IMHE OpenIR  > 山地表生过程与生态调控重点实验室
Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model
Chang Juan1; Wang Genxu2; Mao Tianxu2
Corresponding AuthorChang Juan
2015-10-01
Source PublicationJOURNAL OF HYDROLOGY
ISSN0022-1694
Volume529Pages:1211-1220
SubtypeArticle
AbstractSuprapermafrost 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 region. (C) 2015 Elsevier B.V. All rights reserved.
KeywordSuprapermafrost Groundwater Ann Model Groundwater Level Spatial Variation Climate Change
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
DOI10.1016/j.jhydro1.2015.09.038
WOS Subject ExtendedEngineering ; Geology ; Water Resources
Indexed BySCI
WOS KeywordWATER ; TRANSPORT ; DISCHARGE ; REGION ; ALASKA
Language英语
WOS SubjectEngineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS IDWOS:000364249500041
Funding OrganizationNatural Science Foundation of China(41301024) ; National Basic Research Program of China (973)(2013CBA01807)
Citation statistics
Cited Times:47[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/13837
Collection山地表生过程与生态调控重点实验室
Affiliation1.Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China
2.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
Recommended Citation
GB/T 7714
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.
APA Chang Juan,Wang Genxu,&Mao Tianxu.(2015).Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model.JOURNAL OF HYDROLOGY,529,1211-1220.
MLA Chang Juan,et al."Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model".JOURNAL OF HYDROLOGY 529(2015):1211-1220.
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