Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model

Suprapermafrost groundwater has an important role in the hydrologic cycle of the permafrost region. However, due to the notably harsh environmental conditions, there is little field monitoring data of groundwater systems, which has limited our understanding of permafrost groundwater dynamics. There...

Full description

Bibliographic Details
Main Authors: Chang Juan, Wang Genxu, Mao Tianxu
Format: Article in Journal/Newspaper
Language:English
Published: 2015
Subjects:
Online Access:http://ir.imde.ac.cn/handle/131551/13837
https://doi.org/10.1016/j.jhydro1.2015.09.038
id ftchinacadscimhe:oai:ir.imde.ac.cn:131551/13837
record_format openpolar
spelling ftchinacadscimhe:oai:ir.imde.ac.cn:131551/13837 2023-05-15T17:57:09+02:00 Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model Chang Juan Wang Genxu Mao Tianxu 2015-10-01 http://ir.imde.ac.cn/handle/131551/13837 https://doi.org/10.1016/j.jhydro1.2015.09.038 英语 eng JOURNAL OF HYDROLOGY Chang Juan,Wang Genxu,Mao Tianxu. Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model[J]. JOURNAL OF HYDROLOGY,2015,529:1211-1220. http://ir.imde.ac.cn/handle/131551/13837 doi:10.1016/j.jhydro1.2015.09.038 Suprapermafrost Groundwater Ann Model Groundwater Level Spatial Variation Climate Change Science & Technology Technology Physical Sciences Engineering Geology Water Resources WATER TRANSPORT DISCHARGE REGION ALASKA Civil Geosciences Multidisciplinary Article 期刊论文 2015 ftchinacadscimhe https://doi.org/10.1016/j.jhydro1.2015.09.038 2022-12-19T18:19:25Z Suprapermafrost groundwater has an important role in the hydrologic cycle of the permafrost region. However, due to the notably harsh environmental conditions, there is little field monitoring data of groundwater systems, which has limited our understanding of permafrost groundwater dynamics. There is still no effective mathematical method and theory to be used for modeling and forecasting the variation in the permafrost groundwater. Two ANN models, one with three input variables (previous groundwater level, temperature and precipitation) and another with two input variables (temperature and precipitation only), were developed to simulate and predict the site-specific suprapermafrost groundwater level on the slope scale. The results indicate that the three input variable ANN model has superior real-time site-specific prediction capability and produces excellent accuracy performance in the simulation and forecasting of the variation in the suprapermafrost groundwater level. However, if there are no field observations of the suprapermafrost groundwater level, the ANN model developed using only the two input variables of the accessible climate data also has good accuracy and high validity in simulating and forecasting the suprapermafrost groundwater level variation to overcome the data limitations and parameter uncertainty. Under scenarios of the temperature increasing by 0.5 or 1.0 degrees C per 10 years, the suprapermafrost groundwater level is predicted to increase by 1.2-1.4% or 2.5-2.6% per year with precipitation increases of 10-20%, respectively. There were spatial variations in the responses of the suprapermafrost groundwater level to climate change on the slope scale. The variation ratio and the amplitude of the suprapermafrost groundwater level downslope are larger than those on the upper slope under climate warming. The obvious vulnerability and spatial variability of the suprapermafrost groundwater to climate change will impose intensive effects on the water cycle and alpine ecosystems in the permafrost ... Article in Journal/Newspaper permafrost Alaska IMHE OpenIR (Institute of Mountain Hazards and Environment, Chinese Academy of Sciences)
institution Open Polar
collection IMHE OpenIR (Institute of Mountain Hazards and Environment, Chinese Academy of Sciences)
op_collection_id ftchinacadscimhe
language English
topic Suprapermafrost Groundwater
Ann Model
Groundwater Level
Spatial Variation
Climate Change
Science & Technology
Technology
Physical Sciences
Engineering
Geology
Water Resources
WATER
TRANSPORT
DISCHARGE
REGION
ALASKA
Civil
Geosciences
Multidisciplinary
spellingShingle Suprapermafrost Groundwater
Ann Model
Groundwater Level
Spatial Variation
Climate Change
Science & Technology
Technology
Physical Sciences
Engineering
Geology
Water Resources
WATER
TRANSPORT
DISCHARGE
REGION
ALASKA
Civil
Geosciences
Multidisciplinary
Chang Juan
Wang Genxu
Mao Tianxu
Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model
topic_facet Suprapermafrost Groundwater
Ann Model
Groundwater Level
Spatial Variation
Climate Change
Science & Technology
Technology
Physical Sciences
Engineering
Geology
Water Resources
WATER
TRANSPORT
DISCHARGE
REGION
ALASKA
Civil
Geosciences
Multidisciplinary
description Suprapermafrost groundwater has an important role in the hydrologic cycle of the permafrost region. However, due to the notably harsh environmental conditions, there is little field monitoring data of groundwater systems, which has limited our understanding of permafrost groundwater dynamics. There is still no effective mathematical method and theory to be used for modeling and forecasting the variation in the permafrost groundwater. Two ANN models, one with three input variables (previous groundwater level, temperature and precipitation) and another with two input variables (temperature and precipitation only), were developed to simulate and predict the site-specific suprapermafrost groundwater level on the slope scale. The results indicate that the three input variable ANN model has superior real-time site-specific prediction capability and produces excellent accuracy performance in the simulation and forecasting of the variation in the suprapermafrost groundwater level. However, if there are no field observations of the suprapermafrost groundwater level, the ANN model developed using only the two input variables of the accessible climate data also has good accuracy and high validity in simulating and forecasting the suprapermafrost groundwater level variation to overcome the data limitations and parameter uncertainty. Under scenarios of the temperature increasing by 0.5 or 1.0 degrees C per 10 years, the suprapermafrost groundwater level is predicted to increase by 1.2-1.4% or 2.5-2.6% per year with precipitation increases of 10-20%, respectively. There were spatial variations in the responses of the suprapermafrost groundwater level to climate change on the slope scale. The variation ratio and the amplitude of the suprapermafrost groundwater level downslope are larger than those on the upper slope under climate warming. The obvious vulnerability and spatial variability of the suprapermafrost groundwater to climate change will impose intensive effects on the water cycle and alpine ecosystems in the permafrost ...
format Article in Journal/Newspaper
author Chang Juan
Wang Genxu
Mao Tianxu
author_facet Chang Juan
Wang Genxu
Mao Tianxu
author_sort Chang Juan
title Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model
title_short Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model
title_full Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model
title_fullStr Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model
title_full_unstemmed Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model
title_sort simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model
publishDate 2015
url http://ir.imde.ac.cn/handle/131551/13837
https://doi.org/10.1016/j.jhydro1.2015.09.038
genre permafrost
Alaska
genre_facet permafrost
Alaska
op_relation JOURNAL OF HYDROLOGY
Chang Juan,Wang Genxu,Mao Tianxu. Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model[J]. JOURNAL OF HYDROLOGY,2015,529:1211-1220.
http://ir.imde.ac.cn/handle/131551/13837
doi:10.1016/j.jhydro1.2015.09.038
op_doi https://doi.org/10.1016/j.jhydro1.2015.09.038
_version_ 1766165520258695168