al. 2015 for further details.

For more information please see:
Larned, S, Snelder, T, Unwin, M, McBride, G, Verburg, P, McMillan, H (2015).Analysis of Water Quality in New Zealand lakes and Rivers: data sources, data sets, assumptions, limitations, methods and results. NIWA Client Report no. CHC2015-033. Available at data.mfe.govt.nz/x/DDui3u from the Ministry for the Environment dataservice.

This dataset relates to the "River water quality: Benthic macroinvertebrates" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52534
Data type Table
Row count 512
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Growing degree days monthly data by site, 1972–2016

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Creative Commons Attribution 4.0 International

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4340
33
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 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.
This dataset gives the number of GDD per month and calendar year for all 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 89392
Data type Table
Row count 1290
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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4316
54
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

Lightning Trends, 2001–2016

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Creative Commons Attribution 4.0 International

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4380
4
Added
13 Oct 2017

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

Lightning is the discharge of electricity from thunderstorms and can occur within a cloud, between clouds, or between a cloud and the ground. By international standards, lightning does not occur frequently around New Zealand. However, ground strikes can injure or kill people and livestock, damage property and infrastructure, and, although rarely in New Zealand, spark forest fires. Thunderstorms are often associated with other severe weather events, such as strong wind gusts and hail. Thunderstorms may increase in frequency and intensity with climate change.
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 89427
Data type Table
Row count 1
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Fish licences issued by Fish and Game, 1980/81 to 2013/14 seasons

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Creative Commons Attribution 3.0 New Zealand

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You must attribute the creator in your own works.

4338
31
Added
29 Sep 2015

This dataset was first added to MfE Data Service on 29 Sep 2015.

Fresh water in New Zealand is highly valued for recreational activities, including fishing. Freshwater angling (primarily for introduced ‘sports fish’ such as brown trout, rainbow trout, and chinook salmon) is a popular leisure activity. Fish licences issued by Fish & Game New Zealand provide an indication of recreational fishing activity at freshwater locations around the country.

This dataset relates to the "Participation in recreational fishing" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52543
Data type Table
Row count 238
Services 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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4306
60
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

Oceanic sea surface temperature anomaly

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Creative Commons Attribution 3.0 New Zealand

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4354
22
Added
01 Oct 2015

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

The ocean waters surrounding New Zealand vary in temperature from north to south. They interact with heat and moisture in the atmosphere and affect our weather. Sea surface temperature changes with climate drivers such as El Niño, and will change with climate change. The sea surface temperature anomaly provides an indication of the heat change in the ocean.
Long-term changes and short-term variability in sea-surface temperatures can affect marine processes, habitats, and species. some species may find it hard to survive in changing environmental conditions.
The oceanic sea surface temperature data comes from the NIWA Sea surface temperature Archive (NSA). There are 2 datasets, of NSA Annual means and NSA Annual Anomolies, covering the Tasman, subtropical (STW) and Southern Antarctic (SAW) area and the total area. The data is available from 1993 to 2013 and the unit of measure is degrees celcius.
For further information please see:
Uddstrom, MJ (2015) Sea Surface Temperature Data and Analysis for the 2015 Synthesis Report. For Ministry for the Environment. Available at data.mfe.govt.nz/x/hRbGUJ on the Ministry for the Environment dataservice (data.mfe.govt.nz).
This dataset relates to the "Sea surface temperature" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52582
Data type Table
Row count 84
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Trends in ocean acidification, 1998–2016

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Creative Commons Attribution 4.0 International

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4334
20
Added
14 Oct 2017

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

The pH of New Zealand subantarctic waters is calculated from pCO2 (dissolved carbon dioxide) and alkalinity measurements using refitted Mehrbach constants (see Mehrbach et al, 1973; Dickson & Millero, 1987), and in-situ temperature taken from the Munida time-series transect off the Otago coast. Measurements of pCO2 are taken every two months.
The Munida transect, in the subantarctic waters off Otago, is the Southern Hemisphere’s longest-running record of pH measurements (NIWA, 2015).
Trends were assessed using linear regression 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 89462
Data type Table
Row count 3
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Oceanic sea surface temperature, 1993–2016

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Creative Commons Attribution 4.0 International

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4309
36
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.
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 89406
Data type Table
Row count 960
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed
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