ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China
Precisely quantitative assessments of stream flow response to climatic change and permafrost thawing are highly challenging and urgent in cold regions. However, due to the notably harsh environmental conditions, there is little field monitoring data of runoff in permafrost regions, which has limited...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/9451802 |
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| _version_ | 1832558168950964224 |
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| author | Chang Juan Wang Genxu Mao Tianxu Sun Xiangyang |
| author_facet | Chang Juan Wang Genxu Mao Tianxu Sun Xiangyang |
| author_sort | Chang Juan |
| collection | DOAJ |
| description | Precisely quantitative assessments of stream flow response to climatic change and permafrost thawing are highly challenging and urgent in cold regions. However, due to the notably harsh environmental conditions, there is little field monitoring data of runoff in permafrost regions, which has limited the development of physically based models in these regions. To identify the impacts of climate change in the runoff process in the Three-River Headwater Region (TRHR) on the Qinghai-Tibet Plateau, two artificial neural network (ANN) models, one with three input variables (previous runoff, air temperature, and precipitation) and another with two input variables (air temperature and precipitation only), were developed to simulate and predict the runoff variation in the TRHR. The results show that the three-input variable ANN model has a superior real-time prediction capability and performs well in the simulation and forecasting of the runoff variation in the TRHR. Under the different scenarios conditions, the forecasting results of ANN model indicated that climate change has a great effect on the runoff processes in the TRHR. The results of this study are of practical significance for water resources management and the evaluation of the impacts of climatic change on the hydrological regime in long-term considerations. |
| format | Article |
| id | doaj-art-41425a6be7c14a2ea676752374d1a6e4 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-41425a6be7c14a2ea676752374d1a6e42025-02-03T01:32:56ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/94518029451802ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, ChinaChang Juan0Wang Genxu1Mao Tianxu2Sun Xiangyang3Key Laboratory of Western China’s Environmental Systems, Ministry of Education, College of Earth and Environmental Science, Lanzhou University, Lanzhou 730000, ChinaInstitute of Mountain Hazards and Environment, CAS, Chengdu 610041, ChinaInstitute of Mountain Hazards and Environment, CAS, Chengdu 610041, ChinaInstitute of Mountain Hazards and Environment, CAS, Chengdu 610041, ChinaPrecisely quantitative assessments of stream flow response to climatic change and permafrost thawing are highly challenging and urgent in cold regions. However, due to the notably harsh environmental conditions, there is little field monitoring data of runoff in permafrost regions, which has limited the development of physically based models in these regions. To identify the impacts of climate change in the runoff process in the Three-River Headwater Region (TRHR) on the Qinghai-Tibet Plateau, two artificial neural network (ANN) models, one with three input variables (previous runoff, air temperature, and precipitation) and another with two input variables (air temperature and precipitation only), were developed to simulate and predict the runoff variation in the TRHR. The results show that the three-input variable ANN model has a superior real-time prediction capability and performs well in the simulation and forecasting of the runoff variation in the TRHR. Under the different scenarios conditions, the forecasting results of ANN model indicated that climate change has a great effect on the runoff processes in the TRHR. The results of this study are of practical significance for water resources management and the evaluation of the impacts of climatic change on the hydrological regime in long-term considerations.http://dx.doi.org/10.1155/2017/9451802 |
| spellingShingle | Chang Juan Wang Genxu Mao Tianxu Sun Xiangyang ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China Advances in Meteorology |
| title | ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China |
| title_full | ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China |
| title_fullStr | ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China |
| title_full_unstemmed | ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China |
| title_short | ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China |
| title_sort | ann model based simulation of the runoff variation in response to climate change on the qinghai tibet plateau china |
| url | http://dx.doi.org/10.1155/2017/9451802 |
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