Annual sea surface temperature difference from normal, 2016

Licence

Creative Commons Attribution 4.0 International

You may use this work for commercial purposes.

You must attribute the creator in your own works.

6094
17
Added
12 Oct 2017

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

The oceans store most of the excess energy accumulated due to increased greenhouse gases in the atmosphere warming the surface layer. These long-term increases in temperature caused by climate change are in addition to natural variability where ocean temperatures change in response to climate oscillations like the El Niño Southern Oscillation.
Changes in sea-surface temperatures can affect marine processes, environments, and species. Some species may shift range or find it hard to survive in changing environmental conditions. Warmer water also takes up more space, which contributes to sea-level rise.
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.

Layer ID 89396
Data type Grid
Resolution About 4548.000m
Services Raster Query API, Catalog Service (CS-W), data.govt.nz Atom Feed

Status of widespread indigenous trees - Widespread indigenous tree species, 2002–07 and 2009–14

Licence

Creative Commons Attribution 3.0 New Zealand

You may use this work for commercial purposes.

You must attribute the creator in your own works.

5608
22
Added
28 Sep 2015

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

Eight indigenous tree species, spanning a range of ecological niches, were surveyed twice (between 2002 and 2014) as part of a vegetation monitoring programme. The data from these surveys can be used to assess changes in tree populations. Monitoring the status and trends of these widespread tree species helps us detect large-scale, long-term changes and problems for our forest ecosystems. Surveys assess the average number of trees per hectare.

This dataset relates to the "Status of widespread indigenous trees" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52474
Data type Table
Row count 8
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Long-term average chlorophyll-a concentration, 1997–2014

Licence

Creative Commons Attribution 3.0 New Zealand

You may use this work for commercial purposes.

You must attribute the creator in your own works.

5130
25
Added
08 Feb 2016

This dataset was first added to MfE Data Service on 08 Feb 2016.

The average concentration of chlorophyll-a (chl-a) in phytoplankton over the period 1997–2014.
Concentrations of chl-a in phytoplankton are used to assess primary productivity in our oceans. Phytoplankton are primary producers of biomass and form the basis of the oceans’ food chains.

Layer ID 52826
Data type Grid
Resolution 5000.000m
Services Raster Query API, Catalog Service (CS-W), data.govt.nz Atom Feed

The annual SOI compared with New Zealand's detrended temperature series (1909–2013)

Licence

Creative Commons Attribution 3.0 New Zealand

You may use this work for commercial purposes.

You must attribute the creator in your own works.

5555
22
Added
01 Oct 2015

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

El Niño Southern Oscillation (ENSO). It is an important predictor of how tropical oceans and climate might influence New Zealand’s climate. Being able to predict the timing and intensity of an El Niño or La Niña climate phase is important in predicting and preparing for extreme climatic conditions, such as strong winds, heavy rain, or drought. Such extreme conditions can impact on our environment, industries, and recreational activities. ENSO is commonly measured using the Southern Oscillation Index (SOI).
In New Zealand, an El Niño phase can cause colder winters. In summer it can result in more rain in the west and drought in the east. A La Niña phase can cause warmer temperatures, more rain in the north-east, and less rain in the south and south-west.
This dataset relates to the "El Niño Southern Oscillation" measure on the Environmental Indicators, Te taiao Aotearoa website.

Table ID 52590
Data type Table
Row count 105
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Freshwater pests: Water buttercup

Licence

Creative Commons Attribution 3.0 New Zealand

You may use this work for commercial purposes.

You must attribute the creator in your own works.

9931
16
Added
11 Jan 2016

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

"Freshwater plant and animal pests can have significant negative impacts on ecosystem health by reducing indigenous biodiversity through predation and competition, and destabilising aquatic habitats. Freshwater plant pests can cause economic losses through blocking water intakes for hydroelectricity generation, impeded drainage or irrigation. In addition, pests can affect the suitability for recreational activities.
This dataset relates to the "Freshwater pests" measure on the Environmental Indicators, Te taiao Aotearoa website. "

Layer ID 52742
Data type Vector point
Feature count 283
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Interdecadal Pacific Oscillation, 1871–2016

Licence

Creative Commons Attribution 4.0 International

You may use this work for commercial purposes.

You must attribute the creator in your own works.

7962
75
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

Black carbon concentrations, 2002–17

Licence

Creative Commons Attribution 4.0 International

You may use this work for commercial purposes.

You must attribute the creator in your own works.

5165
14
Added
15 Oct 2018

This dataset was first added to MfE Data Service on 15 Oct 2018.

Black carbon is a particle, often in the PM2.5 or ultra-fine size range, which is emitted from combustion sources and is commonly known as soot. In New Zealand most black carbon is emitted from vehicles (especially diesel vehicles), burning wood and coal for home heating, and outdoor burning. Both long and short-term exposure to black carbon is linked to serious health effects, such as respiratory and cardiovascular disease, cancer, and premature death (World Health Organization (WHO), 2013).
Black carbon warms the climate globally and regionally because it is efficient at absorbing energy from sunlight. Black carbon also increases ice and snow melt when deposited on these surfaces, darkening them and lowering albedo (proportion of light that is reflected) so they absorb more solar energy (Ramanathan & Carmichael, 2008).
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 98417
Data type Table
Row count 19077
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Long term soil erosion North Island 2012

Licence

Creative Commons Attribution 3.0 New Zealand

You may use this work for commercial purposes.

You must attribute the creator in your own works.

5142
46
Added
09 Feb 2016

This dataset was first added to MfE Data Service on 09 Feb 2016.

"Long-term soil erosion is the average mass of soil lost per square kilometre per year (tonnes/km2/year) over a period of approximately 10 years. It is extrapolated from long-term measurements of sediment load in rivers. Extrapolation is based on mean annual rainfall, rock type, and land cover. The total sediment in rivers (tonnes/year) for a particular region is the sum of all soil erosion over the entire region (Dymond et al, 2010). Soil-erosion rates were calculated to 2012.

This data set relates to the "Estimated long-term soil erosion" measure on the Environmental Indicators, Te taiao Aotearoa website."

Layer ID 52832
Data type Grid
Resolution 100.000m
Services Raster Query API, Catalog Service (CS-W), data.govt.nz Atom Feed

Predicted average Macroinvertebrate Community Index (MCI) score, 2007–2011

Licence

Creative Commons Attribution 3.0 New Zealand

You may use this work for commercial purposes.

You must attribute the creator in your own works.

9067
58
Added
11 Jan 2016

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

"Macroinvertebrates are small animals without backbones that live on and under submerged logs, rocks, and aquatic plants in the stream bed during some period of their life cycle. They play a central role in stream ecosystems by feeding on periphyton (algae or slime), macrophytes (aquatic plants), dead leaves and wood, or on each other. High Macroinvertebrate Community Index (MCI) scores generally indicate better stream health. Macroinvertebrates are good continuous indicators of the health of their stream environment. This is because they are relatively sedentary and long–lived (a year or more) which means they live with any stresses or changes that occur in their location (eg, pollution, habitat removal, floods and droughts). They complement discrete measures like chemical monitoring, which only reflects the condition at the exact time and place of sampling. Such monitoring might miss effects of a short–lived pollutant or an unanticipated type of disturbance.
This dataset relates to the ""River water quality: benthic macroinvertebrates"" measure on the Environmental Indicators, Te taiao Aotearoa website. "

Layer ID 52713
Data type Vector point
Feature count 512
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

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

Licence

Creative Commons Attribution 4.0 International

You may use this work for commercial purposes.

You must attribute the creator in your own works.

6136
27
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
Results 181 to 190 of 996