Tagged: Temperature

Mean annual temperature data layer used in the creation of Land Environments of New Zealand (LENZ) classification. The classification layers have been made publicly available by the Ministry for the Environment (see koordinates.com/#/layers/?q=LENZ for to access these layers).

Mean annual temperature is recorded in °C. The climate station data used in the development of this climate surface were derived from summaries of climate observations published by the New Zealand Meteorological Service, using data collected over the period from 1950-1980. The resulting data layer was created by coupling a 100 m DEM with a thin-plate spline surface fitted to an irregular network of 300 meteorological stations. The resulting 100 metre layer was then interpolated to 25 metres using bilinear interpolation. Following conventions used in the calculation of climate summary statistics, the values used to fit the surface consisted of the mean of the 12 monthly averages for daily average temperature.

This layer has been multiplied by a factor of 10 (i.e. converted into an integer grid) to save space and make the grids more responsive. A value of 136 is actually 13.6 °C.

Additional details such as the climate station locations used in the creation of the layer and error maps are defined in the attached LENZ Technical Guide.

Mean minimum temperature of the coldest month data layer used in the creation of Land Environments of New Zealand (LENZ) classification. The classification layers have been made publicly available by the Ministry for the Environment (see koordinates.com/#/layers/?q=LENZ for to access these layers).

Mean minimum temperature of the coldest month is recorded in °C. The climate station data used in the development of this climate surface were derived from summaries of climate observations published by the New Zealand Meteorological Service, using data collected over the period from 1950-1980. Estimates of the mean minimum temperature in July, the coldest month of winter, were derived from a surface fitted to monthly estimates of mean daily temperatures. The resulting data layer was created by coupling a 100 m DEM with a thin-plate spline surface fitted to an irregular network of 346 meteorological stations. The resulting 100 metre layer was then interpolated to 25 metres using bilinear interpolation.

This layer has been multiplied by a factor of 10 (i.e. converted into an integer grid) to save space and make the grids more responsive. A value of 53 is actually 5.3 °C.

Additional details such as the climate station locations used in the creation of the layer and error maps are defined in the attached LENZ Technical Guide.

The New Zealand Fundamental Soil Layer originates from a relational join of features from two databases: the New Zealand Land Resource Inventory (NZLRI), and the National Soils Database (NSD). The NZLRI is a national polygon database of physical land resource information, including a soil unit. Soil is one in an inventory of five physical factors (including rock, slope, erosion, and vegetation) delineated by physiographic polygons at approximately 1:50,000 scale. The NSD is a point database of soil physical, chemical, and mineralogical characteristics for over 1500 soil profiles nationally. A relational join between the NZLRI dominant soil and derivative tables from the NSD was the means by which 14 important soil attributes were attached to the NZLRI polygons. Some if these attributes originate from exact matches with NSD records, while others derive from matches to similar soils or professional estimates. The layers contains the soil temperature regime attribute. The soil temperature regime classes relate to the soil temperature at 0.3 m depth. The classes used originate from and are described more fully in Webb and Wilson (1995), which in turn is based on the work of Aldridge (1982, 1984) and Aldridge and Cook (1983).

A 25m grid of mean annual soil temperature at 30cm depth derived by regression analysis from 175 data loggers and NIWA standard climate stations recording soil temperature. This clipped version is provided for use as a regression variable for calculating the BASE carrying capacity. For use with Model 1.

Soil temperature surfaces for the South Island of New Zealand are based on analysis of a combination of monthly mean soil temperature data from the NIWA (National Institute of Water and Atmospheric Research) 3 years data from 175 mini-data-loggers (1997-2000) laid out in a stratified sampling scheme at 7 climatically representative locations in the South Island. At each location a cluster of about 25 data loggers sampled a range of elevations between 100 and 1800 m. At each elevation grouping the 4 primary aspects (N, S, E, W) and a flat site were sampled at a depth of 30cm. Multiple regression used site characteritics of latitude, Distance from coast, elevation, aspect, slope and forest/non-forest cover to predict topographic effects on soil temperatures.

Soil temperature surfaces for the South Island of New Zealand are based on analysis of a combination of monthly mean soil temperature data from the NIWA (National Institute of Water and Atmospheric Research) 3 years data from 175 mini-data-loggers (1997-2000) laid out in a stratified sampling scheme at 7 climatically representative locations in the South Island. At each location a cluster of about 25 data loggers sampled a range of elevations between 100 and 1800 m. At each elevation grouping the 4 primary aspects (N, S, E, W) and a flat site were sampled at a depth of 30cm. Multiple regression used site characteritics of latitude, Distance from coast, elevation, aspect, slope and forest/non-forest cover to predict topographic effects on soil temperatures.

Soil temperature surfaces for the South Island of New Zealand are based on analysis of a combination of monthly mean soil temperature data from the NIWA (National Institute of Water and Atmospheric Research) 3 years data from 175 mini-data-loggers (1997-2000) laid out in a stratified sampling scheme at 7 climatically representative locations in the South Island. At each location a cluster of about 25 data loggers sampled a range of elevations between 100 and 1800 m. At each elevation grouping the 4 primary aspects (N, S, E, W) and a flat site were sampled at a depth of 30 cm. Multiple regression used site characteristics of latitude, Distance from coast, elevation, aspect, slope and forest/non-forest cover to predict topographic effects on soil temperatures.

Soil temperature surfaces for the South Island of New Zealand are based on analysis of a combination of monthly mean soil temperature data from the NIWA (National Institute of Water and Atmospheric Research) 3 years data from 175 mini-data-loggers (1997-2000) laid out in a stratified sampling scheme at 7 climatically representative locations in the South Island. At each location a cluster of about 25 data loggers sampled a range of elevations between 100 and 1800 m. At each elevation grouping the 4 primary aspects (N, S, E, W) and a flat site were sampled at a depth of 30 cm. Multiple regression used site characteristics of latitude, Distance from coast, elevation, aspect, slope and forest/non-forest cover to predict topographic effects on soil temperatures.

Soil temperature surfaces for the South Island of New Zealand are based on analysis of a combination of monthly mean soil temperature data from the NIWA (National Institute of Water and Atmospheric Research) 3 years data from 175 mini-data-loggers (1997-2000) laid out in a stratified sampling scheme at 7 climatically representative locations in the South Island. At each location a cluster of about 25 data loggers sampled a range of elevations between 100 and 1800 m. At each elevation grouping the 4 primary aspects (N, S, E, W) and a flat site were sampled at a depth of 30cm. Multiple regression used site characteritics of latitude, Distance from coast, elevation, aspect, slope and forest/non-forest cover to predict topographic effects on soil temperatures.

Soil temperature surfaces for the South Island of New Zealand are based on analysis of a combination of monthly mean soil temperature data from the NIWA (National Institute of Water and Atmospheric Research) 3 years data from 175 mini-data-loggers (1997-2000) laid out in a stratified sampling scheme at 7 climatically representative locations in the South Island. At each location a cluster of about 25 data loggers sampled a range of elevations between 100 and 1800 m. At each elevation grouping the 4 primary aspects (N, S, E, W) and a flat site were sampled at a depth of 30 cm. Multiple regression used site characteristics of latitude, Distance from coast, elevation, aspect, slope and forest/non-forest cover to predict topographic effects on soil temperatures.