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...
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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 |