Integrating climate and local factors for geomorphological distribution models

ABSTRACT Earth surface processes (ESPs) drive landscape development and ecosystem processes in high‐latitude regions by creating spatially heterogeneous abiotic and biotic conditions. Ongoing global change may potentially alter the activity of ESPs through feedback on ground conditions, vegetation a...

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Published in:Earth Surface Processes and Landforms
Main Authors: Aalto, Juha, Luoto, Miska
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2014
Subjects:
Online Access:http://dx.doi.org/10.1002/esp.3554
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spelling crwiley:10.1002/esp.3554 2024-09-15T18:29:07+00:00 Integrating climate and local factors for geomorphological distribution models Aalto, Juha Luoto, Miska 2014 http://dx.doi.org/10.1002/esp.3554 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fesp.3554 https://onlinelibrary.wiley.com/doi/pdf/10.1002/esp.3554 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Earth Surface Processes and Landforms volume 39, issue 13, page 1729-1740 ISSN 0197-9337 1096-9837 journal-article 2014 crwiley https://doi.org/10.1002/esp.3554 2024-08-27T04:27:01Z ABSTRACT Earth surface processes (ESPs) drive landscape development and ecosystem processes in high‐latitude regions by creating spatially heterogeneous abiotic and biotic conditions. Ongoing global change may potentially alter the activity of ESPs through feedback on ground conditions, vegetation and the carbon cycle. Consequently, accurate modeling of ESPs is important for improving understanding of the current and future distributions of these processes. The aims of this study were to: (1) integrate climate and multiple local predictors to develop realistic ensemble models for the four key ESPs occurring at high latitudes (slope processes, cryoturbation, nivation and palsa mires) based on the outputs of 10 modern statistical techniques; (2) test whether models of ESPs are improved by incorporating topography, soil and vegetation predictors to climate‐only models; (3) examine the relative importance of these variables in a multivariate setting. Overall, the models showed high transferability with the mean area under curve of a receiver operating characteristics (AUC) ranging from 0.83 to 0.96 and true skill statistics (TSS) from 0.52 to 0.87 for the most complex models. Even though the analyses highlighted the importance of the climate variables as the most influential predictors, three out of four models benefitted from the inclusion of local predictors. We conclude that disregarding local topography and soil conditions in spatial models of ESPs may cause a significant source of error in geomorphological distribution models. Copyright © 2014 John Wiley & Sons, Ltd. Article in Journal/Newspaper palsa Wiley Online Library Earth Surface Processes and Landforms 39 13 1729 1740
institution Open Polar
collection Wiley Online Library
op_collection_id crwiley
language English
description ABSTRACT Earth surface processes (ESPs) drive landscape development and ecosystem processes in high‐latitude regions by creating spatially heterogeneous abiotic and biotic conditions. Ongoing global change may potentially alter the activity of ESPs through feedback on ground conditions, vegetation and the carbon cycle. Consequently, accurate modeling of ESPs is important for improving understanding of the current and future distributions of these processes. The aims of this study were to: (1) integrate climate and multiple local predictors to develop realistic ensemble models for the four key ESPs occurring at high latitudes (slope processes, cryoturbation, nivation and palsa mires) based on the outputs of 10 modern statistical techniques; (2) test whether models of ESPs are improved by incorporating topography, soil and vegetation predictors to climate‐only models; (3) examine the relative importance of these variables in a multivariate setting. Overall, the models showed high transferability with the mean area under curve of a receiver operating characteristics (AUC) ranging from 0.83 to 0.96 and true skill statistics (TSS) from 0.52 to 0.87 for the most complex models. Even though the analyses highlighted the importance of the climate variables as the most influential predictors, three out of four models benefitted from the inclusion of local predictors. We conclude that disregarding local topography and soil conditions in spatial models of ESPs may cause a significant source of error in geomorphological distribution models. Copyright © 2014 John Wiley & Sons, Ltd.
format Article in Journal/Newspaper
author Aalto, Juha
Luoto, Miska
spellingShingle Aalto, Juha
Luoto, Miska
Integrating climate and local factors for geomorphological distribution models
author_facet Aalto, Juha
Luoto, Miska
author_sort Aalto, Juha
title Integrating climate and local factors for geomorphological distribution models
title_short Integrating climate and local factors for geomorphological distribution models
title_full Integrating climate and local factors for geomorphological distribution models
title_fullStr Integrating climate and local factors for geomorphological distribution models
title_full_unstemmed Integrating climate and local factors for geomorphological distribution models
title_sort integrating climate and local factors for geomorphological distribution models
publisher Wiley
publishDate 2014
url http://dx.doi.org/10.1002/esp.3554
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fesp.3554
https://onlinelibrary.wiley.com/doi/pdf/10.1002/esp.3554
genre palsa
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op_source Earth Surface Processes and Landforms
volume 39, issue 13, page 1729-1740
ISSN 0197-9337 1096-9837
op_rights http://onlinelibrary.wiley.com/termsAndConditions#vor
op_doi https://doi.org/10.1002/esp.3554
container_title Earth Surface Processes and Landforms
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