Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Test of statistical means for the extrapolation of soil depth point information using overlays of spatial environmental data and bootstrapping techniques
SMHI, Samhälle och säkerhet.ORCID-id: 0000-0001-7490-7949
2009 (engelsk)Inngår i: Hydrological Processes, ISSN 0885-6087, E-ISSN 1099-1085, Vol. 23, nr 21, s. 3017-3029Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Hydrological modelling depends highly on the accuracy and uncertainty of model input parameters such as soil properties. Since most of these data are field Surveyed, geostatistical techniques Such as kriging, classification and regression trees or more sophisticated soil-landscape models need to be applied to interpolate point information to the area. Most of the existing interpolation techniques require a random or regular distribution of points Within the study area but are not adequate to satisfactorily interpolate soil catena or transect data. The soil landscape model presented in this study is predicting soil information from transect or catena point data using a statistical mean (arithmetic, geometric and harmonic mean) to calculate the soil information based on class means of merged spatial explanatory variables. A data set of 226 soil depth measurements covering a range of 0-6.5 m was used to test the model. The point data were sampled along four transects in the Stubbetorp catchment, SE-Sweden. We overlaid a geomorphology map (8 classes) with digital elevation model-derived topographic index maps (2-9 classes) to estimate the range of error the model produces with changing sample size and input maps. The accuracy of the soil depth predictions was estimated with the root mean square error (RMSE) based oil a testing and training data set. RMSE ranged generally between 0.73 and 0.83 m +/- 0.013 m depending on the amount of classes the merged layers had, but were smallest for a map combination with a low number of classes predicted with the harmonic mean (RMSE = 0.46 m). The results show that the prediction accuracy of this method depends oil the number of point values in the sample, the value range of the measured attribute and the initial correlations between point values and explanatory variables, but suggests that the model approach is in general scale invariant. Copyright (C) 2009 John Wiley & Sons, Ltd.

sted, utgiver, år, opplag, sider
2009. Vol. 23, nr 21, s. 3017-3029
Emneord [en]
soil-landscape modelling, hydrological modelling, soil depth, bootstrapping, soil attributes, soil attribute prediction, statistical mean, root mean square error
HSV kategori
Forskningsprogram
Hydrologi
Identifikatorer
URN: urn:nbn:se:smhi:diva-603DOI: 10.1002/hyp.7413ISI: 000270935400004OAI: oai:DiVA.org:smhi-603DiVA, id: diva2:808022
Tilgjengelig fra: 2015-04-27 Laget: 2015-04-21 Sist oppdatert: 2018-01-11bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Søk i DiVA

Av forfatter/redaktør
Dahlke, Helen E.Seibert, JanAndersson, Lotta
Av organisasjonen
I samme tidsskrift
Hydrological Processes

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 46 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf