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

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7552
52
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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7550
43
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

Lake water quality trends, 2004-2013

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Creative Commons Attribution 3.0 New Zealand

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7475
88
Added
29 Sep 2015

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

When nutrients accumulate in lakes (referred to as ‘nutrient enrichment’) above certain levels, they can make the lakes murky and green with algae, and lower oxygen levels. Lakes with extremely poor water quality are rarely suitable for recreation and provide poor habitats for aquatic species.

Trends in the following parameters are provided:
Trophic Level Index 3 (TLI)
Chlorophyll-a (CHLA)
Bottom-water dissolved oxygen (DObottom)
Ammoniacal nitrogen (NH4N)
Oxidised nitrogen (NO3N)
Total nitrogen (unfiltered) (TN)
Dissolved reactive phosphorus (DRP)
Total phosphorus (unfiltered) (TP)

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 "Lake water quality: Trophic Level Index" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52538
Data type Table
Row count 3724
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Area of seabed trawled by BOMEC habitat classes (1990–2011)

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Creative Commons Attribution 3.0 New Zealand

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7552
11
Added
19 Oct 2016

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

Seabed trawling and dredging (where fishing gear is towed near or along the ocean floor) can physically damage seabed (benthic) habitats and species. These fishing methods can also stir up sediment from the seabed, creating sediment plumes that can smother sensitive species. Recovery times for affected habitats and species depend on their sensitivity and the area affected by trawling or dredging. Bottom trawling is carried out on or near the seabed in both shallow and deep waters. Dredging is carried out on the seabed in shallow waters and targets marine creatures such as scallops. This measure focuses on deepwater areas (waters deeper than 200m).

Table ID 53488
Data type Table
Row count 30
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Coastal extreme waves (2008–15)

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Creative Commons Attribution 3.0 New Zealand

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7534
23
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 wave-height thresholds for each year from 2008 to 2015 in coastal areas.
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.

Table ID 53476
Data type Table
Row count 54
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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7525
29
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

Southern Annular Mode monthly values, January 1979–December 2016

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Creative Commons Attribution 4.0 International

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7504
31
Added
12 Oct 2017

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

A consistent band of westerly wind flows across the Southern Hemisphere and circles the South Pole. The Southern Annular Mode (SAM) describes how this band moves, either north towards the equator (negative phase) or south towards Antarctica (positive phase). A negative phase typically causes increased westerlies, unsettled weather, and storms in New Zealand. A phase can last several weeks, but changes can be rapid and unpredictable.
The SAM is one of three climate oscillations that affect our weather. The resulting changes in air pressure, sea temperature, and wind direction can last for weeks to decades, depending on the oscillation.
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 89384
Data type Table
Row count 456
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Forest carbon stocks trends, 1990–2015

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Creative Commons Attribution 4.0 International

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7507
16
Added
12 Oct 2017

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

New Zealand’s indigenous and exotic forests absorb carbon dioxide (CO2) from the atmosphere through photosynthesis and store the carbon as biomass and in the soil. On average, more than twice as much carbon per hectare is stored in New Zealand’s mature indigenous forests than in exotic forests planted for wood production. Regenerating indigenous forests are also an important store of carbon, adding carbon every year as they grow. Total carbon stored in exotic forests will fluctuate over decades as the forests grow from seedlings to mature trees, are harvested, and replanted. Because CO2 is the major driver of climate change, forests provide important mitigation services and help New Zealand meet its climate change commitments.
The trend 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 89410
Data type Table
Row count 2
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Southern Annular Mode annual values, 1887–2016

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Creative Commons Attribution 4.0 International

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You must attribute the creator in your own works.

7482
36
Added
12 Oct 2017

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

A consistent band of westerly wind flows across the Southern Hemisphere and circles the South Pole. The Southern Annular Mode (SAM) describes how this band moves, either north towards the equator (negative phase) or south towards Antarctica (positive phase). A negative phase typically causes increased westerlies, unsettled weather, and storms in New Zealand. A phase can last several weeks, but changes can be rapid and unpredictable.
The SAM is one of three climate oscillations that affect our weather. The resulting changes in air pressure, sea temperature, and wind direction can last for weeks to decades, depending on the oscillation.
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 89383
Data type Table
Row count 168
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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7497
11
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
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