Oceania Climatology / Meteorology / Atmosphere Data

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 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 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 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.