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A new GIS landscape classification method for rain/snow temperature thresholds in surface based models

Feiccabrino, James M. LU and Grigg, Laurie D. (2017) In Hydrology Research 48(4). p.902-914
Abstract

Landscape air temperature thresholds (TA) and percent misclassified precipitation (error) for 12 years of meteorological observations from 40 stations across the Scandinavian Peninsula were derived and compared using both manual and geographic information system (GIS) landscape classification methods. Dew-point, wet-bulb, and wet bulb 0.5 were also tested. Both classification methods used the following west to east landscape categories: windward (WW) ocean, coast, fjord, hill, and mountain in Norway; and leeward (LW) mountain, hill, rolling terrain, and coast in Sweden. GIS landscape classification has the advantages of automating the classification process and increasing objectivity. The GIS classification was based on... (More)

Landscape air temperature thresholds (TA) and percent misclassified precipitation (error) for 12 years of meteorological observations from 40 stations across the Scandinavian Peninsula were derived and compared using both manual and geographic information system (GIS) landscape classification methods. Dew-point, wet-bulb, and wet bulb 0.5 were also tested. Both classification methods used the following west to east landscape categories: windward (WW) ocean, coast, fjord, hill, and mountain in Norway; and leeward (LW) mountain, hill, rolling terrain, and coast in Sweden. GIS landscape classification has the advantages of automating the classification process and increasing objectivity. The GIS classification was based on station location (LW or WW) relative to the Scandinavian mountain range, and the % water or range of elevation change within 15 km. The GIS and manual method had the same TA for 20 stations, and similar total reduction in error (2.29 to 2.17% respectively) when compared to country TA. Therefore, automated GIS landscape classification can be used to decrease error from common country or global scale TA. Wet-bulb temperature thresholds for GIS landscapes resulted in a greater reduction in error (8.26%) compared to air (2.29%), and dew-point (-16.67%) thresholds. However, finding stations reporting relative humidity or wetbulb temperature may limit its widespread use.

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author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Geographic landscape, Hydrology, Precipitation phase, Snow, Snow model, Temperature threshold
in
Hydrology Research
volume
48
issue
4
pages
13 pages
publisher
IWA Publishing
external identifiers
  • scopus:85026284849
  • wos:000409334100003
ISSN
1998-9563
DOI
10.2166/nh.2016.055
language
English
LU publication?
yes
id
1a4cd920-e0a4-4461-9237-eed92da4f795
date added to LUP
2017-08-29 14:55:14
date last changed
2025-01-07 19:31:30
@article{1a4cd920-e0a4-4461-9237-eed92da4f795,
  abstract     = {{<p>Landscape air temperature thresholds (T<sub>A</sub>) and percent misclassified precipitation (error) for 12 years of meteorological observations from 40 stations across the Scandinavian Peninsula were derived and compared using both manual and geographic information system (GIS) landscape classification methods. Dew-point, wet-bulb, and wet bulb 0.5 were also tested. Both classification methods used the following west to east landscape categories: windward (WW) ocean, coast, fjord, hill, and mountain in Norway; and leeward (LW) mountain, hill, rolling terrain, and coast in Sweden. GIS landscape classification has the advantages of automating the classification process and increasing objectivity. The GIS classification was based on station location (LW or WW) relative to the Scandinavian mountain range, and the % water or range of elevation change within 15 km. The GIS and manual method had the same T<sub>A</sub> for 20 stations, and similar total reduction in error (2.29 to 2.17% respectively) when compared to country T<sub>A</sub>. Therefore, automated GIS landscape classification can be used to decrease error from common country or global scale T<sub>A</sub>. Wet-bulb temperature thresholds for GIS landscapes resulted in a greater reduction in error (8.26%) compared to air (2.29%), and dew-point (-16.67%) thresholds. However, finding stations reporting relative humidity or wetbulb temperature may limit its widespread use.</p>}},
  author       = {{Feiccabrino, James M. and Grigg, Laurie D.}},
  issn         = {{1998-9563}},
  keywords     = {{Geographic landscape; Hydrology; Precipitation phase; Snow; Snow model; Temperature threshold}},
  language     = {{eng}},
  month        = {{08}},
  number       = {{4}},
  pages        = {{902--914}},
  publisher    = {{IWA Publishing}},
  series       = {{Hydrology Research}},
  title        = {{A new GIS landscape classification method for rain/snow temperature thresholds in surface based models}},
  url          = {{http://dx.doi.org/10.2166/nh.2016.055}},
  doi          = {{10.2166/nh.2016.055}},
  volume       = {{48}},
  year         = {{2017}},
}