Simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the Qinghai–Tibetan plateau
Abstract The thermal regime of the active layer temperature (ALT) is a key variable with which to monitor permafrost changes and to improve the precision of simulations and predictions of land surface processes. The dynamics of the active layer thermal regime can differ substantially under various l...
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crwiley:10.1002/ppp.2003 2024-09-15T17:34:48+00:00 Simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the Qinghai–Tibetan plateau Chang, Juan Wang, Genxu Guo, Linmao National Natural Science Foundation of China 2019 http://dx.doi.org/10.1002/ppp.2003 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fppp.2003 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.2003 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Permafrost and Periglacial Processes volume 30, issue 3, page 195-207 ISSN 1045-6740 1099-1530 journal-article 2019 crwiley https://doi.org/10.1002/ppp.2003 2024-08-06T04:12:21Z Abstract The thermal regime of the active layer temperature (ALT) is a key variable with which to monitor permafrost changes and to improve the precision of simulations and predictions of land surface processes. The dynamics of the active layer thermal regime can differ substantially under various land surface types and climatic conditions. The proper simulation of these different processes is essential for accurately predicting the changes in water cycles and ecosystems under a warming climate scenario. In this paper, an artificial neural network (ANN) forecasting model system was developed using only two accessible parameters, air and ground surface temperatures, to predict and simulate the ALT thermal regime. The model results show that the ANN model has better real‐time prediction capability than other physics‐based models and performs well at simulating and forecasting variations in soil temperature with a step size of 12 days in permafrost regions on the Qinghai–Tibetan Plateau. The influence of an increase in air temperature on the ALT thermal regime was more intense during the thawing process than during the freezing process, and this influence decreased with an increase in soil depth. Article in Journal/Newspaper Active layer temperature permafrost Permafrost and Periglacial Processes Wiley Online Library Permafrost and Periglacial Processes 30 3 195 207 |
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Open Polar |
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Wiley Online Library |
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crwiley |
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English |
description |
Abstract The thermal regime of the active layer temperature (ALT) is a key variable with which to monitor permafrost changes and to improve the precision of simulations and predictions of land surface processes. The dynamics of the active layer thermal regime can differ substantially under various land surface types and climatic conditions. The proper simulation of these different processes is essential for accurately predicting the changes in water cycles and ecosystems under a warming climate scenario. In this paper, an artificial neural network (ANN) forecasting model system was developed using only two accessible parameters, air and ground surface temperatures, to predict and simulate the ALT thermal regime. The model results show that the ANN model has better real‐time prediction capability than other physics‐based models and performs well at simulating and forecasting variations in soil temperature with a step size of 12 days in permafrost regions on the Qinghai–Tibetan Plateau. The influence of an increase in air temperature on the ALT thermal regime was more intense during the thawing process than during the freezing process, and this influence decreased with an increase in soil depth. |
author2 |
National Natural Science Foundation of China |
format |
Article in Journal/Newspaper |
author |
Chang, Juan Wang, Genxu Guo, Linmao |
spellingShingle |
Chang, Juan Wang, Genxu Guo, Linmao Simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the Qinghai–Tibetan plateau |
author_facet |
Chang, Juan Wang, Genxu Guo, Linmao |
author_sort |
Chang, Juan |
title |
Simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the Qinghai–Tibetan plateau |
title_short |
Simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the Qinghai–Tibetan plateau |
title_full |
Simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the Qinghai–Tibetan plateau |
title_fullStr |
Simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the Qinghai–Tibetan plateau |
title_full_unstemmed |
Simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the Qinghai–Tibetan plateau |
title_sort |
simulation of soil thermal dynamics using an artificial neural network model for a permafrost alpine meadow on the qinghai–tibetan plateau |
publisher |
Wiley |
publishDate |
2019 |
url |
http://dx.doi.org/10.1002/ppp.2003 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fppp.2003 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.2003 |
genre |
Active layer temperature permafrost Permafrost and Periglacial Processes |
genre_facet |
Active layer temperature permafrost Permafrost and Periglacial Processes |
op_source |
Permafrost and Periglacial Processes volume 30, issue 3, page 195-207 ISSN 1045-6740 1099-1530 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/ppp.2003 |
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Permafrost and Periglacial Processes |
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30 |
container_issue |
3 |
container_start_page |
195 |
op_container_end_page |
207 |
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1810295105818460160 |